Helai Huang

Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis

Most crash severity studies ignored severity correlations between driver-vehicle units involved in the same crashes. Models without accounting for these within-crash correlations will result in biased estimates in the factor effects. This study developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in traffic crashes at signalized intersections. Crash data in Singapore were employed to calibrate the model. Model fitness assessment and comparison using intra-class correlation coefficient (ICC) and deviance information criterion (DIC) ensured the suitability of introducing the crash-level random effects. Crashes occurring in peak time and in good street-lighting condition as well as those involving pedestrian injuries tend to be less severe. But crashes that occur in night time, at T/Y type intersections, and on right-most lane, as well as those that occur in intersections where red light cameras are installed tend to be more severe. Moreover, heavy vehicles have a better resistance on severe crash and thus induce less severe injuries, while crashes involving two-wheel vehicles, young or aged drivers, and the involvement of offending party are more likely to result in severe injuries.

County-Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling

An increasing research effort has been made on spatially disaggregated safety analysis models to meet the needs of region-level safety inspection and recently emerging transportation safety planning techniques. However, without explicitly differentiating exposure variables and risk factors, most existing studies alternate the use of crash frequency, crash rate, and crash risk to interpret model coefficients. This procedure may have resulted in the inconsistent findings in relevant studies. This study proposes a Bayesian spatial model to account for county-level variations of crash risk in Florida by explicitly controlling for exposure variables of daily vehicle miles traveled and population. A conditional autoregressive prior is specified to accommodate for the spatial autocorrelations of adjacent counties. The results show no significant difference in safety effects of risk factors on all crashes and severe crashes. Counties with higher traffic intensity and population density and a higher level of urbanization are associated with higher crash risk. Unlike arterials, freeways seem to be safer with respect to crash risk given either vehicle miles traveled or population. Increase in truck traffic volume tends to result in more severe crashes. The average travel time to work is negatively correlated with all types of crash risk. Regarding the population age cohort, the results suggest that young drivers tend to be involved in more crashes, whereas the increase in elderly population leads to fewer casualties. Finally, it is confirmed that the safety status is worse for more deprived areas with lower income and educational level and higher unemployment rate in comparison with relatively affluent areas.

Modeling fault among motorcyclists involved in crashes

Singapore crash statistics from 2001 to 2006 show that the motorcyclist fatality and injury rates per registered vehicle are higher than those of other motor vehicles by 13 and 7 times, respectively. The crash involvement rate of motorcyclists as victims of other road users is also about 43%. The objective of this study is to identify the factors that contribute to the fault of motorcyclists involved in crashes. This is done by using the binary logit model to differentiate between at-fault and not-at-fault cases and the analysis is further categorized by the location of the crashes, i.e., at intersections, on expressways and at non-intersections. A number of explanatory variables representing roadway characteristics, environmental factors, motorcycle descriptions, and rider demographics have been evaluated. Time trend effect shows that not-at-fault crash involvement of motorcyclists has increased with time. The likelihood of night time crashes has also increased for not-at-fault crashes at intersections and expressways. The presence of surveillance cameras is effective in reducing not-at-fault crashes at intersections. Wet-road surfaces increase at-fault crash involvement at non-intersections. At intersections, not-at-fault crash involvement is more likely on single-lane roads or on median lane of multi-lane roads, while on expressways at-fault crash involvement is more likely on the median lane. Roads with higher speed limit have higher at-fault crash involvement and this is also true on expressways. Motorcycles with pillion passengers or with higher engine capacity have higher likelihood of being at-fault in crashes on expressways. Motorcyclists are more likely to be at-fault in collisions involving pedestrians and this effect is higher at night. In multi-vehicle crashes, motorcyclists are more likely to be victims than at-fault. Young and older riders are more likely to be at-fault in crashes than middle-aged group of riders. The findings of this study will help to develop more targeted countermeasures to improve motorcycle safety and more cost-effective safety awareness program in motorcyclist training.

Applying Bayesian hierarchical models to examine motorcycle crashes at signalized intersections

Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and T signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag-1 dependence specification in the error term is the most suitable. Results show that the number of lanes at the four-legged signalized intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median and an uncontrolled left-turn lane at major roadways of four-legged intersections exacerbate this potential hazard. For T signalized intersections, the presence of exclusive right-turn lane at both major and minor roadways and an uncontrolled left-turn lane at major roadways increases motorcycle crashes. Motorcycle crashes increase on high-speed roadways because they are more vulnerable and less likely to react in time during conflicts. The presence of red light cameras reduces motorcycle crashes significantly for both four-legged and T intersections. With the red light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.

