Spatial modelling of the transport mode choice: application on the Vienna transport network
Abstract
A new approach for spatial modelling of transport mode choice is presented in the paper. The approach tackles the problem by considering the trade-off between subjective and objective factors. To obtain mode Preference Rates (PRs) based on subjective factors, the Analytic Hierarchy Process (AHP) method is applied. The objective factors are expressed with the journey time from any point in the map to destination according to the available transport mode choice on the specific connection. The results are presented as PRs of individual transport modes. The model is validated on the conducted the survey, with students of Vienna University of Economics and Business (WU) as a target audience. Members of different target groups (age, national, employment) decide differently regarding the transport choice, so it is better to analyse them separately. The presented model can be used for the city transport planning in any urban area. It can help promote the sustainable modes of transport in the areas that are less adjusted in sustainable manner.
Keyword : AHP, decision-making policy, GIS, students, mode choice, objective and subjective factors, transport management
How to Cite
Šinko, S., Rupnik, B., Prah, K., & Kramberger, T. (2021). Spatial modelling of the transport mode choice: application on the Vienna transport network. Transport, 36(5), 386-394. https://doi.org/10.3846/transport.2021.16128
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Kroes, E. P.; Sheldon, R. J. 1988. Stated preference methods: an introduction, Journal of Transport Economics and Policy 22(1): 11–25.
Loidl, M.; Wallentin, G.; Cyganski, R.; Graser, A.; Scholz, J.; Haslauer, E. 2016. GIS and transport modeling – strengthening the spatial perspective, ISPRS International Journal of Geo-Information 5(6): 84. https://doi.org/10.3390/ijgi5060084
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Mehdizadeh, M.; Fallah Zavareh, M.; Nordfjaern, T. 2019. Monoand multimodal green transport use on university trips during winter and summer: hybrid choice models on the normactivation theory, Transportation Research Part A: Policy and Practice 130: 317–332. https://doi.org/10.1016/j.tra.2019.09.046
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Nasrin, S. 2020. Private university students’ mode choice behaviour for travel to university: analysis in the context of Dhaka city, Lecture Notes in Civil Engineering 45: 299–310. https://doi.org/10.1007/978-981-32-9042-6_24
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Rodriguez, D. A.; Joo, J. 2004. The relationship between non-motorized mode choice and the local physical environment, Transportation Research Part D: Transport and Environment 9(2): 151–173. https://doi.org/10.1016/j.trd.2003.11.001
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Thill, J.-C. 2000. Geographic information systems for transportation in perspective, Transportation Research Part C: Emerging Technologies 8(1–6): 3–12. https://doi.org/10.1016/S0968-090X(00)00029-2
Uttley, J.; Lovelace, R. 2016. Cycling promotion schemes and long-term behavioural change: a case study from the University of Sheffield, Case Studies on Transport Policy 4(2): 133–142. https://doi.org/10.1016/j.cstp.2016.01.001
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Whalen, K. E.; Paez, A.; Carrasco, J. A. 2013. Mode choice of university students commuting to school and the role of active travel, Journal of Transport Geography 31: 132–142. https://doi.org/10.1016/j.jtrangeo.2013.06.008
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WU. 2021. Vienna University of Economics and Business (Wirtschaftsuniversitat Wien). Available from Internet: https://www.wu.ac.at/en/
Xiong, H.; Ma, L.; Wei, C.; Yan, X.; Srinivasan, S.; Chen, J. 2019. Exploring behavioral heterogeneities of elementary school students’ commute mode choices through the urban travel big data of Beijing, China, IEEE Access 7: 22235–22245. https://doi.org/10.1109/ACCESS.2019.2897890
Zhan, G.; Yan, X.; Zhu, S.; Wang, Y. 2016. Using hierarchical treebased regression model to examine university student travel frequency and mode choice patterns in China, Transport Policy 45: 55–65. https://doi.org/10.1016/j.tranpol.2015.09.006
Zhou, J. 2012. Sustainable commute in a car-dominant city: factors affecting alternative mode choices among university students, Transportation Research Part A: Policy and Practice 46(7): 1013–1029. https://doi.org/10.1016/j.tra.2012.04.001
Boyd, B.; Chow, M.; Johnson, R.; Smith, A. 2003. Analysis of effects of fare-free transit program on student commuting mode shares: BruinGO at University of California at Los Angeles, Transportation Research Record: Journal of the Transportation Research Board 1835: 101–110. https://doi.org/10.3141/1835-13
Buehler, R. 2011. Determinants of transport mode choice: a comparison of Germany and the USA, Journal of Transport Geography 19(4): 644–657. https://doi.org/10.1016/j.jtrangeo.2010.07.005
Button, K.; Kramberger, T.; Šinko, S. 2018. Catchment areas of small airports: a hybrid analysis of the Alps-Adriatic region, Journal of Airport Management 12(4): 399–411.
