The Sioux Falls scenario provided a convenient test-case, combining fully dynamic demand tted with realistic socio-economic and demographic attributes with a small-scale road network including an integrated public transportation system. Based on the Sioux Falls road network commonly used for tests and demonstration purposes in transportation literature (Bar-Gera, 2013), it allowed quick and convenient experiments on new policy or soware implementations with MATSim on a heterogeneous agent population, with a high degree of spatial resolution, but without signicant computational requirements. However, it is important to stress that, despite the use of real world data for the generation of the enriched Sioux Falls scenario, it did not aim to replicate the real City of Sioux Falls in South Dakota, US and remains a ctitious test case. Detailed report on scenario generation and its characteristics is provided by Chakirov and Fourie (2014) and can also be found at http://www.matsim.org/scenario/sioux-falls.

### **59.1 Demand**

A realistic, socio-economically and demographically diverse demand population—with heterogeneous use preferences—was crucial for unlocking the full potential of an agent-based simulation like MATSim. However, generation of a disaggregated demand description on individual and household levels close to reality was challenging; not only for trip origins and destinations, but also with respect to travel pattern relation and socio-demographic travelers' characteristics.

To address this challenge for the Sioux Falls scenario, and represent the household structure, demographic prole and income distribution as realistically as possible, a synthetic household population, using the Bar-Gera et al. (2009) entropy optimization technique, was generated. It matched the aggregate distribution of demographic attributes (age, sex and household income) recorded during the 2010 US Census. It contained census tracts inside, and adjoining, the city center of

### **How to cite this book chapter:**

Chakirov, A. 2016. Sioux Falls. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 385–388. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/ baw.59. License: CC-BY 4.0

Sioux Falls and was composed of household and person records taken from the (anonymous) 5-year American Community Survey sample (2007-2011), covering 5 % of all households.

To keep the scenario accessible, as well as facilitating interpretation and understanding of possible eects on policies studied, only two simple activity chains were initially included: "home – work – home" and "home – other – home". Activity locations were identied using building stock data set provided by the City of Sioux Falls GIS division. Each household's home location was assigned randomly to a residential unit within the household's tract. Because no information on the real number and distribution of work places within the relevant area was easily accessible, the static O-D matrix from LeBlanc et al. (1975) was taken as a workplace attraction indicator for each zone. Then, assignment of work places to individual workers, as well as locations of secondary (other) activities, was performed using a parameter-free radiation model presented by Simini et al. (2012).

To exploit the full potential of disaggregated demand and add another degree of realism to the scenario, car ownership on the household level was modeled using an ordered probit model, presented by Giuliano and Dargay (2006) and based on the NPTS (US Nationwide Personal Transportation Survey) 1995. In addition to socio-demographic household characteristics (number of adults, children, pensioners, household income), the model used residential location attributes (population density, public transport access and dwelling type), which better described specic Sioux Falls scenario characteristics, as well as its area-wide bus network.

### **59.2 Supply**

A realistic transportation test network should ensure sucient complexity of travelers' choice dimensions while limiting computational eort. To this end, the Sioux Falls test network was introduced by Morlok et al. (1973) and later adapted as a benchmark and test scenario in many publications (see Chakirov and Fourie (2014) for overview). The network structure captured the major arterial roads of Sioux Falls, South Dakota, but was never intended to replicate the real city, or all characteristics of its transportation system, such as travel times or modal split. The original network was comprised of 76 arcs, 24 nodes and 552 O-D pairs. For this scenario, road capacities were adjusted according to values provided by the Highway Capacity Manual Transportation Research Board (2010) and other related research publications (e.g., Ng and Small, 2012). The public transportation network added to the scenario included ve bus lines, as initially proposed by Abdulaal and LeBlanc (1979), with bus stops placed at regular intervals of 600 meters.

Due to the design of MATSim queue simulation, agents were handled only at the beginning and end of each network link and could not enter or leave a link along its length. Therefore, origins and destinations located along very long links led to spatial detail loss, as all origins and destinations along the length of the link were eectively assigned the same coordinates. Consequently, to improve spatial detail level, all links of the Sioux Falls network were evenly split into smaller links, with maximum length of 500 meters each. Following this operation, number of nodes was increased to 282 and number of links to 334, without changing eective network topology.

In addition to car and bus modes, walking as "teleported" mode, with constant travel speeds, and with no interaction with other users, is used as the non-motorized transportation mode.

### **59.3 Behavioral Parameters**

Behavioral parameters used in utility functions were based on estimated demand model for Sydney by Tirachini et al. (2014). Before applying parameters in an activity-based context, time-related parameters had to be adjusted to account for utility gained from activity performance. Thus, to provide a value for marginal utility of performing an activity, the travel mode with smallest the disutility was set as a baseline, under the assumption that traveling with this mode was equivalent to idling/doing nothing. Corresponding parameters were split into opportunity costs of time and a mode-specic disutility of traveling, as has been done in previous MATSim-related publications (e.g., Kickhofer et al., 2011). ¨

## **59.4 Results, Drawbacks and Outlook**

Sioux Falls scenario stability and performance was tested using two sets of activity timing constraints, as well as ve dierent random seeds, which all delivered stable and realistic results. Chakirov and Fourie (2014) also investigated MFD (Macroscopic Fundamental Diagram) existence and hysteresis characteristics, as discussed in Geroliminis and Daganzo (2007, 2008); Geroliminis and Sun (2011).

However, recent experience has shown certain coarse network drawbacks; it represented only major arterial roads and neglected minor neighborhood and collector road links. With an elaborate synthetic population and high rush hour demand peaks, the network seemed to be sensitive to network breakdowns under high loading conditions.

Along with the coarse road network, the coarse public transport network level and the resulting low level of accessibility (for parts of the population) represented another drawback, particularly relevant to simulation and evaluation of policies sensitive to, or requiring, a certain share of public transport users.

Replacing the original Sioux Falls network with a ner network obtained from the crowdsourced OSM and adding additional public transport lines would address the above-mentioned scenario weakness. However, this introduces a dierent set of drawbacks and would require further attention. First, the signicantly larger number of network links and nodes increases time and resources for routing and dynamic queue simulation and could erase the advantages of a small-scale network. Extended simulation times can be tackled with the new pseudo-simulation methodology, currently developed by Fourie et al. (2013). Second, total network capacity increase leads to reduction or even disappearance of congestion during peak hours, although including freight and through trac in the scenario can make it more realistic and address congested conditions during peak-hours.

# **Aliaga**

# Pelin Onelcin, Mehmet Metin Mutlu and Yalcin Alver

Aliaga, in Turkey, is situated about 50 kilometers north of Izmir; it is one of the 30 Izmir province districts in the Aegean region of Turkey and is crucial to the national economy.

Aliaga is home to Petkim, one of the largest petrochemical enterprises of Turkey. In 2011, Petkim was ranked as the 12th largest company in Turkey's 500 top industrial list (Istanbul Chamber of Industry, 2012, accessed 03.07.12); the enterprise includes 14 plants and seven auxiliary units.

According to the Turkish Statistical Institute, the 2011 population of Aliaga was 68 432; 56 440 lived in central neighborhoods and 11 992 in surrounding villages (Turkish Statistical Institute, 2011).

Many chemical factories are located near residential areas. The evacuation zone was determined using a scenario developed for a chemical accident in one of the Petkim factories. Chemical substance elements and NFPA (National Fire Protection Association) (704) ratings, ranging from 1 to 4 for ammability, health and reactivity, were compared. The most dangerous substance was acrylonitrile (ACN), rated 3, 4 and 2 for ammability, health and reactivity, respectively.

Risk zone radii were found using Aloha soware developed by the Oce of Emergency Management and Emergency Response Division. The soware divided the risk area into three zones, based on the chemical substance type, wind speed and wind direction. The wind data, obtained from Aliaga wind measurement station, showed that maximum wind speed was 17 meters per second (WolframAlpha, 2012, accessed 02.08.12) and the prevailing wind direction was WNW. Wind blowing from the west would be the most dangerous for Aliaga, carrying the smoke over residential areas and increasing the number of persons to be evacuated.

The evacuation zone was divided into 19 TAZs (Trac Analysis Zones). Trips generated from these zones were directed to six destinations TAZs, three of which are health care centers and three gathering places. The Petkim area iwa divided into six zones; the rst in the impact area. The evacuation planning zone is shown in Figure 60.1.

Onelcin, P, Mutlu, M M and Alver, Y. 2016. Aliaga. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 389–392. London: Ubiquity Press. DOI: http://dx.doi.org/ 10.5334/baw.60. License: CC-BY 4.0

**Figure 60.1:** Evacuation planning zone.

Number of evacuees was calculated considering both permanent residents and employees, who were classied as transients. The scenario was prepared assuming the following conditions:

There were 3 883 employees in the area studied; number of employees to be evacuated from factories was computed using the following assumptions:

Evacuations from residential buildings were calculated using these steps:

• Number of persons living in an evacuation zone neighborhood was divided into the number of neighborhood buildings, giving the mean number of persons living in one building.

• Number of buildings that remained in the evacuation zone was multiplied by the mean number of persons in one building.

To estimate the number of evacuation vehicles needed, car occupancy ratio rate was used. This rate was 1.57 in normal situations—as given in the Urban Transportation Plan of the Istanbul Metropolitan Area by the Istanbul Metropolitan Municipality Directorate of Transportation Planning—however, in emergency situations, it was expected to be higher. In this study, car occupancy ratio rate was taken as two, number of evacuees was computed as 14 472 and number of vehicles 7 236.

The Aliaga network was taken from OSM and converted to a shape le and MATSim network le with the tutorial's PNetworkGenerator class. Zones used in generating synthetic population for MATSim were created in QGIS.

Three dierent scenarios were identied for the evacuation simulation. In Figure 60.2, O-D matrices for each scenario can be seen. These three scenarios were selected based on destination zone location and trac demand criteria; free spaces close to the risk zone were designated as gathering areas. The time required for evacuees to reach health care centers was very important in emergency situations like this. The trac demand generated for health care centers was distributed between zones 26, 27 and 30 in the scenarios, though the rst risk zone was always directed to Aliaga State Hospital, which had the most capacity of all health care centers; severely injured persons would be transferred to this state hospital. Evacuating vehicles departing from the second risk zone were directed to health care centers in zones 26 and 27. Changing the number of evacuating vehicles in any given zones resulted in dierent evacuation times; thus, these dierent scenarios enabled observation of trac demand eect on trac and whether this led to evacuation time reduction.

Initial demand refered to synthetic population derived from numbers and locations of evacuees to be transferred to health care centers or gathering-areas, sorted by distance. A starting place for

**Figure 60.2:** MATSim simulation snapshot.

initial demand generation was found in the tutorial's DemandGenerator class. Zones were modied according to both actual population density in given zones and road links where evacuees could start their trips at the time of a possible chemical accident to generate a relatively realistic scenario. Population zones were set for a group of origin zones, which were assigned for a predened destination. For each agent, random activity coordinates were generated: home, work and leisure, or in this case, evacuation zones, hospitals and gathering areas. Agents' departures and arrivals took place on the nodes closest to activity coordinates and, in the rst iteration, shortest path was calculated for route choice.

MATSim assigned trip start to the node closest to agent activity coordinates (i.e., home or work) for each agent. MATSim simulation results were analyzed by Senozon AG Via. Figure 60.3 shows a simulation snapshot.

Clearance time for three risk zones and total arrival time for three dierent scenarios were listed in Table 60.1. For the rst scenario, evacuation times were 45 minutes for the rst risk zone, 83 minutes for the second, and 86 minutes for the third. For the second scenario, evacuation times were 44 minutes for the rst risk zone, 82 minutes for the second, and 91 minutes for the third. Finally, for the third scenario, evacuation times were 47 minutes for the rst risk zone, 86 minutes for the second, and 88 minutes for the third. The third scenario yielded the best results, with minimum clearance time for the entire risk area. Scenario results conrmed that clearance times were insucient for people to evacuate safely, especially from the rst risk zone.

**Figure 60.3:** OD matrices for evacuation scenarios.

**Table 60.1:** Risk zones evacuation times in minutes.

# **Baoding: A Case Study for Testing a New Household Utility Function in MATSim**

Chengxiang Zhuge and Chunfu Shao

### **61.1 Introduction**

Baoding is a medium-sized city in Hebei Province, China. The Baoding case study—testing a new household utility function—proposed two scenarios to compare the performance of two utility functions: the household and individual utility functions. In Scenario 1, it was assumed that each household sought to maximize their overall household utilities when they scheduled; thus, family members' communication and coordination was communal in each household. In Scenario 2, the individual utility function—the default utility function in MATSim—was utilized to score plans; here, each agent tried only to maximize his own utilities without communicating with other family members.

