Safety Evaluation of Alternative Signal Head Design

Sayed, Tarek; Abdelwahab, Walid; Nepomuceno, John

doi:10.3141/1635-19

Cited by 10 publications

(12 citation statements)

References 3 publications

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“…Sayed et al found that total rate of collisions was reduced by about 24% and that the rates of both fatal and injury collisions were reduced by about 16% when an improved signal head design was used at 10 urban signalized intersections in British Columbia, Canada (15). The improved signal head design used in the study comprised a 300-mm lens for green, amber, and red lights, in addition to retroreflective tape 50 mm wide on the outer edge of the backplate.…”

Section: Improved Signal Visibility At Urban Signalized Intersectionsmentioning

confidence: 98%

Traffic Conflict–Based Before–After Study with Use of Comparison Groups and the Empirical Bayes Method

Reyad

Sacchi

Ibrahim³

et al. 2017

Transportation Research Record

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Road safety evaluations mainly rely on the analysis of crash data that are challenged by well-recognized availability and quality issues. The statistical models used to predict the safety level of road sites—that is, safety performance functions—have recently been successfully developed with the use of traffic conflict observations instead of crashes. As such, it is possible to adopt and transfer the statistical techniques used in crash-based road safety analysis to conflict-based analysis. The use of statistically rigorous techniques in crash-based before-and-after (BA) studies is essential for evaluation of the effectiveness of road safety countermeasures. In particular, the use of Bayesian methods, such as the empirical Bayes (EB) technique, is vital to control for confounding factors that can operate simultaneously with the countermeasure and may affect road safety performance. The main objective of this paper was to estimate the treatment effectiveness of two traffic signal (visibility) improvement projects in the city of Edmonton, Alberta, Canada, with a conflict-based BA study using the comparison group and the EB methods. More than 300 h of video data with traffic conflict observations was automatically collected and analyzed by computer vision techniques for two treatment intersections and two control (untreated) intersections before and after the signal improvement projects. The results of the comparison group method showed a statistically significant 24% reduction in the average number of rear-end conflicts per hour, whereas the EB method showed a statistically significant 24.5% reduction in the average number of total conflicts per hour.

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Section: Improved Signal Visibility At Urban Signalized Intersectionsmentioning

confidence: 98%

Traffic Conflict–Based Before–After Study with Use of Comparison Groups and the Empirical Bayes Method

Reyad

Sacchi

Ibrahim³

et al. 2017

Transportation Research Record

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“…Moreover, sites with potential for improvement are usually chosen based on their recent poor safety record (as was the case in this study), resulting in RTM bias; this is a situation where the count of crashes in the "after" period will generally revert toward the expected mean value even if the site was not treated (Washington et al 2003, Abbess et al 1981, Hauer and Persaud 1983. This can overstate the effect of a treatment by 5% to 10%, depending on the length of the "before" period (Sayed et al 1998, Econometric Analysis 2004.…”

