Evaluation of modal-choice rules through ground transportation modeling using subway data

One of the most important issues in transportation and urban planning is an understanding of passenger choice while commuting in existing transport infrastructure. Big modern cities offer a multimodal selection of methods to transfer between the parts of the city, including various types of ground transportation and a subway. On the other hand, passengers’ model choice depends on the available routes, often historically formed without regard to modern passenger needs and fast-changing of cities life. As a result, for the optimization of public transport systems we should understand how the rules are formed, followed by passengers when choosing a specific route. This paper discusses the evaluation of modal-choice rules through ground transportation modeling using historical data collected from turnstiles in the subway and census data. The results should help in identifying the critical places in the existing infrastructure and transportation planning of big cities. The modeling results are shown on the example of St. Petersburg.

[1]  Stephen Law,et al.  Towards a multimodal space syntax analysis: A case study of the London street and underground network , 2012 .

[2]  Konstantinos G. Zografos,et al.  Algorithms for Itinerary Planning in Multimodal Transportation Networks , 2008, IEEE Transactions on Intelligent Transportation Systems.

[3]  Mark E.T. Horn,et al.  AN EXTENDED MODEL AND PROCEDURAL FRAMEWORK FOR PLANNING MULTI-MODAL PASSENGER JOURNEYS , 2003 .

[4]  J. Gil Integrating public transport networks in the axial model , 2012 .

[5]  Cyril Ray,et al.  Multi-scale and multi-modal GIS-T data model , 2011 .

[6]  Hu Shao,et al.  Travel Time Reliability-Based Optimal Path Finding , 2010, 2010 Third International Joint Conference on Computational Science and Optimization.

[7]  Anastasia A. Lantseva,et al.  Modeling Transport Accessibility with Open Data: Case Study of St. Petersburg , 2016 .

[8]  S. Graham,et al.  Networked infrastructures, technological mobilities, and the urban condition , 2001 .

[9]  Alain J. Chiaradia,et al.  Configurational exploration of public transport movement networks: a case study, the London Underground , 2005 .

[11]  Jianjun Tan,et al.  A Multiscale and Multimodal Transportation GIS for the City of Guangzhou , 2009, IF&GIS.

[12]  Fabio Stella,et al.  An Integrated Forecasting and Regularization Framework for Light Rail Transit Systems , 2006, J. Intell. Transp. Syst..

[13]  Wang Hong,et al.  An Optimal Transit Path Algorithm Based on the Terminal Walking Time Judgment and Multi-mode Transit Schedules , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[14]  W DeWitt,et al.  Intermodal Freight Transportation , 2000 .