# Modeling Sustainable City Trips: Integrating CO2 Emissions, Popularity, and
Seasonality into Tourism Recommender Systems

Ashmi Banerjee<EMAIL_ADDRESS>Technical University of
MunichBoltzmannstrasse 3GarchingGermany85748 ,  Tunar Mahmudov
<EMAIL_ADDRESS>Technical University of MunichBoltzmannstrasse
3GarchingGermany85748 ,  Emil Adler<EMAIL_ADDRESS>Technical University of
MunichBoltzmannstrasse 3GarchingGermany85748 ,  Fitri Nur Aisyah
<EMAIL_ADDRESS>Technical University of MunichBoltzmannstrasse
3GarchingGermany85748 and  Wolfgang Wörndl<EMAIL_ADDRESS>Technical
University of MunichBoltzmannstrasse 3GarchingGermany85748

###### Abstract.

In an era of information overload and complex decision-making processes,
Recommender Systems (RS) have emerged as indispensable tools across diverse
domains, particularly travel and tourism. These systems simplify trip planning
by offering personalized recommendations that consider individual preferences
and address broader challenges like seasonality, travel regulations, and
capacity constraints. The intricacies of the tourism domain, characterized by
multiple stakeholders, including consumers, item providers, platforms, and
society, underscore the complexity of achieving balance among diverse
interests. Although previous research has focused on fairness in Tourism
Recommender Systems (TRS) from a multistakeholder perspective, limited work
has focused on generating sustainable recommendations.

Our paper introduces a novel approach for assigning a sustainability indicator
(SF index) for city trips accessible from the users’ starting point,
integrating CO2 emission analysis, destination popularity, and seasonal
demand. Our methodology involves comprehensive data gathering on
transportation modes and emissions, complemented by analyses of destination
popularity and seasonal demand. A user study validates our index, showcasing
its practicality and efficacy in providing well-rounded and sustainable city
trip recommendations. Our findings contribute significantly to the evolution
of responsible tourism strategies, harmonizing the interests of tourists,
local communities, and the environment while paving the way for future
research in responsible and equitable tourism practices.

Recommender Systems, City Trip Recommendations, Responsible Tourism,
Sustainability, Societal Fairness

††copyright: none

##  1\. Introduction

Recommender Systems (RS) provide tailored content to individual preferences,
spanning diverse domains like e-commerce, social media, news, and more,
effectively managing information to prevent overload (Abdollahpouri et al.,
2020). In travel and tourism, RS is pivotal in simplifying trip planning by
providing personalized recommendations for destinations, accommodations,
activities, and more (Isinkaye et al., 2015). However, this is a particularly
challenging domain due to the influence of dynamic factors such as seasonality
and travel regulations (Balakrishnan and Wörndl, 2021), as well as constraints
related to capacity-limited resources such as airline seats, hotel rooms, and
event tickets (Abdollahpouri and Burke, 2021).

Traditionally, RS focused on delivering accurate user recommendations, but in
practice, they function as a convergence point for multiple stakeholders,
making it a multistakeholder scenario (Abdollahpouri et al., 2020).
Recognizing the interests of all stakeholders becomes crucial in this dynamic.
Our stakeholder classification, inspired by Balakrishnan and Wörndl (2021),
identifies four key categories: consumers, item providers, platform, and
society, aligning with common touristic recommendation scenarios. Despite this
seemingly straightforward categorization, real-world stakeholder relationships
are often more intricate. Each stakeholder is vested in the traveler’s
journey, and optimizing consumer recommendations can yield benefits for all
involved parties (Abdollahpouri et al., 2020). Complexities arise as the goals
of one stakeholder may clash with another, leading to inevitable trade-offs
(Jannach and Bauer, 2020). Thus, achieving fairness in Tourism Recommender
Systems (TRS) requires adopting a multistakeholder approach, acknowledging
stakeholders’ interdependence and balancing their objectives.

Tourism’s impact goes beyond active participants, affecting the local
environment and businesses and profoundly influencing the balance of nature.
Constructing a fair TRS involves recommending sustainable options and
fostering responsible tourism practices. World Tourism Organization and United
Nations Development Programme define sustainable tourism as ”tourism that
takes full account of its current and future economic, social and
environmental impacts, addressing the needs of visitors, the industry, the
environment, and host communities” (Gössling, 2017). Achieving sustainability
in tourism requires interventions at various levels, including municipal
policies and regulations (Werthner et al., 2015). However, measuring
sustainability at destinations poses a significant challenge, impeding
effective decision-making, management, and meeting destination needs
(Fernández and Rivero, 2009). A destination’s sustainability is crucial for
long-term competitiveness and visitor satisfaction, not solely determined by
arrival numbers or bed nights (Irem et al., 2017).

Regulating tourist numbers is a crucial intervention to manage the impact of
tourism, and this is where a well-designed TRS can play a vital role. Such a
system proves particularly beneficial in addressing the twin challenges of
overtourism and undertourism, both on the rise due to factors like low-cost
aviation, affordable transportation, social media influence, and platforms
like Airbnb 111https://www.airbnb.com/ (Gowreesunkar and Vo Thanh, 2020).
Overtourism, witnessed in popular destinations like Venice, Barcelona, Rome,
and Dubrovnik, poses threats to historic preservation, the environment,
residents, and overall tourist experiences, making it challenging to find
reasonably priced housing in these cities (Dastgerdi and De Luca, 2023; Dodds
and Butler, 2019). Conversely, undertourism, prevalent in under-explored
destinations, results from a lack of infrastructure, publicity, and
accessibility. Both scenarios have adverse consequences. For instance, the
recent COVID-19 pandemic highlighted the adverse effects of undertourism,
causing significant disruptions to the tourism and hotel industries (Galí,
2022). To address these issues, a TRS must be designed to provide responsible
recommendations, considering the interests of all stakeholders. These systems
should advocate for sustainable tourism practices, promoting responsible
tourism while offering personalized suggestions to users. This involves
recommendations encouraging tourists to visit destinations with minimal
environmental impact, promoting less popular yet attractive locations, and
balancing the tourist load uniformly throughout the year. In the context of
this paper, we use the terms sustainability and responsibility
interchangeably.

A substantial amount of research has been conducted on developing fair
recommendation systems that consider the interests of all stakeholders
involved in tourism (Rahmani et al., 2022; Shen et al., 2021; Weydemann et
al., 2019; Wu et al., 2021). However, there has been limited focus on
generating sustainable recommendations (Banerjee et al., 2023). This paper
delves into the concept of modeling Societal Fairness or S-Fairness,
emphasizing the impact of tourism on individuals not directly involved, such
as residents, environment, etc. (Banerjee, 2023). These stakeholders often
encounter challenges such as rising housing prices, environmental pollution,
and traffic congestion due to heightened tourism activities in their vicinity.

In the context of tourism, a destination is characterized by ”a country,
state, region, city, or town that is actively marketed or markets itself as an
appealing place for tourists to visit” (Beirman, 2020). For this paper, we
designate destinations as a compilation comprising the 200 most densely
populated European cities. Our focus is on assisting travelers seeking
vacation recommendations for visiting these cities from their respective
starting points. We propose a method to measure sustainability at destinations
relative to the users’ starting point by assigning a composite S-Fairness
index (SF index) to all cities accessible from the users’ initial location.

To mitigate the adverse effects on the environment and society, we identify
three key factors in the calculation of the SF index:

  * •

Destinations with environmentally friendly travel options, minimizing CO2
emissions incurred during the travel to the destination.

  * •

Suggesting less popular yet attractive destinations.

  * •

Choosing destinations with lower demand during the specific month.

Our approach is designed to assist travelers in making more sustainable travel
choices. As depicted in Figure 1, an example user interface shows the top
travel recommendations from Munich (Germany) for July. The SF index is
featured in the top right corner, accompanied by its components, including
emissions, travel time for each mode of transport, aggregated popularity, and
seasonal demand of each city for the month. Such an application can assist
users in making informed decisions. A lower overall index for a city indicates
a more environmentally friendly travel option from the user’s starting point.
This paper delves into the methodology behind collecting the information in
the figure. Showcasing the SF index for analyzing specific tourist
destinations provides a concrete perspective on their sustainability status,
encouraging responsible tourism practices crucial for the well-being of
communities and the environment.

To this end, our work makes the following contributions:

  * •

Defining the concept of sustainability and its elements within a city trip
recommender system.

  * •

Collecting and examining data on transportation CO2 emissions, as well as
determining popularity and seasonality indices for cities. This involves
assigning a composite SF index to cities based on a specific starting point
and month of travel.

  * •

Executing a user study to explore fundamental travel trade-offs and ascertain
the SF index weights.

  * •

Verifying the effectiveness of our newly introduced SF index through an
additional user study to assess its appropriateness.

Figure 1.  An example user interface showing top travel recommendations from
Munich, Germany, for July while highlighting the S-Fairness index in the top
right corner

Our paper is structured as follows: Section 2 reviews prior research on city
trip recommendations and sustainability promotion. In Section 3, we detail
methodologies for calculating transportation-related CO2 emissions. Section 4
outlines methods for estimating city popularity and data collection. Section 5
explores seasonal demand for the destinations. Section 6 presents findings
from a user study on factors influencing destination choices and openness to
sustainable tourism recommendations, offering empirical insights. In Section
7, we elucidate the concept of Societal Fairness, assign the relevant indices,
and subsequently validate our index through a dedicated user study. Finally,
Section 8 concludes the paper, summarizing key findings and suggesting future
studies in sustainable tourism and city trip planning.

