Resilient Spatiotemporal Truck Monitoring Framework using Inductive Signature and 3D Point Cloud-based Technologies

Understanding the spatiotemporal distribution of commercial vehicle is essential for facilitating strategic pavement design, freight planning, and policy making. Hence, analysts and researchers have been increasingly interested in collecting more diverse high granularity truck data across different truck characterization schemes to meet these variegated needs across the roadway network to better understand their distinct operational characteristics and dissimilar impacts on infrastructure and the environment. Existing truck monitoring infrastructure are limited in spatial coverage across the roadway network due to their high installation and maintenance costs. The recently developed Truck Activity Monitoring System (TAMS) by the University of California Irvine Institute of Transportation Studies provides a cost-effective solution for monitoring truck movements statewide across California along major freeways networks through existing inductive loop infrastructure enhanced with inductive signature technology. Nonetheless, it possesses three major limitations: model bias against underrepresented truck classes, spatial coverage limitation on rural highways, and system obsolescence over time.

This dissertation explored a resilient spatiotemporal truck monitoring framework using inductive loop signature and multi-array Light Detection and Ranging (LiDAR) sensor technologies to address the aforementioned issues and to improve truck monitoring capabilities across the roadway network. The designed framework comprises three major parts: Inductive loop sensors for major highway truck monitoring, multi-array LiDAR sensors for rural highway truck monitoring, and a self-learning truck classification through a sensor integration framework.

The first part of the framework was built upon the existing Truck Activity Monitoring System (TAMS) developed by ITS Irvine and addresses prediction model biasness caused by inherently imbalanced truck datasets to provide reliable truck speed estimation and truck classification data.

The second part explored non-intrusive LiDAR-based sensing technologies to fill the surveillance gap along rural highway corridors. This section developed a truck classification method using a LiDAR sensor oriented to provide a wide field-of-view of roadways.

Finally, a self-learning framework for truck classification systems was designed to address system obsolescence through the integration of inductive loop sensors and LiDAR sensors, the latter of which has been proved to have the ability to recognized the truck axle configuration in this dissertation. This framework enhances the resilience of signature-based FHWA classification model with an intelligent system update to accommodate the change of the truck designations over time and significantly reduces the overall burden of periodic model calibration by utilizing the information stored in the legacy model.