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MODELING SCHEDULE DEVIATIONS OF BUSES USING AUTOMATIC VEHICLE-LOCATION DATA AND ARTIFICIAL NEURAL NETWORKS.
| 题名： | MODELING SCHEDULE DEVIATIONS OF BUSES USING AUTOMATIC VEHICLE-LOCATION DATA AND ARTIFICIAL NEURAL NETWORKS. |
| 作者： | Kalaputapu-R; Demetsky-MJ |
| 关键词： | ADVANCED-PUBLIC-TRANSPORTATION-SYSTEMS; AUTOMATIC-VEHICLE-LOCATION; BUS-TRANSIT; SCHEDULE-DEVIATIONS; ARTIFICIAL-NEURAL-NETWORKS; SCHEDULE-BEHAVIOR-MODELS; TIME-SERIES-ANALYSIS; CASE-STUDIES; TIDEWATER-REGIONAL-TRANSIT-VIRGINIA |
| 摘要： | The establishment of the Advanced Public Transportation Systems program has encouraged bus transit operators to experiment with implementing automatic vehicle-location systems for real-time monitoring and supervision of operations. While the focus has primarily been on the implementation of technologies, such as automatic vehicle-location systems, it is necessary to experiment and develop advanced performance analysis and evaluation procedures that can assist in schedule planning and real-time service-control tasks. One potentially useful and effective approach to these tasks is system behavior modeling. In this study this method is used to model schedule behavior of buses on a route using schedule-deviation information. The primary objective of this study is to investigate the application of artificial neural networks, which have been shown to hold promise then applied to nonlinear dynamic system-modeling problems, for developing schedule behavior models. Models are developed using the schedule-deviation information obtained from Tidewater Regional Transit's automatic vehicle-location system. The time-series analysis approach is adopted for the development of schedule behavior models at the route level. The results of a case study are encouraging and demonstrate the usefulness of artificial neural network techniques, especially the Jordan networks and the Elman networks, for modeling schedule deviations of buses on a route. |
| 总页数： | Transportation Research Record. 1995. (1497) pp44-52 (6 Fig., 1 Tab., 14 Ref.) |
| 报告类型： | 科技报告 |
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