||The Institute of Transportation, affiliated to the Ministry of Transportation and Communications, plans to adopt the Demand Responsive Transportation System (DRTS) which has been used for years overseas to manage public transit in remote areas. The problem with the public transit in remote areas lies in the low and scattered demand. The conventional bus transportation can’t efficiently meet the demand in a centralized manner, but causes operating loss instead. In contrast with buses which run through fixed routes at fixed intervals, DRTS can flexibly plan routes and frequency according to the passengers’ boarding and alighting points, and use medium/small-sized vehicles for transshipment and carpooling.|
Because DRTS haven’t been put in operation domestically, this study uses system simulation as its theoretical basis and simulates the application of DRTS domestically in the future. By simulating the mode of operation, we are allowed to obtain quantitative performance indicators and analyze them so as to provide the future service suppliers with some reference data.
According to the operating characteristics of DRTS, we come up with the items to taken into account when assign vehicles. They include passengers in vehicle, passenger tolerance time and operating costs. Operation mode simulation just uses these items as the basis in constituting seven processes, where interaction between modules is simulated, and seven different performance indicators. In addition, this study takes the DRTS demonstration project in Fuxing Town of Taoyuan as the research object, bases on its road net and bus-stops, collects and analyzes information related to the characteristics of trips, and generates demand data as the input for operating mode simulation. This study also witnesses that we design three adjustable operating situations, including the layout of waiting stops (centers, terminals, villages), the tolerance time limit (10minutes, 20 minutes) and the number of vehicles (5, 10, 15, 20, 25, 30).
Performance results of the operating mode simulation where real-time appointments are made are compared and analyzed under the most suitable operating situation for DRTS. And we come to the conclusion that none of the three layout solutions is apparently better than the other two. 10 minutes is better when it comes to tolerance time. Number of vehicles, however, depends on different performances. For example, it is recommended that the number of vehicles should be at least 17 so that passenger’s waiting time can be effectively shortened. On the condition that the time value of the service time and the vehicle idle time is equal, the number of vehicles is recommended to be set at 22 in order to minimize the total cost.
2. 交通部運輸研究所，需求反應式公共運輸系統之整合研究，民國99 年、100年、101年。
12. P. Bakker, Large scale Demand Responsive Transit Systems - A Local Suburban Transport Solution for the Next Millennium? Proceedings of European Transport Conference, Stream: Public Transport Planning and Management, pp 109-126, 1999.
13. Sergio Grosso, Jonathan Higgins, Jenny Mageean, John D. Nelson, Demand Responsive Transport: Towards best practice in rural applications, European Transport Conference, Homerton College, Cambridge, 2002.
14. Marcus Enoch, Stephen Potter, Graham Parkhurst, Mark Smithm, INTERMODE: Innovations in Demand Responsive Transport, Intermode Final Report, 2004.
15. G. Ambrosino, J.D. Nelson, M. Romanazzo, Demand Responsive Transport Services: Towards the Flexible Mobility Agency, ENEA Rome, 2004.
16. Xiugang Li, Luca Quadrifoglio, Feeder transit services: Choosing between fixed and demand responsive policy, Transportation Research Part C, Volume: 18, pp. 770-780, 2010.
17. SYSTEMS FOR ADVANCED MANAGEMENT OF PUBLIC TRANSPORT, A Basic Sysem Architecture and Technical Solutions for DRT, 2000.
18. Jenny Mageean and John D. Nelson, The Evaluation of Demand Responsive Transport Services in Europe, Journal of Transport Geography, Vol.11, Issue 4, pp.255-270, 2003.
19. David Koffman, Operational Experiences with Flexible Transit Services, Transportation Research Board, Washington, USA, 2004.
20. System for Advanced Management of Public Transport Operations, Analysis of User Needs for Demand Responsive Transport Services, 1997.
21. NelsonNygaard Consulting Associates, Optimal Split of Dedicated and Non-Dedicated Services for Demand-Responsive Paratransit Case Study Report, San Francisco, CA, 2006.
22. Donald R. Drew, Traffic Flow Theory and Control, McGraw-Hill Book Company, Newyork, 1968, pages 255-297.
23. Robert E. Shannon, Systems Simulation – the Art and Science, Prentice-Hall, Inc., Englewood Cliffs, N.J., 1975, page 1-14.
24. Adolf D. May,”Traffic Flow Fundamentals,” Prentice Hall, Englewood Cliffs, NJ, pp. 383-384, 1990.
25. ART - Arlington Transit, http://www.arlingtontransit.com/
26. PVTA- Pomona Valley Transportation Authority, http://www.pvtrans.org/