Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach

Introduction: Traffic crashes could result in severe outcomes such as injuries and deaths. Thus, understanding factors associated with crash severity is of practical importance. Few studies have deeply examined how prior violation and crash experience of drivers and roadways are associated with crash severity. Method: In this study, a set of risk indicators of road users and roadways were developed based on their prior violation and crash records (e.g., cumulative crash frequency of a roadway), in order to reflect certain aspect or degree of their driving risk. To explore the impacts of those indicators on crash severity and complex interactions among all contributing factors, a Bayesian network approach was developed, based on citywide crash data collected in Kunshan, China from 2016 to 2018. A variable selection procedure based on Information Value (IV) was developed to identify significant variables, and the Bayesian network was employed to explicitly explore statistical associations between crash severity and significant variables. Results: In terms of balanced accuracy and AUCs, the proposed approach performed reasonably well. Bayesian modeling results indicated that the prior crash/violation experiences of road users and roadways were very important risk indicators. For example, migrant workers tend to have high injury risk due to their dangerous violation behaviors, such as retrograding, red-light running, and right-of-way violation. Furthermore, results showed that certain variable combinations had enhanced impacts on severity outcome than single variables. For example, when a migrant worker and a non-motorized vehicle are involved in a crash happening on a local road with high cumulative violation frequency in the previous year, the probability for drivers suffering serious injury or fatality is much higher than that caused by any single factor. Practical applications: The proposed methodology and modeling results provide insights for developing effective countermeasures to reduce crash severity and improve traffic system safety performance.     

Investigation of crashes at pedestrian hybrid beacons: Results of a large-scale study in Arizona

Introduction: The pedestrian hybrid beacon (PHB) is a traffic control device used at pedestrian crossings. A recent Arizona Department of Transportation research effort investigated changes in crashes for different severity levels and crash types (e.g., rear-end crashes) due to the PHB presence, as well as for crashes involving pedestrians and bicycles. Method: Two types of methodologies were used to evaluate the safety of PHBs: (a) an Empirical Bayes (EB) before-after study, and (b) a long-term cross-sectional observational study. For the EB before-after evaluation, the research team considered three reference groups: unsignalized intersections, signalized intersections, and both unsignalized and signalized intersections combined. Results: For the signalized and combined unsignalized and signalized intersection groups, all crash types considered showed statistically significant reductions in crashes (e.g., total crashes, fatal and injury crashes, rear-end crashes, fatal and injury rear-end crashes, angle crashes, fatal and injury angle crashes, pedestrian-related crashes, and fatal and injury pedestrian-related crashes). A cross-sectional study was conducted with a larger number of PHBs (186) to identify relationships between roadway characteristics and crashes at PHBs, especially with respect to the distance to an adjacent traffic control signal. The distance to an adjacent traffic signal was found to be significant only at the α = 0.1 level, and only for rear-end and fatal and injury rear-end crashes. Conclusions: This analysis represents the largest known study to date on the safety impacts of PHBs, along with a focus on how crossing and geometric characteristics affect crash patterns. The study showed the safety benefits of PHBs for both pedestrians and vehicles. Practical Applications: The findings from this study clearly support the installation of PHBs at midblock or intersection crossings, as well as at crossings on higher-speed roads.     

Highway safety assessment and improvement through crash prediction by injury severity and vehicle damage using Multivariate Poisson-Lognormal model and Joint Negative Binomial-Generalized Ordered Probit Fractional Split model

Introduction: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources. Methods: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity when the sample mean of severe injury crashes (e.g., fatal crashes) is very low. Results: The model estimation results highlight the presence of correlations of crash counts among severity levels, as well as the crash counts in total and crash proportions by different severity levels. A comparison of results indicates that injury severity and vehicle damage are highly consistent. Conclusions: Crash severity counts are significantly correlated and should be accommodated in crash prediction models. Practical application: The findings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity.     

推算交通事故汽车碰撞速度的综合约束方法

推算汽车交通事故中车辆的碰撞速度是进行交通事故技术鉴定的重要内容。笔者提出不同于理论计算和经验方法的综合计算方法,来推算汽车的碰撞速度。这种方法可避免单参数方法造成的失误,经实践检验可行     

Speed choice versus celeration behavior as traffic accident predictor

INTRODUCTION: The driver celeration behavior theory predicts that this variable is superior to all other variables as a predictor of individual traffic accident involvement, including the ever-important speed parameter. The study was undertaken to test this prediction. Also, it was expected that most variables would associate fairly strongly. METHOD: The use of speed choice as a predictor of individual traffic accident record was discussed, and four different variants of this variable (maximum, net mean, gross mean, and standard deviation of speed) identified. These variables were then compared to celeration behavior as predictors of accident record of bus drivers in the same set of data. RESULTS: Celeration behavior was found to be slightly superior, in accordance with the prediction made from the driver celeration behavior theory, although the differences were not significant. Furthermore, the predictor variables were found to associate fairly strongly between themselves, with the exception of gross mean speed, and to have fair stability over time, especially when aggregated. CONCLUSIONS: These results tentatively confirm some of the predictions made from the driver celeration behavior theory. As the results for accidents were in the expected direction, but not significant, and the maximum speed variable may have suffered from a ceiling effect, the conclusion is provisional. Impact on industry: The correlations found were strong enough to warrant the use of celeration behavior as a predictive variable for transportation companies in their safety work.     

