Passenger flow control with multi-station coordination in subway networks: algorithm development and real-world case study

ABSTRACT This paper addresses the problem of passenger flow control in a multi-line subway network during peak hours. A new multi-station coordinated passenger flow control model is proposed to simultaneously adjust the number of inbound and transfer passengers entering multiple stations or lines. The implementation of passenger flow control governs the redundant passengers to queue at given facilities, which is similar to ramp metering that regulates the number of vehicles entering a highway segment. The proposed model is a bi-level programming, whose upper level aims to optimize the system performance with different passenger flow control strategies, while the lower level is a logit-based stochastic user equilibrium assignment problem under a given strategy, considering passenger flow evolution, dynamic path cost, and route choice. An algorithm, integrating the method of successive averages with genetic algorithm, is developed to solve the model. A real-world case study is conducted to examine the validity.

[1]  Keping Li,et al.  A multi‐objective subway timetable optimization approach with minimum passenger time and energy consumption , 2016 .

[2]  Nigel H. M. Wilson,et al.  A behavioural comparison of route choice on metro networks: Time, transfers, crowding, topology and socio-demographics , 2014 .

[3]  Sangjin Han,et al.  Dynamic traffic modelling and dynamic stochastic user equilibrium assignment for general road networks , 2003 .

[4]  W. Y. Szeto,et al.  An intersection-movement-based stochastic dynamic user optimal route choice model for assessing network performance , 2015 .

[5]  Keemin Sohn,et al.  An investigation into passenger preference for express trains during peak hours , 2016 .

[6]  David Bernstein,et al.  The Holding Problem with Real - Time Information Available , 2001, Transp. Sci..

[7]  André de Palma,et al.  Departure Time and Route Choice , 1986 .

[8]  Tomonori Sumi,et al.  Departure time and route choice of commuters on mass transit systems , 1990 .

[9]  Shing Chung Josh Wong,et al.  A dynamic schedule-based model for congested transit networks , 2004 .

[10]  Marta C. González,et al.  The path most traveled: Travel demand estimation using big data resources , 2015, Transportation Research Part C: Emerging Technologies.

[11]  Carlo G. Prato,et al.  Route choice modeling: past, present and future research directions , 2009 .

[12]  Hai Yang,et al.  Efficiency of a highway use reservation system for morning commute , 2015 .

[13]  Yanshuo Sun,et al.  Rail Transit Travel Time Reliability and Estimation of Passenger Route Choice Behavior , 2012 .

[14]  Omar J. Ibarra-Rojas,et al.  Planning, operation, and control of bus transport systems: A literature review , 2015 .

[15]  Haiying Li,et al.  A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway , 2017 .

[16]  Cynthia Barnhart,et al.  Incremental bus service design: combining limited-stop and local bus services , 2013, Public Transp..

[17]  Kentaro Wada,et al.  The corridor problem with discrete multiple bottlenecks , 2015 .

[18]  Özgür Yalçinkaya,et al.  Modelling and optimization of average travel time for a metro line by simulation and response surface methodology , 2009, Eur. J. Oper. Res..

[19]  Sonia Baee,et al.  Passenger Boarding/Alighting Management in Urban Rail Transportation , 2012 .

[20]  Haiying Li,et al.  Metro passenger flow control with station-to-station cooperation based on stop-skipping and boarding limiting , 2017 .

[21]  Liping Xie,et al.  Learning the route choice behavior of subway passengers from AFC data , 2018, Expert Syst. Appl..

[22]  E. Cascetta,et al.  A MODIFIED LOGIT ROUTE CHOICE MODEL OVERCOMING PATH OVERLAPPING PROBLEMS. SPECIFICATION AND SOME CALIBRATION RESULTS FOR INTERURBAN NETWORKS , 1996 .

[23]  Nigel H. M. Wilson,et al.  Real-time holding control for high-frequency transit with dynamics , 2016 .

[24]  Wei Li,et al.  The Optimize Management of Passenger Organization in Transfer Station Based on Dynamic Passenger Flow Analysis , 2013 .

[25]  Bin Ran,et al.  MODELING DYNAMIC TRANSPORTATION NETWORKS : AN INTELLIGENT TRANSPORTATION SYSTEM ORIENTED APPROACH. 2ND REV. ED. , 1996 .

[26]  Zhuang Fengqing,et al.  Patients’ Responsibilities in Medical Ethics , 2016 .

[27]  Lorenzo Meschini,et al.  Schedule-based transit assignment: new dynamic equilibrium model with vehicle capacity constraints , 2009 .

[28]  Ricardo Giesen,et al.  Analysis of real-time control strategies in a corridor with multiple bus services , 2015 .

[29]  Hai Yang,et al.  Modeling the capacity and level of service of urban transportation networks , 2000 .

[30]  Bin Ran,et al.  MODELING DYNAMIC TRANSPORTATION NETWORKS , 1996 .

[31]  Carlos F. Daganzo,et al.  Dynamic bus holding strategies for schedule reliability: Optimal linear control and performance analysis , 2011 .

[32]  Huachun Tan,et al.  Robust tensor decomposition based on Cauchy distribution and its applications , 2017, Neurocomputing.

[33]  Hai Yang,et al.  Equilibrium properties of the morning peak-period commuting in a many-to-one mass transit system , 2007 .

[34]  Ricardo Giesen,et al.  How much can holding and/or limiting boarding improve transit performance? , 2012 .

[35]  Lori Tavasszy,et al.  A freight transport optimization model for integrated network, service, and policy design , 2015 .

[36]  Sajad Shiravi,et al.  A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network , 2016 .

[37]  Haiying Li,et al.  Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study , 2016 .

[38]  Anthony Chen,et al.  Modeling capacity flexibility of transportation networks , 2011 .

[39]  Jun Liu,et al.  Analysis of subway station capacity with the use of queueing theory , 2014 .