Comparing Perceptions and Measures of Congestion

014546562012Final report; Jan. 2011-Oct 2012.PDFTech ReportUTCM 11-00-72DTRT06-G-0044Traffic congestionCongestion management systemsSurveysTraffic surveysHuman factorsPerformance measurementResearch HubCongestionTravel surveyPerformance measureHuman factorsMobilityOperations and traffic managementTexasUnited StatesTexas Transportation Institute. University Transportation Center for MobilityLe, MinhTurner, ShawnLomax, TimWikander, JohnPoe, ChrisTexas Transportation Institute. University Transportation Center for MobilityUnited States. Dept. of Transportation. Research and Innovative Technology AdministrationUniversity Transportation Centers Program (U.S.)NTL-OPERATIONS AND TRAFFIC CONTROLS-CongestionNTL-SAFETY AND SECURITY-Human FactorsNTL-PLANNING AND POLICY-SurveysUS Transportation CollectionPeople\u2019s perception of congestion and the actual measured congestion do not always agree. Measured congestion relates to the delay resulting from field measurements of traffic volume, speed, and travel time. People\u2019s perception of congestion can be influenced by relative year to year growth in congestion, improved or new transportation infrastructure, and societal attitudes on transportation. IBM publishes an annual study on the attitudes of commuters from across the world on their daily travel (known as the Commuter Pain Survey). The Texas A&M Transportation Institute (TTI) publishes an annual Urban Mobility Report that measures urban mobility based on public and private traffic data for highways, streets, and transit. This research attempts to connect the relationships between perceived congestion as determined by IBM\u2019s Commuter Pain Survey results and measured congestion from the Urban Mobility Report (UMR) in 10 cities across the United States. The raw Pain Index values (the unadjusted index values based entirely on survey responses) had higher correlation with TTI-based measures than the published Pain Index. The Raw Pain Index was found to correspond to a composite model of two of the four core UMR measures examined\u2014the Travel Time Index and the Roadway Congestion Index. This study also examines the correlation of measured congestion between the UMR and two measures of the INRIX National Traffic Scorecard. The Travel Time Tax (T3) and the Worst Hour T3 were found to correspond to a model of the Travel Time Index of the UMR. The Travel Time Tax correlates especially well with the Travel Time Index