Factors contributing to commercial vehicle rear-end conflicts in China: A study using on-board event data recorders

Application of naturalistic driving data: A systematic review and bibliometric analysis

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

Can non-crash naturalistic driving data be an alternative to crash data for use in virtual assessment of the safety performance of automated emergency braking systems?

INTRODUCTION

Developers of in-vehicle safety systems need to have data allowing them to identify traffic safety issues and to estimate the benefit of the systems in the region where it is to be used, before they are deployed on-road. Developers typically want in-depth crash data. However, such data are often not available. There is a need to identify and validate complementary data sources that can complement in-depth crash data, such as Naturalistic Driving Data (NDD). However, few crashes are found in such data. This paper investigates how rear-end crashes that are artificially generated from two different sources of non-crash NDD (highD and SHRP2) compare to rear-end in-depth crash data (GIDAS).

METHOD

Crash characteristics and the performance of two conceptual automated emergency braking (AEB) systems were obtained through virtual simulations - simulating the time-series crash data from each data source.

RESULTS

Results show substantial differences in the estimated impact speeds between the artificially generated crashes based on both sources of NDD, and the in-depth crash data; both with and without AEB systems. Scenario types also differed substantially, where the NDD have many fewer scenarios where the following-vehicle is not following the lead vehicle, but instead catches-up at high speed. However, crashes based on NDD near-crashes show similar pre-crash criticality (time-to-collision) to in-depth crash data.

CONCLUSIONS

If crashes based on near-crashes are to be used in the design and assessment of preventive safety systems, it has to be done with great care, and crashes created purely from small amounts of everyday driving NDD are not of much use in such assessment.

PRACTICAL APPLICATIONS

Researchers and developers of in-vehicle safety systems can use the results from this study: (a) when deciding which data to use for virtual safety assessment of such systems, and (b) to understand the limitations of NDD.

Survival analysis of the unsafe behaviors leading to urban expressway crashes

A common cause of vehicle crashes on urban expressways lies in the unsafe behaviors of drivers. This study focused on analyzing the influence of various unsafe behaviors on crash duration. Based on actual video image of vehicle crashes, 14 unsafe behaviors were identified for the analysis of crashes on urban expressways. Using the correspondence analysis method, the correlation among unsafe behaviors and collision types was obtained. Nonparametric survival analysis was then presented to obtain the survival rate curves of sideswipe crashes and rear-end crashes. Finally, parametric survival analysis method can get the influence of unsafe behaviors on crash duration. The survival rate of any time was quantified through the reasoning of key unsafe behaviors for different types of crashes. The results show that there were striking differences in the duration among different types of crashes. The unsafe behaviors had a significant impact on duration for different types of crashes. This study focused on the duration under the influence of unsafe behaviors before the crash, and the results provide valuable information to prevent crashes, which can improve traffic safety.

Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network

The causes of crashes on urban expressways are mostly related to the unsafe behaviors of drivers before the crash. This study focuses on sideswipe collisions on urban expressways. Through real and visual crash data, 17 unsafe behaviors were identified for the analysis of sideswipe collisions on an urban expressway. The chains of high-risk and unsafe behaviors were then revealed to investigate the relationship between drivers’ unsafe behaviors and sideswipe collisions. A Bayesian network diagram of unsafe behaviors was used to obtain the correlation between unsafe behaviors and their influence. A topology diagram of unsafe behaviors was then constructed, and relational reasoning of typical behavioral chains was conducted. Finally, the unsafe behaviors and behavior chains that were likely to cause sideswipe collisions on the urban expressway were determined. The possibility of each behavior chain was quantified through the reasoning of variable structures constructed by the Bayesian network. The result shows that the significant influential single unsafe behavior leading to sideswipe collision on urban expressways was lane change without checking the rearview mirror or not scanning the road around and queue-jumping; moreover, based on unsafe behavior chains analysis, the most influential chains leading to sideswipe collision were: improper driving behavior in an emergency—failure to turn on signal when changing lanes—distracted and inattentive driving. Some safety precautions and countermeasures aimed at unsafe behaviors could be taken before the crash. The results of the study can be used to reduce the number of sideswipe collisions, thereby improving traffic safety on urban expressways.