Collins, C. M.; Chambers, S. M. 2005. Psychological and situational influences on commuter-transport-mode choice, Environment and Behavior 37(5): 640–661. https://doi.org/10.1177/0013916504265440
Conley, J.; McLaren, A. T. 2009. Car Troubles: Critical Studies of Automobility and Auto-Mobility. Routledge. 272 p.
Danaf, M.; Abou-Zeid, M.; Kaysi, I. 2014. Modeling travel choices of students at a private, urban university: Insights and policy implications, Case Studies on Transport Policy 2(3): 142–152. https://doi.org/10.1016/j.cstp.2014.08.006
Delmelle, E. M.; Delmelle, E. C. 2012. Exploring spatio-temporal commuting patterns in a university environment, Transport Policy 21: 1–9. https://doi.org/10.1016/j.tranpol.2011.12.007
Dijkstra, E. W. 1959. A note on two problems in connexion with graphs, Numerische Mathematik 1: 269–271. https://doi.org/10.1007/BF01386390
Ding, L.; Zhang, N. 2016. A travel mode choice model using individual grouping based on cluster analysis, Procedia Engineering 137: 786–795. https://doi.org/10.1016/j.proeng.2016.01.317
Eluru, N.; Chakour, V.; El-Geneidy, A. M. 2012. Travel mode choice and transit route choice behavior in Montreal: insights from McGill University members commute patterns, Public Transport 4(2): 129–149. https://doi.org/10.1007/s12469-012-0056-2
Ermagun, A.; Samimi, A. 2018. Mode choice and travel distance joint models in school trips, Transportation 45(6): 1755–1781. https://doi.org/10.1007/s11116-017-9794-y
ESRI. 2021. ArcGIS Desktop 10.6.1 Quick Start Guide. Environmental Systems Research Institute (ESRI), Redlands, CA, US. Available from Internet: https://desktop.arcgis.com/en/arcmap/10.6/get-started/setup/arcgis-desktop-quick-startguide.htm
Ewing, R.; Schroeer, W.; Greene, W. 2004. School location and student travel analysis of factors affecting mode choice, Transportation Research Record: Journal of the Transportation Research Board 1895: 55–63. https://doi.org/10.3141/1895-08
Fitch, D. T.; Thigpen, C. G.; Handy, S. L. 2016. Traffic stress and bicycling to elementary and junior high school: evidence from Davis, California, Journal of Transport & Health 3(4): 457–466. https://doi.org/10.1016/j.jth.2016.01.007
Gotschi, T.; De Nazelle, A.; Brand, C.; Gerike, R. 2017. Towards a comprehensive conceptual framework of active travel behavior: a review and synthesis of published frameworks, Current Environmental Health Reports 4(3): 286–295. https://doi.org/10.1007/s40572-017-0149-9
Haslauer, E.; Delmelle, E. C.; Keul, A.; Blaschke, T.; Prinz, T. 2015. Comparing subjective and objective quality of life criteria: a case study of green space and public transport in Vienna, Austria, Social Indicators Research 3(124): 911–927. https://doi.org/10.1007/s11205-014-0810-8
Jankovič, A.; Popović, M. 2019. Methods for assigning weights to decision makers in group AHP decision-making, Decision Making: Applications in Management and Engineering 2(1): 147–165.
