Presented to presented by Cambridge Systematics, Inc. Transportation leadership you can trust. Development of a Tour-Based Truck Travel Demand Model using.

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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

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

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

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

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

Truck GPS Data from Phoenix One Truck in April ATRI GPS Truck ID April 2011

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

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 /1/20113:47:08 A.M First Starting884 Indus- trial /1/20114:00:00 A.M moving /1/20114:02:38 A.M moving /1/20114:13:44 A.M Stopping /1/20114:14:00 A.M stopped413 Indust- rial /1/20114:16:06 A.M stopped413 Indust- rial /1/20114:22:36 A.M stopped413 Indust- rial /1/20114:23:14 A.M stopped413 Indust- rial /1/20114:23:28 A.M stopped413 Indust- rial /1/20115:02:38 A.M stopped413 Indust- rial /1/20115:22:38 A.M stopped413 Indust- rial /1/20115:30:00 A.M Starting413 Indus- trial /1/20115:45:00 A.M moving /1/20116:00:00 A.M moving /1/20116:01:14 A.M Stopping /1/20116:01:24 AM stopped745Landfill /1/20116:02:38 AM stopped745Landfill /1/20117:23:14 AM stopped745Landfill /1/20117:33:38 AM stopped745Landfill /1/20117:34:30 A.M Starting745Landfill /1/20117:45:00 AM Moving /1/20117:48:54 A.M Stopping884 Indus- trial /1/20118:02:40 AM Last Stopped Processing of One Truck Tour 8 Primary Anonymized Data Processed Data

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 /1/ :47:08 A.M First Starting884 Indus- trial ……………………………………… /1/ :13:44 A.M Stopping ……………………………………… /1/ :30:00 A.M Starting413 Indus- trial ……………………………………… /1/ :01:14 A.M Stopping ……………………………………… /1/ :34:30 A.M Starting745 Land- fill ……………………………………… /1/ :48:54 A.M Stopping884 Indus- trial ……………………………………… Primary Anonymized Data Processed Data

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

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

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

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)

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

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

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

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.)

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

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

Initial Findings 20

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

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