##  2\. Related Work

In the context of this study, related work encompasses two dimensions: city
trip recommendations and sustainable recommendations, often referred to as
S-Fairness. This reflects the evolving paradigm in recommender systems where
personalization and user preference in city trip recommendations are
increasingly integrated with the broader and critical perspective of
sustainability and societal fairness.

City trip recommender systems have traditionally focused on modeling user
preferences to deliver personalized recommendations. Techniques like
collaborative filtering are prevalent, leveraging users’ past activities,
similarities with other users, and network-based preferences (Lu et al., 2012;
Dadoun et al., 2019; Massimo and Ricci, 2019). Constraints defined by users,
such as budget or time preferences, are also critical components in tailoring
travel packages (Xie et al., 2010; Lim et al., 2015). However, challenges like
the cold start problem and data sparsity have led to the adoption of content-
based approaches. These approaches construct domain models using relevant
features for tourism, often derived from expert opinions, literature, or data-
driven methods (Liu et al., 2011; Pu et al., 2020). While expert-driven models
offer nuanced insights, their cost and complexity necessitate complementing
them with diverse data sources, such as Location-based Social Networks (LBSNs)
for venue data (Lu et al., 2012; Dadoun et al., 2019).

Sustainable tourism is a complex concept such that defining objectives and
indicators for a sustainable TRS can be complicated. Ko (2005) claim that
concerns on sustainability vary between destinations, hence dimensions for
sustainability along with the methodology and data gathering should be
particular to the destination. In respond to this approach the issue of
comparability arises, where meaningful insights from comparing measurement
between destination can’t be done since the sets of indicators are different.
To address this, Cernat and Gourdon (2012) formulate the Sustainable Tourism
Benchmarking Tool (STBT) comprises 54 indicators along seven dimensions
(assets, activities, linkages, leakages, sustainability, attractiveness, and
infrastructure). The interactions of these indicators are then tested on 75
countries but only perform well in Indonesia, Malaysia, and Thailand, where
the data for most indicators is available. Conversely, Hoffmann et al. (2022)
took a fully data-driven approach by analyzing data from TripAdvisor to make
sense of the sustainability measurement between hotels with the sustainable
label granted by the platform and those without. The study conducts
unsupervised statistical learning to classify sustainable hotels by their
features. The model results in significantly higher performance compared to
random draw but the explainability of the model is not adequate. For instance,
it is shown that larger hotels are more likely to be sustainable but it can’t
be inferred as explicitly causal. It is heavily noted that the model can only
be used as a probabilistic estimate to label sustainable destinations. Such
methods might reveal relevant aspects of sustainability as well as solve the
comparability issue however it is constrained by the completeness of the
dataset and hard to generalize beyond the determined item space.

Irem et al. (2017) argues that it is more feasible to analyze existing
sustainable tourism indicators than to introduce new measures lacking in
direct practical applicability. Gorantla and Bansal (2023) adopt the Circles
of Sustainability framework to describe how sustainable a city is. It measures
sustainability along four domains –– ecology, economics, politics, and
culture, with seven subdomains under each domain. The study then consolidates
different data sources for each subdomain, which in the end used to calculate
the sustainability index calculator. The sustainability index is calculated by
summing up all the subdomain scores with equal weights and then normalizing
them. Although the algorithm is simple, it gives a good outlook on the
sustainability of a city compared to the others. Irem et al. (2017) proposes
implementing data envelopment analysis (DEA) on existing tourism information
systems to model destination competitiveness. DEA allows one to identify the
efficiency of a destination, as well as benchmark it to another destination to
give clues on which aspects can be improved. These studies show that it is
possible to adopt existing frameworks into a partial model of city
sustainability. The utility function, for instance, the sustainability index,
can be calculated using the model and is useful to nudge policymakers and city
developers to improve specific aspects of the city.

While numerous studies explore the quantification of sustainability in
tourism, there is a notable scarcity of their application within recommender
systems in this domain. Sustainable recommender system seeks to create a
balance across economic, environmental, and social dimensions which is
essential for creating a resilient ecosystem (Gorantla and Bansal, 2023). One
can initiate the integration of features that capture user interest while
simultaneously promoting sustainability. In the work by Herzog et al. (2019),
attributes such as air quality, greenery, traffic, and pollution are employed
to model the appeal of a travel route. The research demonstrates that tourists
exhibit a preference for visually appealing routes, even if they entail longer
travel distances Indirectly, the recommender system improves the distribution
of tourists by suggesting less congested routes, also serving as a nudge to
cities to develop more attractive alternative routes.

However, a sustainable recommender system should not be fully user-centric.
The tourism industry involves different stakeholders, including consumers,
providers, platforms, and society, thus it is natural for sustainable RS to
implement a multistakeholder approach. Merinov et al. (2022) introduces a
multistakeholder model that not only considers user preferences but also the
occupancy level of the destination. In another study by Patro et al. (2020),
the multi-objective model focuses on providers’ sustainability by maintaining
their exposure while also preventing overcrowding. Pachot et al. (2021) added
local authorities as stakeholders, with economic growth, productive
resilience, prioritizing basic necessities, and greener production as its
objective. In a recent examination of multistakeholder fairness within tourism
recommender systems, it became apparent that existing studies predominantly
concentrate on provider and consumer stakeholders. Surprisingly, the societal
aspect, despite bearing significant impacts from tourism, is often overlooked
as a stakeholder (Banerjee et al., 2023).

Our methodology differs from the state-of-the-art in terms of data collection
and analysis techniques. We employ a combination of qualitative insights from
participant feedback and quantitative data from various sources. This approach
allows for a more nuanced understanding of tourist behavior and preferences,
leading to more effective recommendations for city trip planning that align
with S-Fairness principles.

##  3\. Destinations, Transportation, and Emission Estimations

One of the crucial sustainability indicators in tourism, pertinent for city
tourism policymakers, encompasses factors such as CO2 emissions associated
with travel to/from the city, the mix of transportation modes chosen by
guests, the average distance covered by travelers to reach their destination,
and the average length of stay, as highlighted by Önder et al. (2017). For
example, longer-distance trips that necessitate air travel between cities tend
to incur higher emissions, posing a more significant environmental impact than
shorter trips feasible with public transport like trains. Conversely, a short-
distance trip by car may result in more emissions than a train.

This paper adopts the estimation of greenhouse gases (GHG) emitted by various
transportation modes to indicate the trip’s environmental responsibility
concerning the travelers’ starting point. We examine the trade-off between
travel times, transportation modes, their respective emissions, and the
associated costs. Our approach encourages travelers to opt for public
transport, particularly for shorter to medium-distance destinations.
Ultimately, we assign an emission trade-off indicator $\mathit{Z(c_{i})}$ to
each city $\mathit{c_{i}}$ reachable from the user’s point of origin.
Analyzing trade-off values provides insights into user behavior, enabling the
formulation of effective policies. These policies might include strategies to
improve travel time on routes with lower emissions but higher costs. A lower
$\mathit{Z(c_{i})}$ indicates a more environmentally friendly and responsible
tourism approach.

Subsequent sections delve into detailed discussions on these aspects.

###  3.1. Extracting Destinations

In our scenario, a traveler is seeking a suitable city to visit for vacation
from a specified starting location. The initial step involves collecting data
on potential destinations for city trips. The experimental evaluation has been
conducted on diverse datasets. Our item space comprises 200 European cities or
destinations, spanning across 43 countries, chosen due to Europe’s extensive
connectivity through various modes of transportation—flights, rail, and
road—making it a prominent destination for tourism.

Data for European cities is sourced from the world cities database (World
Cities Database, 2023), filtering for cities in Europe within latitudes 35°N
to 72°N and longitudes 25°W to 65°E, with a population exceeding 100,000. The
top 200 most populated cities, each featuring at least one airport, are
selected. In our data, Moscow followed by Istanbul has the highest population,
Ancona, and Klagenfurt have the lowest. Subsequently, we calculate
transportation emissions for travel to each city using three modes — flights,
driving, and rail where applicable as explained below. Figure 2 illustrates
the geographical dispersion of the 200 European cities, each possessing at
least one airport. It also shows the subset of cities specifically chosen for
in-depth examination regarding driving and train connections in this study.

Figure 2. The geographical distribution of 200 European cities with at least
one airport is depicted in blue, cities prioritized for driving highlighted in
orange, and those considered for their train networks shown in green

###  3.2. Data Gathering: Transportation

####  3.2.1. Flights

In obtaining flight information, a major step involves associating cities with
their respective airport details and IATA
222https://en.wikipedia.org/wiki/IATA_airport_code codes. We gathered data
from the Flugzeuginfo.net 333https://www.flugzeuginfo.net/ website, aligning
the information with our city list. Notably, cities featuring multiple
international airports were also incorporated into the dataset. This resulted
in a compilation of 222 unique airports across 200 cities. 14 cities with more
than one airport, including Baku, Berlin, Copenhagen, Hamburg, Istanbul,
London, Lyon, Madrid, Milan, Minsk, Moscow, Paris, Rome, and Stockholm were
also modeled.

