Inter-city bus routing and timetable setting under stochastic demands

Vehicle fleet routing and timetable setting are essential to the enhancement of an inter-city bus carrier's operating cost, profit, level of service and competitiveness in the market. In past research the average passenger demand has usually served as input in the production of the final fleet routes and timetables, meaning that stochastic disturbances arising from variations in daily passenger demand in actual operations are neglected. To incorporate the stochastic disturbances of daily passenger demands that occur in actual operations, in this research, we established a stochastic-demand scheduling model. We applied a simulation technique, coupled with link-based and path-based routing strategies, to develop two heuristic algorithms to solve the model. To evaluate the performance of the proposed model and the two solution algorithms, we developed an evaluation method. The test results, regarding a major Taiwan inter-city bus operation, were good, showing that the model and the solution algorithms could be useful in practice.

[1]  Lazar N Spasovic,et al.  BUS TRANSIT SERVICE COVERAGE FOR MAXIMUM PROFIT AND SOCIAL WELFARE , 1994 .

[2]  R. Hall,et al.  Fleet sizing and empty equipment redistribution for center-terminal transportation networks , 1997 .

[3]  Paul Schonfeld,et al.  BRANCHED TRANSIT SERVICES: AN ANALYSIS , 1984 .

[4]  P. Schonfeld,et al.  Multiple period optimization of bus transit systems , 1991 .

[5]  Peter Kall,et al.  Stochastic Programming , 1995 .

[6]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[7]  Shangyao Yan,et al.  A simulation framework for evaluating airport gate assignments , 2002 .

[8]  Nigel H. M. Wilson,et al.  The allocation of buses in heavily utilized networks with overlapping routes , 1982 .

[9]  Shangyao Yan,et al.  Airline Scheduling for the Temporary Closure of Airports , 1997, Transp. Sci..

[10]  Chris Hendrickson,et al.  Design of Local Bus Service with Demand Equilibration , 1982 .

[11]  Dirk Van Oudheusden,et al.  A103 (1997) 18–27 in heavily congested cities , 1997 .

[12]  David P. Morton,et al.  Stochastic Vehicle Routing with Random Travel Times , 2003, Transp. Sci..

[13]  Satya Prakash,et al.  Optimizing dead mileage in urban bus routes through a nondominated solution approach , 1999, Eur. J. Oper. Res..

[14]  Nigel H. M. Wilson,et al.  Bus network design , 1986 .

[15]  Dean A. Jones,et al.  Robust optimization for fleet planning under uncertainty , 2003 .

[16]  Shangyao Yan,et al.  A scheduling model and a solution algorithm for inter-city bus carriers , 2002 .

[17]  Rob van Nes,et al.  DESIGN OF PUBLIC TRANSPORT NETWORKS , 1988 .

[18]  F. J. M. Salzborn,et al.  Scheduling Bus Systems with Interchanges , 1980 .

[19]  Dirk Van Oudheusden,et al.  A network approach to trip frequency scheduling for bus routes in heavily congested cities , 1997 .

[20]  Paul Schonfeld,et al.  OPTIMIZATION OF ZONAL TRANSIT SERVICE , 1983 .

[21]  Tiong An Chua,et al.  The planning of urban bus routes and frequencies: A survey , 1984 .

[22]  John M. Mulvey,et al.  A New Scenario Decomposition Method for Large-Scale Stochastic Optimization , 1995, Oper. Res..

[23]  Seong Kyu Chang.  ANALYTIC OPTIMIZATION OF BUS SYSTEMS IN HETEROGENEOUS ENVIRONMENTS , 1990 .

[24]  Jossef Perl,et al.  Closure of "Optimization of Feeder Bus Routes and Bus Stop Spacing" , 1988 .

[25]  S. B. Pattnaik,et al.  Urban Bus Transit Route Network Design Using Genetic Algorithm , 1998 .