Transit agencies have the opportunity to improve the delivery of services by using data from Intelligent Transportation Systems (ITS). On-time performance is an important measure. The objective of this paper is to adjust the timetables so that the probability of on-time performance is maximized. For this purpose we analyze data distributions of travel time and also consider the general case that data distribution is unknown. Statistical procedures are presented to find scheduled time for some selected distributions. Monte Carlo simulation is introduced for the purpose of finding scheduled time when data distribution is not known. Simulation studies indicate that the on-time performance would increase using the proposed methodology. The contribution of this paper is to provide transit system a procedure to set up or update their timetables based on current ITS data and its distribution, and hence increase level of service.
| Identifer | oai:union.ndltd.org:fiu.edu/oai:digitalcommons.fiu.edu:etd-1601 |
| Date | 04 November 2011 |
| Creators | Wang, Xiaobo |
| Publisher | FIU Digital Commons |
| Source Sets | Florida International University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | FIU Electronic Theses and Dissertations |
Page generated in 0.0022 seconds