1. presented to presented by Cambridge Systematics, Inc. Transportation leadership you can trust. Development of a Tour-Based Truck Travel Demand Model using Truck GPS Data 14 th TRB National Transportation Planning Applications Conference May 7, 2013 Arun Kuppam co-authored by Jason Lemp, CS Dan Beagan, CS Vladimir Livshits, MAG Lavanya Vallabhaneni, MAG Sreevatsa Nippani, MAG

2. Overview 2 Why ? Truck trip diary surveys are expensive, with poor response rates Who ? Third party commercial GPS vendors What ? Deploy GPS units and collect GPS records from truck fleets How ? Process GPS data to obtain truck trips and tours Where ? CS applied in Los Angeles, Chicago, Phoenix, BaltimoreExample focuses on specifics from the Phoenix experience

3. Commercial Vehicle GPS Data 3 GPS information should be processed before it can be used for truck travel models Condition of dissemination is that truck ID’s are anonymized Entities (e.g., ATRI) collect and store historical GPS from operators (2004 to present) Automatic Vehicle Location (AVL); Events Activated Tracking Systems (EATS); Fleet Telematics Systems (FTS) Truck fleet operators subscribe to GPS services for operational and maintenance purposes GPS devices are widely deployed in cell phones, autos, and trucks

4. ATRI Data Specifics 4 Cost for one month of data – $10K ATRI was purchased to supplement heavy truck (FHWA Classes 8-13) trip diary surveys 3.5 million positional records from 22,000 trucks Close to 60,000 truck tours Heavy Trucks Large Sample of Trucks Relatively Cheap Heavy Trucks Large Sample of Trucks Relatively Cheap

5. Truck GPS Data from Phoenix All Trucks in April 2011 5 ATRI GPS April 2011 ATRI GPS All Truck IDs April 2011

6. Truck GPS Data from Phoenix One Truck in April 2011 6 ATRI GPS Truck ID 3570452 April 2011

7. Truck GPS Data from Phoenix One Truck on April 1, 2011 7 ATRI GPS Truck ID 3570452 April 1, 2011

8. Truck_ ID DateTimeLongitudeLatitudeEvent Trip Distance (miles) Time From Last (min) Time To Next Min) Speed From Last (MPH) Speed To Next Speed To Next(MPH) Event Type Event Type TAZ LU LastNext 3570424/1/20113:47:08 A.M.-112.169533.435330.066.39-12.870.2129.79First Starting884 Indus- trial 3570424/1/20114:00:00 A.M.-112.263733.48384 6.39 2.5312.872.6329.7957.6moving 3570424/1/20114:02:38 A.M.-112.268933.525 2.53 4.562.6311.157.624.67moving 3570424/1/20114:13:44 A.M.-112.340933.547564.560.0011.10.2724.670.42Stopping 3570424/1/20114:14:00 A.M.-112.340833.54757 0.00 0.060.272.10.421.78stopped413 Indust- rial 3570424/1/20114:16:06 A.M.-112.339833.54758 0.06 0.222.16.51.782.01stopped413 Indust- rial 3570424/1/20114:22:36 A.M.-112.338133.54479 0.22 0.036.50.632.012.64stopped413 Indust- rial 3570424/1/20114:23:14 A.M.-112.337733.544910 0.03 0.020.630.232.644.07stopped413 Indust- rial 3570424/1/20114:23:28 A.M.-112.33833.544911 0.02 0.010.2339.174.070.01stopped413 Indust- rial 3570424/1/20115:02:38 A.M.-112.338133.544912 0.01 0.0539.17200.010.15stopped413 Indust- rial 3570424/1/20115:22:38 A.M.-112.338333.545613 0.05 0.13207.370.151.08stopped413 Indust- rial 3570424/1/20115:30:00 A.M.-112.338533.5475140.135.227.37151.0820.88Starting413 Indus- trial 3570424/1/20115:45:00 A.M.-112.267333.500915 5.22 7.5315 20.8830.11moving 3570424/1/20116:00:00 A.M.-112.139133.480416 7.53 0.30151.2330.1114.61moving 3570424/1/20116:01:14 A.M.-112.134533.4784170.30 0.001.230.1714.611.45Stopping 3570424/1/20116:01:24 AM-112.134633.478418 0.00 0.171.231.450.07stopped745Landfill 3570424/1/20116:02:38 AM-112.134633.478419 0.00 0.031.2380.60.070.02stopped745Landfill 3570424/1/20117:23:14 AM-112.135133.478420 0.03 80.610.40.020.2stopped745Landfill 3570424/1/20117:33:38 AM-112.134533.478521 0.03 0.0410.40.870.22.91stopped745Landfill 3570424/1/20117:34:30 A.M.-112.134433.4791220.04 2.980.8710.52.9117.04Starting745Landfill 3570424/1/20117:45:00 AM-112.169233.447223 2.98 0.8310.53.917.0412.77Moving 3570424/1/20117:48:54 A.M.-112.170233.4352240.830.003.913.7712.770.01Stopping884 Indus- trial 3570424/1/20118:02:40 AM-112.170233.435225 0.00 -13.77-0.01-Last Stopped Processing of One Truck Tour 8 Primary Anonymized Data Processed Data