Exploring the associations between driving volatility and autonomous vehicle hazardous scenarios: Insights from field operational test data

With the promising development and deployment trends of autonomous vehicles (AVs), AVs' operation safety has become a key issue worldwide. Studies have been conducted to reveal the risk factors of AV operation safety based upon AV-involved crash reports. However, the crash data sample size was limited and the crash reports only recorded static information, thus it failed to identify crash contributing factors and further provide feedbacks to AV algorithm development. In this study, the risk factors were investigated based upon hazardous scenarios, which were claimed to possess consistent causal mechanisms with crash events. First, contributing factors were extracted from both vehicle kinematics and traffic environment aspects, and their volatility features were obtained. Then, path analysis models were developed to reveal the concurrent relationships between scenario volatility and hazardous scenario occurrence probability. Besides, to understand the varying risk factors for hazardous scenarios caused by human drivers and AVs, a logit regression model was further established. The modeling results showed that large volatility of space headway held direct impacts on increasing the AV driving risks. And the volatility of the drivable road area had no significant impacts on AV driving risks while it indirectly influenced human driving risks. Finally, result implications for AV driving behavior improvements have been discussed.

Effect of daily car-following behaviors on urban roadway rear-end crashes and near-crashes: A naturalistic driving study

The rear-end crash is one of the most common types of crashes, and key risk factors have been broadly identified in the car-following behaviors preceding a crash. However, the relationships between rear-end crash risk and daily car-following behaviors, or habits, have not been well examined. This study aims to identify the daily car-following behaviors on urban surface roads and urban expressways that have the most influence on rear-end crashes and near-crashes (CNC). Two months of naturalistic driving study data were used to investigate the daily car-following behavior of 54 drivers. A paired t-test and a Wilcoxon matched-pairs signed rank test were conducted to find the differences in behaviors on the two road types, and basic Poisson regression and Poisson hurdle regression models were used to explore significant risk factors. Results revealed that (1) drivers' longitudinal vehicle control, time control, and emergency behaviors are significantly different on urban surface roads and urban expressways; (2) for surface roads, three key influencing factors were ranked, in descending order, as the standard deviation of relative speed, percentage of time gap less than 1 s, and maximum acceleration; (3) for expressways, four key factors were ranked: minimum time gap, maximum deceleration, percentage of TTC less than 5 s, and the percentage of large positive jerk. The knowledge achieved on risky daily driving behaviors can be applied to training drivers to improve safe practices, assist insurance companies in creating usage-based insurance strategies, and support driver assistant systems design.

Exploring transition durations of rear-end collisions based on vehicle trajectory data: A survival modeling approach

The time-to-collision (TTC) index and its extended variants have been widely utilized to assess rear-end collision risks, but the characteristics of the time-series data have not been fully explored, especially for the transition from safe to risky conditions. This study proposes a novel approach in rear-end collision risk analysis based on the concept of transition durations. The vehicle trajectory data were extracted and the TTC index was used to identify risky and safe conditions. Three important transition durations are defined and their rationalities for evaluating rear-end collision risks are examined by developing random-parameters accelerated failure time (AFT) survival models. Furthermore, a typical case from real trajectory data is taken to discuss the limitations of using TTC and its variants, and the advantage of the proposed transition durations. The results of random-parameters AFT models reveal contributing factors affecting the length of three durations and demonstrate the rationality of transition durations in rear-end collision risks analysis. It is indicated that the proposed method outperforms TTC and its variants in evaluating rear-end collision risks, because it could not only provide the information of time point but also the variation of time-series data.

Truck drivers' behavior in encounters with vulnerable road users at intersections: Results from a test-track experiment

Crashes involving cyclists and pedestrians in Europe cause the deaths of about 7600 persons every year. Both cyclists and pedestrians are especially exposed in crashes with motorized vehicles and collisions with trucks can lead to severe injury outcomes. The two most frequent crash scenarios between trucks and these vulnerable road users (VRU) are: a) when the truck wants to turn right at an intersection, with a cyclist riding parallel and planning to cross the intersection and b) when a pedestrian crosses in front of the truck in perpendicular direction to the movement of the truck. Advanced Driver Assistance Systems (ADAS)-that are expected to prevent or mitigate these crashes-benefit from detailed information about the behavior of truck drivers. This study is a first exploration of this research area, with the aim to assess how drivers negotiate the encounters with VRUs in the two scenarios described above. Thirteen participants drove an instrumented truck on a test-track. After some baseline recordings, the drivers experienced two laps where they encountered a cyclist target and a pedestrian target crossing their path. The results show that the truck drivers adapted their kinematic and visual behavior in the laps where the VRU targets were crossing the intersection, compared to the baseline laps. The speed profiles of the drivers diverged approximately 30 m from the intersection and glances were directed more often towards front right and right, during the scenario with the cyclist in comparison to baseline laps. For the scenario with the pedestrian crossing, the drivers changed their speed about 14 m from the intersection and glances were directed more often towards the front center, compared to baseline laps. As a result, both the speed and distance from the intersection at the end of the maneuver were significantly different between VRU and baseline laps. Overall, the findings provide valuable information for the design of ADAS that warn the drivers about the presence of a cyclist travelling in parallel direction or that intervene to avoid a collision with a cyclist or pedestrian.