Overall, Scenario 1 diered from Scenario 2 only in the utility function; other input data and parameters in these two scenarios were kept the same. The scenarios simulated only urban residents' travel behavior. In 2007, the study area population was 1 060 783, in 299 850 households, encompassing 355 465 privately owned cars.

### **61.2 Population and Demand Generation**

**Population** The scenarios' agent population was created using a new population synthesis,which starts with initial household weights obtained from the 2007 Baoding Household Travel Survey. The nal household weights, used for creating the population, were calculated by iteratively adjusting initial household weights in a directed way. Gender and household car ownership were also used as person- and household-level control variables, respectively. In the scenarios, only 20 % of Baoding's total population, approximately 212 000, was synthesized and used, to speed up the simulation.

Zhuge, C and Shao, C. 2016. Baoding: A Case Study for Testing a New Household Utility Function in MATSim. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 393–396. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/baw.61. License: CC-BY 4.0

**Travel Demand Generation** For initial demand generation, a GA (Genetic Algorithm), adopting utility maximization theory, was implemented. For Scenario 1, this GA used the new proposed household utility function as the tness function; this was employed to generate initial individual daily plans for each household in the synthetic population. Specically, in the GA, each chromosome represented a household's set of daily plans and each gene represented a family member's daily plan. During evolution (including mutation, crossover and selection), each chromosome was scored; only those with higher household utilities remained. Then, a set of daily plans with the highest household utility function were selected and allocated to the household. Similarly, other daily household plans in the synthetic population were generated, one by one. It should also be noted that the travel time in the initial daily plans was estimated. Therefore, elements like travel time and activity duration in the initial daily plans would be adapted (optimized) when executed in MATSim.

In Scenario 2, the GA incorporated the individual utility function to search for each agent's (family member's) plans.

### **61.3 Activity Locations, Network and Transport Modes**

**Activity Locations** Five typical activity types, including work, home, leisure, education and shopping, were taken into account in the scenarios. The activity facilities numbers for these ve types were: 1 647, 462, 246, 372 and 445, respectively.

**Transport Network** The scenarios contained two network types, including road and public transit networks. Figure 61.1 demonstrated Baoding's 2007 road and transit network. The road network was composed of 1 650 nodes and 539 links; the transit network contained transit routes and transit schedules, with 49 transit lines and (98 transit routes).

**Transport Modes** The simulated transport modes included car, public transport, bike and walk. Car drivers and public transport passengers used the road network and transit network. Because agents who traveled by bike or on foot had no access to the transport network, they were teleported from origin to destination and assigned no exact routes, but their travel time was calculated.

### **61.4 Historical Validation**

Historic validation. composed of the following two steps, was carried out to assess MATSim's performance and applied to both scenarios.

Step 1: Comparison of both real and simulated car ows and comparison of real and simulated transit passenger ows were carried out in each scenario, to assess MATSim's performance for car and transit simulation. The MRE (Mean Relative Error), calculated by the equation (61.1), was employed to assess performance.

$$\text{MRE} = \frac{\|F\_{\text{simulated}} - F\_{\text{real}}\|}{F\_{\text{real}}} \times 100\,\% \tag{61.1}$$

where, Fsimulated and Freal denotes the simulated and the real ow (car ow or passenger ow), respectively.

Step 2: Comparison of both scenarios' performance for car and transit simulation, based on results from step 1.

### **61.4.1 Comparison of Two Scenarios: Car Trac**

Car ow data on six road links (equal to 12 links in MATSim scenario) from 7 am to 9 am, was used for comparison of car simulation and was manually counted in 2007.

**Figure 61.1:** Road and transit network of Baoding in 2007.

Figure 61.2(a) demonstrated car simulation performance for both scenarios. Four dots were approximately located in the y = x line and the other two dots, below the line, also were very close to it. Mean relative error margins of Scenario 1 and Scenario 2 were 44.8 % and 47.5 %, respectively. It can thus be concluded that the performance of Scenario 1 (using household utility function) was slightly better than Scenario 2 (using individual utility function).

### **61.4.2 Comparison of Two Scenarios: Transit Trac**

Data (passenger ow for nine transit lines from 7 am to 9 am) used for transit simulation comparison was also manually counted in 2007. Figure 61.2(b) illustrated both transit simulation scenarios' performance. Clearly, most dots did locate close to the y = x line, however, two dots below the line were signicantly distant from it. Also, mean relative errors of Scenario 1 and Scenario 2 were 38.7 % and 47.9 %, suggesting that Scenario 1 better represented transit passenger ows than Scenario 2.

### **61.5 Achieved Results**

A proposed MATSim household utility function was tested comparing two scenarios using household and individual utility function. Historical validation conrmed that MATSim improved its own car and transit simulation performance by using the new utility function. However, more case studies are needed to further conrm this new proposed utility function's advantages.

More information on the Baoding scenario can be found in Zhuge (2014) (in Chinese).

**Figure 61.2:** Performance comparison of Scenario 1 and 2.

# **Barcelona**

# Miguel Picornell and Maxime Lenormand

The Barcelona scenario is one of the three case studies (together with London and Zurich) ¨ carried out under the framework of the EUNOIA (Evolutive User-centric Networks fOr Intraurban Accessibility) project. The main goal of the Barcelona case study was to evaluate the impact of dierent public bike-sharing schemes in the city. The study area covers the metropolitan Barcelona area, with special focus on the city center, where public bike-sharing stations are located. For this study a novel bike-sharing module was developed by ETH Zurich. ¨

## **62.1 Transport Supply: Network and Public Transport**

The road network was extracted from the TransCAD (Transportation Computer Assisted Design) model used by the city of Barcelona. Public transport supply was also considered, comprising: bus, underground, tram, train and bike-sharing. Information about stops and schedules was obtained from the public information available at the Barcelona Open Data platform, as well as from the Barcelona transport authority website.

### **62.2 Transport Demand: Population**

Agent plans were dened using anonymised mobile phone registers CDRs (Call Detail Records). From mobile phone data, it is possible to identify places where agents perform activities and corresponding trips. Activities have been classied as "home", "work" and "other" (including as "other", "leisure", "shopping", etc.). A sample of around 15 % of the population was used in the simulation. Modes used in the simulation model include: walking, cycling, public transport and car.

Picornell, M and Lenormand, M. 2016. Barcelona. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 397–398. London: Ubiquity Press. DOI: http://dx.doi.org/ 10.5334/baw.62. License: CC-BY 4.0

# **62.3 Calibration and Validation**

Dierent data sources were used to calibrate and validate the model. First, to validate agent plans obtained from mobile phone data, results were compared to EMEF (Enquesta de Mobilitat en dia Feiner), indicating that mobile phone data provides information similar to traditional surveys. Additionally, agents' utility function was calibrated using the modal split from EMEF and road counts.

# **62.4 Results and More Information**

At the time this summary was written, the calibration process was still ongoing. More detailed information about the scenario and main results can be found at: http://www.eunoia-project. eu/publications/ (project deliverables/Report on Case Study 3: Barcelona).

# **Belgium: The Use of MATSim within an Estimation Framework for Assessing Economic Impacts of River Floods**

Isma¨ı Saadi, Jacques Teller and Mario Cools

### **63.1 Problem Statement**

With the history of river oods in Belgium and the signicant probability that such events will again take place in the near future, assessment of both direct and indirect economic impact was deemed essential to allow formulation of an adequate policy program and ecient ood risk management. One proposal would assess ood risk at the micro-scale level: i.e., individual buildings for exposure analysis and direct economic damage estimation, individual companies for indirect economic damage estimation, 10 meter grid spacing for land-use modeling and individuals/ vehicles for transportation models. To enable this assessment, an integrated modeling framework combining dierent simulation theories from a multidisciplinary perspective is being developed. Figure 63.1 describes the procedure to measure the annual ood risk. A more detailed description of the whole modeling chain is available in Dewals et al. (2015).

A basic modeling framework premise is that dierent spatial pattern 'families' might inuence the damage intensity caused by river oods (e.g., land use change, transportation systems). In this chapter, we focus on how MATSim is being integrated into this overall framework, thus focusing on the TSA (Transport System Analysis) within the overall estimation procedure. For TSA, two congurations (freight and passenger model) are distinguished. For the passenger model, a MATSim scenario is developed on a national scale to simulate travel demand at base year 2010 and its evolution during the following years. The main objective is to study the eects of river oods on the transportation network and, consequently, on travel demand from an economic point of view. In addition, a freight travel demand model has been developed, to enable interactions between

Saadi, I, Teller, J and Cools, M. 2016. Belgium: The Use of MATSim within an Estimation Framework for Assessing Economic Impacts of River Floods. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 399–404. London: Ubiquity Press. DOI: http://dx.doi.org/ 10.5334/baw.63. License: CC-BY 4.0

**Figure 63.1:** Economic impact estimation procedure.

passenger and goods ows. Note: at this time, this is still an aggregate four-step model, but development is ongoing to develop an agent-based model for the freight side.

## **63.2 Data Collection**

As inputs, MATSim requires a synthetic population (or travel demand) le, as well as the related transportation network. Unfortunately, no recent census is available for the rst input; the latest dates from 2001. To compensate, a synthetic population was derived from more recent travel surveys (e.g., Cornelis et al., 2012) by employing a Gibbs sampler (Farooq et al., 2013). The Belgian ´ National Household Travel Survey (e.g., Cornelis et al., 2012) contains socio-demographics and ´ activity travel diaries with a detailed description of activity start, end times and durations. Activity locations are also available, but at the municipality code level. They are generally accessed by using the new municipalities referencing system: LAU (Local Administrative Unit) level 2. For the transportation network, OSM network data has been used.

# **63.3 Input Preparation**

### **63.3.1 Network**

The network data of Belgium, downloaded in 2015, is available online from the OSM server. It consists of 100 467 nodes and 232 715 links. Network quality is generally acceptable, according to many MATSim users, even if manual adjustment is necessary for specic links.

### **63.3.2 Synthetic Population**

Preparation of a synthetic population presents a signicant challenge for this case study; only micro-data are available to enable population synthesis. From these partial views of the actual population, use of a Gibbs sampler enables the joint distribution (re-)construction. The outputs seem to be encouraging when comparing computed predictions to the reference dataset. Here, we propose testing the methodology by synthesizing some relevant variables for both transportation and urban systems simulations at the household level (see Figure 63.2).

## **63.3.3 Activity-Based Pattern Generation**

Aer the synthetic population has been generated, activity types, activity times and activity locations are generated and associated to the agents, using an activity-based pattern generator. Using a combined set of machine learning techniques, daily activity planners are generated for each agent. As shown in Figure 63.3, the model suggests some promising rst results. The activity-pattern generator is calibrated by using micro-data, such as activity travel diaries extracted from travel surveys.

**Figure 63.2:** Examples of households synthesized attributes.

**Figure 63.3:** Activity chains generation.

Calibration quality will be measured aer analyzing MATSim scenario outputs when trac counts are compared. If the comparison between observed and simulated trac counts suggests a significant deviation, a direct approach based on trac counts (Cools et al., 2010) could work to adjust activity-based pattern generator parameters.

As outlined by Cools et al. (2011), uncertainties introduced by statistical distributions of random components in most activity-based models might be signicant. Thus, some key indicators (e.g., sequences type proportions) will be investigated to measure micro-simulation error impact.

## **63.4 General Modeling Framework**

In Saadi et al. (2014), the overall modeling framework is presented, as well as the integration of scheme components. This paper covers all concepts expected to be used in building the future MATSim scenario. Figure 63.4 is a partial view of the overall modeling framework being researched at the moment.

# **63.5 Modeling Network Disruption**

As mentioned, this study also suggests modeling network inaccessibility occurring aer river oods. This approach assumes that link capacities subjected to river oods are reduced, depending on ood intensity. Given that damage is mainly a function of water depth, the idea is to intersect a steady-state inundation map with the transportation network or, at least, the area impacted by oods (Saadi et al., 2014). Then, an analysis extension will be achieved by including a time series of river oods for a better understanding of dynamic eects: e.g., response to river oods propagation, return way and time to the new equilibrium point between transport supply and demand.

**Figure 63.4:** Partial modeling framework.

A similar problem was studied in a tsunami evacuation scenario simulation in the city of Padang (Lammel et al., 2010) (Chapter 76) and was particularly interesting in terms of network ¨ dynamic evolution during the scenario simulation.

# **63.6 Next Development Steps**

When the complete integrated agent-based transportation model is ready, combination with the land-use change CA (Cellular Automaton) based model proposed by Mustafa et al. (2014) will to allow more interactions between those two patterns. This connection will be the basis for an innovative micro-scale LUTI (Land-Use and Transport Interaction) model, allowing more accurate predictions about future river oods inuenced by dierent micro-scale patterns.