Problem driver remediation: a meta-analysis of the driver improvement literature

PROBLEM: Given the public safety risk posed by violation and crash repeaters and the substantial costs for state driver improvement programs, it is important that their effectiveness be scientifically demonstrated and that intervention programs are based on sound research findings. METHOD: Crash and traffic violation standardized effect sizes (d) representing 106 individual interventions were coded from 35 methodologically sound studies and analyzed using meta-analysis. RESULTS: Driver improvement intervention in general was associated with small but significant reductions in both crashes (dw = 0.03) and violations (dw = 0.06). Significant effects were found on both measures for warning letters, group meetings, individual hearings, and license suspense/revocation. Of the driver improvement interventions studied, license suspension/revocation was by far the most effective treatment for both crashes and violations (dw = 0.11 and 0.19). Since one of the objectives of license suspension/revocation is to eliminate driving for the period of suspension, it is possible that much or all of the effect is due to reduced exposure and/or more careful driving during the suspension interval. Results were mixed for other types of interventions, although distributing educational or informational material was not associated with any reductions. Interventions associated with violation reduction tended to also be associated with crash reduction, although the relationship was not very strong (r = .30). DISCUSSION: Although interpretation of the effect size estimates was complicated by almost ubiquitous heterogeneity, the results do suggest an overall positive impact of driver improvement interventions in general. IMPACT ON INDUSTRY: The results support the continued use of driver improvement interventions, chiefly warning letters, group meetings, individual hearings, and especially license suspension/revocation. The results also suggest that court-triggered traffic violator programs are less effective than interventions triggered by drivers license agencies.     

Graduated driver licensing in the United States: evaluation results from the early programs

BACKGROUND: Seventeen states enacted graduated driver licensing (GDL) programs that were implemented from 1996 through 1999 and for which evaluations are of interest. METHODS: We received evaluation results reported for six states for which data were available. Summarizing results is difficult in other than the most global terms because of differences in pre-GDL programs, differences in GDL programs, and differences in evaluation methodology. RESULTS: All states identified some crash reduction among teen drivers following GDL implementation. This positive effect was observed across different geographic regions, and with different GDL programs. Simple counts are down-fewer teens are experiencing crashes and becoming injured. After calculating crash rates to adjust for changes over time in populations or licensed drivers, reductions generally were still found. Population-adjusted risks of injury/fatal crash involvement of 16-year-old drivers in Florida and Michigan were reduced by 11% and 24%, respectively. Population-adjusted risks of any crash involvement of 16-year-old drivers in Michigan and North Carolina were reduced by 25% and 27%, respectively. Reductions in night (restricted hours) crash risk were impressive in Florida, Michigan, and North Carolina. A comparison state design was only possible in the Florida evaluation, and results showed greater crash reductions under GDL. Change-point analyses of Michigan's crash data trends over time provided additional support of GDL's effectiveness in reducing crashes. DISCUSSION: Taken as a whole, and including the preliminary findings from California, Ohio, and Pennsylvania, these reports demonstrate the early effectiveness of GDL in reducing the crash risk of teen drivers. The impact of these studies and others to come will guide future research, practice, and policy.     

餐厨垃圾发酵制备生物燃料丁醇的研究

餐厨垃圾含丰富有机质,是潜在的廉价优质生物质资源;丁醇是继乙醇后的一种极具潜力的新型生物燃料。探索了以餐厨垃圾糖化液为原料,通过微生物发酵制取生物燃料丁醇。首先从实验室现有5株产丁醇梭菌中,优选出Clostridium beijerinckii NCIMB 8052作为丁醇生产菌,并考察在未添加任何营养物质,且不调节pH的条件下(简称非调控状态),以糖化液作为底物进行丁醇发酵的可行性。结果表明:非调控状态发酵的丁醇产量仅为5.96 g/L,并产生"酸崩"抑制;添加0.3%(w/V)Ca CO_3提高了糖化液pH缓冲能力,可解决"酸崩"问题,并将丁醇产量、总溶剂产量及丁醇生产速率分别提高了57.8%、53.1%和90.6%。这证明餐厨垃圾做为原料用于燃料丁醇生产是完全可行的。     

Characteristics and availability of fatal road-crash databases in 20 countries worldwide

INTRODUCTION: This study examined the characteristics and availability of fatal road-crash databases worldwide. METHOD: The study involved two parts. First, the major international road databases were briefly reviewed. Second, the national databases in 20 selected countries were examined. RESULTS: (a) the available international databases of fatal road crashes typically include aggregated data; (b) there is a national database of fatal road crashes in each country examined; (c) all countries provide aggregated crash data, but there are substantial restrictions on the availability of disaggregated data; and (d) overall, the crash data at the accident level are relatively similar, but there are substantial differences in the information at the person level. CONCLUSION: The results imply that international road safety research would greatly benefit from expanded availability of disaggregated fatal crash data worldwide.