Occupant-level injury severity analyses for taxis in Hong Kong: A Bayesian space-time logistic model

A comparative analysis of risk factors in taxi-related crashes using XGBoost and SHAP

Taxis play a crucial role in urban public transportation, but the traffic safety situation of taxi drivers is far from optimistic, especially considering the introduction of ride-hailing services into the taxi industry. This study conducted a comparative analysis of risk factors in crashes between traditional taxi drivers and ride-hailing taxi drivers in China, including their demographic characteristics, working conditions, and risky driving behaviors. The data was collected from 2,039 traditional taxi drivers and 2,182 ride-hailing taxi drivers via self-reported questionnaires. Four XGBoost models were established, taking into account different types of taxi drivers and crash types. All models showed acceptable performance, and SHAP explainer was used to analyze the model results. The results showed that for both taxi drivers, risk factors related to risky driving behaviors are more important in predicting property damage (PD) crashes, while risk factors related to working conditions are more important in predicting person injury (PI) crashes. However, the relative importance of each risk factor varied depending on the type of crashes and the type of taxi drivers involved. Furthermore, the results also validated certain interactions among the risk factors, indicating that the combination of certain factors generated a greater impact on crashes compared to individual factors alone. These findings can provide valuable insights for formulating appropriate measures to enhance road safety for taxi driver.

Analysis of the injury severity of motor vehicle-pedestrian crashes at urban intersections using spatiotemporal logistic regression models

This paper conducted a comprehensive study on the injury severity of motor vehicle-pedestrian crashes at 489 urban intersections across a dense road network based on high-resolution accident data recorded by the police from 2010 to 2019 in Hong Kong. Given that accounting for the spatial and temporal correlations simultaneously among crash data can contribute to unbiased parameter estimations for exogenous variables and improved model performance, we developed spatiotemporal logistic regression models with various spatial formulations and temporal configurations. The results indicated that the model with the Leroux conditional autoregressive prior and random walk structure outperformed other alternatives in terms of goodness-of-fit and classification accuracy. According to the parameter estimates, pedestrian age, head injury, pedestrian location, pedestrian actions, driver maneuvers, vehicle type, first point of collision, and traffic congestion status significantly affected the severity of pedestrian injuries. On the basis of our analysis, a range of targeted countermeasures integrating safety education, traffic enforcement, road design, and intelligent traffic technologies were proposed to improve the safe mobility of pedestrians at urban intersections. The present study provides a rich and sound toolkit for safety analysts to deal with spatiotemporal correlations when modeling crashes aggregated at contiguous spatial units within multiple years.

Aberrant driving behaviors as mediators in the relationship between driving anger patterns and crashes among taxi drivers: An investigation in a complex cultural context

OBJECTIVE

Taxis have become an integrated component of Qatar's urban transportation network due to their convenience, comfort, and flexibility. Qatar has seen an uptick in the demand for professional taxi drivers. Most Qatari taxi drivers come from developing countries with poor awareness of road safety; therefore, they regularly engage in aberrant driving behavior, leading to traffic violations and crashes. For taxi rides to be safer, it is essential to determine the association between driving aberration and road traffic crashes (RTCs), with an emphasis on the underlying factors that trigger these behaviors.

METHODS

To this end, we collected the data from taxi drivers relying on standard questionnaires, namely the Driving Anger Scale (DAS) and the Driver Behavior Questionnaire (DBQ), together with the real crash data of the same taxi drivers obtained from the police department. We relied on factor analysis to identify the main factors of these tools and then structural equation modeling to predict their causal relationship with RTCs.

RESULTS

The results indicated that the component of DAS, namely "illegal driving", triggered all dimensions of aberrant driving behaviors, whereas hostile gestures had a positive correlation with lapses. In addition, the factor "error" was identified as a significant direct predictor, while the factor "illegal driving" was identified as a significant indirect predictor for RTCs. Regarding demographic characteristics, professional driving experience was found to be negatively associated with RTCs.

CONCLUSION

Driving aberration mediated the impact of driving anger on RTCs. The findings from this study could help road safety practitioners and researchers better understand these relations. In addition, these results could also be very helpful for driving instructors to train taxi drivers in a way to cope with provoking situations.

Jointly analyzing freeway primary and secondary crash severity using a copula-based approach

A copula-based model is developed in this study to jointly model the severity of freeway primary crashes and secondary crashes. The copula-based model can concurrently account for the severity levels in the crash and the correlation among primary-secondary crash pairs' severity. The model comprehensively considers a series of explanation variables, including temporal characteristics, crash characteristics, roadway characteristics and real-traffic conditions, and is estimated using traffic crash data from 2016 through 2019 for Los Angeles County, California. The proposed copula model is then contrasted with the traditional binary probit model and the results show a remarkable advantage of the copula model, which is evidenced by better fitting performance. It is found that weather, whether towed away, unsafe speed, collision type, road condition, terrain, road weaving and truck involvement have significant impact on primary crash severity propensity and collision type, road width, road condition, traffic volume and vehicle speed have significant impact on secondary crash severity propensity. In light of the findings, a number of countermeasures are proposed to mitigate freeway crashes, including emergency services, vehicle and roadway engineering, traffic law enforcement and driver education.