We established connections between airports $\mathit{a_{1}}$ and
$\mathit{a_{2}}$, corresponding to cities $\mathit{c_{1}}$ and
$\mathit{c_{2}}$, respectively. A dedicated connection was established for
each unique route between the two cities, resulting in 16,261 unique one-way
routes. It is important to clarify that the calculation of unique routes did
not account for connections to return, although data for return connections
was also gathered. Subsequently, each connection served as input for querying
Google Flights 444https://www.google.com/travel/flights, allowing us to
extract detailed information. We prioritized the best departing flight options
based on criteria such as travel time and number of stops for each trip,
focusing on economy class. The gathered data included information on flight
carriers, departure and arrival timings, the number of stops, and the duration
of the flights.

We classified each route based on the Eurocontrol’s
555https://www.eurocontrol.int/ definition for distances, segmenting them into
four categories: very short haul, short haul, medium haul, and long haul.
Specifically, very short-haul flights covered less than 500 km, short-haul
flights ranged from 500 to 1,500 km, medium-haul flights spanned 1,500 to
4,000 km, and long-haul flights exceeded 4,000 km in flying distance. The
scraped data from Google Flights lacked information on the distances between
the two cities. Therefore, we employed the Great Circle Distance (GCD)
(Wikipedia, 2023a, b) measurement to calculate the distances for each route.
As indicated in Table 1, our dataset predominantly comprised short-haul and
medium-haul flights, with a comparatively smaller number of long-haul flights.

We observed specific distance and duration patterns among different city pairs
in analyzing various flight categories. Examples include Copenhagen to Malmö,
showcasing the minimum distance in very short-haul flights, and Paris to
Stuttgart having the minimum distance in short-haul flights. For medium-haul
flights, Adana to Podgorica exhibited the minimum distance, while Ufa to
Sevilla presented the maximum. In long-haul flights, Erzincan to Braga showed
the minimum distance, while Baku to Nantes recorded the maximum. For a
detailed overview of the numerical aspects, including minimum, maximum, and
mean values for travel time, distances, and emissions (CO2e) in each category,
please refer to Table 1.

####  3.2.2. Driving

We assume that driving between cities is not viable when the distance is above
1,000 kilometers, so we eliminate the longer connections between city pairs.
This refinement yielded 10,056 unique (two-way) connections spanning 200
European cities. We derived the driving distance and travel time for these
connections using two prominent data sources.

Initially, we leveraged the Google Maps Routes API
666https://developers.google.com/maps/documentation/routes in the eco-friendly
mode to acquire the most fuel-efficient driving distance and time, factoring
in real-time traffic conditions. However, due to cost constraints for larger
datasets and limited availability in some countries, we obtained details for
4,718 unique one-way routes.

Subsequently, we utilized the Open Source Routing Machine (OSRM) API
777https://project-osrm.org in driving mode to compute the driving distance
and time between connections. OSRM, an open-source routing engine, relies on
OpenStreetMap 888https://www.openstreetmap.org data, providing information for
all 1056 connections in our dataset. The overview of both datasets can be
found in Table 1.

Both datasets contained data regarding distance, travel time, and specific
route details between two cities. Furthermore, the Google Maps Routes API
offered an estimate of the fuel consumption for each route. Upon conducting a
comparative analysis, it was observed that Google and OSRM demonstrated a mean
absolute percentage difference of 5.63% in their respective distance
calculations. This divergence may be ascribed to the different methodologies
employed by Google and OSRM in estimating distances, considering factors such
as routes and traffic conditions. It is essential to note that this paper does
not explore the accuracy assessment of each data source.

####  3.2.3. Trains

The railway network across Europe exhibits a diverse and country-specific
management infrastructure. Unfortunately, no open-source API or standardized
pan-European platform would enable us to aggregate data seamlessly from
various railway networks. Consequently, our approach relied on country-
specific rail networks, focusing on Germany due to easier data accessibility.
We utilized web scraping techniques on the Bahn.Expert website to gather the
necessary information. This platform uses the Deutsche Bahn APIs
999https://data.deutschebahn.com/dataset.groups.apis.html to provide valuable
insights into past and present data on trains and stations, particularly for
Deutsche Bahn (DB) or German trains (Clasen, 2020). For instance, users can
search for specific train lines and review all stops on a selected day along
its route. Additionally, the website offers information on schedules and real-
time data for the arrival and departure of trains.

In our data collection process, our primary emphasis was on three prominent
long-distance train categories in Germany— Intercity (IC), EuroCity (EC), and
Intercity-Express (ICE). This focus was chosen because these categories
encompass most of the major cities in Germany and extend to significant cities
in neighboring countries, including Amsterdam, Vienna, Basel, Brussels, and
others as illustrated in Figure 2. The stations chosen for data extraction
encompassed major locations in Germany such as Berlin, Dortmund, Erfurt,
Frankfurt, Hamburg, Hannover, Karlsruhe, Köln, Mannheim, München, Nürnberg,
and Stuttgart. As depicted in Figure 2, the dataset includes 566 unique
stations, reflecting the wide geographical coverage of the resulting train
network. With 26,390 trips and 386,367 observations, the dataset provides a
detailed view of connections and train stops. This dataset comprises 64 EC,
106 IC, and 63 ICE train lines, ensuring a thorough representation. The data
collection period extended from January 1, 2023, to May 31, 2023, providing a
robust timeframe for capturing diverse train travel patterns and facilitating
thorough analyses.

We chose to focus on the data from DB, as it offers valuable insights into
connections with major cities in Germany and neighboring countries. However,
it’s important to note that our methodologies are adaptable and can be
extended to incorporate data from other train providers across different
European regions.

Table 1. Summary of data sources and basic statistics for calculating
emissions upon arrival at the destination along with the CO2e values used for
each category

Mode | Data Sources | Category |  # Unique Routes | Distance (km) | Travel Time (hrs) |  CO2e (g/km)
---|---|---|---|---|---|---
Min | Mean | Max | Min | Mean | Max
Flights | Google Flights | Very Short Haul | 1,617 | 30.46 | 331.68 | 499.70 | 1.08 | 6.79 | 50.25 | 155
Short-Haul | 7,086 | 501.34 | 995.33 | 1,499.78 | 1.08 | 7.21 | 83.17 | 110
Medium-Haul | 7,450 | 1,500.42 | 2292.91 | 3,986.05 | 2.03 | 10.82 | 53.75 | 75
Long-Haul | 108 | 4,004.87 | 4,317.96 | 4,986.29 | 8.33 | 18.41 | 55.25 | 95
Driving | Google Maps API | – | 4,718 | 47.24 | 851.51 | 2,440.34 | 0.77 | 10.14 | 35.24 | 96
OSRM API | – | 5,028 | 43.67 | 917.03 | 2,967.52 | 0.72 | 11.08 | 37.67
Trains | Deutsche Bahn | ICEs | 104 | 116.17 | 524.75 | 849.67 | 2.10 | 6.85 | 10.88 | 24
ICs | 144 | 24.51 | 236.86 | 672.77 | 0.68 | 3.66 | 9.27
ECs | 101 | 30.77 | 350.17 | 779.25 | 0.68 | 5.95 | 14.15

###  3.3. Estimation of Emissions from Transportation Modes

The emission of greenhouse gases (GHGs) from transportation plays a pivotal
role in contributing to environmental damage, serving as a significant driver
of climate change and environmental degradation. Greenhouse gases, such as
water vapor, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and
ozone (O3), are atmospheric gases that absorb and re-emit heat, thereby
maintaining the Earth’s atmosphere at a warmer temperature (Lashof and Ahuja,
1990). Among these, CO2 is the most prevalent greenhouse gas emitted by human
activities, both in quantity and its overall impact on global warming. While
the term ”CO2” is occasionally used as a shorthand reference for all
greenhouse gases, this can lead to confusion. In this paper, we use ”carbon
dioxide equivalent” or ”CO2e,” collectively referring to multiple greenhouse
gases as suggested by Brander and Davis (2012).

While there were discrepancies among individual studies regarding the exact
emissions per kilometer, the overall consensus indicates that short-distance
flights have a higher environmental impact. In contrast, trains and public
transport significantly reduce CO2 emissions. Our modeling employs an average
approach, in line with the above consensus. Table 1 presents a summary of
values used for CO2e calculations across various transportation modes. The
subsequent sections provide a detailed exploration of the emission calculation
process and our assumptions for each mode of transportation.

####  3.3.1. Flights

Responsibility for aviation greenhouse gas emissions involves addressing
several complexities. An additional challenge arises from the variability in
passenger occupancy rates among aircraft. While some planes are full of
charter passengers, others may operate with less than half of their seats
occupied, particularly during scheduled flights. This discrepancy may
necessitate considering a higher rate of CO2 emissions for those flying in
partly empty aircraft. Additionally, individuals traveling in Business Class
or First Class contribute to a higher share of CO2 emissions, as highlighted
by Carbon Independent (2023).

