Day-to-day modal choice with a Pareto improvement or zero-sum revenue scheme

We investigate the day-to-day modal choice of commuters in a bi-modal transportation system comprising both private transport and public transit. On each day, commuters adjust their modal choice, based on the previous day's perceived travel cost and intraday toll or subsidy of each mode, to minimize their perceived travel cost. Meanwhile, the transportation authority sets the number of bus runs and the tolls or subsidies of two modes on each day, based on the previous day's modal choice of commuters, to simultaneously reduce the daily total actual travel cost of the transportation system and achieve a Pareto improvement or zero-sum revenue target at a stationary state. The evolution process of the modal choice of commuters, associated with the strategy adjustment process of the authority, is formulated as a dynamical system model. We analyze several properties of the dynamical system with respect to its stationary point and evolutionary trajectory. Moreover, we introduce new concepts of Pareto improvement and zero-sum revenue in a day-to-day dynamic setting and propose the two targets’ implementations in either a prior or a posterior form. We show that, although commuters have different perceived travel costs for using the same travel mode, the authority need not know the probability distribution of perceived travel costs of commuters to achieve the Pareto improvement target. Finally, we give a set of numerical examples to show the properties of the model and the implementation of the toll or subsidy schemes.

[1]  Giulio Erberto Cantarella,et al.  A General Fixed-Point Approach to Multimode Multi-User Equilibrium Assignment with Elastic Demand , 1997, Transp. Sci..

[2]  S. Lawphongpanich,et al.  Solving the Pareto-improving toll problem via manifold suboptimization , 2010 .

[3]  Hai-Jun Huang,et al.  Pricing and logit-based mode choice models of a transit and highway system with elastic demand , 2002, Eur. J. Oper. Res..

[4]  Xuehao Chu,et al.  Alternative congestion pricing schedules , 1999 .

[5]  Ziyou Gao,et al.  Modal Split and Commuting Pattern on a Bottleneck-Constrained Highway , 2007 .

[6]  Ning Jia,et al.  Day-to-day traffic dynamics considering social interaction: From individual route choice behavior to a network flow model , 2016 .

[7]  Hai-Jun Huang,et al.  Equilibrium and Modal Split in a Competitive Highway/Transit System Under Different Road-use Pricing Strategies , 2014 .

[8]  D. Watling STABILITY OF THE STOCHASTIC EQUILIBRIUM ASSIGNMENT PROBLEM: A DYNAMICAL SYSTEMS APPROACH , 1999 .

[9]  Hong Kam Lo,et al.  Stability and attraction domains of traffic equilibria in a day-to-day dynamical system formulation , 2010 .

[10]  Carlos F. Daganzo,et al.  Morning Commute with Competing Modes and Distributed Demand: User Equilibrium, System Optimum, and Pricing , 2012 .

[11]  Hai-Jun Huang,et al.  Modeling the modal split and trip scheduling with commuters' uncertainty expectation , 2015, Eur. J. Oper. Res..

[12]  E. Verhoef,et al.  Congestion pricing in a road and rail network with heterogeneous values of time and schedule delay , 2014 .

[13]  C. Daganzo A pareto optimum congestion reduction scheme , 1995 .

[14]  Anna Nagurney,et al.  On the local and global stability of a travel route choice adjustment process , 1996 .

[15]  T J Higgins CONGESTION PRICING: IMPLEMENTATION CONSIDERATIONS , 1994 .

[16]  Hai Yang,et al.  The Downs–Thomson Paradox with responsive transit service , 2014 .

[17]  Eric J. Gonzales Coordinated pricing for cars and transit in cities with hypercongestion , 2015 .

[18]  S. Boyles,et al.  Dynamic pricing in discrete time stochastic day-to-day route choice models , 2016 .

[19]  Giulio Erberto Cantarella,et al.  Model Representation & Decision-Making in an Ever-Changing World: The Role of Stochastic Process Models of Transportation Systems , 2015 .

[20]  Jian Wang,et al.  Sensitivity analysis based approximation models for day-to-day link flow evolution process , 2016 .

[21]  Yosef Sheffi,et al.  Urban Transportation Networks: Equilibrium Analysis With Mathematical Programming Methods , 1985 .

