Do we need multivariate modeling approaches to model crash frequency by crash types? A panel mixed approach to modeling crash frequency by crash types

Bhowmik, Tanmoy; Yasmin, Shamsunnahar; Eluru, Naveen

doi:10.1016/j.amar.2019.100107

Cited by 35 publications

(18 citation statements)

References 26 publications

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“…Collision with a fixed object will increase the probability of serious crashes by 72.2% compared to collision with a non-fixed object [16]. A similar finding can be found in other studies [13,23]. In particular, collisions resulting in motor vehicle overturning were more likely to have serious crash consequences [24].…”

Section: Crash-specific Characteristicssupporting

confidence: 81%

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

et al. 2021

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The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

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Section: Crash-specific Characteristicssupporting

confidence: 81%

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

et al. 2021

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“…), particularly in China. ere is strong empirical evidence that the prediction model of crash frequency based on collision type can help better understand the influence of the crash occurrence contributing factors on specific collision type, especially in real-time risk assessment [11,12].…”

Section: Introductionmentioning

confidence: 99%

Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data

Zhang

et al. 2020

Journal of Advanced Transportation

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Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. In addition, the traffic characteristics are considered to be a potentially important predictor of traffic accidents; however, their impact on crashes has been controversial. Therefore, a crash frequency predicting model for urban expressway considering collision types is proposed in this study. The loop detector traffic data and historical crash data were aggregated based on the similarities of the traffic conditions 5 minutes before crash occurrence, among which crashes were divided by collision type (rear-end collision and side-impact collision). The impact of traffic characteristics along with weather variables as well as their interactions on crash frequency was modelled by using negative binomial regression model. The results indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level. For rear-end collisions, crash frequency increased with lower average speed and high traffic volume under low speed limit. And when the speed limit is high, higher average speed coupled with larger volume increases the probability of crash. Higher average speed and traffic volume increase the probability of side-impact collisions, without being affected by the speed limit. The findings of the present study could help to determine efficient safety countermeasures aimed at improving the safety performance of urban expressway.

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“…The injury severity information of pedestrians was not linked to hospital data or cross-checked with external sources and solely relied on the judgment of law enforcement officers, which is another limitation of this study. Future studies could adopt a more parsimonious approach in terms of losing samples by using the full data with the party at fault as a variable to address the reduction in the sample size from data segmentation [46]. The city of San Antonio, the Texas Department of Transportation, and other stakeholders should assign policies based on the suggested recommendations in the previous sections to reduce severe pedestrian crash incidents in San Antonio.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers have adopted several techniques in traffic safety analyses in previous studies: the simultaneous equations model [34], the negative binomial model [35][36][37], the random effect ordered logit model [38], the ordinal probit model [39], the random effect negative binomial model [40], and the Bayesian hierarchical binomial logistic model [41]. More recent approaches that have analyzed the injury severity from accidents include the generalized ordered approach [42], the zero inflated model [43], the fractional split approach [44], the copula approach [45], and the panel mixed approach [46]. Logistic regression can measure associations, can control for confounding variable effects [47], and has been widely used in previous pedestrian crash analysis studies to determine the associations between the injury severity and the contributory factors [48], to identify the fatal crash-related risk factors [49,50], and to analyze the vulnerability of older pedestrians [51].…”

Section: Statistical Approaches and Scope Of Studymentioning

confidence: 99%

Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas

2021

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Pedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian injury severity based on the party at fault and to identify high-risk locations. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian crashes. High-risk locations were identified through heat maps and hotspot analysis. A failure to yield the right of way and driver inattention were the primary contributing factors to pedestrian–vehicle crashes. Fatal and incapacitating injury risk increased substantially when the pedestrian was at fault. The strongest predictors of severe pedestrian injury include the lighting condition, the road class, the speed limit, traffic control, collision type, the age of the pedestrian, and the gender of the pedestrian. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, raised medians, and the use of leading pedestrian interval and hybrid beacons are recommended.

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Do we need multivariate modeling approaches to model crash frequency by crash types? A panel mixed approach to modeling crash frequency by crash types

Cited by 35 publications

References 26 publications

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data

Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas

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