Measuring Freeway Traffic Conditions with Transit Vehicles

Abstract

Many public transit agencies have equipped their fleet with automatic vehicle location (AVL) systems, which periodically provide the location of each vehicle in the fleet. Although the AVL is deployed for transit operations, the vehicles also provide valuable information about the traffic stream throughout the road network. This study developed a methodology to mine the transit AVL data to find all trips that use any portion of a prespecified freeway segment. These trips are then used to measure travel time and average speed over the freeway and thereby quantify conditions on the facility. The results are validated against concurrent loop detector data from a corridor. The greatest benefits, however, are in areas without fixed vehicle detection, so the methodology is also demonstrated on such a freeway corridor. The study corridors typically have fewer than 50 observations per day per kilometer per direction, so this paper includes a process for selecting those segments with at least one observation per hour. Even with this low density of observations, the data are aggregated to show clearly the recurring congestion patterns. Nonrecurring events are also evident, but they take longer to detect. With a higher frequency of observations (e.g., from other fleet AVL systems, cell phone tracking, or vehicle–infrastructure integration probe data), the methodology should also be effective for rapidly identifying nonrecurring congestion.