Transit Delay Estimation Using Stop-Level Automated Passenger Count Data

Despite the potential use of global positioning system (GPS) based automatic vehicle location (AVL) data in the development of reliability improvement strategies for transit systems, issues with privacy and accuracy have presented challenges to the open sharing of the AVL data. However, data driven methods of estimating the performance measures of transit vehicles on the basis of the location data are still the most prominent means of reliability studies on transit systems. This paper aims to propose a new method for developing transit performance measures, namely traffic delay, based on the stop-level location data, which does not share the issues concerning privacy and accuracy that hinder the use of GPS-based data. The paper presents a case study on route 16 of the Metro Transit system in the Twin Cities, Minnesota. The delay measures that resulted from the proposed model are at the segment level of resolution, delimited by the route timepoints, and are found to be described well by the gamma distribution. These results allow the localization of excessive delay on a segment level, and the distinction between the traffic delay and dwell time delay.