University of Minnesota FUSING PUBLIC AND PRIVATE TRUCK DATA TO SUPPORT REGIONAL FREIGHT PLANNING AND MODELING Chen-Fu Liao Minnesota Traffic Observatory Department of Civil Engineering University of Minnesota Innovations in Freight Demand Modeling and Data A Transportation Research Board SHRP 2 Symposium September 14-15, 2010, Washington, DC

University of Minnesota Outline Freight Data Challenges Objectives Data Processing and Analysis Analysis Results Concluding Remarks and Potential Applications

University of Minnesota Freight Data Challenges  Freight data availability  Limited public data  Proprietary nature of private data  Data reliability, quality and cost  Data fusion, integration and management  Transform data into information

University of Minnesota Objectives  Use I90/94 as an example to integrate public & private data to analyze freight activity  Compare variations of truck speed and travel time to identify potential freight bottleneck and forecast future freight demand  Identify the needs of regional highway infrastructure improvement to sustain growing freight demand  Generate performance measures to support freight modeling, planning and decision making

University of Minnesota Data Summary FAF2 Data from FHWA 12 months (May 08 ~ Apr. 09) of Truck AVL/GPS Data from ATRI Highway Speed Data from MNDOT, Wisconsin DOT and IL State Toll Highway Authority

University of Minnesota I-94/90 (TWIN CITIES – CHICAGO) Average Truck Speed By Location

University of Minnesota MadisonSt. PaulChicagoTomah Average Speed (EB) I-90 Toll Highway

University of Minnesota MadisonSt. PaulChicagoTomah Average Speed (WB) I-90 Toll Highway

University of Minnesota I-94/90 (Twin Cities – Chicago) Truck Speed and Volume Variation By Time of Day

University of Minnesota St. Paul, MN Mean Median WB Mean=46.4, Median=54.6, N=33,788 EB Mean=46.9, Median=57.7, N=33,610 I-94 at US52

University of Minnesota St. Paul, MN I94 & US52

University of Minnesota 95% TT – Avg. TT Performance Index Buffer Time Index (BTI) = Avg. TT Peak Travel Time Travel Time Index (TTI) = Free Flow Travel Time Congestion Measure Reliability Measure

University of Minnesota Buffer Time Index (BTI) of Apr MadisonSt. PaulChicagoTomah I-90 Toll Highway BTI = 95% TT – Avg. TT Avg. TT

University of Minnesota Travel Time Index (TTI) of Jan Peak Travel Time Travel Time Index (TTI) = Free Flow Travel Time

University of Minnesota St. PaulChicago St. Paul Trip Destinations Analysis

University of Minnesota I-90 (S. BELOIT – O’HARE) Truck vs. General Traffic

University of Minnesota I-90 Annual Average Speed (EB) General Traffic Speed (Segment Speed) Derived from Illinois Toll Highway Authority Inter-Plaza Travel Time S. BeloitO’Hare All Traffic Truck Truck Speed Limit 55 MPH BelvidereMarengoElgin

University of Minnesota I-90 Annual Average Speed (WB) General Traffic Speed (Segment Speed) Derived from Illinois Toll Highway Authority Inter-Plaza Travel Time S. BeloitO’Hare All Traffic Truck Truck Speed Limit 55 MPH BelvidereMarengoElgin

University of Minnesota I-94/90 (TWIN CITIES - CHICAGO) Truck Stops

University of Minnesota EB Truck Stops (Apr. 2009) Truck Speed < 5MPH and Travel Distance < 100 meters, N=72,543 Twin Cities Tomah Madison Beloit Chicago Eau Claire Hudson Belvidere Mauston Black River Falls Portage

University of Minnesota WB Truck Stops (Apr. 2009) Truck Speed < 5MPH and Travel Distance < 100 meters, N=74,405 Twin Cities Tomah Madison Beloit Chicago Eau Claire Hudson Belvidere Mauston Black River Falls Portage

University of Minnesota Truck Stops from TruckMaster

University of Minnesota I-94/90 (TWIN CITIES - CHICAGO) Truck Stop Durations

University of Minnesota

Summary  FPM data can be used to measure performance over time and by location  Truck travel time reliability and level of congestion  Truck volume variation and impact  Performance Index (BTI, TTI)  Truck Destinations  Truck Stops and Rest Durations

University of Minnesota Possible Causes of Bottlenecks  Top 30 freight bottlenecks occurred at highway interchange (ATRI Report)  Roadway geometry (grade, sight distance)  Capacity (number of lanes), toll booths  Required lane of travel for trucks  Speed limit and free flow speed  Volume ratio of truck vs. general traffic  Weather and Others

University of Minnesota Potential Applications  Truck travel time reliability and impact of congestion on cost of freight  Identify truck stop facility needs  Speed gap between general traffic and truck influenced by traffic volume  Develop national standard to report freight performance measures more regularly  Use derived performance measures to support freight modeling and planning

University of Minnesota Acknowledgements  USDOT, FHWA  Minnesota DOT and ATRI  Illinois State Toll Highway Authority  CTS, University of Minnesota (UMN)  Minnesota Traffic Observatory (MTO), Department of Civil Engineering, UMN