[22]  Fan Yang,et al.  Day-to-day stationary link flow pattern , 2009 .

[23]  Marvin Kraus,et al.  A new look at the two-mode problem , 2003 .

[24]  Giulio Erberto Cantarella,et al.  Modelling sources of variation in transportation systems: theoretical foundations of day-to-day dynamic models , 2013 .

[25]  Hai Yang,et al.  Dynamics of modal choice of heterogeneous travelers with responsive transit services , 2016 .

[26]  Xiaolei Guo,et al.  A link-based day-to-day traffic assignment model , 2010 .

[27]  Hai Yang,et al.  Interactive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision , 2017 .

[28]  Hai Yang,et al.  Physics of day-to-day network flow dynamics , 2016 .

[29]  W. Vickrey Congestion Theory and Transport Investment , 1969 .

[30]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[31]  Martin L. Hazelton,et al.  Statistical methods for comparison of day-to-day traffic models , 2016 .

[32]  Giulio Erberto Cantarella,et al.  Dynamic Processes and Equilibrium in Transportation Networks: Towards a Unifying Theory , 1995, Transp. Sci..

[33]  D. Hensher,et al.  Multimodal Transport Pricing: First Best, Second Best and Extensions to Non-motorized Transport , 2012 .

[34]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[35]  Hong Kam Lo,et al.  Day-to-day departure time modeling under social network influence , 2016 .

[36]  Jonas Eliasson,et al.  Road pricing with limited information and heterogeneous users: A successful case , 2001 .

[37]  Erik T. Verhoef,et al.  Competition in Multi-Modal Transport Networks: A Dynamic Approach , 2012 .

[38]  Ki-jung Ahn,et al.  Road Pricing and Bus Service Policies , 2009 .

[39]  H. Lo,et al.  Discrete-time day-to-day dynamic congestion pricing scheme considering multiple equilibria , 2017 .

[40]  Wei Liu,et al.  Managing morning commute with parking space constraints in the case of a bi-modal many-to-one network , 2016 .

[41]  Giulio Erberto Cantarella,et al.  Day-to-day Dynamics & Equilibrium Stability in A Two-Mode Transport System with Responsive bus Operator Strategies , 2015 .

[42]  Henry X. Liu,et al.  Modeling the day-to-day traffic evolution process after an unexpected network disruption , 2012 .

[43]  Nikolas Geroliminis,et al.  Doubly dynamics for multi-modal networks with park-and-ride and adaptive pricing , 2017 .

[44]  Renaud Foucart,et al.  Modal choice and optimal congestion , 2014 .

[45]  Bingsheng He,et al.  Road pricing for congestion control with unknown demand and cost functions , 2010 .

[46]  Michael J. Smith,et al.  The Stability of a Dynamic Model of Traffic Assignment - An Application of a Method of Lyapunov , 1984, Transp. Sci..

[47]  Hai Yang,et al.  Day-To-Day Departure Time Choice under Bounded Rationality in the Bottleneck Model , 2017 .

[48]  S. Peeta,et al.  A Marginal Utility Day-to-day Traffic Evolution Model , 2016 .

[49]  R. Arnott,et al.  The Two‐Mode Problem: Second‐Best Pricing and Capacity , 2000 .

[50]  Yafeng Yin,et al.  Nonnegative Pareto-Improving Tolls with Multiclass Network Equilibria , 2009 .

[51]  Carlos F. Daganzo,et al.  On Stochastic Models of Traffic Assignment , 1977 .

[52]  William H. K. Lam,et al.  Modeling intermodal equilibrium for bimodal transportation system design problems in a linear monocentric city , 2012 .

[53]  G. Cantarella,et al.  A general stochastic process for day-to-day dynamic traffic assignment: Formulation, asymptotic behaviour, and stability analysis , 2016 .

[54]  Hai Yang,et al.  Pareto-improving congestion pricing and revenue refunding with multiple user classes , 2010 .

[55]  Hai-Jun Huang,et al.  Fares and tolls in a competitive system with transit and highway: the case with two groups of commuters , 2000 .