Multivariate random parameters collision count data models with spatial heterogeneity

et al. 2020

IJERPH

This paper analyzes the influence of single and combined unfavorable road geometry on rollover and skidding risks of D-class mid-sized sport utility vehicles (SUVs) with front-wheel drive for roads with design speeds at 80 km/h. A closed-loop simulation model of human-vehicle-road interactions is established to examine the systematic influence of road geometry on vehicle rollover and skidding. The effects of different road geometry on rollover and skidding on SUVs are studied for pavement surface with good and poor friction when vehicles are in the action of steady state cornering. The rollover and skidding risks of the most unfavorable road segments are assessed. The critical wheel is defined by the threshold of skidding during curve negotiation. The results found that SUVs are not easy to rollover on the most unfavorable roads, regardless of good or poor friction of pavement surface. The safety margin of rollover is greater than that of skidding. The safety margin of skidding is minimal on poor friction roads. Therefore, for the sake of driving safety, it is not recommended to design the roads with these unfavorable road geometry combinations

Section: Introductionmentioning

confidence: 99%

The Influence of Road Geometry on Vehicle Rollover and Skidding

Yin

et al. 2020

IJERPH

“…The multivariate conditional autoregressive (CAR) model is one of the most advanced methods for multivariate spatial modeling under the Bayesian framework. It has been successfully applied to analyze crash frequency by severity or transportation mode at the macro level (e.g., canton and census tract) [24][25][26] and the micro level (e.g., roadway segment and intersection) [1,[27][28][29]. With the ability of accounting for heterogeneous and spatial effects and their correlations among crash severities, the multivariate CAR model is expected to improve the accuracy of identifying the contributing factors to freeway crash frequency by severity.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach

Journal of Advanced Transportation

Zeng

et al. 2018

This study sets out to investigate the effects of traffic composition on freeway crash frequency by injury severity. A crash dataset collected from Kaiyang Freeway, China, is adopted for the empirical analysis, where vehicles are divided into five categories and crashes are classified into no injury and injury levels. In consideration of correlated spatial effects between adjacent segments, a Bayesian multivariate conditional autoregressive model is proposed to link no-injury and injury crash frequencies to the risk factors, including the percentages of different vehicle categories, daily vehicle kilometers traveled (DVKT), and roadway geometry. The model estimation results show that, compared to Category 5 vehicles (e.g., heavy truck), larger percentages of Categories 1 (e.g., passenger car) and 3 (e.g., medium truck) vehicles would lead to less no-injury crashes and more injury crashes. DVKT, horizontal curvature, and vertical grade are also found to be associated with no-injury and/or injury crash frequencies. The significant heterogeneous and spatial effects for no-injury and injury crashes justify the applicability of the proposed model. The findings are helpful to understand the relationship between traffic composition and freeway safety and to provide suggestions for designing strategies of vehicle safety improvement.

“…To account for important issues related to crash data such as overdispersion, underdispersion, excess zero observations, spatiotemporal correlation, multilevel structures, and unobserved heterogeneity, a great many Poisson model variations have been proposed, which significantly improve model fit and predictive performance [3,6]. With more recent advances in crash prediction modeling, Bayesian inference has been extensively applied to traffic safety analysis because of its ability to deal with complex models (often without closed-form likelihood functions) such as the hierarchical model [1,2], spatiotemporal model [7,8], random parameters model [9], and multivariate model [10] and through these [8].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation

Zeng

Journal of Advanced Transportation

2017

As the basis of traffic safety management, crash prediction models have long been a prominent focus in the field of freeway safety research. Studies usually take years or seasons as the observed time units, which may result in heterogeneity in crash frequency. To eliminate that heterogeneity, this study analyzes monthly crash counts and develops Bayesian hierarchical models with random effects, lag-1 autoregression (AR-1), and both (REAR-1) to accommodate the multilevel structure and temporal correlation in crash data. The candidate models are estimated and evaluated in the freeware WinBUGS using a crash dataset obtained from the Kaiyang Freeway in Guangdong Province, China. Significant temporal effects are found in the three models, and Deviance Information Criteria (DIC) results show that taking temporal correlation into account considerably improves the model fit compared with the Poisson model. The hierarchical models also avoid any misidentification of the factors with significant safety effects, because their variances are greater than in the Poisson model. The DIC value of the AR-1 model is substantially lower than that of the random effect model and equivalent to that of the REAR-1 model, which indicates the superiority of the lag-1 autoregressive structure in accounting for the temporal effects in crash frequency.