A STOCHASTIC TIMETABLE OPTIMIZATION MODEL IN SUBWAY SYSTEMS

With fixed running times at sections, cooperative scheduling (CS) approach optimizes the dwell times and the headway time to coordinate the accelerating and braking processes for trains, such that the recovery energy generated from the braking trains can be used by the accelerating trains. In practice, trains always have stochastic departure delays at busy stations. For reducing the divergence from the given timetable, the operation company generally adjusts the running times at the following sections. Focusing on the randomness on delay times and running times, this paper proposes a stochastic cooperative scheduling (SCS) approach. Firstly, we estimate the conversion and transmission losses of recovery energy, and then formulate a stochastic expected value model to maximize the utilization of the recovery energy. Furthermore, we design a binary-coded genetic algorithm to solve the optimal timetable. Finally, we conduct experimental studies based on the operation data from Beijing Yizhuang subway line. The results show that the SCS approach can save energy by 15.13% compared with the current timetable, and 8.81% compared with the CS approach.

[1]  Leo G. Kroon,et al.  A Variable Trip Time Model for Cyclic Railway Timetabling , 2003, Transp. Sci..

[2]  Erhan Kozan,et al.  Optimal scheduling of trains on a single line track , 1996 .

[3]  Yanfeng Ouyang,et al.  Optimal fueling strategies for locomotive fleets in railroad networks , 2010 .

[4]  Ziyou Gao,et al.  A green train scheduling model and fuzzy multi-objective optimization algorithm , 2013 .

[5]  K. Nachtigall,et al.  Minimizing waiting times in integrated fixed interval timetables by upgrading railway tracks , 1997 .

[6]  Ove Frank.  Two-Way Traffic on a Single Line of Railway , 1966, Oper. Res..

[7]  Miguel A. Salido,et al.  Robustness for a single railway line: Analytical and simulation methods , 2012, Expert Syst. Appl..

[8]  Antonio González,et al.  A FILTER PROPOSAL FOR INCLUDING FEATURE CONSTRUCTION IN A GENETIC LEARNING ALGORITHM , 2012 .

[9]  A. V. MOGILENKO,et al.  Development of Fuzzy Regression Models Using Genetic Algorithms , 2003, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[10]  Hans van Maaren,et al.  Generation of classes of robust periodic railway timetables , 2006, Comput. Oper. Res..

[11]  Anita Schöbel,et al.  Engineering the Modulo Network Simplex Heuristic for the Periodic Timetabling Problem , 2011, SEA.

[12]  Xuesong Zhou,et al.  Stochastic Optimization Model and Solution Algorithm for Robust Double-Track Train-Timetabling Problem , 2010, IEEE Transactions on Intelligent Transportation Systems.

[13]  Xiang Li,et al.  A Cooperative Scheduling Model for Timetable Optimization in Subway Systems , 2013, IEEE Transactions on Intelligent Transportation Systems.

[14]  Marco Laumanns,et al.  The periodic service intention as a conceptual framework for generating timetables with partial periodicity , 2011 .

[15]  Wolfgang Domschke,et al.  Schedule synchronization for public transit networks , 1989 .

[16]  Matteo Fischetti,et al.  Light Robustness , 2009, Robust and Online Large-Scale Optimization.

[17]  Roberto Cordone,et al.  Optimizing the demand captured by a railway system with a regular timetable , 2011 .

[18]  Gilbert Laporte,et al.  Evaluating passenger robustness in a rail transit network , 2012 .

[19]  Te-Wei Chiang,et al.  Knowledge-Based System for Railway Scheduling , 1998, Data Knowl. Eng..

[20]  Alex Chong,et al.  Fuzzy Cognitive Maps With Genetic Algorithm For Goal-Oriented Decision Support , 2004, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[21]  Malachy Carey,et al.  A model and strategy for train pathing with choice of lines, platforms, and routes , 1994 .

[22]  M. Salicrú,et al.  Timetable-based operation in urban transport: Run-time optimisation and improvements in the operating process , 2011 .

[23]  Sebastian Stiller,et al.  Computing delay resistant railway timetables , 2010, Comput. Oper. Res..

[24]  Matteo Fischetti,et al.  A Lagrangian Heuristic for Robustness, with an Application to Train Timetabling , 2012, Transp. Sci..

[25]  Dario Pacciarelli,et al.  Bi-objective conflict detection and resolution in railway traffic management , 2012 .

[26]  Rommert Dekker,et al.  Stochastic Improvement of Cyclic Railway Timetables , 2006 .

[27]  Francisco Herrera,et al.  A Multi-Objective Genetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems , 2007, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[28]  Ziyou Gao,et al.  Train Timetable Problem on a Single-Line Railway With Fuzzy Passenger Demand , 2009, IEEE Transactions on Fuzzy Systems.

[29]  Malachy Carey,et al.  Extending a train pathing model from one-way to two-way track , 1994 .

[30]  Ferenc Szidarovszky,et al.  A multi-objective train scheduling model and solution , 2004 .

[31]  A. J. Taylor,et al.  A Structured Model for Rail Line Simulation and Optimization , 1982 .

[32]  Monte Zweben,et al.  Scheduling and rescheduling with iterative repair , 1993, IEEE Trans. Syst. Man Cybern..

[33]  M. J. Fuente,et al.  CHECKING ORTHOGONAL TRANSFORMATIONS AND GENETIC ALGORITHMS FOR SELECTION OF FUZZY RULES BASED ON INTERPRETABILITY-ACCURACY CONCEPTS , 2012 .

[34]  Paolo Toth,et al.  Nominal and robust train timetabling problems , 2012, Eur. J. Oper. Res..