Mapping of bus travel time to traffic stream travel time using econometric modeling
mode - bus, place - asia, place - urban, technology - intelligent transport systems, technology - geographic information systems, operations - traffic, operations - performance, planning - methods
Bus probes, panel data modeling, stream travel time, travel time estimation
Travel time is one of the most important traffic parameters for travelers, traffic managers, planners, and operators alike. Travel time estimation is a significant component of any intelligent transportation systems (ITS) operations. In countries like India, which are at a nascent stage of ITS deployment, one of the main hurdles is the lack of accurate and automated systems for travel time data collection. However, in most of the cities in India, public transit buses are equipped with global positioning system (GPS) devices providing rich bus travel time data. Privacy issues deter the use of such GPS devices in private vehicles present in the stream. Hence, estimating the stream travel time using buses as probes is a relevant problem. In countries like India, where a heterogeneous and less lane disciplined traffic condition exists and the buses have to share the road space with other vehicles (no separate bus lanes); this mapping of bus travel time to stream travel time becomes challenging. The present study attempts to estimate stream travel time using buses as probes in a heterogeneous and less lane disciplined urban traffic scenario. Preliminary analysis of the collected travel time data indicated significant variability across time and links. Panel modeling can capture both this cross-sectional and time-dependent behavior and hence is used in this study. The estimation model is corroborated with field data and the performance is found to be satisfactory.
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Banik, S., Vanajakshi, L., & Bullock, D.M. (2022). Mapping of bus travel time to traffic stream travel time using econometric modeling. Journal of Intelligent Transportation Systems, Vol. 26(2), pp. 235-251.