# **Brussels**

# Daniel Roder ¨

The MATSim scenario for Brussels was developed as part of the SustainCity project. This project's goal was to couple an urban land use model, in this case UrbanSim, with the MATSim mobility simulation, to evaluate transport policy impact on urban land use and vice versa. A detailed description of this coupling is given by Nicolai (2013) and others. A detailed description of the scenario development is found in Roder et al. (2013). ¨

The scenario covered the greater Brussels area in Belgium; input data was derived from two main sources. The population was directly generated from the UrbanSim model, covering a total of 860 214 persons. At home- and at work-locations (per person) were given and converted into a daily home-work-home plan. For computational reasons, a randomly-drawn population sample of one percent was used. OSM was sourced for the street network generation, which consisted of 10 861 nodes and 19 830 links, i.e., using mainly the trunk road network.

For the modeling of public transport, two dierent approaches were tested: rst, the MATSim default approach for scenarios where no detailed transit schedule is available, based on either: beeline distance and average speed, or network-based freespeed travel times and a designated factor. The second approach was not part of the MATSim core during the project, but was available as a contribution (matrixBasedPtRouter, see Chapter 20). It was based on O-D travel time matrices between transit stops, i.e., travel times for all relations were computed in a pre-process. The travel times can based on a real-world-schedule or certain assumptions which can take spatial coverage into account. Advantages of this model are obvious; on one hand, it may depict spatial coverage with public transport supply—here, distance to the next transit stop inuences travel time. On the other hand, it may depict the real network, i.e., routes and lines and possible waiting times for switching. Both approaches were compared against travel times and mode share measures from a SATURN (Simulation and Assignment of Trac to Urban Road Networks) model. Since the matrix-based approach came closer to this model, further investigations were based on that.

Roder, D. 2016. Brussels. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) ¨ The Multi-Agent Transport Simulation MATSim, Pp. 405–406. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/baw.64. License: CC-BY 4.0

To evaluate the model's sensitivity to certain policies, a cordon toll scenario was set up, where a toll is charged between 6 and 10 am every time a car passed a cordon border, i.e., every time a car entered a link crossing a cordon border dened by the Brussels freeway ring. Accessibility was calculated and compared for both scenarios. Roder et al. (2013) provides a detailed analysis. ¨

# **Caracas**

# Walter J. Hernandez B. and H ´ ector E. Navarro U. ´

Capital of the country, Caracas is the largest city in Venezuela, with serious vehicle trac issues. Its daily estimated circulation of 1.5 million units represents three times the load originally estimated for the city's growth. Despite the lack of ocial statistics, it is possible to estimate the amount of Caracas' trac using other national gures, such as the Time Travel Index employed by the Federal Highway Administration (2013). This index estimates approximately 50 % longer than free-ow travel to traverse inner city circles and 75 % around metropolitan areas. This is in stark contrast to an average city in the US, which normally does not go beyond 35 %, even in the worst case.

Apart from obvious budget-related decits these delays cause in work force productivity for companies and organizations, the country itself loses an estimated \$2.1 billion per year. This includes the precious subsidies that have helped to maintain the country's world lowest prices of gas for decades (Wilson, 2008); \$1 billion could be saved by reducing the average circulation time by just 30 minutes. Equally important, the accompanying signicant reduction in CO<sup>2</sup> (Carbon Dioxide) emissions would help meet greenhouse targets for the country.

In recent years, several measures have been initiated to cope with increased trac in Caracas:

In addition to the these measures, other mechanisms could be implemented, such as weekday circulation restrictions (e.g., based on license plate numbers) and smart trac lights. Especially in the case of smart trac devices and planning of special lanes, careful study and simulation of trac

Hernandez B., W J and Navarro U., H E. 2016. Caracas. In: Horni, A, Nagel, K and Axhausen, K ´ W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 407–410. London: Ubiquity Press. DOI: http:// dx.doi.org/10.5334/baw.65. License: CC-BY 4.0

(a) Snapshot obtained from OSM. (b) Three snapshots of a simulation ran in MATSim between 6:00 and 6:30 am, depicting the increasing trac in the zone.

**Figure 65.1:** An area of "Los Cortijos" in Caracas, Venezuela.

**Figure 65.2:** Interface of the soware tool developed in Java showing the area studied. Blue dots over the roads in the map represent the counters positioned in the area to capture vehicle ow used as input for the simulation.

patterns must be undertaken. To help achieve this, a soware tool was envisioned with the following objectives:

The tool was tested with real data trac in a Caracas area "Los Cortijos" (Figure 65.1(a)), one of the most heavily traveled zones on the city's east side. The simulation model belongs to the microscopic category, made by Gartner et al. (2001), since only individual elements are taken into account (i.e., vehicles).

The network was created by using data from OSM, then manually modifying it (i.e., setting correct speed, capacity attributes) based on information delivered by a company conducting a study in the same area. Demand was given in a O-D matrix by the same company, but only for the morning period. As the area researched is mainly a consuming zone in the morning and a producing zone in the aernoon, values from the O-D matrix were used to create day-plans for the agents. An initial departure time around 7:30 am was assigned to the plans.

Several scenarios with dierent re-planning rates were run to test how much agents have to change their departure time in the morning to allow the network to accommodate all travel demand. Figure 65.1(b) shows how trac jams builds up in the scenario where simulated demand best matches real-world trac count.

Figure 65.2 shows the interface of the tool providing options to: load a map from OSM, load a network from MATSim, load counters (blue dots in the map image), save the map and export to a shape le (an open le format for GIS systems).

Figure 65.3(a) shows an image output of the same area aer running a simulation generated with MATSim and including a vehicle-density color map. Figure 65.3(b) shows a sample score

(b) MATSim sample score statistic for one of thescenarios dened.

**Figure 65.3:** Simulation results.

graph from one of the many tests run: avg. trip time of 02h:06m:38s and avg. distance per agent of 1727.83 meters; total run time of the simulation was 41 minutes 56 seconds.

In spite of diering approaches between the company's study and our own results, through simulations, the numbers were quite similar with a range of dierence not exceeding 3 % (-0.42 % to 2.52 %). Examining these promising results, but also the limitations encountered, the following future lines of work were dened:

**Acknowledgements** The authors wish to acknowledge Daniel Ampuero Anca and Jesus´ Francisco Gomez Ort ´ ´ız, for their Bachelor's degree nal work at Universidad Central de Venezuela. Also to oscar Anzola, founder of ´ URVISA S.A., who provided the logistics for the capture of real trac data used on the simulations.

# **Cottbus: Trac Signal Simulation**

# Joschka Bischo and Dominik Grether

The Cottbus (Germany) scenario is used for trac light simulation (see Chapter 12). It is explained by Grether (2014, pp. 87); this chapter briey reviews the main points. The scenario data is generally available to the public, and can be found from http://matsim.org/datasets.

The network was derived from OSM data in summer 2010 (Bischo, 2010), and covers all streets within the city boundaries, as well as main roads in the surrounding Spree-Neiße administrative district. It is designed as a 100 % sample. The population is based on the German federal employment agency commuter statistics for both Cottbus and Spree-Neiße (Wietholter et al., ¨ 2010). As such, the population has only home-work-home plans spread over the usual commuting times, resulting in two peaks, including 33 479 agents traveling exclusively by car. The scenario is generally not very busy; the area does not usually have major congestion issues.

Figure 66.1(a) shows the network over the "Corine Land Cover" landuse (European Environment Agency, 2011), provided by European Environmental Agency. Woods and agricultural areas are depicted; most of the region is agricultural use area. Virtual persons in MATSim need a geographic coordinate for their activities. If this coordinate is drawn randomly (solely based on municipality borders), home and work activity locations are uniformly distributed over the area, i.e., most of them in woods and elds. Thus, activity locations are drawn randomly in combination with land use data. The coordinate must be in the municipality area and for home activity, it must be located in urban fabric areas; for work locations, industrial or commercial areas are also allowed. The resulting home activity locations are shown in Figure 66.1(b).

The scenario contains data for 22 trac signals within the city center, based on the city's 2009 signal plans; junction layout is also modeled in detail. Fixed-time control data is taken from Kohler ¨ and Strehler (2010). Due to higher transport network resolution, several originally recorded xedtime control schedules are invalid and were removed; data for 22 junctions is available. Figure 66.2 shows their transport network location.

Public transit, not part of the original scenario, is available based on 2011 schedules, although it is not currently used.

### **How to cite this book chapter:**

Bischo, J and Grether, D. 2016. Cottbus: Trac Signal Simulation. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 411–412. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/baw.66. License: CC-BY 4.0

(a) Cottbus network and municipality borders. (b) Synthetic population for the Cottbus scenario, geospatial location of home activities.

**Figure 66.1:** Cottbus scenario: Network and population. Source: Grether (2014)

**Figure 66.2:** Cottbus scenario: Network, area with trac signals within the city of Cottbus. Source: Grether (2014)

# **Dublin**

# Gavin McArdle, Eoghan Furey, Aonghus Lawlor and Alexei Pozdnoukhov

### **67.1 Introduction**

To demonstrate a new spatial choice model, a microsimulation of urban trac ows for the greater Dublin region was implemented using MATSim. The scenario simulated leisure activities and commuting trips completed by individuals using private cars over a twenty-four hour period. For commuting trips, detailed information from the Irish Census was used; a new spatial choice model, inspired by the radiation model, was developed for leisure trips. The eectiveness of the approach was validated using hourly data from count stations on the main motorways around Dublin City. The results show that the microsimulation accurately reproduced trac volumes.

### **67.2 Study Area**

County Dublin, in Ireland, covers an area of approximately 115 square kilometers and encompasses several administrative areas. Dublin is a coastal county with the Irish Sea lying to the east. To capture both intra-city and inter-city ows, the scenario considered individuals who live or work in Dublin, capturing those who commute to or out of Dublin, as well as those who live and work there.

### **67.3 Network**

To capture the desired study area for the scenario, the network consisted of all roads in the greater Dublin region and major roads for the remainder of the country. The road network was a mix of motorway, national routes and local roads and was extracted from OSM, along with other information such as speed limits and number of trac lanes. This OSM network was prepared for use in

**How to cite this book chapter:**

McArdle, G, Furey, E, Lawlor, A and Pozdnoukhov, A. 2016. Dublin. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 413–418. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/baw.67. License: CC-BY 4.0

MATSim. This study focused on private vehicles; the public transport network was not considered, but can be incorporated into the microsimulation in future studies.

### **67.4 Population Generation**

The population for this scenario consisted of all car drivers who live or work in the greater Dublin region and was prepared from a variety of data sets. To obtain home and work locations, the 2011 Irish Census was used, particularly a census subset called POWSCAR (Place of Work, School or College Census of Anonymised Records). This provided home and work locations, mode of commuting transport used, time of departure for work or school and a variety of socio-economic data at an individual level. The individuals relevant to this scenario (drivers who live or work in Dublin) were extracted from the data set. In POWSCAR, home locations are anonymized by aggregating them into a statistical unit called the small area, consisting of 80 to 100 households. In the greater Dublin region, this represents a street or an apartment complex. We translated this to an individual address point by selecting a random address point within the small area. For this process, we used a commercial database of addresses and their coordinates in Ireland called Geodirectory. To account for non-workers, we used census statistics to generate the spatial distribution of the number of sick, unemployed and retired persons along with car ownership details to produce the non-working population for the greater Dublin region. These were also assigned to individual address points, providing us with a population of 600 000 agents for the scenario (see Figure 67.1).

### **67.5 Demand Generation**

Individuals from the population were assigned work and school locations according to POWSCAR (Figure 67.1). In POWSCAR, work and school locations are given at a 250 meters grid level and we translated them into individual address points using Geodirectory. For school and collage locations, the address point was checked using NACE (from the French title 'Nomenclature gen´ erale des Activit ´ es´ economiques dans les Communaut ´ es Europ ´ eennes') codes, to conrm its sta- ´ tus as an educational institute. Departure times for work and schoolwere assigned using a Gaussian curve centered at the declared 30 minute departure time from POWSCAR. INTS (Irish National Travel Survey) was used to create non-commuter demand for the road network. Through a survey, the INTS collected a 24 hour travel diary for an Irish population sample recording journey origin, destination, departure time and mode. We extracted the private car mode and combined the data with the commuter data to create a 24 hour activity chain for each individual in the population.

