Proposal Summary

A significant proportion of goods movement is transported by trucks, and the value and tonnage of goods are expected to grow over time. Trucks have a significant impact on pavement infrastructure, traffic congestion, pollution and “quality of life”. To provide a better understanding of the behavior of freight-related truck movements, it is necessary to obtain comprehensive high resolution truck data at key truck corridors within the State of California.

Objectives: The proposed study comprises three main phases. The objective of the first phase is enhance the truck body classification models developed by University of California, Irvine (ITS Irvine) in a separate study funded by the California Air Resources Board (ARB), and to develop algorithms to investigate the potential of anonymous truck tracking using the selected technologies. In the second phase, the objective is to setup the technology at selected Weigh-In-Motion (WIM) and Vehicle Detector Station (VDS) sites for advanced truck data collection. In the final phase, the objective is to develop advanced analytics tools to provide detailed reports on the collected truck data, and perform a shakedown of the system and maintenance of installed locations.

Study Sites: The data to be used for the enhancement of the classification model development will be obtained from multiple sites in California to generate a dataset that contains a significant sample size of key truck configurations. The collect data will be merged with the truck dataset previously collected by ITS Irvine at four sites in California. The investigation into anonymous vehicle tracking using WIM and inductive will involve a separate short term data collection effort that will involve concurrent data collection between adjacent WIM sites.

Analysis: The investigators will use a combination of advanced mathematical models and data analysis tools such as artificial neural networks, data clustering, Bayesian and heuristic algorithms to develop the models in this study.

Anticipated Results: The implementation of the above objectives is expected to yield data that will help to address a significant data gap in truck movement statistics in relation to body configuration, which has implications for truck function and associated industries. This is especially beneficial in providing the freight movement data for improved calibration and validation of the California Statewide Freight Forecasting Model. Without this data, freight modeling efforts can only be calibrated against aggregate truck volumes instead of detailed truck volumes by truck type. Another advantage is that this data could enhance the Vehicle Inventory and Use Survey (VIUS) data for California by providing spatial and temporal characteristics to the vehicle type classifications.