Freight Performance Measures Using Truck GPS Data and the Application of National Performance Measure Research Data Set (NPMRDS)
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1 Freight Performance Measures Using Truck GPS Data and the Application of National Performance Measure Research Data Set (NPMRDS) Chen-Fu Liao Department of Civil, Environmental, and Geo- Engineering University of Minnesota NATMEC 2014, Chicago IL, June 29 July 2, 2014
2 Outline Motivation and objectives Data summary Probe vehicle GPS data processing and analysis results Performance measures (mobility, delay & reliability) Truck bottlenecks NPMRDS example Q & A
3 Motivation Truck trip data usually are difficult to obtain due to the proprietary nature of the data ATRI truck GPS data availability and FHWA initiatives Opportunities to better understand trucking activities, freight movement, pattern and characteristics at local, regional and national level
4 Objectives & Goals Integrate private and public data sources to generate freight mobility and reliability measures Better identify system impediments such as truck bottlenecks Investigate truck volume/flow contributing to traffic congestion and delay Use derived performance measures as a guide for decision support on infrastructure investment, freight planning, and forecasting
5 Data Summary Road network (GIS files) greater Twin Cities Metro Area (TCMA) 38 key corridors, 1265 centerline miles 12 months of truck GPS data WIM per vehicle record 4 stations ATR per vehicle data 14 stations Loop detector data in TCMA
6 Key Freight Corridors in Twin Cities Metro Area St. Cloud ATR Volume ATR Volume/Speed ATR Volume/Speed/Class WIM HIGHWAY COUNTY County Road Interstate State Highway US Highway Mankato Rochester
7 Data Set DS-A DS-B DS-C Time Zone GMT/UTC GMT/UTC GMT/UTC Spot Speed Yes No Yes Static ID Yes Rotate every 15 days Rotate every 24 hours Data Accuracy Truck GPS Data Summary 2013 Within 3 meters Within meters at 90% probability and meters at 95% probability. Within meters at 90% probability and meters at 95% probability. Snap Tolerance Used 50 m 150 m 50 m 2013 Number of Truck Trips 74,823 35,179 76, Raw Data Size 50,170,591 3,142,634 38,871, Snapped 18,792, ,076 13,270, Snapped Percentage 37.5% 30.5% 34.1% Average (SD) Sampling Time 8 (15) min 18 (26) min 1 (5) min
8 Speed (MPH) GPS vs. WIM37 Vehicle Speed Comparison I-94 Mile Post Hour of Day Probe Vehicle SD WIM37 Truck SD WIM37 Car SD WIM37 Truck Mean WIM37 Car Mean Probe Vehicle Mean
9 Speed (MPH) Speed Comparison - Truck GPS vs. Loop Detector (I-394 E of TH-100) Loop Detector Speed (All Traffic) Hour of Day GPS Point Speed SD Loop Detector Speed SD GPS Space Mean Speed GPS Space Mean Speed SD GPS Spot Speed Loop Detectors (All Traffic) Speed Average
10 Truck vs. General Traffic Speed (4PM Oct 2013) Truck All Vehicle Speed (MPH) -30 ~ ~ 0 0 ~ 10 No Data
11 Volume % 8% ATR 382 vs. Truck GPS Compariosn of Volume Percentage by Hour 7% 6% 5% 4% 3% 2% 1% 0% Hour of Day ATR 382 Probe Vehicle
12 Correlation of GPS vs. ATR Hourly Volume % ATR Data Probe Vehicle Data Correlation ID HCAADT Route ID Segment ID Count Coefficient 188 2, , , , , , , , * 5, , , , , , , , ** 1, , , , , , , , , NA * ATR341 - S OF CSAH35 (50TH ST N) IN OAKDALE (I-694) ** ATR365 - S OF CSAH116 (BUNKER LAKE BLVD NE) IN HAM LAKE (State Highway 65)
13 Truck Mobility Performance Measures Percent of freight corridor miles in TCMA with average speed < 45 MPH in AM or PM Peak Truck Daily Delay Travel Time Reliability Index Threshold Speed = 45 MPH, Max Throughput Speed
14 Performance Measures
15 Truck Congestion Speed < 45 MPH PM Peak (2-7 PM) Number of Hours Speed < 45 MPH
16 Truck Mobility Measure Time Period (2012 Weekdays TCMA) AM Peak 5-10 AM PM Peak 2-7 PM # of Miles with Average Speed < 45 MPH Total Miles of RTMC Stations in TCMA Percentage of Miles < 45 MPH 12.4% 19.0%
17 Hours State Highway 55 I-394 US Highway 212 I-35E I-35W State Highway 77 Speed (MPH) TH I-494 Average Daily Delay and Speed Delay Dir (+1) Delay Dir (-1) Speed Dir (+1) Speed Dir (-1) HCAADT 5,900~8,900 HCAADT 5,500~8,400 HCAADT 4,150~7, Mile Post Threshold Speed 45 MPH Total Truck Delay = 37 (+1) + 95 (-1) = 132 Hours / Day
18 Truck Delay PM Peak (2-7 PM) Delay (hour)
19 Reliability Index (RI-80) 3.5 I-694 Travel Time Reliability at I-35W Hour of Day EB WB
20 Truck Reliability Index PM Peak (2-7 PM) RI_80 RI 80 < 1.5 reliable 1.5 RI 80 < 2.0 moderate reliable RI unreliable
21 Truck Bottleneck in PM Peak (Based on Delay) Rank Location Dir. PM Peak Delay (hours) / Mile PM Peak Reliability RI80 HCAADT Number of Lanes Length (miles) 1 I-35W at I-694 NB I-35W at I-94 SB I-494 between I-35W & 169 EB I-394 between TH 100 & I-94 EB I-694 between I-35E & I-35W WB I-694 at I-35W EB TH 62 at TH 169 WB US52 at I-94 NB I-94 at I-35W WB I-94 at I-35E EB I-494 at TH 55 NB I-35E at I-94 NB TH 169 at I-394 NB I-696 at TH 100 WB I-35W at I-94 NB TH 100 at TH 7 NB TH 36 at I-35W EB TH 62 at TH 169 EB
22 NPMRDS Analysis Example
23 Sample NPMRDS Data TMC Date Epoch Travel time All vehicles Travel time Passenger vehicles Travel time Freight trucks 118N N N N N N N N
24 Average Truck Speed from NPMRDS Nov (PM Peak)
25 Average Truck Speed Nov (PM Peak) TCMA
26 Future Opportunities Analyze and monitor FPM in TCMA monthly, quarterly or annually Incorporate FPM into regional freight or truck model Take advantage of NPMRDS (NHS only) Truck bottlenecks Truck parking duration and facility needs
27 Acknowledgements MnDOT OFCVO & TDA ATRI Tara Sasank Sunkara & Dayakar Reddy Singana Graduate Research Students Project TL John Tompkins & other TAP Members CTS & Civil Engineering, UMN FHWA
28
Project Title: Using Truck GPS Data for Freight Performance Analysis in the Twin Cities Metro Area Prepared by: Chen-Fu Liao (PI) Task Due: 9/30/2013
MnDOT Contract No. 998 Work Order No.47 213 Project Title: Using Truck GPS Data for Freight Performance Analysis in the Twin Cities Metro Area Prepared by: Chen-Fu Liao (PI) Task Due: 9/3/213 TASK #4:
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