In its analysis of CO2 emissions from commercial aviation, the International
Council on Clean Transportation (ICCT) offers a detailed breakdown of the
carbon intensity, measured in grams of CO2 emitted per passenger kilometer,
highlighting variations based on flight distance (Graver et al., 2019). This
variability stems from the fact that take-off demands a considerably higher
energy input than a flight’s cruise phase. Consequently, for very short
flights, the additional fuel required for take-off becomes more substantial
when contrasted with the more fuel-efficient cruise phase. The ICCT also
observes that shorter flights often involve less fuel-efficient aircraft. For
an overview of various methods used to estimate greenhouse gas (GHG) emissions
in the aviation sector, the study by Iken and Aguessy (2022) provides valuable
insights.

Google Flights calculate emissions per person using various factors, including
the GCD between origin and destination airports, aircraft type, fuel burn, and
flight occupancy following the Tier 3 methodology for emission estimates
outlined by the European Environment Agency (EEA) (Google, 2019). However, the
calculation results in a per-passenger CO2e contribution, which can be
misleading compared to the overall fuel consumption for the entire journey. To
facilitate a fair comparison with alternative transportation modes, such as
rail and driving, we adopt a distance-based estimation model proposed by
Graver et al. (2019) for standard economy class flights. Table 1 displays the
distinct CO2e values applied to three flight categories, categorized according
to the covered distance. We also add an extra 9% correctional adjustment
factor to the great-circle distance to account for delays and indirect flight
paths, as noted by DEFRA (2007).

####  3.3.2. Driving

Estimating driving emissions involves several factors: elevation, car model,
fuel type, car size, number of occupants, and traffic conditions (Ghosh et
al., 2020). Driving alone in a car, particularly one running on fossil fuels,
contributes more emissions than carpooling or using an electric vehicle.
Additionally, route variations, traffic, time of day, distance, and travel
time can lead to variable GHG emissions. In our simplified scenario, we
consider a small gasoline-powered car occupied by a single person, and we
calculate the estimates based on the distance for both Google and OSRM data.
To calculate the CO2e for the given route, we utilize the per-kilometer
emission estimation from Ian Tiseo (2023), which is set at 96 grams per
kilometer.

The Google data also included fuel consumption estimates, allowing us to
derive the CO2e values. We also compute the fuel consumption-based CO2e for
the Google data, with an emission rate of 2.3 kilograms of CO2 per liter of
gasoline (Hilali and Belmaghraoui, 2019). The mean absolute difference
percentage of 10.90 between CO2e values calculated from per-kilometer distance
and those derived from fuel consumption estimates for the Google data
indicates moderate variability or discrepancy between the two methods.
Additional analysis or investigation is required to comprehend the factors
contributing to this difference. However, delving into this aspect is beyond
the scope of this paper. To maintain simplicity and ensure standardization in
the estimation calculation, we adopt distance-based estimates as listed in
Table 1 computed for the minimum distance returned by either the Google or the
OSRM data.

####  3.3.3. Trains

Much like other modes of transportation discussed earlier, train emissions can
vary depending on the type of fuel used. In Europe, where electric trains are
prevalent, emissions are considerably lower than diesel-powered counterparts.
However, reported values exhibit discrepancies even within Europe’s
predominantly electric rail network. For instance, Statista UK cites 41 grams
of CO2e per kilometer (Ian Tiseo, 2023), while Deutsche Bahn reports 32 grams
of CO2e per passenger kilometer (Statista Research Department, 2023b). Our
estimations are based on values obtained for trains from Larsson and Kamb
(2022), specifying 24 grams of CO2 per kilometer. We acknowledge the inherent
challenges in establishing a universally accurate emission figure for this
context.

###  3.4. Estimating the Transportation trade-offs

The values representing the trade-off between emissions, travel time, and cost
are relevant in pinpointing users with stronger pro-environmental attitudes
and formulating effective policies. We utilize the propensity function
$\mathit{Z(c_{i})}$ for city $\mathit{c_{i}}$, as suggested by Aziz and
Ukkusuri (2014), to compute the trade-off among travel time ($TT$), CO2
emissions ($EM$), and cost across all available transportation modes when
selecting a trip, as outlined below:

(1)
$\mathit{Z(c_{i})}=\alpha_{TT}\cdot\tau_{TT}({\mathit{c_{i}}})+\alpha_{EM}\cdot\tau_{EM}({\mathit{c_{i}}})+\alpha_{Cost}\cdot\tau_{Cost}({\mathit{c_{i}}})\
$

In this equation, $\alpha_{j}$ represents the weight associated with each
element, $\tau_{j}({\mathit{c_{i}}})$ signifies the normalized trade-off score
associated with each element for the city, where $j\in\\{TT,EM,Cost\\}$.

Modeling transportation costs presents challenges due to their variability
influenced by external factors such as booking timing and method.
Consequently, we have chosen to incorporate estimation methods into our study.
In the case of calculating flight costs to reach a destination, we leverage
per kilometer estimates provided by Rome2Rio (2018) for the top 200 airlines
and their respective domestic and international flights. Our approach involves
mapping the airline’s per kilometer price, specifically when booked four weeks
in advance, to the international and domestic categories based on whether the
destination is within the same country or a different country, respectively.
We acknowledge the challenges in mapping costs for multi-carrier airlines,
limiting our current model to direct flights or those with a single airline.
While these estimates may not be entirely precise, they serve as a useful tool
for cost modeling.

For train travel, utilizing data from Deutsche Bahn, we adopt the estimation
provided by Euronews (2023), setting the cost at 0.14 euros per kilometer for
tickets booked four weeks in advance. When it comes to driving, we determine
costs based on estimations of the average cost per kilometer of fuel for the
country where the journey originates. To achieve this, we utilize data from
European Commission – Alternative Fuels Observatory (2022) for country-
specific petrol price estimations. While recognizing the approximations and
potential deviations from actual costs, our robust methodology provides a
flexible framework for assessing transportation costs, with potential
enhancements through future research exploring real-time price extraction for
each trip and transportation mode.

Our paper focuses on data involving up to three transportation modes between
cities. We compute trade-offs in travel time, emissions, and cost for each
trip across all available transportation modes. To ensure consistency, we
normalize time (in hours), emissions (in kilograms), and costs (in euros)
across all modes between values 0 and 1 using min-max normalization (Patro and
Sahu, 2015). This normalization also yields relative values, facilitating
comparisons across different transportation modes. Mathematically, the
normalized trade-off for each element can be calculated as follows:

(2)
$N_{j}({\mathit{c_{i}}})=\frac{V_{j}^{(i)}-\min(V^{(i)})}{\max(V^{(i)})-\min(V^{(i)})}$

Where $V^{(i)}=\\{V_{TT}^{(i)},V_{EM}^{(i)},V_{Cost}^{(i)}\\}$ is the set of
factors across all modes of transportation involved in the emissions score
trade-off for a trip. In this context, each element of
$\tau_{j}({\mathit{c_{i}}})$ varies between 0 and 1. Zero signifies the most
favorable alternative, whereas one indicates the least favorable one. We learn
the weights $\alpha_{TT}$, $\alpha_{EM}$ and $\alpha_{Cost}$ from the user
study explained in Section 6.

By examining trade-off values, we can gain insights into user behavior and
formulate effective policies, such as implementing strategies to enhance
travel time on routes with lower emissions and higher costs. The lower the
$\mathit{Z(c_{i})}$ for a city $\mathit{c_{i}}$ , the less damaging it is to
visit that city from the users’ point of origin and thus more fair from a
societal perspective. However, it is crucial to recognize that the
interpretation is context-sensitive, emphasizing that travel time, emissions,
and costs are not interchangeable variables. The model specifically informs us
about the trade-off involved in travel decision-making for sampled users,
considering factors like trip duration, emission, and context.

##  4\. Tourist Destination Popularity

Cities frequently grapple with the challenges of overcrowding caused by
tourism, impacting residents’ daily lives and leading to effects such as
elevated housing prices, intensified traffic, and increased congestion
(Camarda and Grassini, 2003; Gowreesunkar and Vo Thanh, 2020). If not managed
properly, overcrowding can drive the anti-tourism movement by locals who are
concerned about their life quality (Seraphin et al., 2018). One of the initial
efforts to mitigate overcrowding is de-tourism, where local governments
promote alternative destinations to places that are already overcrowded (San
Tropez, 2020). For example, The City of Venice website recommends less popular
routes packaged as ”authentic experiences” to promote sustainable tourism.
Furthermore, San Tropez (2020) proposes social marketing where other
stakeholders such as tourism enterprises and publishing companies also
participate in showcasing these alternative destinations. Therefore, when
suggesting sustainable destinations from a specific city, it becomes pivotal
to consider well-known options in the vicinity and explore the hidden gems or
lesser-known cities that attract fewer tourists. This approach is designed to
distribute the tourist load more evenly among cities.

The collective popularity of a destination significantly shapes its overall
appeal, influencing the preferences and decisions of tourists. Examining
online presence, encompassing factors like search interest on platforms such
as Google and engagement on social media, provides valuable insights into
tourists’ active pursuit of information about a destination (Weng et al.,
2022). According to the research conducted by Pan et al. (2007), a substantial
83% of tourists utilize Google Images for destination-related searches before
embarking on their travels. Tasci and Gartner (2007) discovered that online
images are pivotal in shaping tourists’ perceptions of a destination and
influencing their travel decisions.

