Crash Risk Research Articles

Validation of an urban environment for pedestrian behavior analysis in full immersive virtual reality

The consequences of a road crash for vulnerable road users (VRUs), especially children, are more severe compared to car drivers; in addition, child mortality rates are predominantly attributed to road crashes. With the recent advancements in technology, virtual reality (VR) has emerged as a valuable tool for researching crash-related phenomena. This study's primary objective is to validate the use of a VR headset as a tool for investigating pedestrian behavior. We conducted tests on 122 middle school children, subjecting them to 15 scenarios involving road-crossing and strolling tasks. Kinematic data, specifically speed, was collected and statistically analyzed, comparing it to existing literature. The results obtained align with prior naturalistic studies, affirming the device's suitability for studying pedestrian behavior. Notably, no abnormal behaviors were observed during strolls. Furthermore, the riskiest behaviors, characterized by higher speeds, were predominantly associated with non-signalized crossings. These findings, in addition to validating the scenarios and tools used in this study, highlight the importance of designing intersections that minimize crash risks and educating children about road safety and crossing-related dangers.

High-frequency trading and stock price crash risk

High-frequency trading and stock price crash risk

Development of Modified Temporal Safety Performance Function Considering Various Time Flows

Safety performance functions (SPFs) have become valuable tools for estimating the relationships between crashes and various causal factors when constructing crash‐prediction models. However, the commonly used independent variable, the annual average daily traffic (AADT) is data on a yearly basis, which has limitations in capturing the temporal characteristics of traffic flows influenced by the passage of time. Accordingly, there have also been many studies using 15 min data to reflect real‐time, which is an important time unit to understand changes in highway traffic flow. However, such a short time unit has the limitation of high instability and randomness. In light of this, this study recognizes the importance of the 15 min time interval and proposes a new approach by developing a modified hourly model that aggregates data at fine‐grained 15 min intervals (00, 15, 30, and 45 min, both at the beginning and end), instead of the traditional hourly data that starts and ends at the peak of each hour to compensate for the existing limitations. The analysis focused on South Korea’s nationwide highways, and models were developed based on both statistical and machine‐learning approaches to compare their performances for selecting the final model. Additionally, a modified temporal SPF is introduced to predict crashes by assigning weights based on a Dirichlet distribution to models with overlapping time intervals aggregated in 15 min increments. This innovative approach overcomes the limitations of existing 15 min models, where the number of crashes is too small for effective training if the model is simply developed by dividing the time. The anticipated outcome is that the proposed model will demonstrate excellent performance and serve as an effective tool for predicting highway crash risks.

Comparing fatal crash risk factors by age and crash type by using machine learning techniques.

This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver's age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

Corporate social responsibility and stock price crash risk: the mediating effect of accounting conservatism

Corporate social responsibility and stock price crash risk: the mediating effect of accounting conservatism

Revisiting the Stock Price Crash Risk Puzzle: The Opacity and Overinvestment Channels through an Updated Sample Analysis

Revisiting the Stock Price Crash Risk Puzzle: The Opacity and Overinvestment Channels through an Updated Sample Analysis

Classification and Prediction of Vehicle Lane‐Changing Crash Risk Levels Based on Video Trajectory Data

To investigate the potential lane‐changing collision risks that may arise between vehicles during lane changes and those in the original lane, a model for vehicle lane‐changing collision risk is constructed specifically for this scenario, and a research analysis is conducted. First, based on vehicle trajectory data, a sample set capturing the relationships between vehicles traveling in a straight line and those changing lanes laterally is extracted and built. Interpolation methods are then applied to fill in missing values, outliers are eliminated, and data noise is smoothed during preprocessing. After preprocessing, a total of 468 vehicle pairs and 265,392 data points are obtained. Second, a real‐time collision time model is established based on the preprocessed data, and collision risk probabilities are calculated accordingly. Then, the collision risks are classified into four levels based on whether the vehicle on the side actually changes lanes and the severity of the collision risks. Finally, a light gradient boosting machine (LightGBM) learning method is adopted to predict the risk levels and analyze the main factors that significantly impact the severity of collision risks. The results indicate that the longitudinal distance between the target vehicle and the preceding vehicle is the most critical influencing factor, followed by the speed of the target vehicle itself, and then the speed difference between the target vehicle and the preceding vehicle. The influence of other factors is relatively similar and does not have a significant impact.

