Prediction of Occurrence and Severity of Run-off-Roadway Crashes on Rural Two-Lane Roadways Using Bayesian Networks

Run-off-roadway (ROR) crashes are among the most common crash types on rural two-lane roadways. Current methodologies to predict their occurrence and severity by considering conditional nature and interactions between independent variables require complex mathematical procedures. This study employs Bayesian networks (BNs), a non-functional form graphical model, to determine factors associated with the occurrence and severity of ROR crashes. The study used five-year (2014–2018) crash data collected from 397 randomly selected road segments within Texas. Out of 397 segments, 279 did not experience ROR crashes. The first BN model used all 397 segments and explored factors associated with occurrences of ROR crashes. The second BN model used the remaining 118 segments that involved ROR crashes and focused on factors associated with different crash types (guardrail [GR], overturning [OT], and fixed object [FO] crashes) and their associated severity levels. Study results revealed that the presence of horizontal curves and utility poles within the clear zone on the road individually increased the chance of ROR crashes by about 35%. Moreover, FO crashes resulted in 36% more fatal and injury crashes than GR crashes, which showed the effectiveness of guardrails in reducing severity. This study also explored the combined influence of variables on ROR crash occurrence and severity, as well as the interrelation between several independent variables. The proposed methodology can be used to evaluate the effectiveness of countermeasures.

Motor vehicle crashes on tribal reservations: mapping and statistics

Background Motor vehicle crashes (MVC’s) in the American Indian/Alaska Native (AI/AN) communities account for 43% of unintentional injury deaths. This article introduces MVC data and geographic information system (GIS) mapping for tribal reservations. Methods Utilizing a sample of Montana Department of Transportation (DOT) data for the Flathead reservations to calculate frequencies and proportions of crash types (i.e., property damage or no-injury, injury, fatality or unknown), while also mapping these data to provide a cross-sectional snapshot of MVC’s. Results Overall, 515 MVC’s occurred for years 2016 through 2018, with no-injury, injury, and fatality accounting for 72.2%, 24.9% and 1.8% of all crashes, respectively, with the number of MVC’s ranging up to 30 per square mile. Conclusion Examining DOT data and utilizing it for visual representation of MVC’s can be used as an additional source in uncovering patterns and trends on Tribal reservations and supporting MVC prevention efforts.

Identifying Future Vehicle Safety Priority Areas in Australia for the Light Vehicle Fleet

Formulating priorities for future road safety strategies requires supporting analysis to predict what the future crash population will look like and to assess how the countermeasures either already in place or planned will address the crash problems forecast. This analysis aimed to identify future priority action areas for light vehicle safety by identifying crash types that will not be fully addressed in the future by projected improvements in active and passive safety in the Australian light vehicle fleet. The future crash profile was modelled from 2017 to 2030 using crash data from 5 Australian jurisdictions overlayed with available evidence on vehicle safety feature fitment and effectiveness. The methodology can be applied to larger sets of safety technologies when sufficient evidence and supporting crash data become available. Three future vehicle safety priority areas were identified from the analysis: (i) fatal pedestrian crashes, (ii) single vehicle frontal crashes with objects, and (iii) front-to-front vehicle crashes both at intersections and midblocks, and front-to-side impacts at intersections including straight crossing path and right turn across path crash types. These crash types were projected to be the largest contributors to fatalities by 2030. Projections showed that remaining crash types in 2030 will be poorly addressed by current vehicle safety technologies such as autonomous emergency braking, lane departure warning and electronic stability control. Future vehicle safety policy priorities should address these crash types through the development of additional or enhanced vehicle safety technologies and where vehicle safety technology proves inadequate other countermeasures such as road infrastructure treatments and appropriate speed limit setting for high risk environments that address the key crash types remaining in the system.

Case studies on estimating crash modification factors for treatment combinations with empirical investigation of dual rumble strip application in Ontario

Crash modification factors (CMFs) are used to quantify the impact of safety treatments. These treatments are often used in combination and so the need for estimating CMFs for simultaneous applications arises. Applications of new heuristic methods in combining treatments showed mixed results, indicating a need for sound judgement in their usage. A case study for centreline and edgeline rumble strips on Ontario highways resulted in combined CMFs of 0.805, 0.79, 0.743, 0.799, and 0.689 for total, injury, PDO, single vehicle, and approach & sideswipe crash types, respectively. The estimates were comparable to the CMFs estimated in other research for actual dual rumble strip application. CMFs developed separately for tangent and curved segments showed that both rumble strip types are more effective on curved segments.

