ANALYSIS TOOL TO PROCESS PASSIVELY-COLLECTED GPS DATA FOR COMMERCIAL VEHICLE DEMAND MODELLING APPLICATIONS

1. ANALYSIS TOOL TO PROCESS PASSIVELY-COLLECTED GPS DATA FOR COMMERCIAL VEHICLE DEMAND MODELLING APPLICATIONS Bryce Sharman & Matthew Roorda University of Toronto Presentation for the TRB - SHRP2 Symposium: Innovations in Freight Demand September 15, 2010, Washington DC

2. Presentation Outline • Motivation • Data • Data Analysis Methods • Preliminary Results • Conclusions • Future Work

3. Shortcomings of Existing Commercial Vehicle Survey Data • A freight data survey was conducted in 2006 by University of Toronto researchers • Small sample size (n=600) • Survey limited to one suburban region outside of Toronto • Low survey response rate (25%) • GPS add-on revealed differences between reported and observed behavior • Single day observations only (practical limit for response burden) • Cost of better data collection is very high

4. Benefits of Supplementing Travel Survey Data Using GPS Data • GPS data provide precise and continuous spatial and temporal information about a large number of vehicles for long periods of time. • Many firms already subscribe to GPS tracking services to monitor their vehicle fleets.

5. Research Goals • Use GPS data to develop a model for forecasting urban commercial vehicle tours, incorporating dynamics of business operations over time. • Develop analysis procedure and computer software to process GPS data such that it is suitable for developing a disaggregate travel demand model

6. Provider: Xata Turnpike Global Technologies Inc. • Provides fleet management services to > 300 firms, that own > 30,000 trucks • GPS location tracking • routing, stop dwell time • Engine diagnostics • speed, braking, fuel consumption, idling

7. Database for this Study • 77 Firms • 1618 Vehicles • 91 Days: April 1, 2009 – June 30, 2009 • 147,238 vehicle days • ~ 7 million GPS motion points • 308,575 stops identified by GPS units

8. Study Area

9. GPS Resolution • Xata Turnpike is tracking vehicles for fleet monitoring, not travel demand surveys • Data resolution • 500 m intervals between GPS points • Distance is extended to 1 or 2 miles as the vehicle reaches freeway speeds ( > 60 mph) • Stop detection threshold: 5 minutes

10. Internal/External Stops • All GPS points are recorded within study area. • When vehicle leaves study area, GPS points are recorded until first stop. • When vehicle enters study area, GPS points are recorded after last stop prior to entering the area.

11. Data Cleaning • False-positive stop removal: • Infeasible that truck is making a delivery, service or "other" stop. (E.g. bad congestion on freeways) • False-negative stop addition: • Time interval between subsequent GPS motion points shows that a stop must have occurred. • Removal of uninteresting trips: (E.g. Repositioning truck within the depot)

12. Identifying Repeat Destinations • Why? • When GPS trip ends are linked, then repeated travel behavior to locations can be analyzed • When commercial vehicles repeatedly make deliveries to a customer, the GPS unit does not record exactly the same coordinates. • Differences due to: • GPS error • Choice of loading bay or parking spot. • Research – use spatial clustering techniques to best predict which GPS stops are for the same destinations

13. Example Clustering -- One Firm in Toronto CBD (3 months, 7 trucks) Driver logs were obtained from this firm to test the performance of various methods

14. Clustering Method: • Found issues due to very different scales of land parcel sizes • Factories, warehouses and truck yards can occupy very large areas • Testing different algorithms found that a two-step clustering approach worked the best. • Cluster using Ward’s Hierarchical Agglomerative Clustering method aiming to form reasonably compact clusters • Combine any two clusters whose median point lies within the same land parcel

15. Identifying the Depot • Firm identities and attributes not provided with GPS data • Identification of depots is important to distinguish visits to a firm’s own location vs. visits to customers and suppliers • Using the number of visits to the location and the average time spent as determining attributes

16. Tour Creation • Tours are automatically created when a vehicle visits a depot location or a location outside of the study region.

17. Comparison of Vehicle Ownership • Toronto region survey (2006) • Avg. of 4.4 vehicles per firm (single-unit trucks and tractors) • GPS database (77 firms) • Avg. of 21 vehicles per firm • This difference is expected since transportation and larger retail firms are expected to show a preference for using fleet management services

18. Comparison of Stop Dwell Times

19. Conclusions • Research focused on creating a tool to analyze GPS data recorded for the customers of one fleet management company. • Tasks include data cleaning, clustering stops into destinations, depot identification and tour creation • Goal is to use this processed GPS data to develop commercial travel demand models.

20. Envisioned Travel Demand Models and Analyses from GPS Data • Model of the dwell time at a stop • Model of the number of days between visits to the same destination • Analysis of travel variability (how representative is the travel on one day of other days) • Tour generation model – May use stochastic or deterministic (VRP) approaches. Ideally tour generation will also be specified over a multiple-day time period.