Project 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 an abundant high resolution truck data.
Objectives: The proposed study comprises three main phases. The objective of the first phase is to show that truck body classifications can be accurately obtained from the models developed using inductive signature technology, and that equipment at WIM stations can be readily configured to work with Inductive signature hardware. In the second phase, the objective is to develop a system architecture and to train the developed models at selected VDS and WIM locations. An investigation will be made on how the higher resolution classification data at WIM stations can be extrapolated to VDS locations. In the final phase, the objective is to perform an expanded deployment that will allow data to be usable at a larger geographical scale, such as at the regional or state level.
Study Sites: The data to be used for model development will be obtained from the detector testbed at the southbound San Onofre Truck Weigh and Enforcement Facility equipped with an advanced loop detection system as well as at selected WIM and VDS stations in the state of California.
Analysis: The investigators will use a combination of advanced mathematical models and data analysis tools such as artificial neural networks, data clustering, 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 a current Caltrans research effort in developing a statewide freight forecasting model. Without this data, freight modeling efforts tend to 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.