### **67.6 Activity Locations**

A set of activity locations were obtained from an in-car navigation system's POIs (Points of Interest) database and augmented with additional POIs from OSM. While work locations were assigned from demand generation, locations for secondary activities, such as shopping and leisure, were not specied in the INTS and so had to be modeled to create spatial and temporal activity chains for the population. We developed a radiation model variant that applied emission-absorption ideas to compute interaction probabilities for a set of origins and destinations. The radiation model was parameter-free and distance decay was replaced by a ranked-based decay (Simini et al., 2012). While generally used for modeling movement between regions or cities, we used this approach to produce probabilities of selecting dierent locations capable of fullling a given activity. Where the radiation model uses known populations of locations to produce region ranking, we used attractiveness scores for areas and facilities that could fulll an activity. A facility, venue or area's

**Figure 67.1:** The distribution of work (upper image) and home (lower image) locations for part of the Dublin scenario.

attractiveness was derived from venue size, which was calculated using domain knowledge and the model was calibrated with trip distribution patterns from social media check-in statistics. This radiation model variant was used to assign locations to secondary activitiesin the agents' day chains for the Dublin scenario demand.

# **67.7 Validation and Results**

Network, population and demand data were prepared for use with MATSim. For eciency reasons, a 25 % sample of the population was used for the simulation. The location choice model described above was used to generate the initial demand. On each interaction of the simulation, agents could be rerouted or rescheduled according to the MATSim default settings, but the locations dened in activity chains remained constant. The simulation reached a stable state aer 350 iterations. The road volume data output was scaled according to the sample used, aggregated to an hourly count and compared to the observed count data from 6 count stations on motorways around Dublin. In order to compare the eect of the new location choice model, the simulation was re-run using the MATSim nearest neighbor algorithm for selecting secondary activities' locations.

# **67.8 Achieved Results**

Aggregated hourly counts were compared with those observed at the 6 count stations which determine the number of vehicles traveling in two directions. A typical hourly distribution was obtained by averaging mid-week trac volumes for a 3 month period. The results produced by the radiation model showed a stronger correlation between simulated and observed counts than those from the nearest neighbor approach. Figure 67.2 shows hourly observed and simulated count data for two count stations; the inset shows the relative percentage error for the two approaches being tested. The results indicate that both techniques are eective for estimating commuter trac during morning and evening peaks. This was to be expected as the location of school and work activities were provided from real world data, but it did conrm the MATSim routing algorithm eectiveness. For daytime trac, which consisted mostly of secondary activities, our variant of the radiation model outperformed the nearest neighbor approach; it included individuals who were willing to travel further for better opportunities, producing more accurate results.

# **67.9 Associated Projects and Where to Find More**

The Dublin scenario validation results demonstrated the eectiveness of MATSim as a trac simulation tool and also showed the power of our spatial choice model which adapted the radiation model to predict individual movement at a small spatial scale. In the future, the research will be expanded by considering a multimodal transport network and scaling the scenario from an urban simulation to a national one. Full details of the Dublin scenario can be found in McArdle et al. (2012) and McArdle et al. (2014).

**Figure 67.2:** Hourly observed trac volumes (dashed line) compared to the estimated trac volumes produced by MATSim using the radiation model (green line) and nearest neighbor model (orange line).

# **European Air- and Rail-Transport**

# Dominik Grether

This chapter discusses simulation of air- and rail-transport technology and passengers using MATSim. There is no great dierence in overall travel times between middle-range rail and air transportation. Airports and railway stations are aected by capacity and opening time constraints. For passengers and goods, geospatial location is an important property. Both modes, but especially air transport, are faced with dicult capacity restrictions at certain departure times.

This chapter discusses how MATSim can be applied to capture these constraints and how interaction between passenger demand and constraints on technology supply can be modeled. The public transit model of MATSim (Chapter 16) is applied. Airports and aircra are microscopically modeled the same way as bus stops and buses. Passengers are represented microscopically as multi-agent demand for air transportation. Their choices of transport mode, routes, and departure time are restricted by the air transport technology simulation model's capacity. The modeling of rail transport is based on teleportation. With appropriate data, the modeling approach for air transport could also be applied to rail transport (Quick, 2012).

The modeling of technology and demand is sketched in Section 68.1. On the basis of simulation results for a pure air transport model, rail transport is added and eects of mode choice are presented (Section 68.2). Section 68.3 then interprets simulation results and highlights some modeling aspects requiring further study. The choice set generation and plans removal algorithm of MATSim is discussed in detail; that is also the subject of Section 97.4. Modeling, results, and studies of this chapter present the highlights of Grether (2014, Chapter 6, pp. 119), in more detail.

Grether, D. 2016. European Air- and Rail-Transport. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 419–428. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/baw.68. License: CC-BY 4.0

**Figure 68.1:** Layout of airports in the air transport network: In- and outbound runways are modeled by separate links connected by taxiways and a link representing the apron. There the transit stop facility is attached.

Source: Grether et al. (2013)

### **68.1 Air Transport Scenario**

### **68.1.1 Modeling & Simulation of Air Transport Technology**

The air trac technology model uses data provided by OAG Aviation.<sup>1</sup> Relevant data for schedule and network generation is taken from the September 2009 OAG data, using all ights departing on a Tuesday, taking each specic ight number into account only once. This may not always result in complete ight cycles, e.g., when the outbound and inbound ight operate on dierent days of the week. Compared to using all ights of an entire week, the network may be incomplete, as certain destinations are only served on specic days.

The air network modeling aims at a simulation with MATSim. The network consists of airports, each showing an identical layout and point-to-point connections in between. Every runway is solely used either for inbound or outbound ights, with taxiways connecting the runways to the apron. The latter accommodates a transit stop, i.e., the terminal, where ight movements originate and terminate (Figure 68.1). Each airport pair is directly connected by airway links, one for each ight and direction of travel (Figure 68.2). Maximum speed on any of these links is calculated based on distance and ight duration provided by OAG. Times for taxi, take-o, and landing are also taken into account, i.e., ight duration is reduced by the time needed from push-back to airborne before the maximum speed for an airway link is calculated. Each ight has an individual link that could be interpreted as route, each possessing individual characteristics. Figure 68.3 shows parts of the network for European air trac.

<sup>1</sup> http://www.oagaviation.com, last access 08.08.2012

**Figure 68.2:** Layout of airways in the air transport network: each airport pair is directly connected by two airway links, one for each ight and direction. Source: Grether et al. (2013)

Flight schedules are taken from the OAG data and translated to a MATSim transit schedule containing information about each line, route, and departure. For each airline oering a connection between two airports, a transit line is generated. A transit route, which represents the route on the air trac network, is created for each ight oered by this airline. Mutual interferences of aircras en-route are not included in the studies presented in this chapter.

**Figure 68.3:** European air network with country borders in the background (country borders c http://www.openstreetmap.org). Source: Grether et al. (2013)

To represent individual aircra in the simulation, transit vehicles are created on the basis of OAG data. IATA aircra codes, operating airlines, and seating capacities are reected in the respective aircra representation for every ight. Information about boarding times, i.e., passenger ow per door over time, is not available, but could be set for each aircra type. One aircra per ight is generated, thus delays resulting from a delayed incoming aircra are not modeled. Accordingly, no aircra rotations and vehicle trip chains are implemented at this time. The maximum velocity of each aircra is set to twice sonic speed, since speed limitations are set for each network airway link.

### **68.1.2 Passenger Demand**

As soon as the technology side of air transport is modeled, passenger demand simulation can begin. The passenger demand for trips in Germany created and used for the results of this section is based on O-D data of DESTATIS.<sup>2</sup> For each O-D pair and trip a virtual person is created. Each virtual person performs two activities, one at the origin and the other at the destination airport. Both activities are of same type, thus time spent performing both activities is accumulated before it is evaluated by the utility function according to Section 3.2. A typical duration, ttyp,q, of 21 hours is set for this activity type. The time virtual persons arrive at the origin airport and start waiting for a connection is drawn randomly from a uniform distribution in 4 am to 6 pm, UTC. This reects estimated typical opening hours of European airports. No other time constraints are set, thus the only incentive for virtual persons is to reduce overall travel time and maximize time spent at the activity. A ight leg is scheduled between the two activities, connecting origin and destination. As

<sup>2</sup> Deutsches Statistisches Bundesamt, http://www.destatis.de, Fachserie 8 Reihe 6, last access 10.09.2012

usual, the demand does not specify if a direct ight from O to D is chosen or the virtual person is on a route containing one or more transfers. The synthetic population contains 51 832 virtual persons, 1 550 trips from the original data are neglected as origin and destination are equal.

### **68.2 Simulation Results**

### **68.2.1 Air Transport**

As a scenario for air transport technology, a coverage model from Europe to world wide destinations is used; with the synthetic population, it serves as input for the simulation. The assignment of ights to the desired O-D connection, i.e., the passenger routing, is calculated by MATSim's default public transit routing module.

Each simulation is run for 600 iterations. In each iteration, 10 % of the virtual passengers may shi their departure time randomly within a 2 hour interval. Another 10 % may seek a new route, i.e., a connection between origin and destination. Each passenger chooses from a set of 5 plans using an MNL. The outcome is stable aer 500 iterations, then departure time choice and routing are switched o. For another 100 iterations only the MNL is used by passengers to select a plan.

Results are then taken from the output of the 600th iteration. Filtered by ights in Germany, Figure 68.4 depicts passengers in aircra (red) and seats (black) over time of day and reveals passengers' tendency to depart early.

**Figure 68.4:** Passengers in aircra and available seats over time in Germany: At any time, there are more seats than passengers. Air transport-only scenario based on O-D data for Germany, iteration 600.

Some passengers fail to reach their destination and are "stranded". This is unrealistic; only trips within Germany are modeled. These are usually completed within a few hours, with no requirement for an overnight airport stay. 320 passengers are stranded at the end of the day. Getting stranded is not a result of insucient seating; at any time of day, there are more seats than demand. There are many reasons why passengers could be stranded in such a situation. Further analysis of the clineswitch = 0 scenario simulation results indicates:

Behavioral aspects: neither departing early, nor getting stranded, are explicitly modeled.

### **68.2.2 Adding an Alternative Mode**

To gain further insights, in the following a slightly dierent simulation setup is applied. A second option for mode choice is added. Each virtual passenger can now choose between the microsimulated air transport options and an alternative mode. The alternative mode has no capacity restrictions. Passengers traveling with the alternative mode can start directly at their randomly selected departure time. The travel time, tt, is computed by the microsimulation, with an estimation of the beeline distance between the O-D pair d and a velocity v, i.e., tt = d/v. This velocity is varied in several simulation runs, i.e., v ∈ {100,150,200,250,300}[km/h]. If the alternative mode is chosen, the (dis-)utilities for traveling are calculated accordingly in the scoring.

With this population, the simulation is again run for 600 iterations. As in the previous simulations 10 % of the virtual passengers may shi their departure times, while another 10 % seek a dierent route between origin and destination in the air transport network. Additionally, further 10 % of virtual persons may change mode, i.e., they can switch between the air trac mode and the alternative mode. Aer 500 iterations all choice modules are switched o; thus, for the last 100 iterations, passengers use the logit model to select a plan.

Simulation results for the 600th iteration show that the increasing speed of the alternative mode aects the modal split. While for a v = 100km/h the alternative mode is chosen by 1.2 % of the passengers, a mode alternative with a speed of 300 kilometers per hour attracts 15.69 % of travelers. The number of stranded passengers for the alternative mode with v = 100 kilometers per hour is substantially reduced, from approximately 320 to 67. Higher speeds of the alternative mode further reduce the number of stranded passengers. Slow speeds of the alternative mode imply dominance of the air transport mode. If there is a seat on a ight, travelers receive a higher score than when they use the alternative mode. However, travelers risk getting stranded, which can be hard to analyze and interpret. The implemented algorithm is also an open issue; if the number of plans per traveler exceeds a threshold of 5, the plan with the lowest score is removed from the plan database.

Instead of this deterministic plan removal, a probabilistic algorithm can be implemented: e.g., plans for removal can be selected based on a path size logit model. With this modication, simulation runs are repeated. Figure 68.5 shows the resulting travel patterns over time for alternative modes at speed 100 kilometers per hour and 300 kilometers per hour. Traveler distribution on the alternative mode over time of day is quite homogeneous. The alternative mode speed increase attracts more passengers, as reected by the modal splits in Table 68.1. At most, one passenger is stranded at the end of day.

Simulation results are compared in more detail with DESTATIS data serving as a base for the virtual population. Synthetic population is generated based on O-D pairsthat may contain transfers (odtransfers), while other DESTATIS data counts the number of passengers on actual direct ights

**Figure 68.5:** Passengers waiting for a ight, traveling by plane, or by alternative mode over time of day. Air transport and alternative mode scenario for Germany, iteration 600. Results with random selector for plan removal.

