Exploratory Methods for Truck Re-identification in a Statewide Network Based on Axle Weight and Axle Spacing Data to Enhance Freight Metrics: Phase 2

Importance of freight transportation is recognized universally by all states and it is particularly an important component of Oregon's economy. While other modes are clearly important for freight transportation, trucking is the dominant mode in terms of tons and value. Monitoring freight movement and freight transportation performance is essential in making effective policies and informed decisions to enhance and efficiently manage the freight transportation system. One of the key aspects of monitoring freight over the highways has to do with determining the flow patterns of trucks, which can be achieved by uniquely identifying trucks at specific points along the roads…

Importance of freight transportation is recognized universally by all states and it is particularly an important component of Oregon’s economy. While other modes are clearly important for freight transportation, trucking is the dominant mode in terms of tons and value. Monitoring freight movement and freight transportation performance is essential in making effective policies and informed decisions to enhance and efficiently manage the freight transportation system.
One of the key aspects of monitoring freight over the highways has to do with determining the flow patterns of trucks, which can be achieved by uniquely identifying trucks at specific points along the roads or by tracking individual trucks using technology such as GPS. For example, in Oregon, there are 20 active reporting and equipped stations where trucks carrying Green Light transponders can be uniquely identified. These stations also include WIM (weigh-in-motion) systems which provide axle weights, spacing, and gross vehicle weight estimates uniquely matched to a transponder-equipped truck. This proposed research seeks to develop new methods to determine flow patterns of trucks (those without transponders) by matching archived vehicle-attribute data such as axle spacing and axle weights at multiple geographic locations. These methods for truck re-identification utilize only vehicle-attribute data such as axle spacing and axle weights that are typically collected by AVC and WIM sensors installed on many roadways.
Overall, this proposed research builds on an existing OTREC project. Based on preliminary analyses in the current project, trucks crossing two WIM sites separated by more than 100 miles were successfully matched based on axle spacing and axle weight data. Re-identifying vehicles crossing two sites separated by such large distances has not been done before. Within the scope of the existing OTREC project (Phase I), new re-identification models are being developed to effectively match trucks based on axle data from two sites (one upstream and one downstream). In Phase II, new methods will be developed and additional analyses will be performed to explore three main issues:
• Re-identification in Networks: In Phase I, the re-identification methods are being developed to match vehicles crossing a pair of WIM stations individually. In Phase II, additional capabilities will be developed to re-identify vehicles in networks with more than two stations (e.g., one upstream stations feeding to two downstream stations).
• OD Estimation: Origin-Destination (OD) flows for selected corridors in Oregon will be estimated based on the developed methods. These methods will generate OD flows for all trucks (those with and without transponders) which will be beneficial in planning statewide freight movements.
• Data Accuracy: In Oregon, there are 22 WIM stations across the state. The level of accuracy of the sensors at these stations varies significantly from station to station which impacts the ability to re-identify trucks. The relationship between the accuracy of the measured data and the effectiveness of the re-identification methods will be investigated to understand when and how the re-identification methods can be utilized effectively.

The PIs have already compiled extensive data from 20 WIM stations in Oregon that are currently recording data. These data will be used for model development, validation, and testing. The co-PIs have completed preliminary work on truck re-identification based on axle spacing and weight data, and showed that it is feasible to successfully match trucks crossing two WIM stations. To develop the necessary methods for this study, state-of-the-art mathematical optimization and statistical modeling techniques, such as Bayesian methods, finite mixture models, and nonlinear optimization will be investigated and employed. Extensive analyses will be performed to clearly understand how such systems would perform in real-world applications. The results of this study will benefit not only Oregon but potentially all other states since truck characteristics do not vary significantly from state to state, and many states also collect axle spacing and axle weight data.