9. Processing of One Truck Tour 9 Truck_ ID DateTimeLongitudeLatitudeEvent Trip Distance (miles) Time From Last (min) Time To Next Min) Speed From Last (MPH) Speed To Next Speed To Next(MPH) Event Type Event Type TAZ LU LastNext 357042 4/1/ 2011 3:47:08 A.M.-112.169533.435330.066.39-12.870.2129.79 First Starting884 Indus- trial ……………………………………… 357042 4/1/ 2011 4:13:44 A.M.-112.340933.547564.560.0011.10.2724.670.42Stopping ……………………………………… 357042 4/1/ 2011 5:30:00 A.M.-112.338533.5475140.135.227.37151.0820.88Starting413 Indus- trial ……………………………………… 357042 4/1/ 2011 6:01:14 A.M.-112.134533.4784170.30 0.001.230.1714.611.45Stopping ……………………………………… 357042 4/1/ 2011 7:34:30 A.M.-112.134433.4791220.04 2.980.8710.52.9117.04Starting745 Land- fill ……………………………………… 357042 4/1/ 2011 7:48:54 A.M.-112.170233.4352240.830.003.913.7712.770.01Stopping884 Indus- trial ……………………………………… Primary Anonymized Data Processed Data

10. Truck GPS Data from Phoenix Processing of One Truck on April 1, 2011 10 ATRI GPS Truck ID 357402 - April 1, 2011 – Actual Stops

11. Truck GPS Data from Phoenix TAZ of Trip Ends for One Truck on April 1, 2011 11

12. Truck GPS Data from Phoenix LU of Trip Ends for One Truck on April 1, 2011 12 Industrial Landfill, Sand & Gravel Industrial

13. Trip- and Tour-Based Truck Models 13 Origin/ Destination Stop 5 (Retail) Stop 6 (Retail) Truck Trip Ends (7 trips, 6 LU) Truck Tours (2 tours, 6 LU)

14. Truck Tour-Based Model Structure Tour Generation Heavy truck tour rates by industry type Stop Generation 1 stop2 stops……..11 stops Tour Completion Yes – return to home base No – does not return Stop Purpose One of 10 stop types Retail Constr. Farming Resid. Govt. Warehs. Transp. Office Industrial Service Stop Location One of 3,000 TAZs Stop TOD Choice 1 st Stop TOD (24 1- hr periods) Next Stop TOD (24 1- hr periods) 14

15. Stop Generation Model Predicts number of stops on each truck tour 15 Available Set of Choices Decision Making Variables Outputs = Number of stops made on tour (any value between 1 & 11) Model Structure = MNL Starting LU of the tour Employment and households at starting TAZ Accessibility to employment

16. Tour Completion Model Predicts if truck returns to home base 16 Available Set of Choices Decision Making Variables Outputs = Tour is complete or not Model Structure = Binary Logit No of stops by industry type Total employment in starting TAZ Employment by industry type

17. Stop Purpose Model Predicts purpose of stop 17 Available Set of Choices Decision Making Variables Outputs = One of 10 stop purpose types Model Structure = MNL Previous purpose typeAccessibility to employmentNo of stops on tour Tour purpose type Log (1 + stop sequence no.)

18. Stop Location Model Predicts location TAZ of each stop 18 Available Set of Choices Decision Making Variables Outputs = Location TAZ of each stop Model Structure = MNL Travel time between stops No of stops by type and Employment by type Accessibility to employment

19. Stop TOD Choice Model Predicts TOD of each stop 19 Available Set of Choices Decision Making Variables Outputs = One of 24 hour intervals Model Structure = MNL Indicator for tour completionTravel time between stopsNo of stops on tour Departure shift variable Time / No of stops remaining

20. Initial Findings 20

21. Next Steps 21 Perform more rigorous model calibration and validation Determine adequacy of GPS data both in terms of sample sizes and biases Implement truck tour model and compare with trip-based model applications Acquire GPS data for medium trucks and develop a medium truck tour model

22. Conclusions 22 Truck model parameters are difficult and expensive to obtain by surveys Truck GPS data is cost-effective to obtain large volume of information from trucks Truck tour- based models require large samples by industry type Tour-based models capture trip chaining patterns of trucks by type