Source: Grether (2014)

**Table 68.1:** Modal split for dierent speeds v of the alternative mode. Air transport and alternative mode scenario for Germany, iteration 600. Results with random selector for plan removal.

(oddirect). The latter is used to evaluate model accuracy. For comparison, number of passengers on direct ights is calculated for each O-D pair (simdirect) from the simulation results. Based on these data sets, the mean square error and the mean relative error are calculated.<sup>3</sup>

Table 68.2 shows the outcome of these calculations. The rst line is the comparison of two input data sets from DESTATIS.<sup>4</sup> This serves as reference, as it would assume that all demand is served by direct ights. All simulation runs explain the data better than that reference. Mean square error and variance increase with the speed v of the alternative mode; logical, as the demand covers only air transport trips.

<sup>3</sup> The mean square error σ 2 is computed as σ <sup>2</sup> = P <sup>i</sup>∈OD(simdirect(i)−oddirect(i))<sup>2</sup> |OD| , whereby |OD| denotes the number of O-D pairs, simdirect(i) the simulated passengers on a direct flight between the O-D pair i, and oddirect(i) the same, but retrieved from data. With the same values, the (unsigned) mean relative error for each O-D relation is calculated as mean rel error = P i∈OD |(simdirect(i)−oddirect(i))|/oddirect(i) |OD| .

<sup>4</sup> In the calculation, simdirect is replaced by odtransfers.

**Table 68.2:** Error calculations for dierent speeds v of the alternative mode. Air transport and alternative mode scenario for Germany, iteration 600. Results with random selector for plan removal.

### **68.3 Interpretation & Discussion**

The alternative mode can be dened as a combination of train, bus, or car connection availability. Clearly, the results hinge on the assumption that the alternative mode is always available and not capacity-restricted. All passengers on the alternative mode travel at the same speed, but this assumption is too coarse for the scenario presented. For example, average speed and temporal availability of train connections depends on the O-D pair. In principle, the alternative mode could be rened by including O-D pairs' dependent average speed data. Alternatively, train, bus, and car can be simulated explicitly, featuring capacity restrictions and mutual interactions. Even considering these factors, a homogeneous velocity for the alternative mode seems to be more appropriate for the overall modeling approach illustration. Eects triggered by the alternative mode availability are illustrative. Data for the demand provides O-D pairs for air transport, but not for car, train or bus trips. For more plausible interpretations, further demand data for other modes is required.

All the presented modeling approaches explain passenger routing in more detail than technically possible from the input data. Most passengers use a direct connection, which is very plausible, considering the geospatial demand extent. Flying within Germany is oen not worthwhile if the connection includes a transfer; empirically it is faster to travel by train, car, or bus. For further insights, the geospatial extent of the modeled demand could be increased; but this depends on data availability, not on the overall simulation approach.

Passengers are modeled without specic desired departure or arrival times. This study's input data does not contain any information about time distribution. The simulation approach can capture such individual time constraints and the information can be added, without too much eort, with some more data, thus resolving several departure time choice problems.

Stranded passengers are an unwanted product of the simulation. Without the alternative mode, the only available transport mode is a capacity-restricted ight connection provided in discrete, irregular time intervals. The number of stranded passengers is higher than for the simulation runs with the alternative mode. Passengers are more likely to get stuck in O-D pairs, where demand is higher than seat capacity, for extrinsic and intrinsic model reasons.

The quality of the simulation model's outcome hinges on the data available. For older studies of air transport passenger demand, DESTATIS data for 09-2011 was used, but the air transport technology model was created on an 09-2009 ight schedule. The number of ight starts within Germany increased slightly between 2009 and 2011 (DLR, 2012, p. 23). Assuming that the number of available seats increased accordingly, the simulation model provided too little capacity, at least on certain O-D pairs. As result, the number of passengers not reaching their destination (stranded) was much higher. With the availability of 09-2009 DESTATIS data, the overall quality of results improved. Replacement of the 2011 data with 2009 data reduced the number of stranded passengers signicantly, from around 1 500 to 350 travelers.

Data is provided on a monthly basis, while the simulation model time horizon is one day. Number of trips per day is retrieved using the assumption that trips are uniformly distributed over all days of a month. The remaining 350 stranded passengers might be resolved by a more accurate distribution. Otherwise, a longer time horizon could be simulated.<sup>5</sup> This would also include ights not departing on a Tuesday. With these alternations,the issue of stranded travelers might be solved.

The problem of stranded passenger can be model-intrinsic. The algorithm removing plans is apparently critical to avoid stranded passengers. Replacing the deterministic formulation with the probabilistic resolves most of the stranded passenger problem. The applied path size logit modeling approach seems to be feasible, but requires further studies for parametrization and interpretation. In general, this modeling approach allows the generation of more heterogeneous choice sets, see also Section 97.3. With the deterministic plan, removal plans with a high score (but similar structure) dominate all other generated plans. In combination with capacity restrictions, lack of alternatives results in stranded passengers. All other approaches to simulate more heterogeneity—discussed on the following—should consider these eects.

In further studies, departure time choice and cost structures can be rened. If there is only one early connection to a hub per day, some passengers' departure times might be too late to make connections. The random departure time mutation may not be able to nd a connection for all passengers. This has been ruled out for the current setup, but should be considered in further studies.

Alternatively, passengers could have a connection that works in theory, but are "crowded out" by other passengers arriving earlier at the gate; these passengers would reach their destination if they would take a dierent route. The current approach would not nd such a solution, since passengers do not consider costs they impose on others; see Lammel and Fl ¨ otter ¨ od (2009) for an approach ¨ taking that into account. The real-world solution, presumably, would be to raise prices on seats during congestion periods until a passenger re-routes. Currently, all passengers have homogeneous time values. For a more meaningful price modeling, additional heterogeneous passenger attributes can be included. As the present model is based on only O-D data, it does not include such a process. In principle, other data, e.g., Lorenz curves and median incomes, can be merged with the O-D data (Kickhofer et al., 2011). ¨

An alternative approach to improve heterogeneity is a router generating a greater route diversity for the same departure time. Such a router would be able to direct a passenger to a route where seats are available, without actually knowing about seat availability. That approach would, however, not address the issue that some passengers might need to switch their path to allow others to obtain a feasible path. In Graf (2013), a rst prototype of such a router is tested in a dierent context, with rst tests for the ight model revealing only slight improvement. As more diverse routes are dominated by the direct connection, they are removed by the algorithm similar to routes on slow alternative modes. Aer this general problem is solved, a more diverse routing should be reconsidered.

### **68.4 Conclusion**

Overall, the results show that a microscopic, agent-based simulation of passenger demand for air transport is feasible. Most passengers are able to learn the constraints of air transport technology and arrive at their desired destination.

The technology modeling is similar to the Clarke et al. (2007) approach, although the level of detail is coarser. In the same way as Clarke et al. (2007), further models for, e.g., gates, taxiing, weather

<sup>5</sup> Note, that this requires some changes in the source code that may not be resolved by sole customizations of MATSim. Please ask the developers before running MATSim for a longer time horizon.

or airline operations can be added to the approach. As the open source code of MATSim comes with options for extension, more detailed models of the technology side hinge on the availability of data. In contrast, and going beyond Clarke et al. (2007), passengers are captured at all stages of their trip and passengers traveling on alternative transport modes can be simulated. The chapter discusses certain open general issues not specic to air transport systems. Interested users should support the MATSim team in solving these more general questions rst, which will aid the model in achieving a more detailed picture of mid-distance travel patterns.

Clearly, potential applications of the proposed model depend on type and detail of information included. In general, application for policy planning allows a more detailed evaluation of middistance travel policy eects, including mode alternative consideration. The approach could also be useful for private companies' planning of ight-schedules and capacities to their connections. The impacts of these changes on customers can be assessed in close detail.

# **Gauteng**

Johan W. Joubert

Gauteng is a landlocked province in South Africa, with three main metropolitan areas: the city of Johannesburg, city of Tshwane (formerly Pretoria) and Eurhuleni. Although the province covers less than 3 % of the country's surface, it is the country's economic hub and contributes a third of the country's GDP (Gross Domestic Product). The 2011 census reported a population of 12.2 million inhabitants, a quarter of the South African population.

The rst Gauteng scenario was developed in 2008/9 and appeared in Fourie (2009) and Fourie and Joubert (2009). The population was synthesized from 2001 census data and travel demand was inferred from the 2003 NHTS (National Household Travel Survey). Initially, the network was created from a proprietary source made available for research purposes this has been replaced with a much richer OSM network.

Early comparisons already showed that the Gauteng MATSim scenario provided far more detailed results than the four-step models available at the time (Fourie, 2010). The scenario was also extended to include freight vehicles (Joubert et al., 2010).

With the introduction of an open-road tolling scheme referred to as the GFIP (Gauteng Freeway Improvement Project), the scenario was used to study the diversion patterns of dierent road user groups. The population was extended to included background trac, in the form of public transport (buses and minibus taxis) and external through-trac. This data was taken from Saturn O-D-matrices made available by the sponsor, the SANRAL (South African National Roads Agency Limited). The impact of the tolling scheme, using vehicle-specic values of time, and a complex toll pricing regime was reported in Nagel et al. (2014).

The most recent update to the synthetic population generation for the Gauteng scenario is documented on MATSim's https://matsim.atlassian.net/wiki/display/MATPUB/South+Africa Conuence site.

Joubert, J W. 2016. Gauteng. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 429–430. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/baw.69. License: CC-BY 4.0

# **Germany**

# Johannes Illenberger

The Germany scenario was developed by DB ML AG (DB Mobility Logistics AG), a subgroup of DB AG (Deutsche Bahn AG), the German state-owned railway company, in 2014. To help evaluate MATSim's applicability in the strategic planning process, as well as dening its compatibility in the traditional zone-based four-step process, this scenario has been constructed to establish a Germany-wide O-D matrix for private car travel. A solid understanding of the transport market for private car travel (rail's major competitor) is required for the strategic planning process at the DB ML AG. This scenario intentionally focuses just on road transport since, on one hand, there are already well-established models for rail transport at the DB ML AG and, on the other hand, this scenario is meant to be the rst step towards MATSim's application.

Considering this scenario's objective, MATSim is used here as a tool to build a microscopic representation of the current transport market. Unlike the majority of MATSim studies, the focus is not to build and calibrate a behavioral model with forecasting power to answer the "what if " question. Hence, this study emphasizes reproducing empirical measurements, rather than on modeling plausible causalities and behavioral processes.

The nal outcome of this exercise is a O-D matrix with average daily trip volumes. Although a higher temporal resolution is possible and also available from travel data, this dimension will not be considered during calibration and validation of the scenario. The matrix is based on a zonal structure with approx. 10 K TAZs, with a granularity comparable to municipalities (LAU 2) and a higher detail in large cities.

Illenberger, J. 2016. Germany. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 431–436. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/ baw.70. License: CC-BY 4.0

**Figure 70.1:** Le: Simulation road network including roads down to the level of major arterials. Right: Spatial distribution of activity facilities colored according to activity type.

### **70.1 Demand and Supply Data**

The German national travel survey MiD from 2008 (Follmer et al., 2010) builds the foundation for the synthetic population and its travel plans. The survey features approx. 60 K person records with one-day travel episodes. A travel episode species trip sequences with mode, purpose, travel distance and day of reporting. Home locations are known at the level of states and municipality type (urban/rural). From each record, a synthetic person with one travel plan is generated. Initially, activity locations are set to random facilities and each person is cloned multiple times (according to the person's weight), so that in total a population of 4 M persons results.

The road network is extracted from OSM. The geographical resolution of the O-D matrix allows omission of minor roads (everything below "tertiary" in OSM terminology) and the network is further simplied so that connected nodes with a distance less than 50 meter are merged to one node. The resulting network consists of 126 K nodes and 360 K links (Figure 70.1).

Activity facilities are taken from OSM as well. A facility can be synthesized from a OSM node representing a point-of-interest (shop, restaurant, bar, etc.), a polygon representing a building and a polygon representing a region with specied land use. In the latter case, multiple facilities are generated proportional to the polygon's area. Activity options are inferred from meta information associated with the node or polygon. Given the huge amount of "home" facilities, only a 20 % subsample is used. This still yields a total of 5.6 M activity facilities.

### **70.2 Imputation and Calibration**

The location of activities—origin and destination of trips—are required for building the O-D matrix. The travel survey does not provide any information on activity locations. Thus, the study's main task is to impute plausible activity locations, given the sequence of trip distances and underlying geographical distribution of activity facilities. The intermediate solution resulting from this imputation is calibrated against count stations and selected O-D ows from car navigation devices.