Moreover, user-generated content, including reviews, ratings, and the
abundance of aesthetic attractions or points of interest, significantly
enhances a destination’s overall popularity. In our study, we assess
popularity across three dimensions: the prevalence of city images searched on
Google (GT), the number of points of interest (POI) at a destination, and
various forms of user-generated content (UGC), such as reviews and photos.
These components serve as proxies for popularity, and a strong correlation
between the number of reviews and attractions indicates a highly popular
destination. The collected data is normalized to derive a popularity index for
each city.

The city’s popularity index can be incorporated into a recommender system,
aiding users in decision-making and promoting the selection of destinations
with lower popularity. This integration not only diversifies recommendations
but also aligns with the principles of sustainable tourism, emphasizing the
equitable distribution of economic benefits and mitigating overcrowding. As
illustrated in Figure 1, the prioritization of cities with medium popularity
indices over those with higher indices is evident in our top recommendations.

###  4.1. Data Gathering

To estimate the popularity of a city, we gathered data from two prominent
sources — Tripadvisor and Google Trends. The sections below delve into the
details of the data-gathering process.

Table 2. Table summarizing different data sources and their basic statistics used for calculating the popularity index for a city | Data Sources
---
| Attributes
---
| Statistics
---
|  |  | Min | Mean | SD | Max
Tripadvisor | POI | # Attractions | 5.0 | 683.80 | 1,338.59 | 8,999
UGC | Total # Reviews & Opinions | 217 | 302,935.5 | 853,071.2 | 7,099,844
# Attraction Reviews | 0 | 74,058.54 | 212,555.9 | 1,795,447
# Attraction Photos | 0 | 56,214.04 | 140,800.1 | 1,019,360
Google Trends | GT | Images | 0 | 13.70 | 5.90 | 100

####  4.1.1. Tripadvisor

Tripadvisor is a popular online platform aggregating user-generated reviews
and ratings for travel-related entities, such as accommodations, restaurants,
and attractions. To estimate the popularity of cities, we utilized web
scraping techniques on the Tripadvisor platform to gather key metrics such as
the total number of reviews, number of attractions, reviews on attractions,
and photos for attractions for each of the 200 European cities that we had
gathered in Section 3.1.

Exploratory data analysis revealed that London has the highest number of
attractions, totaling 8,999, while Agri in Turkey reports the fewest
attractions. Regarding user-generated content, London leads with the maximum
count, whereas Siirt has the minimum number of reviews and opinions.
Additionally, London tops the charts in attraction reviews and photos. The
information for attraction reviews and photos is unavailable for Thessaloniki,
Trabzon, Arkhangelsk, and Canakkale on Tripadvisor. The cities with the
maximum number of attractions, reviews, opinions, and photos are consistently
London, Paris, and Rome. Conversely, Siirt, Mus, and Agri in Turkey have the
minimum review counts, while Agri, Batman, and Siirt in Turkey report the
fewest attractions. Table 2 summarizes the basic statistics of the Tripadvisor
data.

The data reveals a striking similarity between attraction review lists and
photo counts. To validate our hypotheses, we conducted a correlation analysis
examining the relationship between the overall reviews and opinions, the
number of attraction reviews, and the number of attraction photos at each
destination. The analysis revealed an exceptionally strong correlation,
exceeding 0.90. Additionally, a T-Test (Eisenhart, 1979) was performed to
assess the significance of this correlation, and the results confirmed its
statistical significance. Therefore, we consider a combined count of reviews
and opinions for a particular city for our popularity index, serving as a
proxy for all the user-generated content elements.

####  4.1.2. Google Trends

Search engines like Google, Yahoo, or Bing serve as primary information
sources on the internet, with Google holding the highest usage percentage at
67% and approximately 5.9 billion daily searches (Önder and Gunter, 2016).
Google Trends 101010https://trends.google.com/trends/ aggregates search data,
offering users insights into trending topics and the popularity of specific
search terms globally.

We used Pytrends 111111https://pypi.org/project/pytrends/ to collect weekly
data from Google Trends (GT) within the travel category for the year 2022.
Focused worldwide and on English-language search results, GT normalizes search
data to facilitate comparisons between terms. This normalization involves
dividing each data point by the total searches within its corresponding
geography and time range, ensuring relative popularity comparisons and
preventing biases toward regions with higher search volumes. The resulting
values are scaled from 0 to 100, reflecting a topic’s proportion to all
searches. It’s important to note that regions with the same search interest
for a term may not always share the same total search volumes.

To gain deeper insights into the data, we conducted a correlation analysis
between the GT and Tripadvisor data, focusing specifically on POI and UGC
components for the respective cities. Surprisingly, the analysis revealed a
notably low correlation but was statistically significant. This suggests that
the patterns in Google search trends for cities, as measured by image
searches, do not strongly align with the popularity of attractions and user-
generated content on Tripadvisor. Therefore, we treat GT as a distinct entity
in our popularity index estimation, recognizing its divergence from other
indicators.

Figure 3. Bar plot showing the normalized values of the popularity index
components for selected cities

###  4.2. Estimating Destination Popularity

Quantifying a city’s popularity is complex due to its multifaceted nature. In
this paper, we propose a method to define city popularity based on metrics
derived from Tripadvisor and the GT data as explained above in Section 4.1.
The popularity index $\rho(c_{i})$ of a city $\mathit{c_{i}}$ is expressed as
a weighted sum of various popularity components —- $\pi_{POI}$, $\pi_{UGC}$,
and $\pi_{GT}$ denoting the points of interest, number of reviews and opinions
available on Tripadvisor and google trends index for the last one year
respectively for a city $\mathit{c_{i}}$. To ensure consistency, we employ
min-max normalization (Patro and Sahu, 2015), as depicted in Equation 2, to
standardize all component values within the range of 0 to 1. Figure 3 displays
the normalized values of various components constituting the popularity index
for a chosen group of cities. The contrast in popularity is evident, with
larger cities like Paris and Barcelona grappling with overtourism, while
Munich and Berlin, comparatively less popular, showcase a distinct difference.
Based on this, we define the popularity index $\rho(c_{i})$ for the city
$\mathit{c_{i}}$ as follows:

(3)
$\rho(c_{i})=\beta_{POI}\cdot\pi_{POI}+\beta_{UGC}\cdot\pi_{UGC}+\beta_{GT}\cdot\pi_{GT}$

Where $\beta_{j}$ for $j\in\\{POI,UGC,GT\\}$ are the weights assigned to each
component of the popularity index. We derive the weights, $\beta_{j}$, for the
popularity index components through a user study, as detailed in Section 6.
This process involves determining the quantitative contributions of factors
within the popularity elements to the overall popularity index, relying on
user preferences and perceptions. By adopting this user-driven approach, the
assigned weights for each component reflect user opinions and behaviors,
thereby incorporating a human-centered dimension into the calculation of city
popularity.

We aim to recommend destinations with a lower popularity index $\rho(c_{i})$,
fostering a balanced distribution of tourist traffic, even in less popular yet
attractive places. This approach seeks to mitigate the issue of overtourism at
popular destinations.

##  5\. Seasonal Destination Demand

The personalized recommendation algorithms in online platforms frequently
highlight certain destinations, resulting in seasonal concentration of
tourists. This pattern of recommendation can inadvertently lead to less-
visited destinations remaining unnoticed. Consequently, the information
presented online tends to direct visitors to these highlighted destinations,
particularly in peak seasons, which can lead to temporary but significant
increases in visitor numbers during specific times of the year (Gowreesunkar
and Vo Thanh, 2020). TRS can intervene in this issue by redirecting tourists
to less crowded destinations and avoiding peak seasons, thereby ensuring a
consistent distribution of tourists throughout the year across all seasons.

Without specific data on destination demand, we employ city seasonality
metrics as a proxy measure to assess its popularity among tourists at
different times throughout the year. Cities can exhibit diverse patterns, with
some having a singular peak, others featuring two peaks, and some maintaining
a steady stream of tourists year-round (Butler, 1998; Corluka, 2019).

When considering the causes of seasonality in tourism, literature primarily
distinguishes between natural and institutional causes. Natural causes are
predominantly related to climatic conditions and are particularly evident in
certain forms of tourism, such as summer vacation tourism. However, for
example, health and business tourism tend to be more resistant to the natural
causes of seasonality. Institutional causes are linked to written and non-
written norms and customs that dictate social practices, including school
holidays, state holidays, or specific festivities (Suštar and Ažić, 2020).

Our objective is to attribute a seasonal demand index to each city for a given
month, providing an estimation of its appeal to tourists. The aim is to
recommend destinations with lower seasonality indices, ensuring a consistent
tourist presence throughout the year or balancing tourist loads across
different destinations. As illustrated in Figure 1, the user interface
provides recommendations tailored to a chosen time frame, such as a particular
month. However, it’s important to differentiate between the data used for
these recommendations: while the popularity index offers an aggregated view of
a destination’s appeal over the entire year, the seasonality index, on the
other hand, provides a more nuanced, month-by-month analysis. This distinction
is essential for travelers planning their trips. The popularity index,
although helpful, might not fully capture the unique characteristics or
visitor trends of a destination in a specific month. In contrast, the
seasonality index, with its monthly granularity, offers a more accurate
representation of what a traveler can expect during their chosen travel
period.

###  5.1. Data Gathering

To gauge the seasonal variations in tourist activity, we explore monthly
visitor counts and bednight statistics from TourMIS and financial seasonality
indicators, such as the average daily rates (ADR) from Airbnb. The ensuing
sections elaborate on these data-gathering processes.

