Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity

Macro-level evaluation of road safety improvement interventions : an evaluation of the Arrive Alive 1 (1997/98) road safety campaign

Please read the abstract in the section 00front of this documentDissertation (M Eng (Transportation Engineering))--University of Pretoria, 2007.Civil Engineeringunrestricte

Buried Concrete Barrier Ends in Washington State

There are over 400 buried concrete barrier ends on Washington state routes. While buried concretebarrier ends are no longer included in standard plans, it is not well understood how vehicles are interacting with these structures. An inventory of buried concrete barrier ends was matched with crash data sourced from the WSDOT Engineering Crash Datamart and the Crash Location & Analysis System (CLAS) database for the years 2011 through 2020 to determine the distribution of crashes in location and severity. During this ten-year period, 36 crashes with these ends were reported and they included only one fatal crash and no serious injury crashes. Most crashes occurred in an urban environment with almost an even split between mainline and exit ramps

Pilot In-service Performance Evaluation of Guardrail Terminals in Washington State

A pilot routine in-service performance evaluation (ISPE) was undertaken for guardrail terminals following the process outlined in NCHRP 22-33. Controlled stop, rollover, vehicle mix, and secondary impacts on the roadside and roadway were evaluated as performance measures using data sourced from the Crash Location & Analysis System (CLAS) database and the WSDOT Engineering Crash Data Mart for years 2016 through 2020. Four Performance Assessment Levels, ranging from no exclusions of crash data to exclusions of crash data limited to vehicle type and speed limit were assessed. For all five performance measures, the study found no measurable differences between the performance of the major types of guardrail terminal in use on state highways within WSDOT jurisdiction

Pilot In-service Performance Evaluation of Impact Attenuators in Washington State

A pilot routine in-service performance evaluation (ISPE) was undertaken for impact attenuatorsfollowing the process outlined in NCHP 22-33. Controlled stop, rollover, vehicle mix, and secondary impacts on the roadside and roadway were evaluated as performance measures using data sourced from the Crash Location & Analysis System (CLAS) database and the WSDOT Engineering Crash Data Mart for years 2016 through 2020. Four Performance Assessment Levels, ranging from no exclusions of crash data to exclusions of crash data limited to vehicle type and speed limit were assessed. For all five performance measures, the study found no measurable differences between the performance of the major types of impact attenuators in use on state highways within WSDOT jurisdiction

Pilot In-service Performance Evaluation of Cable Barrier in Washington State

A pilot routine in-service performance evaluation (ISPE) was undertaken for cable barrier following the process outlined in NCHRP 22-33. Barrier breach, rollover, vehicle mix, and secondary impacts on the roadside and roadway were evaluated as performance measures using data sourced from the Crash Location & Analysis System (CLAS) database and the WSDOT Engineering Crash Data Mart for years 2016 through 2020. Four Performance Assessment Levels, ranging from no exclusions of crash data to exclusions of crash data limited to vehicle type and speed limit were assessed. For all five performance measures, the study found no measurable difference between the performance of the four major types of cable barrier in use on state highways within WSDOT jurisdiction, including three-strand versus four-strand

Important omitted spatial variables in safety models: Understanding contributing crash causes at intersections

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causation—rests upon the pursuit of numerous lines of inquiry. The research community has focused on analytical methods development (negative binomial specifications, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might think of different lines of inquiry in terms of ‘low lying fruit’—areas of inquiry that might provide significant improvements in understanding crash causation. It is the contention of this research that omitted variable bias caused by the exclusion of important variables is an important line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant ability to better understand contributing factors to crashes. This study—believed to represent a unique contribution to the safety literature—develops and examines the role of a sizeable set of spatial variables in intersection crash occurrence. In addition to commonly considered traffic and geometric variables, examined spatial factors include local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools. The results indicate that inclusion of these factors results in significant improvement in model explanatory power, and the results also generally agree with expectation. The research illuminates the importance of spatial variables in safety research and also the negative consequences of their omissions

Evaluation of the Scottsdale Loop 101 automated speed enforcement demonstration program

Speeding is recognized as a major contributing factor in traffic crashes. In order to reduce speed-related crashes, the city of Scottsdale, Arizona implemented the first fixed-camera photo speed enforcement program (SEP) on a limited access freeway in the US. The 9-month demonstration program spanning from January 2006 to October 2006 was implemented on a 6.5 mile urban freeway segment of Arizona State Route 101 running through Scottsdale. This paper presents the results of a comprehensive analysis of the impact of the SEP on speeding behavior, crashes, and the economic impact of crashes. The impact on speeding behavior was estimated using generalized least square estimation, in which the observed speeds and the speeding frequencies during the program period were compared to those during other periods. The impact of the SEP on crashes was estimated using 3 evaluation methods: a before-and-after (BA) analysis using a comparison group, a BA analysis with traffic flow correction, and an empirical Bayes BA analysis with time-variant safety. The analysis results reveal that speeding detection frequencies (speeds> or =76 mph) increased by a factor of 10.5 after the SEP was (temporarily) terminated. Average speeds in the enforcement zone were reduced by about 9 mph when the SEP was implemented, after accounting for the influence of traffic flow. All crash types were reduced except rear-end crashes, although the estimated magnitude of impact varies across estimation methods (and their corresponding assumptions). When considering Arizona-specific crash related injury costs, the SEP is estimated to yield about $17 million in annual safety benefits

Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity