Responsive bus dispatching strategy in a multi-modal and multi-directional transportation system: A doubly dynamical approach

Abstract This paper examines the time-dependent bus dispatching problem in a multi-modal context. Traditional studies along this line often optimize the bus frequency or schedule. However, they may fail as the realized bus frequency or schedule is constrained by the time-varying traffic congestion on the road. Adding more buses to service does not necessarily increase the service frequency. Given this, we look into the time-dependent bus dispatching (number of buses in service on road) when taking into account complex multimodal and multi-directional flow interactions on the road. In particular, the traffic dynamics over clock time is modeled through an aggregate traffic representation with flow interactions between cars and buses, and interactions between traffic in opposite moving directions. Instead of explicitly optimizing the size of dispatched bus fleet, we propose an adaptive fleet size adjustment mechanism where we have a target level of bus loading factor. This adaptive or responsive approach, by taking advantage of the doubly dynamical system proposed in Liu and Geroliminis (2017), adjusts the size of dispatched bus fleet over calendar time and accommodates day-to-day variations of mode choices and traffic patterns. Numerical studies show that the proposed approach can help bus operator to reduce operating cost and improve net benefit while maintaining comparable user costs for passengers. This study offers a new perspective for dynamic bus dispatching strategy and improves our understanding of multi-modal traffic dynamics.

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

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

[3]  Hai-Jun Huang,et al.  Are We Really Solving the Dynamic Traffic Equilibrium Problem with a Departure Time Choice? , 2018, Transp. Sci..

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

[5]  Luigi dell’Olio,et al.  Optimizing bus stop spacing in urban areas , 2010 .

[6]  Nikolas Geroliminis,et al.  A systematic analysis of multimodal transport systems with road space distribution and responsive bus service , 2018 .

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

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

[9]  N. Geroliminis,et al.  Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings - eScholarship , 2007 .

[10]  Vikash V. Gayah,et al.  On the impacts of locally adaptive signal control on urban network stability and the Macroscopic Fundamental Diagram , 2014 .

[11]  N. Geroliminis,et al.  A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks , 2014 .

[12]  Nikolas Geroliminis,et al.  On the distribution of urban road space for multimodal congested networks , 2013 .

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

[14]  Ludovic Leclercq,et al.  Dynamic macroscopic simulation of on-street parking search: A trip-based approach , 2017 .

[15]  Nicolas Chiabaut,et al.  Evaluation of a multimodal urban arterial: the passenger macroscopic fundamental diagram , 2015 .

[16]  Takamasa Iryo An Analysis of Instability in a Departure Time Choice Problem , 2008 .

[17]  H. Mohring Optimization and Scale Economies in Urban Bus Transportation , 1972 .

[18]  Ludovic Leclercq,et al.  Macroscopic Fundamental Diagrams: A cross-comparison of estimation methods , 2014 .

[19]  Vikash V. Gayah,et al.  Clockwise Hysteresis Loops in the Macroscopic Fundamental Diagram , 2010 .

[20]  Terry L. Friesz,et al.  A Variational Inequality Formulation of the Dynamic Network User Equilibrium Problem , 1993, Oper. Res..

[21]  Shing Chung Josh Wong,et al.  Network topological effects on the macroscopic Bureau of Public Roads function , 2016 .

[22]  Jin Zhang,et al.  On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models , 2015 .

[23]  Jack Haddad Robust Constrained Control of Uncertain Macroscopic Fundamental Diagram Networks , 2015 .

[24]  Takatoshi Tabuchi,et al.  Bottleneck Congestion and Modal Split , 1993 .

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

[26]  Moshe Ben-Akiva,et al.  Dynamic model of peak period congestion , 1984 .

[27]  Monica Menendez,et al.  Empirics of multi-modal traffic networks – Using the 3D macroscopic fundamental diagram , 2017 .

[28]  H. Oliver Gao,et al.  Optimal design of sustainable transit systems in congested urban networks: A macroscopic approach , 2017 .

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

[30]  Hai Yang,et al.  On the morning commute problem with bottleneck congestion and parking space constraints , 2013 .

[31]  W. Y. Szeto,et al.  Learning and managing stochastic network traffic dynamics with an aggregate traffic representation , 2020 .

[32]  W. Y. Szeto,et al.  Day-to-day modal choice with a Pareto improvement or zero-sum revenue scheme , 2018 .

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

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

[35]  Huijun Sun,et al.  Overcoming the Downs-Thomson Paradox by Transit Subsidy Policies , 2017 .

[36]  Nikolaos Geroliminis,et al.  Modeling the morning commute for urban networks with cruising-for-parking: An MFD approach , 2016 .

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

[38]  Nikolaos Geroliminis,et al.  Clustering of Heterogeneous Networks with Directional Flows Based on “Snake” Similarities , 2016 .

[39]  Takamasa Iryo,et al.  Day-to-day dynamical model incorporating an explicit description of individuals’ information collection behaviour , 2016 .

[40]  W. Y. Szeto,et al.  A simultaneous bus route design and frequency setting problem for Tin Shui Wai, Hong Kong , 2011, Eur. J. Oper. Res..

[41]  M. J. Smith,et al.  A continuous day-to-day traffic assignment model and the existence of a continuous dynamic user equilibrium , 1995, Ann. Oper. Res..

[42]  Hai-Jun Huang,et al.  Day-to-Day Flow Dynamics and Congestion Control , 2016, Transp. Sci..

[43]  Nikolaos Geroliminis,et al.  Enhancing model-based feedback perimeter control with data-driven online adaptive optimization , 2017 .

[44]  Mike J. Smith,et al.  A route-swapping dynamical system and Lyapunov function for stochastic user equilibrium , 2016 .

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

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

[47]  Nikolas Geroliminis,et al.  Approximating Dynamic Equilibrium Conditions with Macroscopic Fundamental Diagrams , 2014 .

[48]  Markos Papageorgiou,et al.  Exploiting the fundamental diagram of urban networks for feedback-based gating , 2012 .

[49]  Hai Yang,et al.  Traffic dynamics in a bi-modal transportation network with information provision and adaptive transit services , 2018 .

[50]  Agachai Sumalee,et al.  Boundary conditions and behavior of the macroscopic fundamental diagram based network traffic dynamics: A control systems perspective , 2018 .

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

[52]  W. Y. Szeto,et al.  A cell-based variational inequality formulation of the dynamic user optimal assignment problem , 2002 .

[53]  Hai Yang,et al.  The Downs–Thomson paradox with imperfect mode substitutes and alternative transit administration regimes , 2016 .

[54]  Nikolas Geroliminis,et al.  Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing , 2015 .

[55]  Eric J. Gonzales,et al.  Demand responsive transit systems with time-dependent demand: User equilibrium, system optimum, and management strategy , 2016 .

[56]  Giulio Erberto Cantarella,et al.  Day-to-day dynamic models for intelligent transportation systems design and appraisal , 2013 .

[57]  Haijun Huang,et al.  A discrete dynamical system of formulating traffic assignment: Revisiting Smith’s model , 2016 .

[58]  Nikolas Geroliminis,et al.  Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control , 2015 .

[59]  Nikolas Geroliminis,et al.  Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach , 2013, IEEE Transactions on Intelligent Transportation Systems.

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