Table 3. Table summarizing different data sources and their basic statistics for calculating the destination’s seasonality index Data Source | Attributes | Statistics
---|---|---
# Cities | Min | Mean | SD | Max
TourMIS | NFIs | AVC | 64 | 489 | 192,125.56 | 293,783.60 | 2,188,497
BN | 69 | 1,163 | 415,708.25 | 675,550.57 | 5,260,073
Airbnb | FI | ADR | 45 | 68.09 | 316.88 | 418.63 | 2013.13

####  5.1.1. TourMIS

(a) Amsterdam

(b) Barcelona

(c) Berlin

(d) Brussels

(e) Madrid

(f) Munich

(g) Paris

(h) Vienna

Figure 4. Visualization of the normalized monthly arrival visitor count (AVC)
represented in green and monthly average listing price on Airbnb in blue for
selected cities

TourMIS 121212https://www.tourmis.info, a tourism marketing information
system, offers complimentary and electronically accessible market research
data to aid management decisions. Supported by the regional, national, and
international tourist industry, TourMIS provides up-to-date tourism statistics
and analyses, including arrivals and bed nights, for informed decision-making
(Wöber, 2003). The monthly arrival visitor count (AVC), encompassing both
foreign and domestic data, and the number of bed nights (BN) for European
cities in 2022 are considered in our analysis. The dataset includes
information for 65 cities regarding AVC and 70 cities for BN, with an overlap
of 63 cities. Leveraging this data, we estimate the footfall in cities for
respective months.

Exploratory data analyses on the TourMIS data revealed that a minimum AVC of
489.0 was recorded in January for the city of Eisenstadt, while Paris recorded
a maximum AVC of 2,188,497 in July as evident from Table 3. Similarly,
Eisenstadt, Austria documented the minimal number of bednights for January,
while Paris still accounted for the maximum number in July. These insights
illuminate the dynamic nature of tourism, showcasing the fluctuating visitor
counts and bednights across various months and cities. Notably, the data
suggests heightened touristic activity during the summer compared to winter.

We performed correlation analyses on the bednights (BN) and monthly AVC data
from TourMIS. The results unveiled a consistently strong correlation (¿0.98)
for each month among cities where data for both variables were available. To
determine if this correlation trend holds for the entire population of cities,
we carried out a T-Test (Eisenhart, 1979) with a significance level (p) of
0.05. The test yielded significant results, indicating sufficient evidence to
conclude that the correlation significantly differs from zero in the
population. Therefore, for our subsequent calculations, we exclusively
consider the AVC numbers. It’s important to acknowledge that our approach
utilizes absolute figures for AVC without normalizing them by the sizes of the
cities. Although attempts were made to normalize for city sizes, the outcomes
were heavily skewed towards small cities. We believe that our method
accurately reflects the seasonal demands of smaller cities, which often
feature numerous attractions.

####  5.1.2. Airbnb

To quantify the impact of financial indicators like average daily rate (ADR)
on seasonality, we leverage the calendar.csv dataset sourced from Inside
Airbnb 131313http://insideairbnb.com/get-the-data. This dataset includes
details on the availability and daily pricing of all listed accommodations
within a city. Our analysis focuses on the latest data from September 2023 and
covers a one-year duration for 45 European cities.

A foundational exploratory analysis of the data is presented in Table 3. In
February, the lowest ADR was observed in Riga, while Oslo reported the highest
ADR in July. These findings affirm the presence of seasonal demand variations
across months, with increased demand in the summer, and align with the
economic disparities between the cities (Statista Research Department, 2023a).
Figure 4 illustrates the normalized monthly AVC in green and the monthly
average listing price on Airbnb in blue for selected cities. A positive
correlation is observed between the two in most cases, except for Brussels.
Specific peaks in Munich’s accommodation prices during October are
representative of Oktoberfest, while generally, prices are elevated in the
summer months, followed by a gradual decline in the winter months.
Additionally, we applied standard correlation metrics to analyze the
relationship between AVC and the average price of Airbnb listings for each
city every month. The results consistently show a positive correlation for
most cities except Brussels. This deviation in Brussels can be attributed to
more business travelers (Santos and Cincera, 2018).

Despite these observed correlations, the correlation coefficients for each
month across all cities were negatively correlated and statistically
insignificant. Consequently, we opted to include the Average Daily Rate (ADR)
values of the cities for each month as a separate component in our analysis of
the financial indicators of tourism demand.

###  5.2. Estimating Seasonal Demand

In literature, the Gini coefficient stands out as a widely employed tool for
assessing tourism seasonality (Þórhallsdóttir and Ólafsson, 2017; Suštar and
Ažić, 2020; Ferrante et al., 2018). This coefficient offers distinct
advantages, including its ability to consider distribution asymmetry, relative
insensitivity to extreme values, and an indication of stability in the
distribution of overnight stays within a single year (Suštar and Ažić, 2020).
In our paper, the Gini coefficient serves as a numerical metric quantifying
the level of inequality in the distribution (Gini, 1921). Derived from the
Lorenz curve, which illustrates the cumulative frequency of ranked
observations starting from the lowest number (Gastwirth, 1972), the Gini
coefficient provides a comprehensive measure for the demand of the destination
at a particular time of the year. The analytical formula frequently used for
Gini coefficient calculation, applied in this paper, is expressed as
(Gastwirth, 1972; Þórhallsdóttir and Ólafsson, 2017):

(4) $G=\frac{2}{n}\sum_{i=1}^{n}(x_{i}-y_{i})$

where

$\begin{split}&n=\text{the number of fractiles, months, weeks, days, or other
units}\\\ &x_{i}=\text{the rank of fractiles, for example, }\\\
&\quad\frac{1}{12},\frac{2}{12},\ldots\text{ when using months, or when using
weeks }\\\ &\quad\frac{1}{52},\frac{2}{52},\ldots,\text{ or days
}\frac{1}{365},\frac{2}{365},\ldots,\text{ etc. So }x_{i}=\frac{i}{n}\\\
&y_{i}=\text{the cumulated fractiles in the Lorenz curve}\\\ \end{split}$

In the context of seasonal tourism demand, studies suggest that the average
room price is one of the pivotal business performance indicators in the hotel
industry (Israeli, 2002; O’Neill and Carlbäck, 2011; Pine and Phillips, 2005).
We calculate the seasonality Gini index for a city using Gini Coefficients
derived from non-financial and financial indicators, as described by Suštar
and Ažić (2020). Non-financial indicators consist of monthly counts of
arriving visitors (AVC) from TourMIS, while financial indicators involve the
Average Daily Rate (ADR) computed from Airbnb listings. The Gini coefficient
values for each indicator span from zero to one, with zero signifying a
complete lack of seasonality and seems to be an active or equal distribution
of volumes all year round, and a Gini coefficient of one indicates complete
seasonality, i.e., the total volume is registered in one single month (Suštar
and Ažić, 2020). Gini coefficients are computed annually for AVC, while
monthly calculations are performed for ADR.

As illustrated in Figure 4, the monthly average prices of listings exhibit
significant fluctuations depending on the city and month. Therefore, it is
advisable to model these fluctuations daily to enhance the precision of our
estimations. Similarly, if data is available daily or weekly granularity, the
AVC numbers could be modeled at those levels for more accurate analyses. The
aggregated seasonality index $\sigma(c_{i}^{j})$ across all indicators for a
city $c\\_{i}$ for month $j$ for can be calculated as follows:

(5) $\sigma(c_{i}^{j})=\Gamma_{AVC}\cdot G_{AVC}^{(i)}+\Gamma_{ADR}\cdot
G_{ADR}^{(i)(j)}$

Where $\Gamma_{k}$ where $k\in\\{AVC,ADR\\}$ represents the weights as derived
from the user study. We aim to recommend cities with lower
$\sigma(c_{i}^{j})$, ensuring a consistent tourist presence throughout the
year or balancing tourist loads across different destinations.

From a selected list of cities common in all datasets, we calculate the Gini
coefficients for the AVC and ADR, presenting the results in Table 4. Madrid
exhibits the least seasonality in AVC, suggesting consistent demand throughout
the year, while Munich registers the highest seasonality. Additionally,
Brussels displays minimal seasonality in ADR (close to 0), and Munich exhibits
maximum ADR seasonality in September and October. The heightened seasonality
in Munich’s AVC and ADR can also be attributed to the annual Oktoberfest event
occurring in September (Herrmann and Herrmann, 2014).

