Publication details
Analysis of Trends in Data on Transit Bus Dwell Times
| Authors | |
|---|---|
| Year of publication | 2017 |
| Type | Article in Periodical |
| Magazine / Source | Transportation Research Record: Journal of the Transportation Research Board |
| MU Faculty or unit | |
| Citation | |
| web | DOI |
| Doi | http://dx.doi.org/10.3141/2619-07 |
| Field | Informatics |
| Keywords | traffic signal control; bus dwell time; data analysis |
| Description | Transit vehicles create special challenges for urban traffic signal control. Signal timing plans are typically designed for the flow of passenger vehicles, but transit vehicles, with frequent stops and uncertain dwell times, may have very different flow patterns that fail to match these plans. Transit vehicles stopping on urban streets can also restrict or block other traffic on the road. This results in increased overall wait times and delays throughout the system for transit vehicles and other traffic. Transit signal priority (TSP) systems are often used to mitigate some of these issues, primarily addressing delay to the transit vehicles. However, existing TSP strategies give unconditional priority to transit vehicles, exacerbating quality of service for other modes. In networks where transit vehicles have significant effects on traffic congestion, particularly urban areas, using more realistic models of transit behavior in adaptive traffic signal control could reduce delay for all modes. Estimating the arrival time of a transit vehicle at an intersection requires an accurate model of transit stop dwell times. As a first step toward developing a model for predicting bus arrival times, this paper analyzes trends in automatic vehicle location (AVL) data collected over a two-year period, allowing several inferences to be drawn about the statistical nature of dwell times, particularly for use in real-time control and transit signal priority. Based on this trend analysis, we argue that an effective predictive dwell time distribution model must treat independent variables as random or stochastic regressors. |