Specifications TableSubjectSocial SciencesSpecific subject areaTransportation, Travel Behavior AnalysisType of dataTableHow data were acquiredComputer-Assisted Telephonic Interview (CATI) surveyData formatRaw\
Analyzed\
FilteredParameters for data collectionThe survey addressed individuals older than 18, employee active status, living or residing in Cluj Metropolitan Area, and willing to share their current travel behavior and response to public transport improvement policy.Description of data collectionThe respondents were accessed by phone following Random Digit Dialing procedure in order to ensure demographic and spatial coverage for the sample. The respondents were asked to participate in a 15-min telephonic interview on matters of travel behavior.Data source locationCity/Town/Region: Cluj Metropolitan Area, Transylvania\
Country: RomaniaData accessibilityData accompanies the current article.Related research articleCristian Toşa, Hitomi Sato, Takayuki Morikawa, Tomio Miwa\
Commuting behavior in emerging urban areas: Findings of a revealed-preferences and stated-intentions survey in Cluj-Napoca, Romania\
Journal of Transport Geography\
DOI [10.1016/j.jtrangeo.2018.02.011](http://10.1016/j.jtrangeo.2018.02.011){#intref0010}**Value of the Data**•Data contains commuter level information on current and prospective travel behavior, socioeconomic characteristics, derived spatial features, and attitudinal data for transport services in emerging urban areas.•Stakeholders with interest in transport policies can be provided with important insights on public response to non-coercive actions with regards to sustainable transportation in urban areas.•The empirical study could serve as a reliable framework for testing transportation policies, using the same or different modal attributes of interest.•The data brings additional value in explaining travel behavior in emerging urban areas in the context of post-communist countries from Central and Eastern Europe.

1. Data {#sec1}
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The dataset described in this paper contains socio-economic and demographic characteristics, travel behavior, associated spatial features, and attitudinal indicators. Data was collected using a stratified sample representing the daily, morning rush-hour commuters from the Metropolitan Area of Cluj, Romania ([Fig. 1](#fig1){ref-type="fig"}). Several studies were previously conducted \[[@bib1],[@bib2]\] with regards to data collection from individuals for the assessment of sustainable urban mobility in post-socialist urban areas \[[@bib3]\], but data was scarce, and studies were exploratory. Toșa et al. \[[@bib4]\] conducted a comprehensive study by revealing the generational differences and their demographic, socioeconomic, and attitudinal characteristics in quantitatively explaining commuting patterns within Cluj Metropolitan Area, Romania. To those were added elements of transport supply and built environment that contribute to the refinement of the choice process. The associated data and the methodology will be described in the following. The cleaned and processed dataset is part from a total of 1079 respondents who participated in the data collection process, and it comprises of a quota of 544 individuals (50.42%).Fig. 1Study area.Fig. 1

The socio-economic and demographic section in the questionnaire collected information related to the respondent, such as on gender, family size, age, education level and occupation, marital status, driver\'s license, household type, and income and. [Table 1](#tbl1){ref-type="table"} integrates these characteristics by their associated levels and shows the percentage distribution. For each variable, the sum of the elements sample sizes equals the final quota of the respondents, i.e. 544 individuals.Table 1Socio-economic and demographic characteristics.Table 1VariableElementSamplePercentageGenderMale28352.02Female26147.98Family sizeAlone376.8215127.76318033.094 or more17632.35Age cohorts18--25336.0726--3513825.3736--4516129.646--5514226.1\>557012.87EducationBachelor and below19736.21Higher education34763.79OccupationPublic worker16931.07Private worker37568.93Marital statusMarried14426.47Not married40073.53Driver\'s licenseYes42678.31No11821.69Household typeIndividual home12322.61Apartment42177.39Average income household (in RON)\<1000315.71001--200016830.882001--300013324.453001--40007714.154001--5000539.745001--6000264.78\>60005610.29Monthly transportation expenditure (in RON)\<50224.0451--10011821.69101--20015528.49201--3009317.1301--4005710.48\>4009918.2

