Using Archived ITS Data to Improve Transit Performance and Management

10990332007Final ReportPDFTech ReportMN/RC 2007-44(c) 81655 (wo) 241Automatic vehicle locationBus transitBus transit operationsData banksIntelligent transportation systemsRunning timeSchedule maintenanceReliability (Statistics)Analytical modelsAutomated passenger countersService reliabilityMaintenance and PreservationOperations and Traffic ManagementPublic TransportationReliability of serviceITS in transitRunning time variabilitySchedule adherenceTransit operationsMinneapolis (Minnesota)United StatesMinnesota. Dept. of TransportationEl-Geneidy, AhmedHorning, JessicaKrizek, Kevin J.Hubert H. Humphrey Institute of Public AffairsMinnesota. Dept. of TransportationUS Transportation CollectionThe widespread implementation of automated vehicle location (AVL) systems and automatic passenger counters (APCs) in the transit industry has opened new venues in transit operations and system monitoring. Metro Transit, the primary transit agency in the Twin Cities, Minnesota region, has been testing various intelligent transportation systems (ITS) since 1999. In 2005, they fully implemented an AVL system and partially implemented an APC system. To date, however, there has been little effort to employ such data to evaluate different aspects of performance. This research capitalizes on the availability of such data to better assess performance issues of one particular route in the Metro Transit system. The authors employ the archived data from the location systems of buses running on an example cross-town route to conduct a microscopic analysis to understand reasons for performance and reliability issues. They generate a series of analytical models to predict run time, schedule adherence and reliability of the transit route at two scales: the time point segment and the route level. The methodology includes multiple approaches to display ITS data within a geographic information system (GIS) environment to allow visual identification of problem areas along routes. The methodology also uses statistical models generated at the time point segment and bus route level of analysis to demonstrate ways of identifying reliability issues and what causes them. The analytical models show that while headways are being maintained, schedule revisions are needed in order to improve run time. Finally, the analysis suggests that many scheduled stops along this route are underutilized and recommends consolidating them.75