Abstract

This paper presents a new method for estimating traffic density on freeways, and an adaptation for real-time applications. This method uses re-identified vehicles and their travel times estimated from a real-time vehicle re-identification (REID) system which attempts to anonymously match vehicles based on their inductive signatures. The accuracy of the section- 6 based density estimation algorithm is validated against ground-truth data obtained from recorded video for a six-lane, 0.66-mile freeway segment of I-405N in Irvine, California, during the morning peak period. The proposed density estimation algorithm results are compared against a g-factor based method which relies on inductive loop detector occupancy data and estimated vehicle lengths from the Caltrans Performance Measurement System (PeMS) as well as a selected REID method which uses a sparse REID algorithm based on long vehicle detection and volume counts at detector stations. Although the g-factor approach produces real-time density estimates, it requires seasonally calibrated parameters. In addition to the calibration effort to maintain overall accuracy of the system, the g-factor approach will also produce errors in density estimation if the actual composition of vehicles yields a different observed g-factor from the calibrated value. In contrast, the proposed method uses an existing vehicle re-identification model based on the matching of inductive vehicle signatures between two locations spanning a freeway section. This approach does not require assumptions on the vehicle composition, hence does not require calibration. The proposed algorithm obtained section-based density measures with a mean absolute percentage error (MAPE) of less than four percent when compared against groundtruth data and provides accurate density estimates even during congested conditions, improving both the PeMS and selected alternative REID based methods.

Abstract

PATH Task ORder 6328 addresses the optimal deployment of traffic detectors on freeway to ensure that adequate information is collected at the lowest possible cost. The project team produced a study framework and tools that can be applied locally to test the sensitivity of traffic data quality to detectors location and spacing, and ultimately recommend a deployment plan.

Various types of traffic detectors, including loop detectors, radars, toll tag readers and video cameras are deployed on highways. They provide the data needed to run traffic management applications such as ramp metering control, bottleneck identification, and travel times estimation. However, few studies have systematically analyzed the data requirements of these applications in terms of detector spacing and location. In other words, the trade-offs between the cost of the detectors and their benefits for traffic estimation accuracy are not well known. As a result, most highway detectors are installed using ad hoc guidelines or on a case-by-base basis, rather than through the application of measurable objectives. This in turn makes it difficult for practitioners to justify equipment and maintenance expenditures, often slowing deployment.

The product of this research is two-fold. First, we developed a framework to study the sensitivity of traffic information to sensor location and spacing and reached general conclusions. Second, the team created practical tools to assist practitioners at the local level with optimal sensor deployment. These tools include recommendations for rural areas and an Excel-based model for urban areas.