Distractions by work-related activities: The impact of ride-hailing app and radio system on male taxi drivers

Use of ride-hailing mobile apps has surged and reshaped the taxi industry. These apps allow real-time taxi-customer matching of taxi dispatch system. However, there are also increasing concerns for driver distractions as a result of these ride-hailing systems. This study aims to investigate the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers using the driving simulator experiment. In this investigation, fifty-one male taxi drivers were recruited. During the experiment, the road environment (urban street versus motorway), driving task (free-flow driving versus car-following), and distraction type (no distraction, auditory distraction by radio system, and visual-manual distraction by mobile app) were varied. Repeated measures ANOVA and random parameter generalized linear models were adopted to evaluate the distracted driving performance accounting for correlations among different observations of a same driver. Results indicate that distraction by mobile app impairs driving performance to a larger extent than traditional radio systems, in terms of the lateral control in the free-flow motorway condition and the speed control in the free-flow urban condition. In addition, for car-following task on urban street, compensatory behaviour (speed reduction) is more prevalent when distracted by mobile app while driving, compared to that of radio system. Additionally, no significant difference in subjective workload between distractions by mobile app and radio system were found. Several driver characteristics such as experience, driving records, and perception variables also influence driving performances. The findings are expected to facilitate the development of safer ride-hailing systems, as well as driver training and road safety policy.

Application of Bayesian Space-Time interaction models for Deer-Vehicle crash hotspot identification

The objective of this research was to identify and prioritize deer-vehicle crash (DVC) hotspots using five years of crash data. This study applied Bayesian spatiotemporal models for the identification of the DVC hotspots. The Bayesian spatiotemporal model allows to observe area-specific trends in the DVC data and highlights specific locations where DVC occurrence is deteriorating or improving over time. Census Tracts (CTs) were used as the geographic units to aggregate DVC, land use, and transportation infrastructure related data of Minnesota (MN) for the year 2015 to 2019. Several tests were conducted to evaluate the performance of the hotspot identification methods. The result showed that Type-I spatiotemporal interaction model (Model-2) outperforms other four space-time models in terms of predicting DVC frequency in CTs and hotspot identification performance test measures. Results showed that forest area, vegetation, and wetland percentages were positively associated with DVC frequency, whereas the percentage of developed land use was negatively associated with DVC frequency. The findings of this study suggest that the deer population plays an important role in DVCs, which indicates that deer population management is necessary to minimize the DVC risks. Using the final Type-I spatiotemporal interaction model, 65 "High-High" CTs were identified, where both the posterior mean of the decision parameter (potential for safety improvement) and the area-specific trend were higher. The distribution of the identified hotspots showed that the risk of DVCs was more in suburban areas with mixed land use conditions. These CTs represent high-risk zones, which need immediate safety improvement measures to reduce the DVC risks. As DVC can occur at any roadway segment location, DVC hotspots information is important for safety engineers and policymakers to implement area specific countermeasures.

Spatiotemporal analysis of crash severity on rural highway: A case study in Anhui, China

Traffic crashes are the result of the interaction between human activities and different socio-economic, geographical, and environmental factors, showing a temporal and spatial relationship. The temporal and spatial correlations must be characterized in crash severity studies, for which the geographically and temporally weighted ordered logistic regression (GTWOLR) model is an effective approach. However, existing studies using the GTWOLR model only subjectively selected a type of kernel function and kernel bandwidth, which cannot determine the best expression of the spatiotemporal relationship between crashes. This paper explores the optimal kernel function and kernel bandwidth considering the aforementioned problem to obtain the best GTWOLR model to analyze the crash data based on the crash data of rural highways in Anhui Province, China, from 2014 to 2017. First, the GTWOLR models with Gaussian or Bi-square kernel function and fixed (the spatiotemporal distance remains constant of local sample) or adaptive (the quantity of the local sample is constant) bandwidth are compared. Second, the log-likelihood and Akaike information criterion are used to compare the GTWOLR model with the ordered logistic regression (OLR) model. Finally, the spatial and temporal characteristics of the contributing factors in the best GTWOLR model are analyzed, and corresponding countermeasures for improving traffic safety on rural highways are proposed. Model comparison results reveal that although the difference was insignificant, the Bi-square kernel function with fixed bandwidth (BF)- GTWOLR model has a better goodness of fit than the GTWOLR models with other types of kernel function and bandwidth and the OLR model. The BF-GTWOLR model estimation results showed that eight factors, including pedestrian-vehicle crash, middle-aged driver, hit-and-run, truck, motorcycle, curve, slope and mountainous, passed the non-stationary test, indicating their varying effects on the crash severity across space and over time. As a crash severity modeling approach that effectively quantifies the spatiotemporal relationships in crashes, the BF-GTWOLR model, which adapts to crash data, may have implications for future research. In addition, the findings of this paper can help traffic management departments to propose progressive and targeted policies or countermeasures, so as to reduce the severity of rural highway crashes.

