Comparative Analysis of Performance Measures for Network Screening: A Case Study of Brazilian Urban Areas

Xavier, Vanessa Jamille Mesquita; Cunto, Flávio José Craveiro

doi:10.1155/2017/4360414

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Another study in Brazil [6] presented a comparative analysis of safety performance measures, considering its applicability limitations in a sample of signalized intersections from Fortaleza City, Brazil. The safety performance measures assessed in this study are average crash frequency (ACF), crash rate (CR), Equivalent Property Damage Only (EPDO), Level of Service of Safety (LOSS), Excess Predicted Average Crash Frequency using Safety Performance Functions (SPFs), Expected Average Crash Frequency with EB Adjustments (EB), and Excess Expected Average Crash Frequency with EB Adjustment (EEB).…”

Section: Literature Reviewmentioning

confidence: 99%

An Empirical Evaluation of a New Heuristic Method for Identifying Safety Improvement Sites on Rural Highways: An Oregon Case Study

Dhakal,

Al-Kaisy

2024

Sustainability

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Road safety is considered one of the fundamental factors for sustainable mobility, which requires establishing effective highway safety management programs and processes. Identifying safety improvement sites (network screening) is a critical step in the state roadway safety programs. The overall effectiveness of these programs largely relies on the robustness of the network screening method in identifying sites with high potential for safety improvements. This study investigates the network screening performance of a new proposed methodology that employs heuristic scoring schemes to account for roadway and roadside characteristics, crash history, and traffic exposure. The new method, originally proposed for use on rural low-volume roads, is simplistic in that it does not require exact and detailed information on traffic and geometric characteristics and can still be applied in the absence of crash data. The performance evaluation is conducted using a sample of 1495 miles of rural two-lane highway segments in Oregon. The effectiveness of the proposed method is assessed using observed crash history and compared to the well-established Empirical Bayes method as a reference. The effect of traffic level on the performance of the proposed method was evaluated using separate analyses for lower and higher traffic volume segments. The study findings indicate that the effectiveness of the proposed method in identifying sites with potential safety improvements is overall high and slightly exceeded that of the more sophisticated and data-intensive Empirical Bayes method. When data were stratified by traffic volume, the proposed method was found to be more effective for lower volume roads; however, the Empirical Bayes method outperformed the proposed method for higher volume roads.

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Section: Literature Reviewmentioning

confidence: 99%

An Empirical Evaluation of a New Heuristic Method for Identifying Safety Improvement Sites on Rural Highways: An Oregon Case Study

Dhakal,

Al-Kaisy

2024

Sustainability

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“…A study conducted in Brazil [13] compared different safety performance measures and their practical limitations using a sample of signalized intersections located in Fortaleza City, Brazil. The safety performance measures evaluated in this study average crash frequency (ACF), crash rate (CR), Equivalent Property Damage Only (EPDO), Level of Service of Safety (LOSS), Excess Predicted Average Crash Frequency using Safety Performance Functions (SPFs), Expected Average Crash Frequency with EB Adjustments (EB), and Excess Expected Average Crash Frequency with EB Adjustment (EEB).…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Identifying Safety Improvement Sites on Rural Highways: A Validation Study

Dhakal,

Al-Kaisy

2024

Applied Sciences

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The research presented in this paper examines a new proposed approach for identifying safety improvement sites on rural highways. Unlike conventional approaches, the proposed approach does not require crash history, but rather utilizes classified variables for traffic volume, geometric features, and roadside characteristics that do not require access to exact data or extensive technical expertise. The research validates the performance of the proposed approach using field data from a large sample of rural two-lane highway segments in the state of Oregon including traffic, roadway, and crash data. A mathematical model for the prediction of the EB expected number of crashes using multivariate regression analysis is developed and used as the network screening criterion. The model’s independent variables include roadway geometry, roadside characteristics, and traffic exposure, while the dependent variable is the EB expected number of crashes. Using observed crash history as a reference, the performance of the proposed approach was compared to two of the well-established methods in practice, namely, the Empirical Bayes (EB) and the potential for safety improvement (PSI) methods. The study results suggest that by using crash density for highway segments, the performance of the proposed method was lower than that of the EB and PSI methods. This is despite the high R-square value of the predictive model used in the proposed method. However, when using crash frequencies for highway segments, the performance of the proposed method was found comparable to the well-established EB and PSI methods.

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A New Approach for Identifying Safety Improvement Sites on Rural Highways: Validation Study

Dhakal,

Al-Kaisy

2023

Preprint

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The research presented in this paper examines a new proposed approach for identifying safety improvement sites on rural highways. Unlike conventional approaches, the proposed approach does not require crash history, but rather utilizes classified variables for traffic volume, geometric features, and roadside characteristics that do not require access to exact data or extensive technical expertise. The research validates the performance of the proposed approach using field data from a large sample of rural two-lane highway segments in the state of Oregon including traffic, roadway, and crash data. Using observed crash history as a reference, the performance of the proposed ap-proach was compared to two of the well-established methods in practice, namely: the Empirical Bayes (EB) and the Potential for Safety Improvement (PSI) methods. The study results suggest that, using crash density for highway segments, the performance of the proposed method was lower than that of the EB and PSI methods. This is despite the high R-square value of the predictive model used in the proposed method. However, when using crash frequencies for highway segments, the per-formance of the proposed method was found comparable to the well-established EB and PSI methods.

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Comparative Analysis of Performance Measures for Network Screening: A Case Study of Brazilian Urban Areas

Cited by 3 publications

References 6 publications

An Empirical Evaluation of a New Heuristic Method for Identifying Safety Improvement Sites on Rural Highways: An Oregon Case Study

An Empirical Evaluation of a New Heuristic Method for Identifying Safety Improvement Sites on Rural Highways: An Oregon Case Study

A New Approach for Identifying Safety Improvement Sites on Rural Highways: A Validation Study

A New Approach for Identifying Safety Improvement Sites on Rural Highways: Validation Study

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