The commuting behavior section of the questionnaire addressed questions to track the transport modes used for commuting and their corresponding weekly frequency. The transport modes included both motorized modes, such as public transport, car, motorcycle or taxi, and non-motorized modes, such as bicycle, and walking. These travel modes were selected for the questionnaire in order to emphasize their impact on traffic congestion and level of service. Therefore, modes such as motorcycle, car, and taxi represent personal motorized modes, and walking and bicycle represent non-motorized modes. The frequency data was processed in order to obtain the representative transport mode used for commuting, i.e. the mode with the highest weekly frequency. Therefore, the merged modes considered were named (1) non-motorized, (2) Private motorized, and (3) Public transport. The modal split data is revealed in [Table 2](#tbl2){ref-type="table"}. As in [Table 1](#tbl1){ref-type="table"}, for each variable, the sum of the elements sample sizes equals the final quota of the respondents.Table 2Modal split characteristics and mobility tools ownership.Table 2VariableElementSamplePercentageCurrent commuting travel mode before experiment (RP)Non-motorized8615.81Private motorized22240.81Public transport23643.38Intended commuting travel mode after experiment (SP)Non-motorized6912.68Private motorized16931.07Public transport30656.25Cars per household07413.6129654.41214426.473 or more305.51Time travel to work (minutes)\<1071.2910--20407.3521--306912.68\>3042878.68Travel distance to work (kilometers)\<541676.475--1010819.85\>10203.68

Information on the place of residence and work was requested, but this data was considered sensitive, and therefore is not shared within the dataset. Nevertheless, the associated spatial features that include distances, consider the location of respondent origin and destination within the study area, correlated with the relative position of downtown area, and the bus station locations. Accordingly, population density was extracted from the geographic information system (GIS) model, as well as the accessibility of public transport stations to respondent\'s home and workplace and were shared in the dataset. This information was synthesized in [Table 3](#tbl3){ref-type="table"}. For each variable, the sum of the elements sample sizes equals the final quota of the respondents.Table 3Spatial characteristics derived from the GIS model.Table 3VariableElementSamplePercentageOrigin to public transport station\<400 m51193.93400 ... 800 m315.7\>800 m20.37Destination to public transport station\<400 m52295.96400 ... 800 m203.68\>800 m20.37Origin to downtown area (Euclidian distance, kilometers)\<1254.61--210920.042--321339.153--414526.654--5152.76\>5376.8Destination to downtown area (Euclidian distance, kilometers)\<1417.541--210419.122--311420.963--410419.124--55510.11\>512623.16Origin - Destination route through downtown areaYes16730.7No37769.3Population density at origin (inhabitants per sqkm)\<400011521.144000--60005810.666000--8000366.628000--100009617.6510000--12000488.82\>1200019135.11Population density at origin (inhabitants per sqkm)\<200010218.752000--400011921.884000--60008014.716000--80008215.078000--100006111.21\>1000010018.38

Attitudinal questions were selected from the questionnaire to capture opinions related to the adequacy of the public transport network, car dependency, and issues related to traffic congestion within metropolitan area (see [Table 4](#tbl4){ref-type="table"}).Table 4Attitudinal statements regarding traffic and transport system.Table 4Fully disagree (%)Disagree (%)Agree (%)Fully Agree (%)No Response (%)Inappropriate public transport network18.0123.5334.7419.124.6Car necessary in daily life21.1417.6518.242.10.91Traffic is congested in the city2.392.7614.3479.780.73

2. Experimental design, materials, and methods {#sec2}
==============================================

We employed a combined estimation method between Revealed Preferences (RP) and Stated Preferences (SP). While RP-data models describe real-life choices and represent actual travel behavior, SP-data-based choice experiments set hypothetical alternatives and record individuals\' preferences \[[@bib5]\]. The adopted methodology uses disaggregated data on respondents' travel behavior and related individual data and provides rich behavioral predictions \[[@bib6], [@bib7], [@bib8], [@bib9]\]. This modelling methodology has been proven to be highly effective in determining the role of selected variables in the selection process and identifying the effects of new policy interventions within transportation sector \[[@bib10],[@bib11]\].