Assessment of commercial truck driver injury severity based on truck configuration along a mountainous roadway using hierarchical Bayesian random intercept approach

For the last decade, disaggregate modeling approach has been frequently practiced to analyze truck-involved crash injury severity. This included truck-involved crashes based on single and multi-vehicles, rural and urban locations, time of day variations, roadway classification, lighting, and weather conditions. However, analyzing commercial truck driver injury severity based on truck configuration is still missing. This paper aims to fill this knowledge gap by undertaking an extensive assessment of truck driver injury severity in truck-involved crashes based on various truck configurations (i.e. single-unit truck with two or more axles, single-unit truck pulling a trailer, semi-trailer/tractor, and double trailer/tractor) using ten years (2007-2016) of Wyoming crash data through hierarchical Bayesian random intercept approach. The log-likelihood ratio tests were conducted to justify that separate models by various truck configurations are warranted. The results obtained from the individual models demonstrate considerable differences among the four truck configuration models. The age, gender, and residency of the truck driver, multi-vehicles involvement, license restriction, runoff road, work zones, presence of junctions, and median type were found to have significantly different impacts on the driver injury severity. These differences in both the combination and the magnitude of the impact of variables justified the importance of examining truck driver injury severity for different truck configuration types. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (24%-42%) of intra-crash correlation (effects of the common crash-specific unobserved factors) in driver injury severity within the same crash. Finally, based on the findings of this study, several potential countermeasures are suggested.

Applying a Bayesian multivariate spatio-temporal interaction model based approach to rank sites with promise using severity-weighted decision parameters

Ranking sites with promise is an essential step for cost-effective engineering improvement on roadway traffic safety. This study proposes a Bayesian multivariate spatio-temporal interaction model based approach for ranking sites. The severity-weighted crash frequency and crash rate are used as the decision parameters. The posterior expected rank and posterior mean of the decision parameters are adopted as the statistical criteria. The proposed approach is applied to rank road segments on Kaiyang Freeway in China, which is conducted via programming in the freeware WinBUGS. The results of Bayesian estimation and assessment indicate that incorporating spatio-temporal correlations and interactions into the crash frequency model significantly improves the overall goodness-of-fit performance and affects the identified crash-contributing factors and the estimated safety effects for each severity level. With respect to the ranking results, significant differences are found between those generated from the proposed approach and those generated from the naïve ranking approach and a Bayesian approach based on the multivariate Poisson-lognormal model. Besides, the ranks under the posterior mean criterion are found generally consistent with those under the posterior expected rank criterion.

Investigating the uniqueness of crash injury severity in freeway tunnels: A comparative study in Guizhou, China

INTRODUCTION

With the rapid development of transportation infrastructures in precipitous areas, the mileage of freeway tunnels in China has been mounting during the past decade. Provided the semi-constrained space and the monotonous driving environment of freeway tunnels, safety concerns still remain. This study aims to investigate the uniqueness of the relationships between crash severity in freeway tunnels and various contributory factors.

METHOD

The information of 10,081 crashes in the entire freeway network of Guizhou Province, China in 2018 is adopted, from which a subset of 591 crashes in tunnels is extracted. To address spatial variations across various road segments, a two-level binary logistic approach is applied to model crash severity in freeway tunnels. A similar model is also established for crash severity on general freeways as a benchmark.

RESULTS

The uniqueness of crash severity in tunnels mainly includes three aspects: (a) the road-segment-level effects are quantifiable with the environmental factors for crash severity in tunnels, but only exist in the random effects for general freeways; (b) tunnel has a significantly higher propensity to cause severe injury in a crash than other locations of a freeway; and (c) different influential factors and levels of contributions are found to crash severity in tunnels compared with on general freeways. Factors including speed limit, tunnel length, truck involvement, rear-end crash, rainy and foggy weather and sequential crash have positive contributions to crash severity in freeway tunnels. Practical applications: Policy implications for traffic control and management are advised to improve traffic safety level in freeway tunnels.

Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study

A better understanding of injury severity risk factors is fundamental to improving crash prediction and effective implementation of appropriate mitigation strategies. Traditional statistical models widely used in this regard have predefined correlation and intrinsic assumptions, which, if flouted, may yield biased predictions. The present study investigates the possibility of using the eXtreme Gradient Boosting (XGBoost) model compared with few traditional machine learning algorithms (logistic regression, random forest, and decision tree) for crash injury severity analysis. The data used in this study was obtained from the traffic safety department, ministry of transport (MOT) at Riyadh, KSA, and contains 13,546 motor vehicle collisions along 15 rural highways reported between January 2017 to December 2019. Empirical results obtained using k-fold (k = 10) for various performance metrics showed that the XGBoost technique outperformed other models in terms of the collective predictive performance as well as injury severity individual class accuracies. XGBoost feature importance analysis indicated that collision type, weather status, road surface conditions, on-site damage type, lighting conditions, and vehicle type are the few sensitive variables in predicting the crash injury severity outcome. Finally, a comparative analysis of XGBoost based on different performance statistics showed that our model outperformed most previous studies.

Factors associated with consecutive and non-consecutive crashes on freeways: A two-level logistic modeling approach

A consecutive crash consists of a primary crash and one or more secondary crashes that occur subsequently in a short period of time within a certain distance. It often affects a relatively large area of road space and the traffic disruption created can be difficult for traffic managers to control and resolve. This study identifies the factors delineating a primary crash that results in secondary crashes within a minute from a regular crash that does not result in any secondary crashes. Random-effects, random-parameter and two-level binary logistic regression models are applied to data collected on 8779 crashes on the freeway network of the Guizhou Province, China in 2018, of which 299 are consecutive crashes. According to the AIC values, the two-level logistic model outperforms the other two models. Rear-end primary crashes have a significant random effect varying across road segments on the occurrence of consecutive crashes. Various crash types (rear-end, roll-over and side-swipe), tunnel crash and foggy weather are positively associated with the possibility to cause subsequent consecutive crashes, whereas single-vehicle crash, truck involvement and the time periods with poorer natural lighting are less likely to incur consecutive crashes. Recommendations are provided to minimize the possibility of the occurrence of consecutive crashes on a freeway.

Analysing the main and interaction effects of commercial vehicle mix and roadway attributes on crash rates using a Bayesian random-parameter Tobit model

In previous research, the effects of commercial vehicle proportions (CVP) on overall crash propensity have been found to be significant, but the results have been varied in terms of the effect direction. In addition, the mediating or moderating effects of roadway attributes on the CVP-vs-safety relationships, have not been investigated. In addressing this gap in the literature, this study integrates databases on crashes, traffic, and inventory for Hong Kong road segments spanning 2014-2017. The classes of commercial vehicles considered are public buses, taxi, and light-, medium- and heavy-goods vehicles. Random-parameter Tobit models were estimated using the crash rates. The results suggest that the CVP of each class show credible effects on the crash rates, for the various crash severity levels. The results also suggest that the interaction between CVP and roadway attributes is credible enough to mediate the effect of CVP on crash rates, and the magnitude and direction of such mediation varies across the vehicle classes, crash severity levels, and roadway attribute type in four ways. First, the increasing effect of taxi proportion on slight-injury crash rate is magnified at road segments with high intersection density. Second, the increasing effect of light-goods vehicle proportion on slight-injury crash rate is magnified at road segments with on-street parking. Third, the association between the medium- and heavy-goods vehicle proportion and killed/severe injury (KSI) crash rate, is moderated by the roadway width (number of traffic lanes). Finally, a higher proportion of medium- and heavy-goods vehicles generally contributes to increased KSI crash rate at road segments with high intersection density. Overall, the findings of this research are expected not only to help guide commercial vehicle enforcement strategy, licensing policy, and lane control measures, but also to review existing urban roadway designs to enhance safety.

Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors

INTRODUCTION

It has been demonstrated that weather conditions have significant impacts on freeway safety. However, when employing an econometric model to examine freeway crash injury severity, most of the existing studies tend to categorize several different adverse weather conditions such as rainy, snowy, and windy conditions into one category, "adverse weather," which might lead to a large amount of information loss and estimation bias. Hence, to overcome this issue, real-time weather data, the value of meteorological elements when crashes occurred, are incorporated into the dataset for freeway crash injury analysis in this study.

METHODS

Due to the possible existence of spatial correlations in freeway crash injury data, this study presents a new method, the spatial multinomial logit (SMNL) model, to consider the spatial effects in the framework of the multinomial logit (MNL) model. In the SMNL model, the Gaussian conditional autoregressive (CAR) prior is adopted to capture the spatial correlation. In this study, the model results of the SMNL model are compared with the model results of the traditional multinomial logit (MNL) model. In addition, Bayesian inference is adopted to estimate the parameters of these two models.

RESULT

The result of the SMNL model shows the significance of the spatial terms, which demonstrates the existence of spatial correlation. In addition, the SMNL model has a better model fitting ability than the MNL model. Through the parameter estimate results, risk factors such as vertical grade, visibility, emergency medical services (EMS) response time, and vehicle type have significant effects on freeway injury severity. Practical Application: According to the results, corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. For example, steep slopes should be avoided if possible, and in-lane rumble strips should be recommended for steep down-slope segments. Besides, traffic volume proportion of large vehicles should be limited when the wind speed exceeds a certain grade.

Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashes

BACKGROUND

Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods.

METHODS

Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian-motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared.

RESULTS

Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes.

CONCLUSIONS

The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units.

Investigating occupant injury severity of truck-involved crashes based on vehicle types on a mountainous freeway: A hierarchical Bayesian random intercept approach

Earlier research on injury severity of truck-involved crashes focused primarily on single-truck and multi-vehicle crashes with truck involvement, or investigated truck-involved injury severity based on rural and urban locations, time of day variations, lighting conditions, roadway classification, and weather conditions. However, the impact of different vehicle-truck collisions on corresponding occupant injury severity is lacking. Therefore, this paper advances the current research by undertaking an extensive assessment of the occupant injury severity in truck-involved crashes based on vehicle types (i.e., single-truck, truck-car, truck-SUV/pickup, and truck-truck), and identifies the major occupant-, crash-, and geometric-related contributing factors. A series of log-likelihood ratio tests were conducted to justify that separate model by vehicle and occupant types are warranted. Injury severity models were developed using 10 years of crash data (2007-2016) on I-80 in Wyoming through binary logistic modeling with a Bayesian inference approach. The modeling results indicated that there were significant differences between the influences of a variety of variables on the injury severities when the truck-involved crashes are broken down by vehicle types and separated by occupant types. The age and gender of occupants, truck driver occupation, driver residency, sideswipes, presence of junctions, downgrades, curves, and weather conditions were found to have significantly different impacts on the occupant injury severity in different vehicle-truck crashes. Finally, with the incorporation of the random intercept in the modeling procedure, the presence of intra-crash and intra-vehicle correlations (effects of the common crash- and vehicle-specific unobserved factors) in injury severities were identified among persons within the same crash and same vehicle.

Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview

Accident Analysis & Prevention (AA&P) is a leading academic journal established in 1969 that serves as an important scientific communication platform for road safety studies. To celebrate its 50^th anniversary of publishing outstanding and insightful studies, a multi-dimensional statistical and visualized analysis of the AA&P publications between 1969 and 2018 was performed using the Web of Science (WoS) Core Collection database, bibliometrics and mapping-knowledge-domain (MKD) analytical methods, and scientometric tools. It was shown that the annual number of AA&P's publications has grown exponentially and that over the course of its development, AA&P has been a leader in the field of road safety, both in terms of innovation and dissemination. By determining its key source countries and organizations, core authors, highly co-cited published documents, and high burst-strength publications, we showed that AA&P's areas of focus include the "effects of hazard and risk perception on driving behavior", "crash frequency modeling analysis", "intentional driving violations and aberrant driving behavior", "epidemiology, assessment and prevention of road traffic injuries", and "crash-injury severity modeling analysis". Furthermore, the key burst papers that have played an important role in advancing research and guiding AA&P in new directions - particularly those in the fields of crash frequency and crash-injury severity modeling analyses were identified. Finally, a modified Haddon matrix in the era of intelligent, connected and autonomous transportation systems is proposed to provide new insights into the emerging generation of road safety studies.

Severity of passenger injuries on public buses: A comparative analysis of collision injuries and non-collision injuries

INTRODUCTION

Although public buses have been demonstrated as a relatively safe mode of transport, the number of injuries to public bus passengers is far from negligible. Existing studies of public bus safety have focused primarily on injuries caused by collisions. Surprisingly, limited effort has been devoted to identifying factors that increase the severity of passenger injuries in non-collision incidents.

METHOD

Our study therefore investigated the injury risk of public bus passengers involved in collision incidents and non-collision incidents comparatively, based on a police-reported dataset of 17,383 passengers injured on franchised public buses over a 10-year period in Hong Kong. A random parameters logistic model was established to estimate the likelihood of fatal and severe injuries to passengers as a function of various factors.

RESULTS

Our results indicated substantial inconsistences in the effects of risk factors between models of non-collision injuries and collision injuries. The severity of passenger injuries tended to increase significantly when non-collision incidents occurred due to excessive speed of bus drivers, on double-decker buses, in less urbanized areas, in winter, in heavy rains, during daytime, and at night without street lighting. Elderly female passengers were also found more likely to be fatally or severely injured in non-collision incidents if they lost their balance while boarding, alighting from, or standing on a bus. In comparison, the following factors were associated with a greater likelihood of fatal or severe injuries in collision incidents: elderly female passengers, standing passengers who lost balance, buses out of driver control, double-decker buses, collisions with vehicles or objects, and less urbanized areas. Practical Applications: Based on our comparative analysis, more targeted countermeasures, namely "4E" (engineering, enforcement, emergency, and education) and "3A" (awareness, appreciation, and assistance), were recommended to mitigate collision injuries and non-collision injuries to public bus passengers, respectively.