Section: Empirical Bayes Proceduresmentioning

confidence: 99%

Safety Effectiveness of Offsetting Opposing Left-Turn Lanes: A Case Study

Naik¹,

Appiah²,

Khattak³

et al. 2012

J Transp Res Forum

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This paper discusses the benefits to intersection safety of offsetting left-turn lanes by widening the width of the lane-line marking between the left-turn lanes and their adjacent through lanes. The analysis was performed using an empirical Baye's procedure in order to account for potential bias due to regression-to-the-mean. Results from the analysis of 12 treated intersection approaches and 36 non-treated approaches in Lincoln, Nebraska, suggest statistically significant improvements in safety at the treated intersections. Transportation Data Analysis. Chapman and Hall/CRC, Boca Raton, Florida, 2003. Bhaven Naik is a Ph.D. candidate in the Transportation Systems Analysis program at the University of Nebraska-Lincoln. His research interests are in transportation infrastructure, planning and safety, traffic simulation modeling, and intelligent transportation systems. Bhaven holds B.S. and M.S. degrees in Civil Engineering from the University of Nebraska-Lincoln. Justice Appiah is a Ph.D. candidate in the Transportation Systems Analysis program at the University of Nebraska-Lincoln. His research interests are in transportation network modeling and simulation and discrete choice analysis. Justice holds B.S. and M.S. degrees in Civil Engineering from the University of Science and Technology, Ghana, and Texas A&M University, College Station, respectively.Aemal J. Khattak is an associate professor of Civil Engineering at the University of Nebraska-Lincoln. Khattak specializes in Transportation infrastructure, planning and safety, geographic information systems, and intelligent transportation systems. He has authored or co-authored more than 30 refereed journal papers and conference papers. Laurence R. Rilett is a professor of Civil Engineering and the Director of the Mid-AmericaTransportation Center at the University of Nebraska-Lincoln. Rilett specializes in multimodal transportation systems analysis, transportation planning and operations, dynamic network modeling and optimization, and Intelligent Transportation Systems applications. He has been a principal investigator or co-principal investigator on more than 20 research projects and has authored or co-authored more than 90 refereed journal papers and conference papers. He serves as associate editor for the ASCE Journal of Transportation Engineering.

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“…Most of these studies used laboratory or controlled field testing to determine the effects of various factors, such as signal lens size, back plates, weather conditions, and driver characteristics, on the visibility of traffic signals (8)(9)(10)(11)(12). Although the literature on signal visibility is extensive, only a few studies have been conducted to determine the potential safety impacts of improvements to signal visibility (13)(14)(15)(16)(17)(18). Furthermore, many of the studies suffered a number of shortcomings caused by (a) use of simple before-and-after evaluations (13,14), (b) evaluation of a small number of locations (16), or (c) failure to detect the safety impacts on specific collision types.…”

Section: Previous Workmentioning

confidence: 99%

“…Although the literature on signal visibility is extensive, only a few studies have been conducted to determine the potential safety impacts of improvements to signal visibility (13)(14)(15)(16)(17)(18). Furthermore, many of the studies suffered a number of shortcomings caused by (a) use of simple before-and-after evaluations (13,14), (b) evaluation of a small number of locations (16), or (c) failure to detect the safety impacts on specific collision types.…”

Section: Previous Workmentioning

confidence: 99%

Investigating Effect of Collision Aggregation on Safety Evaluations with Models of Multivariate Linear Intervention

El-Basyouny

Sayed

Esawey

et al. 2012

Transportation Research Record

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This study investigated the effect of collision aggregation on safety evaluation through a case study from the 2001 Signal Head Upgrade Program of the Insurance Corporation of British Columbia, Canada. Three types of evaluations were performed. Bivariate intervention models were used in the first two evaluations to assess the impacts of different collision severity levels [severe and property damage only (PDO)] and the impact of the time of the collision (daytime and nighttime) on safety. In the third evaluation, multivariate intervention models were used to determine the safety impacts of the program on each combination of collision severity and time of occurrence (i.e., severe–daytime, severe–nighttime, PDO–daytime, PDO–nighttime). Overall, the results indicated that the program was effective in improving the safety of the treated intersections. However, the results revealed that aggregate analyses could lead to misleading results. Aggregation of collisions over time of day indicated that the treatment resulted in significant reductions in PDO collisions but not in severe collisions. Alternatively, aggregation of collisions over severity levels indicated that the treatment resulted in significant reductions in both daytime and nighttime collisions. These results were different from the results of the disaggregate analysis, in which significant reductions were found for all collision types, except for severe collisions during the daytime.

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Safety Evaluation of Alternative Signal Head Design

Cited by 10 publications

References 3 publications

Traffic Conflict–Based Before–After Study with Use of Comparison Groups and the Empirical Bayes Method

Traffic Conflict–Based Before–After Study with Use of Comparison Groups and the Empirical Bayes Method

Safety Effectiveness of Offsetting Opposing Left-Turn Lanes: A Case Study

Investigating Effect of Collision Aggregation on Safety Evaluations with Models of Multivariate Linear Intervention

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