Data-Driven Support Tools for Transit Data Analysis, Scheduling and Planning

Abstract

Many transit agencies in the U.S. have instrumented their fleet with Automatic Data Collection Systems (ADCS) to \nmonitor the performance of transit vehicles, support schedule planning and improve quality of services. The \nobjective of this study is to use an urban local route (Metro Transit Route 10 in Twin Cities) as a case study and \ndevelop a route-based trip time model to support scheduling and planning while applying different transit \nstrategies. Usually, timepoints (TP) are virtually placed on a transit route to monitor its schedule adherence and \nsystem performance. Empirical TP time and inter-TP link travel time models are developed. The TP-based models \nconsider key parameters such as number of passengers boarding and alighting, fare payment type, bus type, bus \nload (seat availability), stop location (nearside or far side), traffic signal and volume that affect bus travel time. TP \ntime and inter-TP link travel time of bus route 10 along Central Avenue between downtown Minneapolis and \nNorthtown were analyzed to describe the relationship between trip travel time and primary independent variables. \nRegression models were calibrated and validated by comparing the simulation results with existing schedule using \nadjusted travel time derived from data analyses. The route-based transit simulation model can support Metro \nTransit in evaluating different schedule plans, stop consolidations, and other strategies. The transit model provides \nan opportunity to predict and evaluate potential impact of different transit strategies prior to deployment.

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