Social interaction and stock price crash risk: a social network analysis approach

ABSTRACT Using a social network analysis (SNA) approach to construct a retail investor interaction network, this paper examines whether investor social interactions increase stock price crash risk. The results show that social interactions on social media increase the likelihood of a stock price crash, suggesting that such interactions among retail investors breed irrational emotions that spread throughout the network, leading to increased risk in the stock market. We also find that this relation is more pronounced in bear markets.

Audit committee equity incentives and stock price crash risk

Audit committee equity incentives and stock price crash risk

The moderating effect of financial constraints on the relationship between stock price crash risk and managerial ability

PurposeThis study aims to empirically explore the influence of managerial ability on crash risk and the moderating effect of financial constraints on this interrelationship.Design/methodology/approachUsing a sample of listed corporations in the Egyptian Stock Exchange during 2018–2021, the authors test the hypotheses by using the measures and methods well established in prior literature. The authors also conduct multiple robustness analyses to ensure the validity of the empirical results.FindingsThe findings suggest that managerial ability can effectively inhibit crash risk. In addition, the authors report that financial constraints significantly dampen this relationship. Thus, financial restrictions play a striking role in hampering the managerial ability to prevent stock crashes. Furthermore, the authors document that the moderating role of severe financing constraints is more prominent during the Covid-19 pandemic period.Originality/valueThe originality of this study stems from the following considerations. First, this study enriches relevant studies on crash risk by providing evidence from one of the emerging markets in the Middle East; thereby, contrasting with those in developed economies. Second, to the best of the authors’ knowledge, this is the first study investigating the moderating impact of financing constraints on the managerial ability and crash risk nexus. Therefore, this work adds value to the extant knowledge by scrutinizing this important issue and providing novel empirical evidence.

How do drunk-driving events escalate into drunk-driving crashes? An empirical analysis of Beijing from a spatiotemporal perspective

Drunk-driving events often escalate into drunk-driving crashes, however, the contributing factors of this progression remain elusive. To mitigate the likelihood of crashes stemming from drunk-driving events, this paper introduces the notion of ‘the severity of drunk-driving event’ and examines the complex relationship between the severity and its contributing factors, considering spatiotemporal heterogeneity. The study utilizes a Geographically and Temporally Weighted Binary Logistic Regression (GTWBLR) model to conduct spatiotemporal analysis based on police-reported drunk-driving events in Beijing, China. The results show that most factors passed the non-stationary test, indicating their effects on the severity of drunk-driving event vary significantly across different spatial and temporal domains. Notably, during non-workday, drunk-driving events in northeast of Beijing are more likely to escalate into crashes. Furthermore, severe weather during winter in the northwest of Beijing is associated with high risk of drunk-driving crashes. Based on these insights, the authorities can strengthen drunk-driving checks in the northeast region of Beijing, particularly during non-workdays. And it is crucial to promptly clear accumulated snow on the roads during severe winter weather to improve road safety. These insights and recommendations are highly valuable for reducing the risk of drunk-driving crashes.

Corporate Sustainability and Stock Price Crash Risk

Contemporary society requires enterprises to improve the ability of sustainable development, ESG as a major indicator of corporate sustainability, and the study of its impact on stock price crash risk has attracted the attention of academia and society. This paper takes Chinese A-share listed companies as the research object to empirically test the impact of corporate sustainability on stock price crash risk. It is found that good ESG performance implying satisfactory firm sustainable development can alleviate information asymmetry and principal-agent problem, thus reducing stock price crash risk. Further heterogeneity test reveals that ESG has a stronger curbing effect on stock price crash risk for firms with low CFO. The final conclusion also passed the robustness test. The findings not only provide empirical evidence for the construction of ESG system and the suppression of stock price crash risk in emerging market countries, but also have some practical significance in promoting both corporate and so-cial sustainable development.