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.

Exploring the role of delta-V in influencing occupant injury severities - A mediation analysis approach to motor vehicle collisions

This study investigates the impact that delta-V, the relative change in vehicle velocity pre- and post-crash, has on the severity of motor vehicle collisions (MVCs). We study injury severity using two metrics for each occupant - the number of injuries suffered, and the probability of suffering a serious or worse (MAIS 3+) injury. We use a cross-sectional set of generally-representative MVC data between 2010 and 2015 as a basis for our research. Collision factors that influence the crash environment are combined with the injuries that were suffered in MVCs. The influence of delta-V is captured using a mediation analysis, whereby delta-V acts as the focal point between crash factors and injury outcome. The mediation approach adds to existing research by presenting a detailed view of the relationship between injury severity, delta-V and other collision factors. We find evidence of competitive mediation, wherein a collision factor's positive association with injury severity is offset by a negative association with delta-V. Neglecting to include delta-V in our study would have let the factor's association with injury severity go undiscovered. In addition, certain collision factors are found to be related to injury severity solely because of delta-V, while others are found to have a significant impact regardless of delta-V. Our results support the multitude of policy recommendations that promote seatbelt use and warn against alcohol-impaired driving, and support the proliferation of safety-enabled vehicles whose technology can mitigate the bodily damage associated with detrimental crash types.

How does financial burden influence the crash rate among taxi drivers? A self-reported questionnaire study in China

Objective: Taxis play an important role in the transportation system of China, but they have a relatively high accident rate. The current study discusses the driver's financial burden in the Chinese context and explores its correlation with working conditions, risky driving behavior, and other characteristics of taxi drivers who are involved in accidents.Method: A total of 2,391 taxi drivers from 29 companies in four Chinese cities were interviewed and then asked to complete a questionnaire concerning their socio-demographic characteristics, working conditions, risky driving behavior, and accident frequency during the previous two years. Given the increase in the management fee (measured in CNY) charged by taxi companies, the drivers were divided into three groups: the "less than 150" group, the" 150 to 180" group and the "over 180" group, where were named Group 1, Group 2 and Group 3, respectively. Finally, the zero-inflated Poisson model was used to investigate the factors that contributed to the accident rate for each group.Result: The significant factors that lead to accidents differed significantly for drivers with different levels of financial burden. First, most of the factors were weakly correlated with the crash rate among Group 1 drivers. Second, many factors related to working conditions and risky driving behavior were significant for drivers in Groups 2 and 3, while working hours and off-duty days were significant only for drivers in Group 3. Third, working hours were negatively correlated with accident rates for drivers in Group 3, and the drivers who suffered from the heaviest financial burden were most affected by fatigue and sleep problems.Conclusion: Financial burden is the root cause behind the propensity of taxi drivers to be involved in accidents. Taxi companies should find ways to reduce drivers' expenses, and new technologies, such as taxi-calling or location and navigation based on mobile applications, should be introduced into the traditional taxi industry.

Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis

This study presents an empirical investigation of the impacts of real-time weather conditions on the freeway crash severity. A Bayesian spatial generalized ordered logit model was developed for modeling the crash severity using the hourly wind speed, air temperature, precipitation, visibility, and humidity, as well as other observed factors. A total of 1424 crash records from Kaiyang Freeway, China in 2014 and 2015 were collected for the investigation. The proposed model can simultaneously accommodate the ordered nature in severity levels and spatial correlation across adjacent crashes. Its strength is demonstrated by the existence of significant spatial correlation and its better model fit and more reasonable estimation results than the counterparts of a generalized ordered logit model. The estimation results show that an increase in the precipitation is associated with decreases in the probabilities of light and severe crashes, and an increase in the probability of medium crashes. Additionally, driver type, vehicle type, vehicle registered province, crash time, crash type, response time of emergency medical service, and horizontal curvature and vertical grade of the crash location, were also found to have significant effects on the crash severity. To alleviate the severity levels of crashes on rainy days, some engineering countermeasures are suggested, in addition to the implemented strategies.

Comparative risk factor analyses on bi-level injury severity of taxi and private car crashes in Seoul, South Korea

