Abstract Manager

The vast majority of freight in the Ontario is moved by trucks. As such, a better understanding of truck travel is necessary to assess the current needs, develop models to predict future freight demand, and formulate evidence-based public policies. The biggest challenge to achieve these is the limited availability of freight data, which require substantial resources to collect by means of freight surveys – either roadside truck surveys or business establishment surveys. Roadside surveys, as in the case of MTO’s Commercial Vehicle Surveys (CVS), cannot collect full information of the stops, trips, tours, and routes from the surveyed truck drivers. Establishment surveys that make use of GPS loggers could obtain such information, but on a limited scale. On the other hand, large-scale GPS-based truck trajectory data is a valuable source of such information that can be used to complement freight surveys.

This paper will provide an exploratory analysis of how Big Data of raw truck trajectory from passive sources can be used to develop knowledge on truck travel patterns in the region. The following use cases will be covered in this paper: identifying major truck trip generators, analyzing discretionary truck stops by location and dwell time, developing an understanding of the routes preferred by trucks, and exploring truck delays on the road network. The findings will enrich our understanding of truck travel in Ontario and help improve some of the inputs and assumptions of the provincial travel demand model.