Is providing inaccurate pre-trip information better than providing no information in the morning commute under stochastic bottleneck capacity?

Abstract One way to eliminate the negative impact of travel time uncertainty faced by commuters is to provide them with traffic information so that they can make informed travel. Nevertheless, the provided information may not be accurate, thereby affecting commuters’ choices as well as system efficiency. This paper investigates the welfare effects of inaccurate pre-trip information on commuters’ departure time choice under stochastic bottleneck capacity in the morning commute. Three cases concerning commuters’ actions in response to inaccurate pre-trip information, namely compliance, noncompliance, and co-existence, are studied. We consider two types of traffic conditions: good conditions and bad conditions, as well as errors in forecasting good conditions and bad conditions, respectively. We derive all theoretical solutions under different magnitudes of forecast errors, and frequency and severity of bottleneck capacity reductions in the compliance and noncompliance cases, and adopt an iterative algorithm to obtain the departure flow patterns in the co-existence case. Our results show that the benefits of inaccurate information depend on information quality, commuters’ response and heterogeneity, and the frequency and severity of bottleneck capacity reductions. A sensitivity analysis is carried out to further evaluate the benefit gains/losses under different situations in the three cases.

[1]  Rob Hranac,et al.  Decomposition of Travel Time Reliability into Various Sources , 2011 .

[2]  Hai-Jun Huang,et al.  Stochastic bottleneck capacity, merging traffic and morning commute , 2014 .

[3]  André de Palma,et al.  Information and Time-of-Usage Decisions in the Bottleneck Model with Stochastic Capacity and Demand , 1999 .

[4]  Moshe Ben-Akiva,et al.  Dynamic network models and driver information systems , 1991 .

[5]  Agachai Sumalee,et al.  Modeling impacts of adverse weather conditions on a road network with uncertainties in demand and supply , 2008 .

[6]  Hani S. Mahmassani,et al.  Uncertainty in transportation systems evaluation: issues and approaches , 1984 .

[7]  Erik T. Verhoef,et al.  A Monopolistic Market for Advanced Traveller Information Systems and Road Use Efficiency , 2004 .

[8]  Hjp Harry Timmermans,et al.  Costs of Travel Time Uncertainty and Benefits of Travel Time Information: Conceptual Model and Numerical Examples , 2006 .

[9]  Michiel C.J. Bliemer,et al.  Departure time distribution in the stochastic bottleneck model , 2007 .

[10]  Amnon Rapoport,et al.  Vickrey's model of traffic congestion discretized , 2008 .

[11]  Hai-Jun Huang,et al.  Congestion Behavior and Tolls in a Bottleneck Model with Stochastic Capacity , 2015, Transp. Sci..

[12]  Ziyou Gao,et al.  Reliability-based traffic network design with advanced traveler information systems , 2014, Inf. Sci..

[13]  L. Blume The Statistical Mechanics of Best-Response Strategy Revision , 1995 .

[14]  Hai-Jun Huang,et al.  Modeling and solving the dynamic user equilibrium route and departure time choice problem in network with queues , 2002 .

[15]  Asad J. Khattak,et al.  Willingness to pay for travel information , 2003 .

[16]  A. Palma,et al.  Economics of a bottleneck , 1986 .

[17]  Giulio Erberto Cantarella,et al.  Advanced traveller information systems under recurrent traffic conditions: Network equilibrium and stability , 2016 .

[18]  H. Stanley,et al.  Emergence of communities and diversity in social networks , 2016, Proceedings of the National Academy of Sciences.

[19]  Rui Jiang,et al.  Departure Time and Route Choices With Accurate Information Under Binary Stochastic Bottleneck Capacity in the Morning Commute , 2020, IEEE Access.

[20]  Amnon Rapoport,et al.  Pre-trip Information and Route-Choice Decisions with Stochastic Travel Conditions: Experiment , 2014 .

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

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

[23]  Hong Kam Lo,et al.  Equilibrium Trip Scheduling in Congested Traffic under Uncertainty , 2009 .

[24]  P. Varaiya,et al.  Components of Congestion , 2006 .

[25]  Ziyou Gao,et al.  Dynamic modelling of traffic incident impacts on network reliability , 2015 .

[26]  Tao Cheng,et al.  Empirical assessment of urban traffic congestion , 2014 .

[27]  W. Y. Szeto,et al.  Departure Time Choice Equilibrium and Tolling Strategies for a Bottleneck with Stochastic Capacity , 2021, Transp. Sci..

[28]  André de Palma,et al.  Does providing information to drivers reduce traffic congestion , 1991 .

[29]  Randolph W. Hall,et al.  ROUTE CHOICE AND ADVANCED TRAVELER INFORMATION SYSTEMS ON A CAPACITATED AND DYNAMIC NETWORK , 1996 .

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

[31]  Peter Nijkamp,et al.  Information Effects in Transport with Stochastic Capacity and Uncertainty Costs , 1998 .

[32]  Eyran J. Gisches,et al.  Pre-trip Information and Route-Choice Decisions with Stochastic Travel Conditions : Experiment , 2014 .

[33]  Saurabh Amin,et al.  Bottleneck model with heterogeneous information , 2018, Transportation Research Part B: Methodological.

[34]  André de Palma,et al.  INFORMATION AND USAGE OF FREE-ACCESS CONGESTIBLE FACILITIES WITH STOCHASTIC CAPACITY AND DEMAND* , 1993 .

[35]  Hai Yang,et al.  Equilibria and Inefficiency in Traffic Networks with Stochastic Capacity and Information Provision , 2009 .

[36]  Kenneth A. Small,et al.  THE SCHEDULING OF CONSUMER ACTIVITIES: WORK TRIPS , 1982 .

[37]  Woodrow Barfield,et al.  Statistical analysis of commuters' route, mode, and departure time flexibility , 1994 .

[38]  Hai Yang,et al.  Day-to-day evolution of departure time choice in stochastic capacity bottleneck models with bounded rationality and various information perceptions , 2019, Transportation Research Part E: Logistics and Transportation Review.

[39]  Alexander Mendiburu,et al.  A Review of Travel Time Estimation and Forecasting for Advanced Traveler Information Systems , 2012 .

[40]  Francesc Soriguera On the value of highway travel time information systems , 2014 .