Measure of Truck Delay and Reliability at the Corridor Level

2018PDFTech ReportMN/RC 2018-15(c) 99008 (wo) 235Project CTS #2017012Traffic delayTruck trafficTraffic congestionIntegrated corridor managementFreight transportationFreight trafficHighway corridorsTrucksDelaysPerformance measurementTraffic congestionFreight transportationIntegrated corridor managementMinnesotaUnited StatesMinnesota. Dept. of Transportation. Research Services & LibraryLiao, Chen-FuMinnesota Traffic Observatory LaboratoryMinnesota. Dept. of Transportation. Research Services & LibraryUS Transportation CollectionFreight transportation provides a significant contribution to our nation\u2019s economy. A reliable and accessible freight network enables business in the Twin Cities to be more competitive in the Upper Midwest region. Accurate and reliable freight data on freight activity is essential for freight planning, forecasting and decision making on infrastructure investment. A report entitled \u201cTwin Cities Metropolitan Region Freight Study\u201d published by MnDOT and the Metropolitan Council in 2013, suggested a need to understand where and when trucks are most affected by congestion. A framework for truck data collection and analysis was recommended to better understand the relationships between truck traffic and congestion in rush hours. Building upon our previous study to measure freight mobility and reliability along 38 key freight corridors in the Twin Cities Metropolitan Area (TCMA), this study leveraged our previous effort to implement the performance measures using the National Performance Measurement Research Dataset (NPMRDS) from the USDOT. The researcher team first worked with stakeholders to prioritize a list of key freight corridors with recurring congestion in peak periods in the TCMA. We used 24 months of NPMRDS data to measure travel time reliability and estimate truck delay at the corridor level and to identify system impediments during the peak hours. The objective is to use performance measures for assessing impact of truck congestions and identifying operational bottlenecks or physical constraints. Trucking activity nearby a congested area is examined to analyze traffic pattern and investigate possible causes of recurring congestions