A simulation study of an on-demand transportation system

In this paper we present the results of a simulation study aimed at assessing an on-demand transportation system. The on-demand system uses minibuses that have neither fixed itineraries nor fixed stops. The minibuses are dynamically routed to accommodate the requests received by the users. To use the on-demand service, users communicate, close to their desired departure time, the origin and destination of the trip. They accept the service if the estimated arrival time at destination fulfills their service level threshold. In the simulation users may decide whether to walk, to use a standard bus, to call the on-demand service, and, if none of these options is satisfactory, to use a private car. We consider different scenarios to assess the potential benefits of the introduction of an on-demand service. We also analyze the scalability and responsiveness of the service. The results suggest that an on-demand system may be able to satisfy a large portion of user transportation requests and may be put beside standard transportation systems in order to provide a better transportation service to the users and substantially reduce the use of private cars.

[1]  Mathijs de Weerdt,et al.  Impact of Competition on Quality of Service in Demand Responsive Transit , 2010, MATES.

[2]  David Meignan,et al.  Simulation and evaluation of urban bus-networks using a multiagent approach , 2007, Simul. Model. Pract. Theory.

[3]  José Manuel Viegas,et al.  An agent‐based simulation model to assess the impacts of introducing a shared‐taxi system: an application to Lisbon (Portugal) , 2015 .

[4]  Kari Watkins,et al.  Comparing Fixed-Route and Demand-Responsive Feeder Transit Systems in Real-World Settings , 2013 .

[5]  J. Nelson,et al.  Flexible Transport Services: A New Market Opportunity for Public Transport , 2009 .

[6]  Stephan Winter,et al.  Comparing demand responsive and conventional public transport in a low demand context , 2016, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[7]  Mark A. Turnquist,et al.  THE EFFECTS OF NETWORK STRUCTURE ON RELIABILITY OF TRANSIT SERVICE , 1980 .

[8]  Chuntian Cheng,et al.  A Parallel Ant Colony Algorithm for Bus Network Optimization , 2007, Comput. Aided Civ. Infrastructure Eng..

[9]  Mark E.T. Horn,et al.  Procedures for planning multi-leg journeys with fixed-route and demand-responsive passenger transport services , 2004 .

[10]  C. Bellini,et al.  The Demand Responsive Transport Services: Italian Approach , 2003 .

[11]  D. Shoup.  Cruising for Parking , 2006 .

[12]  J. Nelson,et al.  DEMAND RESPONSIVE TRANSPORT: TOWARDS THE EMERGENCE OF A NEW MARKET SEGMENT , 2004 .

[13]  Cristina Pronello,et al.  A methodology for comparing distances traveled by performance-equivalent fixed-route and demand responsive transit services , 2009 .

[14]  C. Daganzo.  Checkpoint dial-a-ride systems , 1984 .

[15]  John D. Nelson,et al.  Key lessons learned from recent experience with Flexible Transport Services , 2007 .

[16]  José Manuel Viegas,et al.  Formulating a New Express Minibus Service Design Problem as a Clustering Problem , 2015, Transp. Sci..

[17]  Gilbert Laporte,et al.  The dial-a-ride problem: models and algorithms , 2006, Ann. Oper. Res..

[18]  Christoph E. Mandl,et al.  Evaluation and optimization of urban public transportation networks , 1980 .

[19]  L. A. Silman,et al.  Planning the route system for urban buses , 1974, Comput. Oper. Res..

[20]  M. G. Speranza,et al.  On-Demand Public Transportation , 2016 .

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

[22]  G. Laporte,et al.  The diala-ride problem : models and algorithms , .

[23]  Fang Zhao,et al.  Simulated Annealing-Genetic Algorithm for Transit Network Optimization , 2006 .

[24]  José Manuel Viegas,et al.  Configuration of Innovative Minibus Service in the Lisbon, Portugal, Municipality , 2011 .

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

[26]  Luis Ferreira,et al.  Evaluating Flexible Transport Solutions , 2007 .

[27]  Paul Logan.  Best practice demand-responsive transport (DRT) policy , 2007 .

[28]  Randy B Machemehl,et al.  Using a Simulated Annealing Algorithm to Solve the Transit Route Network Design Problem , 2006 .

[29]  Hani S. Mahmassani,et al.  Hybrid route generation heuristic algorithm for the design of transit networks , 1995 .

[30]  José Manuel Viegas,et al.  A new optimisation procedure to design minibus services: an application for the Lisbon Metropolitan Area , 2011 .

[31]  Keiichi Uchimura,et al.  Demand responsive services in hierarchical public transportation system , 2002, IEEE Trans. Veh. Technol..

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

[33]  C. Kennedy.  A comparison of the sustainability of public and private transportation systems: Study of the Greater Toronto Area , 2002 .

[34]  Maurizio Bielli,et al.  Genetic algorithms in bus network optimization , 2002 .

[35]  Leena Suhl,et al.  A time-space network based exact optimization model for multi-depot bus scheduling , 2006, Eur. J. Oper. Res..

[36]  Moshe Ben-Akiva,et al.  The concept and impact analysis of a flexible mobility on demand system , 2015 .