Travel modal choice analysis for traffic corridors based on decision-theoretic approaches

The rapid development of multimodal transportation system prompts travellers to choose multiple transportation modes, such as private vehicles or taxi, transit (subways or buses), or park-and-ride combinations for urban trips. Traffic corridor is a major scenario that supports travellers to commute from suburban residential areas to central working areas. Studying their modal choice behaviour is receiving more and more interests. On one hand, it will guide the travellers to rationally choose their most economic and beneficial mode for urban trips. On the other hand, it will help traffic operators to make more appropriate policies to enhance the share of public transit in order to alleviate the traffic congestion and produce more economic and social benefits. To analyze the travel modal choice, a generalized cost model for three typical modes is first established to evaluate each different travel alternative. Then, random utility theory (RUT) and decision field theory (DFT) are introduced to describe the decision-making process how travellers make their mode choices. Further, some important factors that may influence the modal choice behaviour are discussed as well. To test the feasibility of the proposed model, a field test in Beijing was conducted to collect the real-time data and estimate the model parameters. The improvements in the test results and analysis show new advances in the development of travel mode choice on multimodal transportation networks.

[1]  Ennio Cascetta,et al.  Transportation Systems Analysis , 2009 .

[2]  Zhen Sean Qian,et al.  Modeling Multi-Modal Morning Commute in a One-to-One Corridor Network , 2011 .

[3]  Philippe Mongin,et al.  Expected Utility Theory , 1998 .

[4]  Giulio Erberto Cantarella,et al.  Multilayer Feedforward Networks for Transportation Mode Choice Analysis: An Analysis and a Comparison with Random Utility Models , 2005 .

[5]  Hui Zhao,et al.  Transportation serviceability analysis for metropolitan commuting corridors based on modal choice modeling , 2013 .

[6]  Rui Wang,et al.  Autos, transit and bicycles: Comparing the costs in large Chinese cities , 2011 .

[7]  Kenneth A. Small,et al.  The Value of Time and Reliability: Measurement from a Value Pricing Experiment , 2001 .

[8]  Jerome R. Busemeyer,et al.  Computational Models of Decision Making , 2003 .

[9]  C Terence,et al.  The Value of Time and Reliability: Measurement from a Value Pricing Experiment , 2003 .

[10]  B. Verplanken,et al.  Habit, information acquisition, and the process of making travel mode choices , 1997 .

[11]  C. Bhat A heteroscedastic extreme value model of intercity travel mode choice , 1995 .

[12]  Hani S. Mahmassani,et al.  DYNAMICS OF COMMUTING DECISION BEHAVIOR UNDER ADVANCED TRAVELER INFORMATION SYSTEMS , 1999 .

[13]  Adele Diederich,et al.  Survey of decision field theory , 2002, Math. Soc. Sci..

[14]  Karin Baier,et al.  Transportation Systems Analysis Models And Applications , 2016 .

[15]  Chandra R. Bhat,et al.  WORK TRAVEL MODE CHOICE AND NUMBER OF NON-WORK COMMUTE STOPS , 1997 .

[16]  C. J. Goodman,et al.  Metro traffic regulation from the passenger perspective , 2001 .

[17]  Guido Gentile,et al.  Section 7.5 - Dynamic traffic assignment with non separable link cost functions and queue spillovers , 2009 .

[18]  J. Townsend,et al.  Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. , 1993, Psychological review.

[19]  Jerome R. Busemeyer,et al.  The Cambridge Handbook of Computational Psychology: Micro-Process Models of Decision Making , 2007 .

[20]  R. Banomyong,et al.  Multimodal transport: the case of Laotian garment exporters , 2001 .

[21]  Athanasios K. Ziliaskopoulos,et al.  An intermodal optimum path algorithm for multimodal networks with dynamic arc travel times and switching delays , 2000, Eur. J. Oper. Res..

[22]  Karl Rehrl,et al.  Assisting Multimodal Travelers: Design and Prototypical Implementation of a Personal Travel Companion , 2007, IEEE Transactions on Intelligent Transportation Systems.

[23]  R. van Nes,et al.  Design of multimodal transport networks: A hierarchical approach , 2002 .

[24]  Hong Kam Lo,et al.  Modeling competitive multi-modal transit services; a nested logit approach. , 2004 .

[25]  Yi Zhang,et al.  A generalized comfort function of subway systems based on a nested logit model , 2014, Tsinghua Science and Technology.

[26]  Lorenzo Meschini,et al.  A frequency based transit model for dynamic traffic assignment to multimodal networks , 2007 .

[27]  J. Townsend,et al.  Multialternative Decision Field Theory: A Dynamic Connectionist Model of Decision Making , 2001 .

[28]  Ziyou Gao,et al.  Bounded-rationality based day-to-day evolution model for travel behavior analysis of urban railway network , 2013 .

[29]  Li Ji-yun,et al.  The Application of Decision Field Theory in Clothing Style Evaluation , 2010 .

[30]  Alan Geoffrey Wilson,et al.  Optimisation in locational and transport analysis , 1981 .

[31]  Yao-Jan Wu,et al.  Analysis of park-and-ride decision behavior based on Decision Field Theory , 2013 .

[32]  John Dinwoodie,et al.  Congestion and multimodal transport: a survey of cargo transport operators in the Netherlands , 2000 .