Scheduling of Bus Fleet Departure Time Based on Mathematical Model of Number of Bus Stops for Municipality Bus Organization
Operating Urban Bus Transit System is a phenomenon that has a major role in transporting passengers in cities. There are many factors involved in planning and operating an Urban Bus Transit System, one of which is selecting optimized number of stops and scheduling of bus fleet departure. In this paper, we tried to introduce desirable methodology to select number of stops and schedule properly. Selecting the right number of stops causes convenience in accessibility and reduction in travel time and finally increase in public preference of this transportation mode. The achieved results revealed that number of stops must reduce from 33 to 25. Also according to scheduling and conducted economic analysis, the number of buses must decrease from 17 to 11 to have the most appropriate status for the Bus Organization.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3462083Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 183
 Shen, Y., & Xia, J. (2009). Integrated bus transit scheduling for the Beijing bus group based on a unified mode of operation. International Transactions in Operational Research, 16(2), 227-242.
 Shen, Y., Xu, J., & Zeng, Z. (2015). Public transit planning and scheduling based on AVL data in China. International Transactions in Operational Research.
 Wren, A. (1972). Bus scheduling: an interactive computer method. Transportation Planning and Technology, 1(2), 115-122.
 Kettler, K. A., Lehoczky, J. P., & Strosnider, J. K. (1995, December). Modeling bus scheduling policies for real-time systems. In Real-Time Systems Symposium, 1995. Proceedings, 16th IEEE (pp. 242-253). IEEE.
 Rosen, J., Andrei, A., Eles, P., & Peng, Z. (2007, December). Bus access optimization for predictable implementation of real-time applications on multiprocessor systems-on-chip. In Real-Time Systems Symposium, 2007. RTSS 2007. 28th IEEE International (pp. 49-60). IEEE.
 Gleason, J. M. (1973). Set covering approach to the location of express bus stops. Omega, 3, 605-608.
 Reilly, J. (1997). Transit service design and operation practices in western European countries. Transportation Research Record: Journal of the Transportation Research Board, (1604), 3-8.
 Ceder, A., Prashker, J. N., & Stern, J. I. (1983). An algorithm to evaluate public transportation stops for minimizing passenger walking distance. Applied Mathematical Modelling, 7(1), 19-24.
 Ibeas, Á. dell’Olio, L., Alonso, B., & Sainz, O. (2010). Optimizing bus stop spacing in urban areas. Transportation research part E: logistics and transportation review, 46(3), 446-458.
 Vanitchakornpong, K., Indra-Payoong, N., Sumalee, A., & Raothanachonkun, P. (2008). Constrained local search method for bus fleet scheduling problem with multi-depot with line change. In Applications of Evolutionary Computing (pp. 679-688). Springer Berlin Heidelberg.
 Ceder, A. (2011). Optimal multi-vehicle type transit timetabling and vehicle scheduling” Procedia Soc. Behav. Sci. 20, 19–30.
 Kim, W., B. Son, J. Chung, and E. Kim. (2009). Development of Real-Time Optimal Bus Scheduling and Headway Control Models. Transportation Research Record: Journal of the Transportation Research Board, No. 2111.
 Ming, W., Bo, S., Wenzhou, J. (2012). Model and Algorithm of Regional Bus Scheduling with Grey Travel Time. J Transpn Sys Eng & IT, 12(6), 106_112.
 Shuia, X., Zuoa, X., Chena, C., Alice, E. (2015). A clonal selection algorithm for urban bus vehicle scheduling” Applied Soft Computing 36, 36–44.
 Wagale, M., Pratap Singh, A., Ashoke, K., Sarkar and Apkatkar, S. (2013). Real-Time Optimal Bus Scheduling for a City using A DTR Model. Procedia - Social and Behavioral Sciences 104, 845 – 854.
 Wei, M., Jin, W.Z., Sun, B. (2011). Model and algorithm for regional bus scheduling with stochastic travel time. Journal of Highway and Transportation Research and Development, 28(10): 151–156.
 Tirachini, A. (2014). The economics and engineering of bus stops: Spacing, design and congestion. Transportation research part A: policy and practice, 59, 37-57.