Kent, J. L.; Dowling, R. 2013. Puncturing automobility? Carsharing practices, Journal of Transport Geography 32: 86–92. https://doi.org/10.1016/j.jtrangeo.2013.08.014
Klockner, C. A.; Friedrichsmeier, T. 2011. A multi-level approach to travel mode choice – how person characteristics and situation specific aspects determine car use in a student sample, Transportation Research Part F: Traffic Psychology and Behaviour 14(4): 261–277. https://doi.org/10.1016/j.trf.2011.01.006
Ko, J.; Lee, S.; Byun, M. 2019. Exploring factors associated with commute mode choice: An application of city-level general social survey data, Transport Policy 75: 36–46. https://doi.org/10.1016/j.tranpol.2018.12.007
KPIT Technologies Inc. 2018. Smart transportation: a key building block for a smart city, in Forbes India 6 July 2018. Available from Internet: https://www.forbesindia.com/blog/infrastructure/smart-transportation-a-key-building-blockfor-a-smart-city/
Kramberger, T.; Intihar, M.; Vanelslander, T.; Vizinger, T. 2018. On distance decay in port choice, Tehnički vjesnik 25(5): 1314–1320. https://doi.org/10.17559/TV-20161220133114
Kramberger, T.; Rupnik, B.; Štrubelj, G.; Prah, K. 2015. Port hinterland modelling based on port choice, Promet – Traffic & Transportation 27(3): 195–203. https://doi.org/10.7307/ptt.v27i3.1611
Kroes, E. P.; Sheldon, R. J. 1988. Stated preference methods: an introduction, Journal of Transport Economics and Policy 22(1): 11–25.
Loidl, M.; Wallentin, G.; Cyganski, R.; Graser, A.; Scholz, J.; Haslauer, E. 2016. GIS and transport modeling – strengthening the spatial perspective, ISPRS International Journal of Geo-Information 5(6): 84. https://doi.org/10.3390/ijgi5060084
McKelvey, R. D.; Zavoina, W. 1975. A statistical model for the analysis of ordinal level dependent variables, The Journal of Mathematical Sociology 4(1): 103–120. https://doi.org/10.1080/0022250X.1975.9989847
Mehdizadeh, M.; Fallah Zavareh, M.; Nordfjaern, T. 2019. Monoand multimodal green transport use on university trips during winter and summer: hybrid choice models on the normactivation theory, Transportation Research Part A: Policy and Practice 130: 317–332. https://doi.org/10.1016/j.tra.2019.09.046
Miller, H. J.; Shaw, S.-L. 2001. Geographic Information Systems for Transportation: Principles and Applications. Oxford University Press. 480 p.
Miller, H. J.; Shaw, S.-L. 2015. Geographic information systems for transportation in the 21st century, Geography Compass 9(4): 180–189. https://doi.org/10.1111/gec3.12204
Mitra, R.; Buliung, R. N.; Faulkner, G. 2010. Spatial clustering and the temporal mobility of walking school trips in the Greater Toronto Area, Canada, Health & Place 16(4): 646–655. https://doi.org/10.1016/j.healthplace.2010.01.009
Muller, S.; Tscharaktschiew, S.; Haase, K. 2008. Travel-to-school mode choice modelling and patterns of school choice in urban areas, Journal of Transport Geography 16(5): 342–357. https://doi.org/10.1016/j.jtrangeo.2007.12.004
Nasrin, S. 2020. Private university students’ mode choice behaviour for travel to university: analysis in the context of Dhaka city, Lecture Notes in Civil Engineering 45: 299–310. https://doi.org/10.1007/978-981-32-9042-6_24
Nguyen-Phuoc, D. Q.; Amoh-Gyimah, R.; Tran, A. T. P.; Phan, C. T. 2018. Mode choice among university students to school in Danang, Vietnam, Travel Behaviour and Society 13: 1–10. https://doi.org/10.1016/j.tbs.2018.05.003
Obaid, L.; Hamad, K. 2020. Modelling mode choice at Sharjah University city, United Arab Emirates, MATEC Web of Conferences 308: 02004. https://doi.org/10.1051/matecconf/202030802004
ODO. 2021. Katalog: Intermodales Verkehrsreferenzsystem Österreich (GIP.at). Open Data Osterreich (ODO). Available from Internet: https://www.data.gv.at/katalog/dataset/3fefc838-791d-4dde-975b-a4131a54e7c5 (in German).