Analysis of safety climate and individual factors affecting bus drivers' crash involvement using a two-level logit model

Although traffic crashes involving buses are less frequent than those involving other vehicle types, the consequences of bus crashes are high due to the potential for multiple injuries and casualties. As driver error is a primary factor affecting bus crashes, driver safety education is one of the main countermeasures used to mitigate crash risk. In China, however, safety education is not as focused as it should be, largely due to the limited research identifying the specific driver behaviors, and potential influences on those behaviors, that are correlated with crashes. The aim of this study is, therefore, to explore the fleet- and driver-level risk factors underlying bus drivers' self-reported crash involvement, including analyzing the effect of psychological distress on the most influential driver-level factors. A survey was conducted of 725 drivers from a large Shanghai bus company, and a random-effects two-level logit model was developed to integrate fleet and individual variables. Results showed that: 1) the fleet-level safety climate explained about 8.5% of the model's variance, indicating it was a valid predictor of self-reported crash involvement; 2) the driver-level factors of drivers' age, seniority, marital status, positive behavior, and driving anger influenced drivers' self-reported crash involvement, but ordinary violations, lapses, aggressive violations, and insomnia were the most influential variables; 3) psychological distress appeared to associate with the high frequency of risky driving behavior and the high severity of driving anger. This study's findings will help bus companies to give more attention to their safety climate and implement more targeted improvements to their driver safety education programs.

Discovering latent themes in traffic fatal crash narratives using text mining analytics and network topology

The proliferation of digital textual archives in the transportation safety domain makes it imperative for the inventions of efficient ways of extracting information from the textual data sources. The present study aims at utilizing crash narratives complemented by crash metadata to discern the prevalence and co-occurrence of themes that contribute to crash incidents. Ten years (2009-2018) of Michigan traffic fatal crash narratives were used as a case study. The structural topic modeling (STM) and network topology analysis were used to generate and examine the prevalence and interaction of themes from the crash narratives that were mainly categorized into pre-crash events, crash locations and involved parties in the traffic crashes. The main advantage of the STM over the other topic modeling approaches is that it allows the researchers to discover themes from documents and estimate how the topic relates to the document metadata. Topics with the highest prevalence for the angle, head-on, rear-end, sideswipe and single motor vehicle crashes were crash at stop-sign, crossing the centerline, unable to stop, lane change maneuver and run-off-road crash, respectively. Eigenvector centrality measure in network topology showed that event-related topics were consistently central in articulating the crash occurrence. The centrality and association between topics varied across crash types. The efficacy of generated topics in classifying crashes by type was tested using a machine learning algorithm, Random Forest. The classification accuracy in the held-out sample ranged between 89.3 % for sideswipe crashes to 99.2 % for single motor vehicle crashes. High classification accuracy suggests that automation of crash typing and consistency checks can be accomplished effectively by using extracted latent themes from the crash narratives.

How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity effects on freeway safety-based simulation experiments. Two types of vehicle dynamic models were employed to depict dynamic behaviors of manually driven vehicles and adaptive cruise control (ACC) vehicles (a simplified version of ICVs), respectively. Real vehicle trajectories were utilized to calibrate model parameters based on genetic algorithms. Surrogate safety measures were applied to establish the relationship between vehicle behaviors and longitudinal collision risks. Simulation results indicate that the heterogeneity has negative effects on longitudinal safety. With the higher degree of heterogeneity, longitudinal collision risks are increased. Compared to traffic flow consisting of human drivers only, mixed traffic flow may be more dangerous when the market penetration rate of ACC is low, since the ACC system can be recognized as a new source of individual heterogeneity. Findings of this study show that necessary countermeasures should be developed to improve safety for mixed traffic flow from the perspective of transportation safety planning in the near future.

Developing Concentration Index of Industrial and Occupational Accidents: The Case of European Countries

Background

From only frequency rate of industrial accidents, it is difficult to define the industry composition of accident statistics in a nation. This study aims to propose and develop a new index for measuring the degree of concentration of industrial accidents using the concept of the Herfindahl-Hirschman Index in the case of European countries.

Methods

Using the concept of the Herfindahl-Hirschman Index, the concentration index of accidents in the country is developed, and the conditions of European countries are compared using indexes of frequency rate and concentration ratio.

Results

The frequency rate and concentration ratio of fatal and nonfatal accidents in European countries are compared. According to the economic condition and geographical position, different patterns of accidents concentration are presented in terms of frequency rate and concentration ratio.

Conclusion

We develop the concentration index of industrial and occupational accidents that identifies the industrial ratio of accident occurrence, and the differentiated strategy can be formulated such as approaches to reducing frequency and prioritizing target industries.

The role of off-path glances: A quantitative analysis of rear-end conflicts involving Chinese professional truck drivers as the striking partners

INTRODUCTION

Rear-end crashes are one of the most frequent crash types in China, leading to significant economic and societal losses. The development of active safety systems - such as Automatic Emergency Braking System (AEBS) - could avoid or mitigate the consequences of these crashes in Chinese traffic situations. However, a clear understanding of the crash causation mechanisms is necessary for the design of these systems.

METHOD

Manually coded variables were extracted from a naturalistic driving study conducted with commercial vehicles in Shanghai. Quantitative analyses of rear-end crashes and near crashes (CNC) were conducted to assess the prevalence, duration, and location of drivers' off-path glances, the influence of lead vehicle brake lights on drivers' last off-path glance, and driver brake onset, and the influence of off-path glances and kinematic criticality on drivers' response to conflicts.

RESULTS

The results indicate that the Chinese truck drivers in our study rarely engage in distracting activities involving a phone or other handheld objects while driving. Instead, they direct their off-path glances mainly toward the mirrors, and the duration of off-path glances leading to critical situations are shorter compared to earlier analyses performed in Western countries. The drivers also often keep small margins.

CONCLUSIONS

Overall, the combination of short time headway with off-path glances directed toward the mirror originates visual mismatches which, associated to a rapid change in the kinematic situation, cause the occurrence of rear-end CNC. When drivers look back toward the road after an off-path glance, a fast response seems to be triggered by lower values of looming compared to previous studies, possibly because of the short time headways. Practical Application: The results have practical implications for the development of driver models, for the design of active safety systems and automated driving, and for the design of campaigns promoting safe driving.

Road traffic accident severity analysis: A census-based study in China

BACKGROUND

In China, despite the decrease in average road traffic fatalities per capita, the fatality rate and injury rate have been increasing until 2015.

PURPOSE

This study aims to analyze the road traffic accident severity in China from a macro viewpoint and various aspects and illuminate several key causal factors. From these analyses, we propose possible countermeasures to reduce accident severity.

METHOD

The severity of traffic accidents is measured by human damage (HD) and case fatality rate (CFR). Different categorizations of national road traffic census data are analyzed to evaluate the severity of different types of accidents and further to demonstrate the key factors that contribute to the increase in accident severity. Regional data from selected major municipalities and provinces are also compared with national traffic census data to verify data consistency.

RESULTS

From 2000 to 2016, the overall CFR and HD of road accidents in China have increased by 19.0% and 63.7%, respectively. In 2016, CFR of freight vehicles is 33.5% higher than average; late-night accidents are more fatal than those that occur at other periods. The speeding issue is severely becoming worse. In 2000, its CFR is only 5.3% higher than average, while in 2016, the number is 42.0%. Conclusion and practical implementation: A growing trend of accident severity was found to be contrasting to the decline of road traffic accidents. From the analysis of casual factors, it was confirmed that the release way of the impact energy and the protection worn by the victims are key variables contributing to the severity of road traffic accidents.

Exploring crash mechanisms with microscopic traffic flow variables: A hybrid approach with latent class logit and path analysis models

Understanding the occurrence mechanisms of crashes is critical for traffic safety improvement. Efforts have been investigated to reveal the crash mechanisms and analyze the contributing factors from the aspects of vehicle, driver, and operational perspectives. In this study, special attention has been paid to the operational level analyses while bridging the research gaps of: (1) failing to identify the heterogeneous impact of microscopic traffic flow variables on crash occurrence, and (2) focusing on correlation effects without further investigations for the causal relationships. A hybrid modeling approach with latent class logit (LCL) and path analysis (PA) models was proposed to account for the heterogeneous influencing effects and reveal the causal relationships between crash occurrence and microscopic traffic flow variables. Data from Shanghai urban expressway system were utilized for the empirical analyses. First, the LCL model has concluded four latent subsets of crash occurrence influencing factors. Then, PA models were conducted to identify the concurrent relationships (direct and indirect eff ;ects) for the four sets of crash occurrence influencing factors separately. Finally, the results of the LCL model and PA models were compared and the crash-prone scenarios were inferred. And the potential safety improvement countermeasures were discussed.