The imputation process implementation can be considered as a Monte Carlo Markov Chain simulation converging into a distribution where the activity locations conguration best ts the constraints imposed by trip distances, count stations and selected O-D ows. Solving this task in one simulation process would be congruent with theory. However, considering the scenario size and computational limitations, the process is split into three steps: (i) assigning "home" locations, (ii) generating an initial state with assigned "non-home" activity locations, and (iii) varying a subset of "non-home" activity locations to meet car volumes at count stations and selected O-D pairs' ows. Steps (i) and (ii) can be considered as imputation processes and are realized outside of the MATSim iteration framework. Step (iii) can be considered the calibration step and is realized with a MATSim-Cadyts setup congured as a Monte Carlo engine (see Chapter 48).

### **70.2.1 Imputation of Home Locations**

Home locations are known at state and municipality type levels. The municipality type is divided into six categories by number of inhabitants. Initially, each person is placed on a random home facility, while inhabitants' geographical distribution is given at the TAZs level. A Monte Carlo simulation relocates persons to best meet their specied state and municipality. More formally:

	- (a) Randomly assign each person n a home facility.
	- (b) Evaluate the conguration: H (Pk) = P n θ1δ<sup>n</sup> + θ<sup>2</sup> <sup>m</sup><sup>n</sup> <sup>−</sup> <sup>m</sup><sup>∗</sup> n , where δ<sup>n</sup> is 0 if the person is located in the correct state and 1 otherwise, m<sup>n</sup> denotes the category index of the current person's municipality and m<sup>∗</sup> n its target category. Parameters θ<sup>1</sup> and θ<sup>2</sup> control how close the simulation converges to the target values.

Switching home facilities, instead of assigning a random facility, ensures that persons' spatial distribution remains constant. Running the simulation for 10<sup>9</sup> iterations results in a conguration where more than 90 % of persons are located in their correct state and, on average, three of four persons are located in their correct municipality; the forth persons is just one category index distant from its target index.

### **70.2.2 Imputation of Non-Home Locations**

Activity locations (non-home) are assigned with an analogous process, like home locations. The simulation relocates activities so that resulting trip distances best meet their empirical target distances. In this imputation step, distance always refers to the beeline distance and thus avoids expensive route search.

About one forth of all trip chains are composed of more than two trips; that is, trips are not symmetrical. Accordingly, drawing a random activity location on a annulus centered at the origin activity location, with radius according to the target distance, does not necessarily fulll the target distances of succeeding trips. The simulation process is specied as follows:

	- (a) Assign a random facility to each non-home activity by considering the activity type and the facility's activity options.
	- (b) Evaluate the conguration: H (Pk) = P n P q θ3 dnq−d ∗ nq d ∗ nq , where <sup>d</sup>nq denotes the realized distance of n's trip to activity q and d ∗ nq the target distance.

A conguration is evaluated based on the relative error or realized distances to target distances, so that short and long trips are treated equally. Parameter θ<sup>3</sup> controls the randomness. That is, if θ<sup>3</sup> = ∞ each trip would exactly (if possible) meet its target distance, which, however, is not the desired solution. Rather, θ<sup>3</sup> is adjusted so that there is some randomness in realized trips distances, but without distorting global target distance distribution.

### **70.2.3 Calibration**

The outcome of step 2 (Section 70.2.1) and 3 (Section 70.2.2) is a valid population with imputed home and activity locations. In the third step, the population is calibrated against measurements of count stations and ows of selected O-D pairs. This step is implemented in a "standard" MATSim-Cadyts combination. The scoring function accounts only for the "linear plan eect", agents are allowed to have only one plan and SelectExpBetaForRemoval is used for the planSelectorForRemoval parameter. All Cadyts parameters are le to their defaults.

During replanning, non-home activities that are not part of a complex trip chain are relocated. More specically, an activity is valid for relocation if:

The above conditions ensure that complex trip chains that have been adjusted in step 2 (Section 70.2.2) are not distorted. New activity locations are randomly chosen within a distance of ±10 % of the trip's target distance, so that global distance distribution is conserved.

### <IP_ADDRESS> Counts Calibration

The German Federal Highway Research Institute, BASt (Bundesanstalt fur Straßenwesen), ¨ provides average daily vehicle volumes (distinguished in car and trucks) yearly, measured at about 1500 count stations on motor- and highways. Aer separating each station's data into both directions of trac and validating measured vehicle volumes, about 2 500 link volumes are available for calibration. Empirical car occupancy rates (depending on trip purpose) are taken from the MiD to convert person volumes to car volumes.

### <IP_ADDRESS> O-D Calibration

O-D calibration is based on an O-D matrix representing car navigation device ows. Since occupancy rate of devices in cars is unknown, only the distribution of ows is used for calibration. Further, comparison with other data sources shows that the O-D matrix is only valid for O-D pairs above a distance of 100 kilometers. This appears reasonable, considering that navigation devices are probably not switched on for short (likely commuter) trips. Accordingly, only O-D pairs above 100 kilometers distance and, from those, only the 6 000 pairs with the highest volumes are extracted into a reduced calibration matrix. The latter conditions ensures that only pairs with a sucient sample size are used.

The reduced calibration matrix is normalized to the sum of all trips in a reference matrix. The reference matrix contains all trips from the initial population that correspond to all non-null O-D pairs (matrix entries) in the reduced calibration matrix. This yields a calibration matrix with valid absolute trip volumes.

For each O-D pair, a virtual link is inserted into the road network at runtime. A virtual count station with the corresponding count value from the reduced calibration matrix is attached to each virtual link. In the mobility simulation, aer a person arrives at its destination, it then travels the virtual link corresponding to the traveled O-D pair. This travel, however, is only communicated towards the Cadyts calibrator by injecting additional PersonDeparture, LinkLeave, and PersonArrival events.

### **70.3 Simulation Results and Travel Statistics**

The synthetic population of 4 M persons corresponds to approx. 8.5 % of the German population that conducts at least one car trip per day. This yields a scaling factor of 11.8 by which all simulation statistics need to be multiplied. The simulation produces an overall trip volume of 57.5 M trips, quite close to 57.2 M trips in the ocial statistics (DIW, 2014). Passenger mileage of 947 G personkilometers is slightly overestimated compared to 917 G person-kilometers in the ocial statistics, yet still reasonable.

Figure 70.2 visualizes the calibration results in a scatter plot. Each dot represents a count station (le) or a O-D pair (right), respectively. On average, absolute value of the relative error yields 0.18, considering vehicle volumes at count stations and 0.16 considering O-D ows. A median relative

**Figure 70.2:** Comparison of simulation and empirical measurements. Le: count stations. Right: O-D pairs.

count station error of 0.12 indicates that there are a few count station that are signicantly o. These two issues should be noted; rst, there is only a description of the count station's location, which is not always unique and may be misinterpreted by the matching algorithm. This can yield a false assignment of count stations to network links. Second, there is no information on the count stations' measuring error, or on temporarily capacity reductions (for instance, caused by construction sites) modeled in the simulation network.

O-D ows error correlates with the O-D pairs' volumes; thus, pairs with low volume show, on average, a higher error. This is related to this scenario and MATSim's characteristics: a population sample size (8.5 %) and the discrete nature of MATSim. For instance, a real-world O-D ow with 118 individuals is represented by ten agents in the simulation. A variation during re-planning to this O-D pairs by, say, one agent already has a signicant impact on the scaled real-world value. Averaging over multiple iterations reduced the variance but does not entirely remove the eect.

# **Hamburg Wilhelmsburg**

# Hubert Klupfel and Gregor L ¨ ammel ¨

The following describes the evacuation of Hamburg-Wilhelmsburg as a case study. The scenario has been created using MATSim's evacuation contribution. Technical details about the evacuation contribution are given in Chapter 41.

Wilhelmsburg was severely ooded in 1962. Since then, many structural and operational improvements have been implemented. Back then, the housing situation was rather bad, many people lived in provisional housing due to destruction in World War II. Additionally to the by far more stable buildings, precautions for ooding have been taken and the walls have been heightened. Evacuation is nevertheless necessary under certain circumstances. The relocation of one of the major roads in Wilhelmsburg, the B75, will also inuence the evacuation trac, since it is one of the major north-south arterial roads. In this case study, the consequences of this relocation on the evacuation of Hamburg-Wilhelmsburg is investigated.

### **71.1 Brief Description**

The scenario investigated here is the relocation of highway B75 in Wilhelmsburg. Two cases are investigated, as summarized in the following table. The investigation highlights dierences in the evacuation trac for both variants of the B75 trail. As seen in Figure 71.1, the new trail "B75 new" is located generally next to the existing railway track. In the south, the new variant is connected to the existing highway at the junction "Hamburg Wilhelmsburg Sud" (just north of the bridge ¨ across the river Elbe); in the north, it is connected to the existing highway just before the junction

**Table 71.1:** Scenarios.

Klupfel, H and L ¨ ammel, G. 2016. Hamburg Wilhelmsburg. In: Horni, A, Nagel, K and Axhausen, K W. (ed ¨ s.) The Multi-Agent Transport Simulation MATSim, Pp. 437–444. London: Ubiquity Press. DOI: http://dx. doi.org/10.5334/baw.71. License: CC-BY 4.0

**Figure 71.1:** The current trail of highway B75 is shown in the center of the image. The new trail is east of it next to the railroad.

"Hamburg Georgswerder". The main dierences between the two variants are the location of the access routes to highway B75 in the center of Wilhelmsburg.

# **71.2 Road Network**

The MATSim road network is generated ("imported") from the Hamburg OSM le, downloaded from http://www.geofabrik.de. Fortunately, the OSM le already contains the new B75 highway track, marked by an attributed "open 2016". Therefore, the two networks forthe "B75 old" and "B75 new" variants could be derived from the same OSM le. For the variant "B75 old", this le could be directly imported. For the variant "B75 new", the section of B75 to be relocated has been removed in a rst step. In a second step, the new B75 track has been connected to the existing road network, i.e., the B75 north at junction "Georgswerder" in the north and junction "Hamburg Wilhelmsburg Sud" in the south. ¨

**Figure 71.2:** Comparison between network for the old and new track of the B75.

**Figure 71.3:** Roads closed during evacuation.

Additionally, the on and o-ramps to the B75 have been added. The two variants of the resulting road network, i.e., "B75 old" and "B75 new" are shown in Figure 71.2.

In an evacuation, some roads would be blocked to avoid intersecting and inbound trac. The following streets were thus deleted in the OSM le:

An illustration is given in Figure 71.3.

## **71.3 Evacuation Scenario**

The comparison of the two variants is based on overall evacuation time, clearing time of dierent cells (squares in the area that had to be evacuated) and the number of cars using the road network (utilization).

As described in Section 41.4, the input les for the network (OSM), the area (ESRI shp), and the population (ESRI shp), as well as the parameters for sample size and departure time distribution, have been specied and assessed via a GUI. The scenario XML le for the existing (or "old", in German "alt") track of highway B75 is shown in the following listing.

```
<? xml version =" 1.0 " encoding =" UTF -8 " standalone =" yes "? >
< grips_config
 xsi:schemaLocation =" http: // www . matsim . org / files / dtd
 http: // matsim . org / files / dtd / grips_config_v0 .1. xsd "
 xmlns:xsi =" http: // www . w3 . org /2001/ XMLSchema - instance ">
    < networkFile >
         < inputFile > osm / hamburgB75_alt . osm </ inputFile >
    </ networkFile >
    < mainTrafficType > vehicular </ mainTrafficType >
    < evacuationAreaFile >
         < inputFile > area / area . shp </ inputFile >
    </ evacuationAreaFile >
    < populationFile >
         < inputFile > population / population . shp </ inputFile >
    </ populationFile >
    < outputDir >
         < inputFile > matsim_output_B75alt </ inputFile >
    </ outputDir >
    < sampleSize > 0.1 </ sampleSize >
    < departureTimeDistribution >
         < distribution > normal </ distribution >
         < sigma > 1800.0 </ sigma >
         <mu > 1800.0 </ mu >
         < earliest > 0.0 </ earliest >
         < latest > 3600.0 </ latest >
    </ departureTimeDistribution >
</ grips_config >
```
### **71.3.1 Departure Time Distribution**

The departure time distribution is specied in the le scenario.xml. The values were in seconds, i.e., a normal distribution with a mean value (mu) and a standard deviation (sigma) of 30 minutes in the range of zero (earliest) to one hour (latest) was chosen. More details about time distributions are discussed in Section 41.4. This distribution reected certain assumptions made about evacuation procedure. The overall time frame, based on the warning time, is minimum 7 hours. The preparation phase is projected with three hours. Available time for the evacuation is three hours, with a one-hour buer. The warning via radio will start at t=0 hours and local warning (e.g., by police cars, sirens, and via short messages) at t=1 hours; simulation reference point was set to t=3 hours. The overall time acceptance criterion for the simulation is the a required safe evacuation time (for simulation by car) of less than three hours (including reaction time). The reaction time set in the simulation could be interpreted as decision-making time aer readying personal belongings. In short: ASET (Available Safe Evacuation Time) determined by ooding is 3 hours and the RSET (Required Safe Egress Time) is estimated by the simulation. The criterion for a successful evacuation is ASET > RSET.