Objectives: Taxis, one of the main transportation modes that occupy the roadways in Seoul, are semipublic transportation modes for transporting passengers safely and promptly. Considering that one fifth of passenger vehicles on the roads in Seoul are taxis and the crash rate of taxis is double the exposure to traffic, it is important to identify risk factors of taxis from that of private cars. In this paper, crash causes and characteristics in both taxi crashes and private car crashes are investigated to identify the risk factors in accordance with the injury severity.Methods: An eight-year light-vehicle crash dataset was utilized, in which injury levels were defined as severe vs. non-severe. Three binary logit models that estimate the severity of crashes, the injury severity for at-fault drivers, and the injury severity for victims were modeled for taxi crashes and private car crashes. Independent variables were extracted and included in the models to evaluate the odds ratio of each predictor variable.Results: The results indicated that violation of traffic signals and signs was the highest contributor among all violation types for taxi crashes and parties involved (at-fault driver and victims), while driving on the wrong side of the road resulted in the highest increase in the odds ratio for private cars. Head-on collision and nighttime driving increased the likelihood of severe injury risk for all models, while age was the most prominent factor for the injury level of victims. Use of seatbelts had a major impact on the at-fault drivers, especially for taxis.Conclusions: This study identified the risk factors that affect the crash- and party-related severity level when casualties involved taxis and private cars. By employing both crash- and party-level models, the study not only identifies the risk factors among taxis and private car crashes but also provides a comprehensive picture of the injury profile of all vehicular occupants, which helps to devise safety measures that enhance the safety and reduce the injury severity for parties involved in crashes.

Evaluation of penalty and enforcement strategies to combat speeding offences among professional drivers: A Hong Kong stated preference experiment

Speeding has been a great concern around the world due to the occurrence and severity of road crashes. This paper presents an evaluation of the effectiveness of different penalty and camera-based enforcement strategies in curbing speeding offences by professional drivers in Hong Kong. A stated preference survey approach is employed to measure the association between penalty and enforcement strategies and drivers' speed choices. Data suggest that almost all drivers comply with speed limits when they reach a camera housing section of the road. For other road sections, a panel mixed logit model is estimated and applied to understand the effectiveness of penalties and enforcement strategies on driver's speeding behaviors. Driving-offence points (DOPs) are found to be more effective than monetary fines in deterring speeding offences, albeit there is significant heterogeneity in how drivers respond to these strategies. Warning drivers of an upcoming camera-based enforcement section increased speed compliance. Several demographic and employment characteristics, driving history and perception variables also influence drivers' choices of speed compliance. Finally, besides penalty and enforcement strategies, driver education and training programs aimed at addressing aggressiveness/risk-taking traits might help reduce repeated speeding offences among drivers.

Incorporating spatial effects into temporal dynamic of road traffic fatality risks: A case study on 48 lower states of the United States, 1975-2015

The rate of road traffic fatalities has long served as a regular indicator to evaluate and compare road safety performance for different administrative divisions. This article introduces a novel method known as the Markov chain spatial model to incorporate the spatial effects into the temporal dynamic of the fatality rates. Compared to the traditional Markov chain model, the proposed spatial Markov chain model can quantify the influence of neighboring sites explicitly in the transition process. A case study using a long duration dataset, from 1975 to 2015 in the 48 lower states of the United Sates, was conducted to illustrate the proposed model. The fatality rates were measured as the number of traffic fatalities per 100 million vehicle miles or per 10,000 residents. The results show that the probability of transition for one state between different levels of traffic fatality risks depends largely on the context of its surrounding neighbors. Another important finding is that relative to the estimates of traditional Markov chain models, states surrounded by neighborhoods with relatively low fatality rates take a longer time to transform to a higher level of fatality risk in the spatial Markov chain model. On the other hand, those with high-risk neighborhoods takes less time to deteriorate. These findings confirm that it is imperative to incorporate spatial effects when modeling the temporal dynamic of safety indicators to assess and monitor the safety trends in the areas of interest.

Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model

Due to the wide existence of heterogeneous nature in traffic safety data, traditional methods used to investigate motorcyclist rider injury severity always lead to masking of some underlying relationships which may be critical for the formulation of efficient safety countermeasures. Instead of applying one single model to the whole dataset or focusing on pre-defined crash types as done in previous studies, the present study proposes a two-step method integrating latent class cluster analysis and random parameters logit model to explore contributing factors influencing the injury levels of motorcyclists. A latent class cluster approach is first used to segment the motorcycle crashes into relatively homogeneous clusters. A mixed logit model is then elaborately developed for each cluster to identify its unique influential factors. The analysis was based on the police-reported crash dataset (2015-2017) of Hunan province, China. The goodness-of-fit indicators and the Receiver Operating Characteristic curves show that the proposed method is more accurate when modeling the riders' injury severities. The heterogeneity found in each homogeneous subgroup supports the application of the random parameters logit model in the study. More importantly, the results demonstrate that segmenting motorcycle crashes into relatively homogeneous clusters as a preliminary step helps to uncover some important influencing factors hidden in the whole-data model. The proposed method is proved to have great potential for accounting for the source of heterogeneity. The injury risk factors identified in specific cases provide more reliable information for traffic engineers and policymakers to improve motorcycle traffic safety.

The 10 commandments of exsanguinating pelvic fracture management

Background:

Unstable pelvic fractures are highly lethal injuries.

Objective:

The review aims to summarize the landmark management changes in the past two decades.

Methods:

Structured review based on pertinent published literatures on severe pelvic fracture was performed.

Results:

Ten key management points were identified.