Empirical Evaluation of Alternative Approaches in Identifying Crash Hot Spots

This study proposes a framework of a model-based hot spot identification method by applying full Bayes (FB) technique. In comparison with the state-of-the-art approach [i.e., empirical Bayes method (EB)], the advantage of the FB method is the capability to seamlessly integrate prior information and all available data into posterior distributions on which various ranking criteria could be based. With intersection crash data collected in Singapore, an empirical analysis was conducted to evaluate the following six approaches for hot spot identification: (a) naive ranking using raw crash data, (b) standard EB ranking, (c) FB ranking using a Poisson-gamma model, (d) FB ranking using a Poisson-lognormal model, (e) FB ranking using a hierarchical Poisson model, and (f) FB ranking using a hierarchical Poisson (AR-1) model. The results show that (a) when using the expected crash rate–related decision parameters, all model-based approaches perform significantly better in safety ranking than does the naive ranking method, and (b) the FB approach using hierarchical models significantly outperforms the standard EB approach in correctly identifying hazardous sites.

Safety of Public Transportation Occupational Drivers: Risk Perception, Attitudes, and Driving Behavior

Public transportation plays a key role in providing transport services for the public in most cities of China. Safety is a top priority for improving the level of services of public transportation. This study aims to identify crash risk factors associated with demographic characteristics, driving-related experiences, and aberrant driving behaviors of the drivers of public transportation vehicles as well as to establish the influence of risk perception, risk-taking attitudes, and risky driving behaviors. The data used for analyses were obtained from a self-reported questionnaire survey carried out among 248 taxi and bus drivers in Wuhan, China. The results showed that drivers who both reported more tendencies toward aggressive violations and ordinary violations and had previously been involved in crashes were at high risk of crash involvement. Moreover, through the use of a structural equation model, it was found that drivers’ attitudes toward rule violations and speeding significantly affect risky driving behaviors. Two risk perception scales, likelihood of crash and concern, have indirect effects on risky driving behaviors through their influence on drivers’ attitudes toward rule violations and speeding. The significant risk factors and influential paths identified in this study are expected to result in better planning of road safety campaigns aimed at the occupational drivers in public transportation.

Examining Exposure of Motorcycles at Signalized Intersections

Crash statistics in Singapore from 2001 to 2005 have shown that motorcycles were involved in about 54% of intersection crashes. The overall involvement of motorcycles in crashes as the not-at-fault party was about 43%, but at intersections the corresponding percentage is increased to 57%. Quasi-induced exposure estimates have shown that the motorcycle exposure rate at signalized intersections was 41.7% even though motorcycles accounted for only 19% of the vehicle population. This study seeks to examine, in greater detail, the problem of motorcycle exposure at signalized intersections—in particular, the exposure caused by potential crashes with red-light-running vehicles from the conflicting stream. For that purpose, four signalized intersections are investigated. Results show that motorcycles are more exposed because they tend to accumulate near the stop line during the red phase to facilitate an earlier discharge during the initial period of the green, which is the more vulnerable period. At sites in which there are more weaving opportunities because the lanes are wider or there are exclusive right-turn lanes, the accumulation is higher and hence exposure is increased. The analysis also shows that the presence of heavy vehicles tends to decrease motorcycle exposure because motorcyclists’ weaving opportunities become restricted and they are more reluctant to weave past or queue alongside the heavy vehicles; effects intensify for narrower lane widths.

Effect of Red Light Cameras on Accident Risk at Intersections

One major cause of accidents at signalized intersections is vehicles running red lights. To discourage red light running (RLR), a number of authorities have installed red light camera (RLC) systems on the approaches to these signalized intersections. There have been indications from several studies that RLCs have been effective in curbing RLR; this leads to potential reduction in right-angle collisions. However, there are also concerns over a possible increase in rear-end collisions. This paper investigates the effect of RLCs on accident risks at signalized intersections for both right-angle collisions and rear-end collisions. A binary logit model was preliminarily developed to examine how the stopping-crossing decision of drivers at the onset of amber is affected by geometric, traffic, and situational variables. Results showed that the presence of RLCs is one of the five significant factors affecting a drivers decision to cross at the onset of amber. A multinomial logit model further confirmed that RLCs...

Evaluating Spatial-Proximity Structures in Crash Prediction Models at the Level of Traffic Analysis Zones

Zonal crash prediction that considers cross-zonal spatial correlation is a frontline research topic, especially in the context of transportation safety planning. This study presents an evaluation of crash prediction models at the level of traffic analysis zones with four types of spatial-proximity structures: 0–1 first-order adjacency, common-boundary length, geometry-centroid distance, and crash-weighted centroid distance. Bayesian spatial analysis with conditional autoregressive priors was successfully applied, and Hillsborough data were used to compare the model-fitting and predictive performance associated with the proposed models. The results confirmed the extensive existence of cross-zonal spatial correlation in crash occurrence. The best predictive capability, relatively, was associated with the model that considered proximity of neighboring zones by weighing their common-boundary lengths. Furthermore, full consideration of all possible spatial correlations for all zones significantly increased model complexity, which probably resulted in reduced predictive performance.

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach.

This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness.