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

Presentation Outline 1.Motivation 2.Data 3.Data Analysis Methods 4.Preliminary Results 5.Conclusions 6.Future Work

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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.

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work Database for this Study 77 Firms 1618 Vehicles 91 Days: April 1, 2009 – June 30, ,238 vehicle days ~ 7 million GPS motion points 308,575 stops identified by GPS units

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work Study Area

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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.

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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)

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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. 1.Cluster using Ward’s Hierarchical Agglomerative Clustering method aiming to form reasonably compact clusters 2.Combine any two clusters whose median point lies within the same land parcel

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work Tour Creation Tours are automatically created when a vehicle visits a depot location or a location outside of the study region.

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work Comparison of Stop Dwell Times

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work 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.

Motivation Data Data Analysis Methods Preliminary Results Conclusions Future Work Envisioned Travel Demand Models and Analyses from GPS Data 1.Model of the dwell time at a stop 2.Model of the number of days between visits to the same destination 3.Analysis of travel variability (how representative is the travel on one day of other days) 4.Tour generation model – May use stochastic or deterministic (VRP) approaches. Ideally tour generation will also be specified over a multiple-day time period.