When modeling RP and SP as random utility models with discrete choices, the utility associated with each transport mode can be expressed as an additive function between regressor vectors describing characteristics $X_{i}$ of an individual *i*, and characteristics $Y_{ij}\text{~and~}Y_{ik}$, of the transport alternatives *j* or *k*, and characteristics of the specific effect of the SP experiment ($Z_{ik}$), scaled with respect to corresponding parameter vectors $\alpha_{i}$, $\beta_{ij}$, $\beta_{ik}$ and $\gamma_{ik}$, respectively. The RP and SP models could be jointly estimated and subsequently maximizing the log-likelihood of the following function:$$L\left( {\alpha,\beta,\gamma\text{|}X,Y,Z} \right) = \prod_{\forall\ i \in I}\prod_{\forall j,k \in J,K}P_{ij}^{RP}\left( {\alpha,\ \beta\text{|}X,Y} \right)^{\delta_{ji}}\ P_{ik}^{SP}\left( {\alpha,\beta,\gamma,\mu\text{|}X,Y,Z} \right)^{\delta_{ki}}$$where $\delta_{ji}$ and $\delta_{ki}$ represent modal choice indicators (=1 if alternatives j and k are selected by an individual i in the RP and SP models, respectively; and 0 otherwise), and scale parameter $\mu$. $P_{ij}^{RP}$ and $P_{ik}^{SP}$ represent the marginal probabilities of the selection of the *j* or *k* transport mode, in RP, and SP model respectively. The unknown parameters within the three mode-choice models (RP, SP, and the combined RP--SP) were estimated by using the GAUSS econometric software (version 3.2.32).

The commuting data employed in this paper were gathered from a cross-sectional survey conducted in the CNMA in July 2015 by means of a computer assisted telephone interview (CATI). The SP section was based on the experimental design intended to test the respondent\'s likelihood to change the current commuting habits over the introduction of the alternative ticketing policy. The alternatives are characterized by a set of relevant attributes and must offer clear and simple choices to the respondent \[[@bib12],[@bib13]\]. The attributes of the proposed policy included a monthly pass consisting of (1) type, with 2 assigned levels, (2) price, with 3 assigned levels, and (3) incentive bonus, with 3 assigned levels. The public transport types of monthly tickets considered were the two-line and the all-line passes. For each type of monthly pass, 3 levels of pricing were considered, a low, medium, and high costs. The bonus values were considered as percentage of the monthly cost, and set to 2, 5 and 15% points. A full factorial design was employed to generate 18 cases of preference, as a combination of the attributes, as seen in [Table 5](#tbl5){ref-type="table"}. Out of the total 18 scenarios for the transport policy alternative, each respondent was presented a single case to accept or reject during the interview.Table 5Alternative scenarios for public transport.Table 5Case no.Monthly Cost (RON)Monthly Pass TypeBonus (% of cost)1552 lines2%2552 lines5%3552 lines15%4702 lines5%5702 lines15%6702 lines2%7902 lines15%8902 lines2%9902 lines5%1085All line2%1185All line5%1285All line15%13110All line5%14110All line15%15110All line2%16135All line15%17135All line2%18135All line5%

The combined estimation of RP and SP models reveals the values and significance levels of certain coefficients and helps identify the role of certain variables in the choice process \[[@bib14]\]. This study assesses differences between generations and their role in tailoring travel demand in emerging urban areas. In this way, several models can be customized by specific socio-economic and demographic features, in order to reveal idiosyncrasies between groups of interest.

Conflict of Interest {#appsec3}
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data {#appsec1}
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The following are the Supplementary data to this article:Multimedia component 1Multimedia component 1Multimedia component 2Multimedia component 2Multimedia component 3Multimedia component 3Multimedia component 4Multimedia component 4

The author acknowledges financial support for data collection from the KAKENHI Grant-in-Aid for Scientific Research (number 2604707 and 15F14707) received from the Japan Society for the Promotion of Science (JSPS).

This research was partially supported by the project 21 PFE in the frame of the programme PDI-PFE-CDI 2018.

Supplementary data to this article can be found online at <https://doi.org/10.1016/j.dib.2019.104703>.