Popović, M.; Kuzmanović, M.; Savić, G. 2018. A comparative empirical study of analytic hierarchy process and conjoint analysis: literature review, Decision Making: Applications in Management and Engineering 1(2): 153–163.
Rodriguez, D. A.; Joo, J. 2004. The relationship between non-motorized mode choice and the local physical environment, Transportation Research Part D: Transport and Environment 9(2): 151–173. https://doi.org/10.1016/j.trd.2003.11.001
Saaty, Т. L. 1980. The Analytic Hierarchy Process. McGraw-Hill.
Saaty, T. L.; Vargas, L. G. 2012. Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. Springer. 346 p. https://doi.org/10.1007/978-1-4614-3597-6
Shannon, T.; Giles-Corti, B.; Pikora, T.; Bulsara, M.; Shilton, T.; Bull, F. 2006. Active commuting in a university setting: Assessing commuting habits and potential for modal change, Transport Policy 13(3): 240–253. https://doi.org/10.1016/j.tranpol.2005.11.002
Thill, J.-C. 2000. Geographic information systems for transportation in perspective, Transportation Research Part C: Emerging Technologies 8(1–6): 3–12. https://doi.org/10.1016/S0968-090X(00)00029-2
Uttley, J.; Lovelace, R. 2016. Cycling promotion schemes and long-term behavioural change: a case study from the University of Sheffield, Case Studies on Transport Policy 4(2): 133–142. https://doi.org/10.1016/j.cstp.2016.01.001
Wang, D.; Liu, Y. 2015. Factors influencing public transport use: a study of university commuters’ travel and mode choice behaviours, in 7th State of Australian Cities Conference, 9–11 December 2015, Gold Coast, Australia, 1–14.
Whalen, K. E.; Paez, A.; Carrasco, J. A. 2013. Mode choice of university students commuting to school and the role of active travel, Journal of Transport Geography 31: 132–142. https://doi.org/10.1016/j.jtrangeo.2013.06.008
Wien Info. 2021. Cycling in Vienna. Wien Tourismus, Vienna, Austria. Available from Internet: https://www.wien.info/en/lifestyle-scene/sports/cycling
Wilson, K.; Clark, A. F.; Gilliland, J. A. 2018. Understanding child and parent perceptions of barriers influencing children’s active school travel, BMC Public Health 18(1): 1053. https://doi.org/10.1186/s12889-018-5874-y
WU. 2021. Vienna University of Economics and Business (Wirtschaftsuniversitat Wien). Available from Internet: https://www.wu.ac.at/en/
Xiong, H.; Ma, L.; Wei, C.; Yan, X.; Srinivasan, S.; Chen, J. 2019. Exploring behavioral heterogeneities of elementary school students’ commute mode choices through the urban travel big data of Beijing, China, IEEE Access 7: 22235–22245. https://doi.org/10.1109/ACCESS.2019.2897890
Zhan, G.; Yan, X.; Zhu, S.; Wang, Y. 2016. Using hierarchical treebased regression model to examine university student travel frequency and mode choice patterns in China, Transport Policy 45: 55–65. https://doi.org/10.1016/j.tranpol.2015.09.006
Zhou, J. 2012. Sustainable commute in a car-dominant city: factors affecting alternative mode choices among university students, Transportation Research Part A: Policy and Practice 46(7): 1013–1029. https://doi.org/10.1016/j.tra.2012.04.001