Abstract
A new hybrid sensor technology integrating existing Weigh-In-Motion (WIM) axle configuration data with inductive signature data obtained from advanced Inductive Loop Detector (ILD) is gaining interest due to its potential to provide detailed classification of truck body types as well as anonymous tracking of truck movements on freeways. This paper describes the methodologies and analysis of two alternative strategies for optimal deployment locations for this new technology at existing WIM locations by utilizing sampled truck GPS trajectories on California freeways: (1) Flow-interception approach to maximize the total amount of net origin-destination (OD) flows captured; (2) Re-identification approach to maximize insights into origins and destinations of sampled truck trips, as well as routes of those trips. The truck GPS samples used in this study is obtained from the American Transportation Research Institute (ATRI), which provides position and time stamp information of truck movements. The model designed for flow-interception is capable of selecting locations emphasizing different body types by employing the flow-based weight factor. The RSP model investigates the best locations for heavy truck movement identification on freeways by selecting pairwise locations, and is shown to be sensitive to the re-identification decay factor assumed.