A simulation study of demand responsive transit system design

In this paper we study the impact on productivity of specific operating practices currently used by demand responsive transit (DRT) providers. We investigate the effect of using a zoning vs. a no-zoning strategy and time-window settings on performance measures such as total trip miles, deadhead miles and fleet size. It is difficult to establish closed-form expressions to assess the impact on the performance measures of a specific zoning practice or time-window setting for a real transportation network. Thus, we conduct this study through a simulation model of the operations of DRT providers on a network based on data for DRT service in Los Angeles County. However, the methodology is quite general and applicable to any other service area. Our results suggest the existence of linear relationships between operating practices and performance measures. In particular we observe that for each minute increase in time-window size the service saves approximately 2 vehicles and 260 miles driven and that a no-zoning strategy is able to satisfy the same demand by employing 60 less vehicles and driving 10,000 less total miles with respect to the current zoning strategy.

[1]  Thomas D. Clark,et al.  A simulation analysis of demand and fleet size effects on taxicab service rates , 1987, WSC '87.

[2]  Paolo Toth,et al.  Heuristic Algorithms for the Handicapped Persons Transportation Problem , 1997, Transp. Sci..

[3]  Hani S. Mahmassani,et al.  Dynamic Decision Making for Commercial Fleet Operations Using Real-Time Information , 1996 .

[4]  Jacques Desrosiers,et al.  A Request Clustering Algorithm for Door-to-Door Handicapped Transportation , 1991, Transp. Sci..

[5]  Jacques Desrosiers,et al.  VRP with Pickup and Delivery , 2000, The Vehicle Routing Problem.

[6]  Leen Stougie,et al.  On-Line Dial-a-Ride Problems under a Restricted Information Model , 2002, ESA.

[7]  M. Sol.  The general pickup and delivery problem , 2010 .

[8]  Maged Dessouky,et al.  A new regret insertion heuristic for solving large-scale dial-a-ride problems with time windows , 2004 .

[9]  Moshe Ben-Akiva,et al.  Impact of Advanced Public Transportation Systems on Travel by Dial-a-Ride , 1996 .

[10]  Sven Oliver Krumke,et al.  The Online Dial-a-Ride Problem under Reasonable Load , 2000, CIAC.

[11]  Martin Grötschel,et al.  Telebus Berlin: Vehicle Scheduling in a Dial-a-Ride System , 1999 .

[12]  H. Kunzi,et al.  Lectu re Notes in Economics and Mathematical Systems , 1975 .

[13]  Mauro Dell'Orco,et al.  Handling Uncertainty in the Analysis of Traffic and Transportation Systems (Bari, Italy, June 10–13, 2002) , 2005 .

[14]  Maged M. Dessouky,et al.  An Exact Algorithm for the Multiple Vehicle Pickup and Delivery Problem , 2004, Transp. Sci..

[15]  Christoffel Venter,et al.  Cost and Productivity Impacts of a “Smart” Paratransit System , 1997 .

[16]  L. Quadrifoglio,et al.  Insertion Heuristic for Scheduling Mobility Allowance Shuttle Transit ( MAST ) Services : sensitivity to service area , 2004 .

[17]  Bruno Dalla Chiara,et al.  Simulation and performance of DRTS in a realistic environment , 2002 .

[18]  Hideyuki Nakashima,et al.  Is Dial-a-Ride Bus Reasonable in Large Scale Towns? Evaluation of Usability of Dial-a-Ride Systems by Simulation , 2003, MAMUS.

[19]  Marius M. Solomon,et al.  Partially dynamic vehicle routing—models and algorithms , 2002, J. Oper. Res. Soc..

[20]  Nan Xia,et al.  A model for the fleet sizing of demand responsive transportation services with time windows , 2006 .

[21]  N. H. M. Wilson,et al.  Simulation of a Computer Aided Routing System (CARS) , 1969 .

[22]  Liping Fu,et al.  A simulation model for evaluating advanced dial-a-ride paratransit systems , 2002 .

[23]  John R Stone,et al.  Internet-Based Decision Support for Advanced Public Transportation Systems Technology , 2000 .

[24]  Martin W. P. Savelsbergh,et al.  Efficient Insertion Heuristics for Vehicle Routing and Scheduling Problems , 2004, Transp. Sci..

[25]  Maged M. Dessouky,et al.  A new insertion-based construction heuristic for solving the pickup and delivery problem with time windows , 2006, Eur. J. Oper. Res..

[26]  A. Nalevanko,et al.  COMPUTER DISPATCH AND SCHEDULING FOR PARATRANSIT: AN APPLICATION OF ADVANCED PUBLIC TRANSPORTATION SYSTEMS , 1994 .

[27]  Goeddel.  Benefits assessment of Advanced Public Transportation Systems (APTS). Final report, October 1995-July 1996 , 1996 .

[28]  R R Wallace.  PART 2: Paratransit: Paratransit Customer: Modeling Elements of Satisfaction with Service , 1997 .

[29]  Luca Quadrifoglio,et al.  An insertion heuristic for scheduling Mobility Allowance Shuttle Transit (MAST) services , 2007, J. Sched..

[30]  Nigel H. M. Wilson,et al.  A heuristic algorithm for the multi-vehicle advance request dial-a-ride problem with time windows , 1986 .

[31]  David Koffman,et al.  Impact of reliability on paratransit demand and operating costs , 1998 .

[32]  Carol L Schweiger,et al.  DEPLOYMENT OF TECHNOLOGY FOR PARATRANSIT: WHAT ARE THE EFFECTS ON EMPLOYEES? , 1997 .

[33]  Mohamadreza Banihashemi,et al.  HEURISTIC APPROACHES FOR SOLVING LARGE-SCALE BUS TRANSIT VEHICLE SCHEDULING PROBLEM WITH ROUTE TIME CONSTRAINTS , 2002 .

[34]  Marco Diana.  The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services , 2006 .

[35]  Maged Dessouky,et al.  Impacts of management practices and advanced technologies on demand responsive transit systems , 2004 .

[36]  Leen Stougie,et al.  On-line single-server dial-a-ride problems , 2001, Theor. Comput. Sci..