A Contemporary Approach for Visualizing Temporal and Spatial Urban Freight Movement by Leveraging Mobility Portal Data

Analyzing and visualizing traffic data in order to better understand congestion trends, safety concerns, goods movement and capacity needs is a pressing need. Broadly speaking, there is a large amount of traffic data available today, including volume, lane occupancy, speed, and travel time, which can be used to manage transportation networks, provide traveler information and produce performance measures. This broadly disseminated data almost always treats all vehicles alike, without discriminating between trucks and passenger cars. Since trucks are critical and growing components of freeway traffic, monitoring and tracking their dynamics can reveal the impacts of freight movement on current freeway operations and over time will uncover trends useful for future planning and management. This study takes advantage of a unique data stream available for the freeway network of Portland, Oregon, USA. In addition to providing continuous vehicle count, speed and lane occupancies at 20-second intervals at more than 500 stations (1,300 individual detectors), the Portland system reports volume bins at 4 length-based classifications. (<; 20, 20-35, 36-60, >60 ft). Given that most vehicle classification studies are done over very short time intervals at an extremely limited number of locations, this nonstop data stream enables unprecedented insight into where and when trucks are traveling on Portland's freeways and a wealth of opportunities for performance measurement and diagnosis of their impacts. The objective of this paper is to exploit this new data stream and explore new visualization techniques that depict truck volume, truck percentage and volume-weighted average vehicle speeds along Portland's Interstate 5 corridor, an important north-south freight route between the Canada and Mexico borders. Results confirm the merit in analyzing and tracking truck volumes, proportions, and other dynamics for traffic management, information and modeling. The results of this study and the techniques employed here can be used to better understand and visualize freight movement dynamics on a continuous basis.