Search Results: 'cross-site evaluation'

Search

×
×
×
×
7 results for "cross-site evaluation"
Young Adults in the Workplace: A Multisite Initiative of Substance Use Prevention Programs By Jeremy W. Bray, Deborah M. Galvin, Laurie A. Cluff
Paperback: $8.50
Ships in 3-5 business days
The Substance Abuse and Mental Health Services Administration funded the multisite Young Adults in the Workplace (YIW) initiative to study the effectiveness of diverse approaches to workplace-based... More > prevention of substance abuse. Six teams adapted evidence-based programs to target young employees and then implemented the programs in retail, restaurant, health care, construction, skilled trade, and transportation industry workplaces. This book describes the programs, the adaptation and implementation processes, and the YIW cross-site evaluation.< Less
OTREC-RR-12-04 By Christopher Monsere, Mecit Cetin
eBook (PDF): $0.00
Download immediately.
Vehicle re-identification matches vehicles crossing two different locations. Building on a previous study, we investigate different methods for re-identification and explore factors that impact... More > accuracy. Archived data from weigh-in-motion (WIM) stations in Oregon are used to develop, calibrate, and test vehicle re-identification algorithms. In addition to the Bayesian approach developed in the previous study, a neural network model is developed. The results show both methods to be effective while the Bayesian method results are more accurate. A comprehensive analysis employing the Bayesian algorithm matches vehicles that cross upstream and downstream pairs of WIM sites. Data from 14 different pairs of WIM sites are used to evaluate how factors such as distance, travel time variability, truck volumes, and sensors impact accuracy. The testing showed a large variation in accuracy, with sensor accuracy and volumes have the greatest impacts, while distance alone shows no significant impact.< Less
OTREC-RR-12-04 By Christopher Monsere, Mecit Cetin
Paperback: $6.70
Ships in 3-5 business days
Vehicle re-identification matches vehicles crossing two different locations. Building on a previous study, we investigate different methods for re-identification and explore factors that impact... More > accuracy. Archived data from weigh-in-motion (WIM) stations in Oregon are used to develop, calibrate, and test vehicle re-identification algorithms. In addition to the Bayesian approach developed in the previous study, a neural network model is developed. The results show both methods to be effective while the Bayesian method results are more accurate. A comprehensive analysis employing the Bayesian algorithm matches vehicles that cross upstream and downstream pairs of WIM sites. Data from 14 different pairs of WIM sites are used to evaluate how factors such as distance, travel time variability, truck volumes, and sensors impact accuracy. The testing showed a large variation in accuracy, with sensor accuracy and volumes have the greatest impacts, while distance alone shows no significant impact.< Less
OTREC-RR-11-07 By Christopher Monsere, Mecit Cetin, Andrew Nichols
eBook (PDF): $0.00
Download immediately.
This project evaluates the feasibility of re-identifying commercial trucks with data automatically collected at Oregon weigh-in-motion (WIM) stations. The methods consist of two main stages. The... More > first uses a Bayesian model to match each vehicle from the downstream station to the most “similar” upstream vehicle. In the second stage, methods are introduced to screen out vehicles crossing only downstream and to adjust accuracy vs. total number of vehicles matched by calculating the highest and second highest similarity measures for each vehicle. The proposed approach improves the accuracy of re-identification significantly. The models are applied to data from three WIM stations, creating two different “links” of 125 and 145 miles respectively. The algorithms match around 95% of trucks crossing both sites with about 90% accuracy. A threshold parameter allows adjustment of accuracy vs. total matched vehicles. When travel times vary widely between sites the mismatch rate increases.< Less
OTREC-RR-11-07 By Christopher Monsere, Mecit Cetin, Andrew Nichols
Paperback: $7.35
Ships in 3-5 business days
This project evaluates the feasibility of re-identifying commercial trucks with data automatically collected at Oregon weigh-in-motion (WIM) stations. The methods consist of two main stages. The... More > first uses a Bayesian model to match each vehicle from the downstream station to the most “similar” upstream vehicle. In the second stage, methods are introduced to screen out vehicles crossing only downstream and to adjust accuracy vs. total number of vehicles matched by calculating the highest and second highest similarity measures for each vehicle. The proposed approach improves the accuracy of re-identification significantly. The models are applied to data from three WIM stations, creating two different “links” of 125 and 145 miles respectively. The algorithms match around 95% of trucks crossing both sites with about 90% accuracy. A threshold parameter allows adjustment of accuracy vs. total matched vehicles. When travel times vary widely between sites the mismatch rate increases.< Less
OTREC-RR-11-07 By Christopher Monsere, Mecit Cetin, Andrew Nichols
eBook (PDF): $0.00
Download immediately.
This project evaluates the feasibility of re-identifying commercial trucks with data automatically collected at Oregon weigh-in-motion (WIM) stations. The methods consist of two main stages. The... More > first uses a Bayesian model to match each vehicle from the downstream station to the most “similar” upstream vehicle. In the second stage, methods are introduced to screen out vehicles crossing only downstream and to adjust accuracy vs. total number of vehicles matched by calculating the highest and second highest similarity measures for each vehicle. The proposed approach improves the accuracy of re-identification significantly. The models are applied to data from three WIM stations, creating two different “links” of 125 and 145 miles respectively. The algorithms match around 95% of trucks crossing both sites with about 90% accuracy. A threshold parameter allows adjustment of accuracy vs. total matched vehicles. When travel times vary widely between sites the mismatch rate increases.< Less
OTREC-RR-11-07 By Christopher Monsere, Mecit Cetin, Andrew Nichols
eBook (PDF): $0.00
Download immediately.
This project evaluates the feasibility of re-identifying commercial trucks with data automatically collected at Oregon weigh-in-motion (WIM) stations. The methods consist of two main stages. The... More > first uses a Bayesian model to match each vehicle from the downstream station to the most “similar” upstream vehicle. In the second stage, methods are introduced to screen out vehicles crossing only downstream and to adjust accuracy vs. total number of vehicles matched by calculating the highest and second highest similarity measures for each vehicle. The proposed approach improves the accuracy of re-identification significantly. The models are applied to data from three WIM stations, creating two different “links” of 125 and 145 miles respectively. The algorithms match around 95% of trucks crossing both sites with about 90% accuracy. A threshold parameter allows adjustment of accuracy vs. total matched vehicles. When travel times vary widely between sites the mismatch rate increases.< Less