### **71.3.2 Population Size**

As explained previously, the population is not stored in the scenario le, but in the population shape le, possibly consisting of several polygons. The number of persons is stored as an attribute for each polygon. Here, one must assume that only part of the population would have to evacuate; for many, escape to higher ground might be sucient. Detailed information on the dierent procedures can be found at http://www.hamburg.de/sturmflut/3425646/sturmflut-download-1/ (in German).

**Figure 71.4:** The initial distribution of the agents for the evacuation of Wilhelmsburg (for both cases, B75 new and old).

Each agent represented one evacuee traveling by car. To independently check the number of agents based on the simulation les, one could open the le population.dbf with a database or spreadsheet editor. Note that the number specied in the population.shp (resp. population.dbf) is multiplied with the sampleSize when converting the les to MATSim input, i.e., in this case, the population.xml.gz located in the output directory.

The population is initially distributed as shown in Figure 71.4. The algorithm that converted the area and population (i.e., area.shp and population.shp) is described in Section 41.2. It assigns agents to the edges of the network. In the case study, harbor areas are le out and agents are equally distributed to streets in the housing (and agricultural) areas of Wilhelmsburg (Figure 71.4). Of course, this could have been further rened by going to a block, or even house level and assigning the population according to detailed statistical housing data. This has not been undertaken for this simulation, for two reasons. First, many assumptions are made about behavior, initial location, and share of population that had to evacuate. Therefore, the level of detail seemed to be sucient. Second, each agent represented a car driver, i.e., in the simulation, all cars registered in Wilhelmsburg le the area. Considering that inbound, as well as through trac would be prohibited when ooding level exceeded a certain threshold, this is a "worst case" assumption resulting in a heavy trac load. To summarize, the overall approach is justied to assess highway B75 relocation based on heavy trac load with a reaction time span between 0 and 1 hour.

# **71.4 Simulation Results**

The simulation results are summarized in Table 71.2. The 0th iteration is based on shortest distance only. This might have resulted in "strange" behavior, as illustrated in the following Figure 71.5 (south of the bridge across the Elbe river, near the junction "Großmoordamm"). The road network had a circular shape; it was cut out from the OSM road network according to the area.shp, which is, in this case, just a circle. Since all the agents are taking the shortest path in iteration 0, they headed to the nearest road out of the evacuation area. Technically, the boundary links in the network are connected to a super link when creating the MATSim network from the OSM le and the

**Figure 71.5:** Results for B75 old, iteration 0.

**Table 71.2:** Results.

area.shp. This super-link is the destination in all evacuees' plans. A second factor that contributes to congestion in iteration 0 is a short cut via an on- and o-ramp of Autobahn A253 at "Großmoordamm". Capacity of the on- and o-ramp is 1 500 cars per hour, compared to 4 000 cars per hour on the highway. Thus, the short cut (which is shorter in distance, the reason agents chose it) was a bottleneck, resulting in articial congestion in iteration 0. Therefore, the 0th iteration was unsuitable for assessing the overall evacuation time. As can be seen from Table 71.2 above, for both cases, from iteration 10 on, time presumably converges to some realistic value. This was also illustrated in Figure 71.6 where the situation at t=1:30 hours was shown for iteration 20.

In summary, relocation of highway B75 had no major inuence on the overall evacuation time. The evacuation time of about two hours was also within the available safe egress time, as described in the previous section.

It would certainly have been possible to analyze the results further. The two screenshots above, for the situation in iteration 0 at t=3 hours and for iteration 20 at t=1.5 hours, were created with Senozon AG Via (the visualizer presented in Chapter 33). As a conclusion to this chapter and an illustration for the built-in capabilities of the evacuation contribution for analyzing simulation results, road utilizations of the two variants are shown in Figure 71.7.

**Figure 71.6:** Results for B75 old, iteration 20.

**Figure 71.7:** Comparison between network utilization for the old and new track of the B75.

# **Joinville**

# Davi Guggisberg Bicudo and Gian Ricardo Berkenbrock

Joinville Prefeitura Municipal de Joinville (PMJ) (2015) is a mid-sized industrial city in the south of Brazil, with around 550 000 inhabitants. It has a large workforce, including commuters from neighboring cities and an intense industrial activity prole, meaning that companies work oen in three shis, causing peculiar trac patterns. Many people also have 12-hour daily routines, encompassing work and higher education.

The Joinville trac model was built as an initial step of a project to simulate the entire northeast region of Santa Catarina state, including air trac, shipping, state highways and neighboring cities. The project aims to build a complete data base of people and freight movement in the region. The rst version of the urban Joinville model is now complete, produced as a graduate thesis at the Federal University of Santa Catarina (UFSC) http://ufsc.br, Transportation and Logistics Engineering course http://transporteslogistica.joinville.ufsc.br. 1

The scenario population was generated with data from the 2010 Brazilian census combined with demographic information from the city's travel survey; travel demand was generated from the same survey. Both were designed to t into the MATSim, using Tutorial classes (with some adaptations).

The network was produced with vector data provided by the local Urban Sustainable Planning Institute of Joinville (IPPUJ) https://ippuj.joinville.sc.gov.br. The data came as a shapele, with numerous connectivity problems. We were able to x them using scripts in Python with the NetworkX module (Hagberg et al., 2008). Information was transformed from vector data into a graph, addressing issues with the help of QGIS and nally writing as the MATSim XML network format. The facilities were produced from land-use data provided by the city government.

For now, the model runs only with cars, using a full sample of the population. From the available data, we inferred 135 652 agents traveling by car; the rest were removed from the simulation. Figure 72.1 shows a screenshot of the Events using Via.

<sup>1</sup> The authors would like to thank their sponsors Federal University of Santa Catarina (UFSC) and Urban Sustainable Planning Institute of Joinville (IPPUJ).

Guggisberg Bicudo, D and Berkenbrock, G R. 2016. Joinville. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 445–446. London: Ubiquity Press. DOI: http:// dx.doi.org/10.5334/baw.72. License: CC-BY 4.0

Figure 72.2 shows the comparison between simulated and count data for 20 links in the morning peak from 7 to 8 am. The count data available for comparison is still sparse and could not be used as eectively as we hoped; we know that calibration is needed for the next model versions. The good news is that the local authorities are installing more than a hundred counting stations throughout the city within the next couple of months and a new travel survey will be conducted this year.

**Figure 72.1:** Screenshot of the simulation using Via.

**Figure 72.2:** Count comparisons for the morning peak at 7-8 am.

# **London**

# Joan Serras, Melanie Bosredon, Vassilis Zachariadis, Camilo Vargas-Ruiz, Thibaut Dubernet and Mike Batty

The building of a travel demand model for London started to take shape under the EUNOIA Project.<sup>1</sup> The core decisions around the model design were taken aer two meetings with TfL (Transport for London), which was part of the Advisory Board in the project. In that respect, the main suggestion by TfL was the adoption of an activity-based approach.

The main traits from the current implementation of the London model are listed next:

## **73.1 Supply**

The assembly of the supply for our model includes the denition of the following three components:

<sup>1</sup> see http://eunoia-project.eu

Serras, J, Bosredon, M, Zachariadis, V, Vargas-Ruiz, C, Dubernet, T and Batty, M. 2016. London. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 447–450. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/baw.73. License: CC-BY 4.0

The data used to build the road network is the Integrated Transport Network from the Ordnance Survey. The source network, which is dened at the navigation level, has been processed to remove some of the detail included. Decisions on the capacity of each road link has been based on the guidelines proposed by COBA Manual (2002)(Vol. 13),<sup>2</sup> by the UK's Department for Transport.

All the public transport services operating within the case study region have been obtained from timetable data held by the National Public Transport Data Repository from 2009<sup>3</sup> . This dataset, includes a very detailed account of all the services operating in the UK.

Finally, the land-use conguration for the London model has been produced using the Ordnance Survey AddressBase layer which keeps address records for all the United Kingdom with a denition of land-use for each one of them. We have processed the detailed spatial information in order to assign each address point to the nearest road link in the network; this means that aer this process, each link in our network will contain a number of addresses which include the land-use associated to it. We have also mapped the wide categorization associated to each address point to the activity types from the model: home, work, shop, education, leisure and other.

### **73.2 Demand**

In order to dene the travel demand associated to London, we have followed the methodology adopted in TRANSIMS<sup>4</sup> . In this respect, we rst generated a synthetic population representative of the case study area and then, we assigned the sequence of activities to each synthetic individual.

We created our synthetic population using a simulated annealing technique based on Metropolis et al. (1953). We have used the following two datasets: Census 2011 data for each of the 850 wards in London and the HSAR (Household Sample of Anonymised Records) for England in 2001. This technique is based on the selection of survey households from the HSAR which best match the overall socio-demographics from the Census 2011 for each of the 850 wards in London. The output of this technique includes a number of synthetic households associated to each ward and, correspondingly, the synthetic individuals which cohabitate within the household with very detailed socio-demographic information.

The assignment of each synthetic household to our network has been achieved using a probabilistic distribution based on the use of home-only activity locations within each ward.

The assignment of skeletal activity patterns for each synthetic individual has been executed using Classication and Regression Tree Algorithms much like in Speckman et al. (1998). More speci- cally, the multivariate regression tree algorithm. This technique aims to produce clusters of survey households whose activity patterns are similar through the use of socio-demographic data. Once the decision tree is built, it is used to assign each synthetic household to a given survey household through socio-demographic similarities between the two. In this case study, we have used the LTDS (London Travel Demand Survey) 2010/11 to generate the tree.

Aer assigning the skeletal activity patterns to each synthetic individual, the next step consists in assigning a location to each activity. In order to do this, we have used a multinomial logit choice model. This technique allows each synthetic individual to evaluate the benet of performing a specic activity at a particular destination as a composite value based on objective metrics associated with this destination (e.g., number of relevant addresses), objective metrics associated with

<sup>2</sup> retrieved from https://www.gov.uk/government/publications/coba-11-user-manual

<sup>3</sup> see http://data.gov.uk/dataset/nptdr

<sup>4</sup> We used v3.1, corresponding to that developed in Los Alamos National Laboratory.

traveling from origin to destination (e.g., travel time) and subjective components following a probability distribution. The area units being considered in London have been the wards again. And, in this respect, the attractiveness of each ward has been quantied by the number of addresses for each activity type, and the accessibility throughout the region as the travel time across all wards using the crow-y distances and the average speed for each travel mode in the case study. The calibration for each travel mode and activity type pair has been performed, and those parameters have been used to calculate the new activity locations for each synthetic agent.

### **73.3 Calibration and Validation**

In terms of the calibration, the multinomial logit choice model applied described in the previous section could also be included here. On top of this, activity-related time values have been set in MATSim's conguration le using typical duration values observed from the LTDS dataset. Finally, the parameter values set by default in MATSim taking have also been adopted here. This is a limitation as the modal split currently in place is the one provided by the MATSim corresponding module. The related parameters should be rst adjusted so that the observed modal share is similar across modes to the observed modal shares.

In terms of the validation, trac counts from around 600 sensors have been made available to us for London. We hold values for the AM peak (8-9 am), the inter-peak (9 am-5 pm) and the PM peak (5-6 pm). The former and the latter are hourly counts; the inter-peak is an average value. Those counts are organized into so-called cordons (3) and screenlines (3). Comparisons are currently in place and we are still not in a position to evaluate in detail how the model validates to the observed data.

### **73.4 More Information**

More detailed information on the building of the model including some results from each module previously described can be found at: http://eunoia-project.eu/publications/ (Report on Case Study 1: London).

**Figure 73.1:** Snapshot of the road network for London's case study colored by road type (le) and map showing number of bus trips per road segment in London using timetable data (right).

**Figure 73.2:** Visual estimate of activities performed in London at 9 am using the Via soware.

# **Nelson Mandela Bay**

Johan W. Joubert

The Nelson Mandela Bay metropolitan area is in the Eastern Cape province of South Africa and includes the cities of Port Elizabeth and Uitenhage, with a population of approximately 1.2 million inhabitants.

The issue of complexity drove the development of a scenario for this region. We needed an area where we could experiment with various modules and elements oered by MATSim, but one less complex than the mega-city region of Gauteng. The Nelson Mandela Bay case was attractive; it still had a substantial population, only one ocial bus operator (Algoa Bus Company) and one passenger rail operator (Metrorail). It also displayed the characteristic apartheid urban form of South African cities and towns, where many low-income commuters lived on the outskirts of spatially sprawled cities.