Conclusion:

These 10 recommendations help diminish and prevent the mortality. (1) Before the ABCDE management, preparedness, protection, and decision are essential to optimize patient outcome and to conserve resources. (2) Do not rock the pelvis to check stability, avoid logrolling but prophylactic pelvic binder can be life-saving. (3) Computed tomography scanner can be the tunnel to death for hemodynamically unstable patients. (4) Correct application of pelvic binder at the greater trochanter level to achieve the most effective compression. (5) Choose the suitable binder (BEST does not exist, always look for BETTER) to facilitate body examination and therapeutic intervention. (6) Massive transfusion protocol is only a temporizing measure to sustain the circulation for life maintenance. (7) Damage control operation aims to promptly stop the bleeding to restore the physiology by combating the trauma lethal triad to be followed by definitive anatomical repair. (8) Protocol-driven teamwork management expedites the completion of the multi-phase therapy including external pelvic fixation, pre-peritoneal pelvic packing, and angio-embolization, preceded by laparotomy when indicated. (9) Resuscitation endovascular balloon occlusion of aorta can reduce the pelvic bleeding while awaiting hospital transfer or operation theater access. (10) Operation is the definitive therapy for trauma but prevention is the best treatment, comprising primary, secondary, and tertiary levels.

Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors

This study develops a Bayesian spatial generalized ordered logit model with conditional autoregressive priors to examine severity of freeway crashes. Our model can simultaneously account for the ordered nature in discrete crash severity levels and the spatial correlation among adjacent crashes without fixing the thresholds between crash severity levels. The crash data from Kaiyang Freeway, China in 2014 are collected for the analysis, where crash severity levels are defined considering the combination of injury severity, financial loss, and numbers of injuries and deaths. We calibrate the proposed spatial model and compare it with a traditional generalized ordered logit model via Bayesian inference. The superiority of the spatial model is indicated by its better model fit and the statistical significance of the spatial term. Estimation results show that driver type, season, traffic volume and composition, response time for emergency medical services, and crash type have significant effects on crash severity propensity. In addition, vehicle type, season, time of day, weather condition, vertical grade, bridge, traffic volume and composition, and crash type have significant impacts on the threshold between median and severe crash levels. The average marginal effects of the contributing factors on each crash severity level are also calculated. Based on the estimation results, several countermeasures regarding driver education, traffic rule enforcement, vehicle and roadway engineering, and emergency services are proposed to mitigate freeway crash severity.

Temporal patterns of driving fatigue and driving performance among male taxi drivers in Hong Kong: A driving simulator approach

This study uses a questionnaire survey and a driving simulator test to investigate the temporal patterns of variations in driving fatigue and driving performance in 50 male taxi drivers in Hong Kong. Each driver visited the laboratory three times: before, during, and after a working shift. The survey contained a demographic questionnaire and the Brief Fatigue Inventory. A following-braking simulator test session was conducted at two speeds (50 and 80 km/h) by each driver at each of his three visits, and the driver's performance in brake reaction, lane control, speed control, and steering control were recorded. A random-effects modeling approach was incorporated to address the unobserved heterogeneity caused by the repeated measures. In the results, a recovery effect and a lagging effect were defined for the driving fatigue and performance measures because their temporal patterns were concavely quadratic and had a 1-hour delay compared to the temporal patterns of occupied taxi trips and taxi crash risk in Hong Kong. Demographic variables, such as net income and driver age, also had significant effects on the measured driving fatigue and performance. Policies regarding taxi management and operation based on the modeling results are proposed to alleviate the taxi safety situation in Hong Kong and worldwide.

Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models

Traffic crashes are more likely to occur at intersections where the traffic environment is complicated. In this study, a hybrid approach combining cluster analysis and hierarchical Bayesian models is developed to examine driver injury severity patterns in intersection-related crashes based on two-year crash data in New Mexico. Three clusters are defined by K-means cluster analysis based on weather and roadway environmental conditions in order to reveal drivers' risk compensation instability under diverse external environment. Hierarchical Bayesian random intercept models are developed for each of the three clusters as well as the whole dataset to identify the contributing factors on multilevel driver injury outcomes: property damage only (Level I), complaint of injury and visible injury (Level II), and incapacitating injury and fatality (Level III). Model comparison with an ordinary multinomial logistic model omitting crash data hierarchical features and cross-level interactions verifies the suitability and effectiveness of the proposed hybrid approach. Results show that a number of crash-level variables (time period, weather, light condition, area, and road grade), vehicle/driver-level variables (traffic controls, vehicle action, vehicle type, seatbelt used, driver age, drug/alcohol impaired, and driver age) along with some cross-level interactions (i.e., left turn and night, drug and dark) impose significantly influence driver injury severity. This study provides insightful understandings of the effects of these variables on driver injury severity in intersection-related crashes and beneficial references for developing effective countermeasures for severe crash prevention.