Observational Before and After Safety Study of Installing Signals at Rural Intersections: Using the Empirical Bayes (EB) and Conventional Methods

Signalizing an intersection usually results in a reduction in right-angle and left-turn crashes, and an increase in rear-end crashes. This study used the conventional and Empirical Bayes (EB) before and after methods on 45 treated sites (converted from stop to signal control) in California and Minnesota to estimate the safety effect of having signals installed. The results confirm the belief that right-angle and left-turn crashes are reduced and rear-end crashes increase. However, these effects cannot be used to quantitatively assess the benefit gained from the reduction in right-angle and left-turn crashes against the increase in rear-end crashes, simply because crash types have different severities. By performing an economic examination of the safety effects, this study was able to show that by installing signals on 45 treated sites, there was a positive aggregate economic benefit of $155,883,978 which represents a 69 percent reduction in cost. This translated into $616,142 per site-year.

Observational Before and After Safety Study of Installing Signals at Rural Intersections: Using the Empirical Bayes (EB) and Conventional Methods

Signalizing an intersection usually results in a reduction in right-angle and left-turn crashes, and an increase in rear-end crashes. This study used the conventional and Empirical Bayes (EB) before and after methods on 45 treated sites (converted from stop to signal control) in California and Minnesota to estimate the safety effect of having signals installed. The results confirm the belief that right-angle and left-turn crashes are reduced and rear-end crashes increase. However, these effects cannot be used to quantitatively assess the benefit gained from the reduction in right-angle and left-turn crashes against the increase in rear-end crashes, simply because crash types have different severities. By performing an economic examination of the safety effects, this study was able to show that by installing signals on 45 treated sites, there was a positive aggregate economic benefit of $155,883,978 which represents a 69 percent reduction in cost. This translated into $616,142 per site-year.

Calibration of highway safety manual predictive models and validation of safety performance funtions for canadian urban/suburban intersections

In this research, the HSM predictive models for collisions on urban/suburban arterials are calibrated for collision data from the City of Toronto. It has been found that the use of calibration factors for applying HSM models to Toronto intersection data is not appropriate. New collision models are therefore developed by using local data. The HSM and Toronto models are then calibrated to City of Edmonton intersection collision data to determine whether it is better to calibrate HSM models for a Canadian jurisdiction or models from another Canadian jurisdiction. A related aspect of the research is the investigation of models for crash types. There is no safety performance function (SPF) available in the HSM to predict rear end collisions. Instead, rear end collisions are estimated as a proportion of predicted multivehicle collisions. To overcome this deficiency, Toronto data are used in the estimation of models for rear end collisions.

Calibration of highway safety manual predictive models and validation of safety performance funtions for canadian urban/suburban intersections

In this research, the HSM predictive models for collisions on urban/suburban arterials are calibrated for collision data from the City of Toronto. It has been found that the use of calibration factors for applying HSM models to Toronto intersection data is not appropriate. New collision models are therefore developed by using local data. The HSM and Toronto models are then calibrated to City of Edmonton intersection collision data to determine whether it is better to calibrate HSM models for a Canadian jurisdiction or models from another Canadian jurisdiction. A related aspect of the research is the investigation of models for crash types. There is no safety performance function (SPF) available in the HSM to predict rear end collisions. Instead, rear end collisions are estimated as a proportion of predicted multivehicle collisions. To overcome this deficiency, Toronto data are used in the estimation of models for rear end collisions.

Case studies on estimating crash modification factors for treatment combinations with empirical investigation of dual rumble strip application in Ontario

Crash modification factors (CMFs) are used to quantify the impact of safety treatments. These treatments are often used in combination and so the need for estimating CMFs for simultaneous applications arises. Applications of new heuristic methods in combining treatments showed mixed results, indicating a need for sound judgement in their usage. A case study for centreline and edgeline rumble strips on Ontario highways resulted in combined CMFs of 0.805, 0.79, 0.743, 0.799, and 0.689 for total, injury, PDO, single vehicle, and approach & sideswipe crash types, respectively. The estimates were comparable to the CMFs estimated in other research for actual dual rumble strip application. CMFs developed separately for tangent and curved segments showed that both rumble strip types are more effective on curved segments.