The population was, initially, generated from the 2001 census: later revised and updated to the 2011 census data. Travel demand was inferred from the 2006 travel diary conducted in the metro. The process of synthetic population generation is described in detail on the MATSim https://matsim.atlassian.net/wiki/display/MATPUB/South+Africa Conuence site. The population was generated as entire households, using MLIPF (Multi-Level Iterative Proportional Fitting) as published by Muller and Axhausen (2012). Households were also assigned to buildings, based ¨ on census description.

This was the rst South African scenario to include private cars, freight and detailed public transport. The unique minibus taxis, a form of paratransit in South Africa and many developing countries, were incorporated in the Nelson Mandela Bay area and reported in Roder (2013) ¨ and Neumann et al. (2015).

Joubert, J W. 2016. Nelson Mandela Bay. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 451–452. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/ baw.74. License: CC-BY 4.0

# **New York City**

# Christoph Dobler

The MATSim New York model was an example of an agent-based model based on a given activitybased demand generation process outcome: in this case, the NYBPM (New York Best Practice Model) of Parson Brinkerho (Vovsha et al., 2002; Parsons Brinckerho, 2005, 2009). It produced persons with individual activity chains; MATSim was chosen as the simulation-based alternative to conventional assignment processes.

Activity locations were selected on zonal level (3 824 zones), timings (i.e., start time and duration) were chosen using given distributions. As part of the conversion process to MATSim, locations were distributed within the zones, according to land use and buildings. For the route assignment, transport modes were converted into those supported by MATSim. The resulting population contained 5.3 million persons (25 % sample).

A multimodal network was created, containing car and public transport links, for the MATSim model. Car links were derived from the aggregated model network data, including capacity, number of lanes and speed limits. For the public transport network, a shape le containing every lines' routes was available. Aer converting and cleaning the data, the nal multimodal network contained 498 000 nodes and 541 000 links. Based on further public transport-related data, a full schedule was created, including dierent public transport modes (bus, train, etc.).

An example for nal model outcomes was shown in Figure 75.1 and Figure 75.2, depicting the car share of all performed trips within a region. Red indicated a high share, blue a low. In Figure 75.1, trips were aggregated on zonal level. In Figure 75.2, the MATSim model high resolution is shown; there, the trips were aggregated using hexagons with a side length of 500 meters instead of a zonal level.

Finally, Figure 75.3 shows trac ows in Lower Manhattan. Cars were represented by rectangulars, public transport vehicles by arrows. Further model outcomes were presented by Balmer (2014). An online movie can be found at http://senozon.com/news/2014-05/z%C3%BCrich-meetsnew-york

Dobler, C. 2016. New York City. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 453–456. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/ baw.75. License: CC-BY 4.0

**Figure 75.1:** Car share (entire modeled area).

**Figure 75.2:** Car share (Manhattan).

**Figure 75.3:** Trac ows in Lower Manhattan.

# **Padang**

# Gregor Lammel ¨

The Padang scenario demonstrates the MATSim application to large-scale evacuation problems. The scenario has been created as part of the third party funded project "Last-Mile".  Padang is located on the west coast of Sumatra Island, Indonesia. In 2014, the city had a population of about 1 000 000 people. Because of its problematic location on the coast in a so-called "seismically locked" area (McCloskey et al., 2010), Padang is prone to earthquakes and subsequent tsunamis. In the "Last-Mile" project, a realistic tsunami scenario, triggered by an earthquake about 300 km o the coast, was identied (Goseberg and Schlurmann, 2009). The assumed tsunami would leave about 30 minutes for the evacuation. The ooding would reach as far as three kilometers inland, thus threatening up to 330 000 lives. Lammel ¨ (2011) developed a MATSim scenario representing the city with its aected population. One unusual aspect of the Padang situation is the expected universal evacuation by foot; simulating pedestrians with MATSim was a novelty when this project started. The standard simulation model (see, e.g., Section 1.3) was thus adapted to deal with pedestrians. Details are discussed by Lammel ¨ et al. (2009). Another important variation, contrary to most standard transport scenarios, is that network links would ood once the tsunami reached them. Thus, accessibility—ooded or not ooded—of the network links is time-dependent, which is modeled by a time-dependent network (Lammel et al., 2010). In the time-dependent network concept, link attributes—like ¨ freespeed can be changed, while the simulation is running, by precomputed network change events. For the Padang scenario, the network change events have been extracted from microscopic ooding simulation data.

Key Padang scenario facts:

Lammel, G. 2016. Padang. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) ¨ The Multi-Agent Transport Simulation MATSim, Pp. 457–458. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/baw.76. License: CC-BY 4.0

**Figure 76.1:** Evacuation time analysis for downtown Padang. Numbers showing average evacuation time in minutes, which are also indicated by the colors green, yellow, red.

Based on the Padang scenario, various evacuation strategies have been investigated:

# **Patna**

# Amit Agarwal

Patna is a medium-size city in eastern India. As in other developing nations, trac conditions are heterogeneous, composed of: a large number of bikes (37 %, including 4 % cycle rickshaws) and motorbikes (14 %). When this scenario was composed, public transport accounted for 18 % and walk for 29 %; only 2 % of all trips were made by car. Therefore, the MATSim queue simulation was modied to simulate travel demand under mixed trac conditions (Agarwal et al., 2015b).

A detailed Patna scenario description can be found in Agarwal et al. (2013). The scenario was created using household survey data from a comprehensive Patna mobility plan (TRIPP et al., 2009), using the area within the Patna Municipal Corporation. The scenario consisted of 72 zones, with a population of about 1.57 million (year 2008). MATSim demand was generated using trip diaries, with car, motorbike and bike used as main congested modes (Figure 77.1). PCU (Passenger Car Unit) factors for dierent vehicle types were derived using eective area occupied by vehicles. The eective area occupied by a vehicle is calculated, and the ratio of area occupied by this vehicle to the area occupied by a passenger car is taken as PCU factor for the respective vehicle. To allow overtaking of slower vehicles (bike), by faster vehicles (car and motorbike), pre-existing, state-ofthe-art FIFO queue simulation was overridden, using earliest link exit time as shown in Figure 77.2. Trac behavior in modied queue simulation was then analyzed by plotting fundamental diagrams and space time trajectories for car, motorbike and bike (Agarwal et al., 2015b).

To address some special factors of Patna's travel time distributions, MATSim utility function was calibrated so that a mode share from real world data was replicated in the model, performed by allowing agents to switch modes. The model was validated using trac count data and modal travel time distributions. The model's main shortcoming seemed to be overly short average travel times for motorbikes. Although no specic experiment was performed to analyze computational performance, no noticeable loss of performance was found during simulations. Thus, the model seems to be useful for many areas where mixed trac conditions predominate.

Agarwal, A. 2016. Patna. In: Horni, A, Nagel, K and Axhausen, K W. (eds.) The Multi-Agent Transport Simulation MATSim, Pp. 459–460. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/baw.77. License: CC-BY 4.0

**Figure 77.1:** Patna: Various vehicles on network, car in red, motorbike in blue and bike in green.

**Figure 77.2:** Patna: FIFO approach and passing of bicycle by car on a link (not to scale).

# **The Philippines: Agent-Based Transport Simulation Model for Disaster Response Vehicles**

Elvira B. Yaneza

This study's primary aim was adapting an agent-based trac simulation model to assist planning agencies in determining road trac routes for DRVs (Disaster Response Vehicles) in crises or disasters. Aer the initial disaster event period, road network management is crucial for disaster response operations, which must cope with travel demand increase. Depending on level of road damage, sections of the the road network may close. The degraded DRVs road trac routes will result in longer travel times.

The model was developed using an agent-based simulation modeling paradigm implemented through MATSim. Road trac routes were generated using Dijkstra's shortest path algorithm. MATSim output les stored each agent's routes, which represented trac routes for DRVs; here, each route's calculated travel time was equivalent to each agent's running time (in actual motion, while using shortest paths from source to destination).

### **78.1 Literature Review**

Road trac routing studies generally use dierent modeling approaches and shortest path algorithms. In studies using modeling, Lefebvre and Balmer (2007) used MATSim for largescale agent-based transport simulation, also investigating variations of Dijkstra's algorithm and A\*-algorithm. Sumalee and Kurauchi (2006) used the Monte-Carlo simulation approach to approximate network capacity reliability, then evaluated trac regulation policy performance, using the Kobe city (Japan) road network. Teknomo (2008) multi-agent simulation modeling approach considered route probability as a direct simulation output, rather than input, to the network. Sanders and Schultes (2017) outlined algorithms with faster run times than Dijkstra's algorithm for transportation network route planning. Their study focused on successful speedup techniques in static

### **How to cite this book chapter:**

Yaneza, E B. 2016. The Philippines: Agent-Based Transport Simulation Model for Disaster Response Vehicles. In: Horni, A, Nagel, K and Axhausen, K W. (eds.), The Multi-Agent Transport Simulation MATSim, Pp. 461–468. London: Ubiquity Press. DOI: http://dx.doi.org/10.5334/baw.78. License: CC-BY 4.0

road networks with xed edge cost. Elalouf (2012) model incorporated joint analysis of expected route time and its variance, using dynamic-programming, shortest path algorithm as a basis for a fully polynomial time approximation scheme.

#### **78.2 Design Details and Specications**

**Element 1: Study Area** During Tropical Storm Washi (Sendong), areas most aected areas were those near the Cagayan de Oro river (Ramos, 2011). Landslides near river banks, ash oods, as well as the overowing river and its tributaries, caused some barangays (barrios)—already damaged by Tropical Depression Shanshan (Crising)—to be swept away (Del Rosario, 2011). The ve most aected major bridges cross along the Cagayan de Oro River, connecting its two main areas, District 1 (west) and District 2 (east), in Misamis Oriental province (see Figure 78.1). The designated road network coverage has a total area of approximately 73.2 square kilometers, including the riverside (see Figure 78.2).

**Element 2: Road Network and Facilities** The model involved three main entities: road network, facilities and population and is described by two variables: nodes and links. It used graphical representation and had 3 847 nodes and 9 630 directed links (see Figure 78.3). A specic stretch of street consisted of nodes and links, representing intersections and street sections, respectively. MATSim handles only one-way links; in this model, one-way attribute had a default value of 1 and modes attribute were assigned only as car. Facilities were represented by their geographical coordinate locations in the network, which involved 21 entities from the following agencies: 10 hospitals with ambulance services, 3 re stations, 8 police stations and 2 evacuation centers. Facilities were mapped on nearest road network links.

**Element 3: Population and Demand Generation** The population was classied into dierent types of DRVs, representing major agents in the trac simulation model: ambulances, re trucks and police cars. The hospitals, re stations and police stations were assigned as agents' origins,

**Figure 78.1:** Cagayan de Oro City, Philippines urban road network. Source: GIS City Planning Oce, 2012

**Figure 78.2:** Spatial coverage of road network and locations of facilities in the network: it has 73.2 square kilometers including land and surrounding river and coastal areas. The facilities are mapped based on its actual geographical x and y coordinates in the road network. There are 23 facilities located in its nearest link in the network. These are: 10 hospitals, three re stations, eight police stations and two evacuation centers.

**Figure 78.3:** Nodes and links representation: Road Network has 3 837 nodes representing road intersections and 9 630 links representing the streets. It includes ve major bridges along Cagayan River: (A) Kauswagan-Puntod Bridge, (B) Maharlika Bridge (formerly known as Marcos Bridge), (C) Gov. Ysalina Bridge (formerly known as Carmen Bridge), (D) Kagay-an Bridge (Rotunda Bridge) and (E) Emmanuel Pelaez Bridge.

where vehicles start and end their activities; evacuation centers were assigned as agent destinations. Population was characterized by four variables: person, plan, act and leg. The leg variable used a mode dening vehicle type, assigned as car. The model advanced by performing trac routing activities. Each trac routing activity, seven events, was processed in the following sequence: end activity event, agent departure event, wait to link event, enter link event, leave link event, agent arrival event and start activity event. The end activity event prompted the agent to depart from the origin facility and begin again in the same ow of events.

**Figure 78.4:** Screenshot of SCENARIO 1 (without bridge closures) using agent ID#4. DRVs trip starting from the Sabal Hospital (Origin) passing Carmen Bridge (Gov. Ysalina Bridge) going to Balulang Evacuation Center dropping point (destination) then back to its origin.

## **78.3 Model Scenarios**

The simulation model was applied to the network of Cagayan de Oro City in Philippines. Two scenarios were assumed.