Table 4. Gini coefficients illustrating the seasonality indicators in selected
citiesThe bold notations highlight the maximum Gini indices for ADR in the
city, while the italicized and underlined ones denote the minimum

City | $G_{AVC}$ | $G_{ADR}$
---|---|---
Jan | Feb | Mar | Apr | May | Jun | July | Aug | Sep | Oct | Nov | Dec
Amsterdam | 0.146 | 0.033 | 0.010 | 0.019 | 0.017 | 0.014 | 0.011 | 0.044 | 0.014 | 0.063 | 0.060 | 0.026 | 0.047
Barcelona | 0.115 | 0.040 | 0.108 | 0.051 | 0.027 | 0.026 | 0.026 | 0.008 | 0.025 | 0.029 | 0.025 | 0.026 | 0.097
Berlin | 0.158 | 0.028 | 0.019 | 0.020 | 0.015 | 0.026 | 0.051 | 0.046 | 0.026 | 0.040 | 0.022 | 0.018 | 0.056
Brussels | 0.116 | 0.023 | 0.015 | 0.020 | 0.025 | 0.008 | 0.009 | 0.006 | 0.008 | 0.081 | 0.046 | 0.028 | 0.064
Madrid | 0.079 | 0.037 | 0.024 | 0.041 | 0.028 | 0.045 | 0.028 | 0.025 | 0.020 | 0.059 | 0.055 | 0.037 | 0.073
Munich | 0.188 | 0.013 | 0.008 | 0.029 | 0.010 | 0.020 | 0.036 | 0.033 | 0.032 | 0.138 | 0.049 | 0.009 | 0.033
Paris | 0.100 | 0.017 | 0.007 | 0.012 | 0.008 | 0.013 | 0.009 | 0.139 | 0.150 | 0.078 | 0.039 | 0.013 | 0.044
Vienna | 0.184 | 0.058 | 0.016 | 0.032 | 0.020 | 0.024 | 0.021 | 0.015 | 0.101 | 0.062 | 0.031 | 0.027 | 0.101

##  6\. User Perception of Sustainable City Trips

To explore how the users perceive sustainability when looking for city trip
recommendations, we conducted an inclusive user study involving participants
with diverse travel experiences and preferences. This method was selected as a
key approach to unravel the intricacies of decision-making, providing valuable
insights into various facets of the process, as established by prior research
(Wilson, 1981). By directly engaging real-world participants in simulated
scenarios, our aim was to discern how tourists assess different aspects of a
city trip, negotiate trade-offs, align preferences with sustainable tourism
practices, and assign weights to criteria in their decision-making.

###  6.1. User Study Design

The primary objective of our user study was to gain a deeper understanding of
the factors influencing individuals’ decisions when selecting a city for
vacation and their receptiveness to sustainable recommendations for tourism
destinations. Respondents were prompted to imagine planning their next
vacation to another European city and identify the most crucial factors
influencing their choice of destination. Only a limited set of personal
demographic questions pertaining to the users’ age, gender, and nationality
were asked to preserve the participants’ privacy.

The questionnaire was designed using Qualtrics Experience Management Software
141414https://www.qualtrics.com, an online survey platform. We recruited
participants through the online crowdsourcing platform Prolific
151515https://www.prolific.com, renowned for its efficacy in subject
recruitment for the scientific community (Palan and Schitter, 2018). With a
focus on European participants who listed travel as one of their hobbies, the
questionnaire, designed in English, was distributed to 200 individuals through
Prolific’s advanced pre-screening options. To ensure gender diversity, the
preset distribution aimed for an equal representation of 50% males and 50%
females. Demographic analyses of the survey data indicated that 33.8% of the
participants fell within the 25-34 age group, followed by 24.3% in the 18-24
age group, and the remaining were above the age of 35.

###  6.2. Transportation Sustainability Concerns

Figure 5. CO2e, travel time (TT), estimated distance, and cost as shown to the
users for each trip scenario, separated by transportation mode. Each city in
every trip scenario is denoted by its respective IATA code

Participants were presented with seven distinct scenarios, elaborated in
Figure 5. These scenarios illustrated trips between cities involving diverse
modes of transportation—–such as train, flight, and driving with the intention
of gauging their inclination towards making sustainable choices when selecting
their mode of transport. We estimated costs associated with one-way nonstop
flight and train connections up to a maximum distance of 500 kilometers using
the estimations from Rome2Rio (2018), projected for approximately one month in
advance. We approximated the cost of driving based on the estimates from
European Commission – Alternative Fuels Observatory (2022) for a petrol car.
The CO2e estimations, tailored to each mode of transportation, were computed
based on the values in Table 1.

Figure 6. Distribution of user responses (%) for different transport modes in
various trip scenarios. Each city in every trip scenario is denoted by its
respective IATA code

Figure 6 provides a summary of the mode distribution for various city pairs,
indicating the percentages of user responses for different transportation
modes (train, drive, and fly). Key takeaways include preferences for specific
modes for each trip, reflecting the distribution of travel choices considering
the associated distances. Notably, train travel dominates in several
instances, with variations depending on the city pairs and their distances.
The percentages of user responses offer insights into the preferred
transportation modes for different travel scenarios.

Figure 7. Reasonings provided by respondents when choosing modes of transport
across all trips

We delved into the reasons behind selecting trade-offs for various
transportation modes in each trip scenario, as illustrated in Figure 7. Our
analysis reveals notable insights from survey responses. For train travel, a
significant 47.1% of respondents prioritize convenience, while 15.65%
emphasize selecting the mode with the least CO2 emissions. Affordability is a
critical factor for 13.91% of participants, and 8.70% opt for the cheapest
option available. Additionally, 5.22% favor trains over flights for cost
considerations. In contrast, driving is primarily chosen for its convenience
and flexibility, with an overwhelming 73.81% of respondents highlighting this
aspect. Affordability remains a factor, as 18.57% opt for the cheapest driving
option. Some respondents (6.67%) perceive driving as environmentally better
than flying. Flying, chosen by 76.53% for its speed, also reflects a segment
(11.91%) indifferent to CO2 emissions. The findings underline the multifaceted
nature of decision-making, encompassing convenience, environmental concerns,
and cost considerations across different transportation modes. These findings
align with the outcomes presented by Avogadro et al. (2021). They similarly
indicate a preference for public transport over flying for shorter distances.

###  6.3. Understanding Tradeoffs

The user study also aimed to explore the various trade-offs associated with
trip planning. Participants were presented with Likert Scale (Joshi et al.,
2015) statements from ”not at all important” to ”extremely important” to gauge
their agreement levels to the following statements:

  * •

Importance of presence of off-season discounts.

  * •

Climate at the destination.

  * •

Cost savings by traveling during off-season.

  * •

Visiting the city during its best travel time, even during the peak tourist
season.

  * •

Overall attractiveness of the destination.

  * •

The destination in terms of unique attractions, points of interest, etc., even
if that means they are very popular.

  * •

Cities that are widely popular, even if they might be crowded.

The analysis of user responses, as depicted in Figure 8, provides insights
into the trade-offs users are willing to make when considering various
attributes in travel decision-making. Notably, for the attribute ”popular but
crowded,” a minimal percentage (2.99%) strongly agreed, while the majority
(31.34%) agreed. Conversely, for the attribute ”attractive but popular”, a
significant proportion (55.22%) agreed, indicating a higher tolerance for
popularity in the pursuit of attractiveness. The consideration of ”overall
attractiveness” saw 51.24% in agreement. Respondents expressed varying
opinions on the ”visiting in peak season”, with 1.99% strongly disagreeing and
33.83% expressing disagreement. Furthermore, factors such as ”off-season cost
savings”, ”Climate at the destination” during the time of travel, and ”off-
season discounts” revealed nuanced preferences, with notable percentages in
agreement (51.74%, 37.31%, 39.30%, respectively) and distinct proportions
holding dissenting views. These results contribute valuable insights into the
multifaceted considerations influencing users’ travel preferences.

Figure 8. Figure showing the degree of importance of the various factors
during a trip decision-making process

##  7\. Societal Fairness

This section delves into quantifying S-Fairness, a pivotal element in
examining city trip recommendations. Societal Fairness (S-Fairness) involves
ensuring the fair distribution of tourism benefits and impacts among a
spectrum of stakeholders, encompassing not only travelers, item providers, and
platforms but also non-participating entities such as residents, locals, and
the environment (Banerjee, 2023; Banik et al., 2023).

To assess Societal Fairness (S-Fairness) across diverse destinations, our
methodology integrates insights from established data sources and incorporates
findings from an extensive user study, providing a thorough perspective. We
define the S-Fairness index (SF index) by quantifying the overall impact of a
destination on both the environment and society concerning the user’s starting
point.

###  7.1. Defining the S-Fairness Index

Each destination $\mathit{c_{i}}$ during the month $j$ accessible from the
user’s origin is assigned an SF index, denoted as $\psi(c_{i}^{j})$. This
index is determined through a weighted combination of three essential
indicators—

  * •

emissions trade-off indicator $\mathit{Z(c_{i})}$ from Section 3 with its
normalized weight represented by $\alpha$

  * •

popularity index $\rho(c_{i})$ from Section 4 with its normalized weight
denoted as $\beta$

  * •

seasonality index $\sigma(c_{i}^{j})$ from Section 5 with its normalized
weight represented by $\Gamma$.

The formulation is expressed as:

(6) $\psi(c_{i}^{j})=\alpha\cdot
Z(c_{i})+\beta\cdot\rho(c_{i})+\Gamma\cdot\sigma(c_{i}^{j})$

Here, $\psi(c_{i}^{j})$ falls within the range of zero to one, where a higher
index signifies a more adverse impact on society. The allocation of weights to
these indicators reflects the significance of considering emissions,
popularity, and seasonality in evaluating the societal fairness of a
destination. This approach, integrating both quantitative and user-centered
perspectives, strengthens the effectiveness of our S-Fairness indicator.

###  7.2. Learning Weights of the Indicators

Here, we aim to gauge the respondents’ perceived importance of various factors
influencing their selection of travel destinations. In the user study
described in Section 6.1, the participants were also prompted to allocate
maximum weightage among travel time, CO2 footprint, and cost when determining
transportation to their destination, aligning with the coefficients outlined
in the calculation of the emissions trade-off indicator $\mathit{Z(c_{i})}$ in
Equation 1. They also provided insights into the significance of factors like
points of interest counts, Google image search values, and the total number of
Tripadvisor reviews and opinions, detailed in the estimation of the popularity
index $\rho(c_{i})$ in Equation 3, to determine the popularity of a city. They
also indicated the importance of costs associated with available accommodation
and crowd levels in the city during their visit, aligning with the
coefficients of the seasonality index in Equation 5. This aimed to shed light
on the impact of seasonality on the decision-making process.

Finally, the participants were prompted to assess the influence of various
factors on their decision-making process to estimate the weights associated
with the combined S-Fairness index $\psi(c_{i}^{j})$, as illustrated in
Equation 6. They were explicitly asked about the impact of factors such as
having a lower CO2 footprint, opting for a less famous city, and avoiding the
busiest time of the year when making travel decisions. All the responses were
gathered using a 5-point Likert Scale.

Figure 9. Distribution of the absolute values of weights, obtained through the
weighted average of Likert scale results

We calculated weighted averages on Likert scales, spanning from ”not important
at all”, having a minimum weight of 1, to ”extremely important”, with a
maximum weight of 5, enabling us to identify patterns in the composite scores.
The Distribution of the absolute values of these weights, obtained through the
weighted average of Likert scale results, are shown in Figure 9. Additionally,
we applied min-max normalization (Patro and Sahu, 2015) to normalize these
averages within each category, enabling us to gauge their relative
significance in their respective categories. These normalized weights offer
valuable insights into participants’ preferences and priorities, shedding
light on the factors that significantly influence their decision-making when
choosing travel destinations. After incorporating the normalized weights, the
expressions for the emissions trade-off indicator, popularity index, and
seasonality index can be revised as follows:

(7)
$\mathit{Z(c_{i})}=0.352\cdot\tau_{TT}({\mathit{c_{i}}})+0.218\cdot\tau_{EM}({\mathit{c_{i}}})+0.431\cdot\tau_{Cost}({\mathit{c_{i}}})$
(8) $\rho(c_{i})=0.469\cdot\pi_{POI}+0.325\cdot\pi_{UGC}+0.206\cdot\pi_{GT}$
(9) $\sigma(c_{i}^{j})=0.443\cdot G_{AVC}^{(i)}+0.557\cdot G_{ADR}^{(i)(j)}$

Combining the weighted formulations presented in Equation 7, Equation 8, and
Equation 9, we obtain the updated values for the S-Fairness index
$\psi(c_{i}^{j})$ as follows:

(10)
$\psi(c_{i}^{j})=0.281\cdot\mathit{Z(c_{i})}+0.334\cdot\rho(c_{i})+0.385\cdot\sigma(c_{i}^{j})$

The weights here represent the importance assigned to different attributes
within each category. Notably, in the emission trade-off category
$\mathit{Z(c_{i})}$, the highest weight is given to the cost attribute
($\alpha_{Cost}$), indicating that users prioritize the cost factor when
evaluating emission scores. In the popularity category $\rho(c_{i})$, the
points of interest ($\beta_{POI}$) attribute carries the highest weight,
suggesting that users prioritize locations with significant points of
interest. However, in the S-Fairness index category $\psi(c_{i}^{j})$, the
weight distribution is relatively balanced among the attributes $\alpha$,
$\beta$, and $\Gamma$, indicating a more equitable consideration of these
factors. These weights provide insights into user preferences, highlighting
the relative importance of different attributes within each category. The
normalized weights further emphasize the relative significance of
characteristics within their respective categories, aiding in understanding
user priorities and preferences in decision-making.

Figure 10 shows the distribution of the weighted seasonality indices for
selected cities. It captures the pronounced peaks in monthly seasonality for
Munich in September and Paris in July/August as shown in Table 4. We did not
plot the weighted popularity scores as they were similar to the results in
Figure 3.

Figure 10. Distribution of the weighted seasonality index for selected cities

The composite S-Fairness index $\psi(c_{i}^{j})$ assigned to each city
$\mathit{c_{i}}$ for the month $j$ signifies the extent of negative
environmental impact associated with traveling to the city from users’
starting points. A lower value of $\psi(c_{i}^{j})$ indicates a more
environmentally friendly choice and lesser harm caused. Our objective is to
encourage individuals to visit cities with lower S-Fairness indices relative
to their starting points, aiming to minimize the adverse effects of tourism on
the environment and promote sustainable and responsible tourism practices.

###  7.3. Validating S-Fairness Index

The validation of the S-Fairness index is a pivotal part of our study. Here,
we detail the process undertaken for this validation, highlighting how the
index performs in real-world scenarios and reflects a balance between tourist
preferences and sustainable practices.

To evaluate the effectiveness of our SF indices, we conducted a user study
involving a gender-balanced sample of 200 European residents with a listed
interest in travel on Prolific. Participants were presented with the Figure 1
from Section 1, featuring the top travel destinations from Munich for July.
The study showcased six European cities, each accompanied by a photograph and
a brief overview. Featured cities include Vienna, Berlin, Brussels, Madrid,
Barcelona, and Paris. Each destination is accompanied by travel time
information by train and plane from Munich, carbon emissions for each mode of
transport, and three indices, Emission, Popularity, and Seasonality, with
qualitative ratings such as ”Medium” or ”High” for trade-off understanding. In
this scenario, cities within the top 5 percentile of their respective
popularity and seasonality indices are categorized as high, those in the top
50 percentile as medium, and the rest as low.

An overall SF index out of 100 is assigned to each city (derived by
multiplying our SF index from Section 7.2 by 100), representing its overall
sustainability status concerning the starting point, Munich. Vienna and Berlin
score 19/100, being the most sustainable options from Munich, followed by
Brussels 22/100. Madrid 30/100, Barcelona 33/100, and Paris 39/100 are the
least sustainable options at the end of the list. The interface aims to assist
users in choosing a travel destination based on sustainability, popularity
metrics, and personal interest. The presented SF indices indicate the
preference for sustainable destinations. Through the interface, users will
also have the option to choose their preferred mode of transport, and the SF
index value will be updated accordingly.

Following the presentation, participants were tasked with expressing their
opinions on the provided statements using a 5-point Likert Scale (Joshi et
al., 2015) ranging from ”strongly disagree” to ”strongly agree”. The
statements covered the following aspects:

  * •

The assigned SF index scores accurately reflect the sustainability of the
showcased city destinations.

  * •

Cities with lower SF index scores are perceived as more appealing for travel.

  * •

SF indices are deemed helpful in facilitating informed decisions about
preferred travel destinations.

The results are depicted in Figure 11, revealing a generally positive
reception to the SF index as a sustainability indicator. A majority of users
(72%) expressed neutrality or agreement. The ”low SF index, better appeal”
metric showed a similar trend, with 71% ranging from neutral to solid
agreement, suggesting that a lower SF index correlates with higher user
appeal. The ”overall helpful metric” received the most positive responses,
with 77.5% ranging from neutral to solid agreement on its usefulness. Overall,
there is a trend of approval across all metrics, with even the least favorable
response showing a majority of users being neutral to strongly agreeing on the
value of the metrics. In conclusion, the metrics presented in the study are
well-received by most users.

Figure 11. Results of the user study to validate the usefulness of the SF
index, showing a mostly positive response

##  8\. Conclusion

In conclusion, our research introduces a novel approach for assigning a
sustainability indicator (SF index) for city trips accessible from the users’
starting point, integrating CO2 emission analysis, destination popularity, and
seasonal demand. Our methodology, validated through a user study, showcases
the practicality and effectiveness of the model in providing well-rounded and
sustainable city trip suggestions. Our findings indicate that while there is a
general awareness of sustainability, tourists often prioritize convenience and
personal preferences over sustainable choices. This gap highlights the need
for more effective communication and education strategies to promote
S-Fairness in city trip planning.

This work lays the groundwork for significant enhancements in TRS. While
Figure 1 serves as an illustrative user interface, the potential of this work
extends to the development of a practical application. Such an application
could present destinations and their SF indices, incorporating up to date
information like real-time connectivity and cost considerations. Moreover, it
could integrate additional metrics such as accommodation availability and
environmental factors like climate and air quality indices. This integration,
coupled with user preferences, would enable the provision of real-time
recommendations that align with sustainability principles and individual user
preferences.

Additionally, as depicted in Figure 1, destinations are currently ordered by
the SF index. However, in practical scenarios, users may prefer sorting
destinations by factors such as emissions, popularity, and seasonality, given
their varying importance to different individuals. Introducing a user
interface that allows sorting and filtering by the individual components while
making the associated trade-offs explicit would enhance the flexibility of the
application. Similarly, the current representation of popularity and
seasonality labels (e.g., low, medium, or high) in Figure 1 could be refined
through discrete categories to offer meaningful and actionable insights,
contributing to a more user-friendly and informative interface. We intend to
delve deeper into the communication of sustainable recommendations to users as
part of our future research.

In summary, our study marks an essential stride towards a new era in
sustainable tourism. It paves the way for a system that combines
environmentally sensitive travel suggestions with travelers’ evolving needs
and preferences.

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