Details:

Creators:
Monsere, Christopher M. ; Mecit Cetin ; Andrew P. Nichols
Monsere, Christopher M. ; Mecit Cetin ; Andrew P. Nichols Less -
Corporate Contributors:
Oregon Transportation Research and Education Consortium
Subject/TRT Terms:
[+]
Accuracy Automatic Vehicle Identification Data And Information Technology Freight Transportation Highways I72: Traffic And Transport Planning Research Hub Travel Time Weigh In Motion
Publication/ Report Number:
OTREC-RR-11-07
Resource Type:
Tech Report
Geographical Coverage:
Oregon ; United States
Oregon ; United States Less -
TRIS Online Accession Number:
01335393
Edition:
Final Report
Corporate Publisher:
Portland State University
Abstract:
The main objective of this project is to evaluate the feasibility of re-identifying commercial trucks based on vehicle-attribute data automatically collected by sensors installed at traffic data collection stations. To support this work, archived data from weigh-in-motion (WIM) stations in Oregon are used for developing, calibrating, and testing vehicle re-identification algorithms. The vehicle re-identification methods developed in this research consist of two main stages. In the first stage, each vehicle from the downstream station is matched to the most “similar” upstream vehicle by using a Bayesian model. In the second stage, several methods are introduced to screen out those vehicles that cross the downstream site but not the upstream site and to tradeoff accuracy versus the total number of vehicles being matched. These methods involve calculating both the highest and the second highest similarity measures for each vehicle being matched. It is demonstrated that the proposed screening approach improves the accuracy of the re-identification methods significantly. The models are applied to the truck data collected by WIM sensors at three stations in Oregon, which together create two different “links” that are 125 and 145 miles long, respectively. It is observed that the algorithms can match trucks with approximately 90% accuracy while the total number of trucks being matched at this accuracy level is about 95% of the actual common trucks that cross both upstream and downstream sites. These methods allow the user to trade-off the accuracy vs. total vehicles being matched by adjusting a threshold parameter. For example, trucks can be matched with 98% accuracy if one is willing to match about 40% of all common trucks. It is also found that when travel times of vehicles between the upstream and downstream sites exhibit larger variation, mismatch rate increases. Overall, for estimating travel times and origin-destination flows between two WIM sites, the methods developed in this project can be used to effectively match commercial vehicles crossing two data collection sites that are separated by long distances.
Format:
PDF
Collection(s):
US Transportation Collection University Transportation Centers
Main Document Checksum:
[+]
urn:sha256:ba3e010c284a9d990a0154059cc1f24f8ded08b3f8fd1539a3358b7adf11e50f
Download URL:
https://rosap.ntl.bts.gov/view/dot/29456/dot_29456_DS1.pdf
File Type:

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