Research on the Impact of Female Directors on Capital Structure Adjustment -- Based on the perspective of crash risk

This paper selects all non-financial listed companies in Shanghai and Shenzhen A shares from 2010 to 2022 as the research object, based on dynamic capital structure theory, high-level echelon theory and resource dependence theory, through panel regression , to explore the impact of female directors themselves on the speed of capital structure adjustment; And introducing crash risk into the extended model to test the relationship between female directors and the speed of capital structure adjustment from the perspective of crash risk. The results show that the existence of female directors in the board of directors will optimize the capital structure, and the increase in the proportion of female directors in the board of directors will improve the speed of capital structure adjustment; Due to the characteristics of conservative and cautious women, the existence of female directors in the board of directors will reduce the risk of stock price collapse; Based on the perspective of collapse risk, female directors still improve the speed of capital structure adjustment.

Health and economic impact assessment of active travel modes in Tehran megacity

Health and economic benefits of active travel modes were investigated compared to air pollution and crash risk in Tehran, the capital of Iran using Health Economic Assessment Tool (HEAT) developed by World Health Organization (WHO). By increasing walking time duration to the range of 10–30 min from 5 and population proportion to 16.2% from 8.5%, the mortality rate would decrease to ∼112–108 from 113 and ∼735–654 from 764 in the age groups of 20–44 and 45–74 respectively. Similarly, in the cycling mode, increasing the time duration to 15–40 min from 10 and population proportion to 1.2% from 0.9%, would lead to increasing prevented premature deaths. Moreover, results of increasing PM2.5 concentration within the time range of present study showed that the benefits of physical activity outweighed the risks caused by air pollution and crash fatality. So promoting active transportation modes has significant health and economic benefits, especially for older adults.

Exploring the Behavior-Driven Crash Risk Prediction Model: The Role of Onboard Navigation Data in Road Safety

Driving behavior has frequently been overlooked in previous road traffic crash research. Hereby, abnormal (extreme) driving behavior data transmitted by the onboard navigation systems were collected for vehicles involved in traffic crashes, including sharp-lane-change, sharp-acceleration, and sudden-braking behaviors. Using these data in conjunction with expressway crash records, multiple classification learners were trained to establish a behavior-driven risk prediction model. To further investigate the influence of driving behavior on crash risk, partial dependence plots (PDPs) were applied. Regression analyses indicate that models have a stronger effect when derivative features such as frequency of specific deviant behavior, speed, and acceleration in the behavior process are included. The behavioral RUSBoost model surpasses other models, achieving an AUC prediction metric of 0.782 and outperforming traditional traffic-flow-driven machine learning models. PDP analysis demonstrates that the sudden-braking behavior is the leading contributory factor of expressway crashes, particularly when the acceleration exceeds 0.5 G. This study confirms the potential of predicting crash risks through augmenting behavior data from navigation software; the findings lay a foundation for countermeasures.

Proposed Electrical System Design of Streetlights at Megadike Access Road

Due to the absence of streetlights, it is unsafe for drivers to travel during the night in the current state of Mega Dike. The lack of streetlights causes low visibility for drivers, which is one of the major factors that causes road crashes. Thus, this study aims to propose an electrical system design of streetlights at the Mega Dike Access Road. In order to plan an electrical system design, the proponents would take in consideration the following parameters; Illumination formula for pole spacing; voltage drop calculation; economic analysis; and simulation of the system. The results will be presented in tabulated form for the respective parameters. The study examines five (5) different cases, taking into account different lamp wattages, voltage drop considerations, and cost analysis. The study would abide with the standards set by the Philippine Electrical Code (PEC) and Department of Energy (DOE). The goal of the proposed streetlights is to provide additional security for drivers on the Mega Dike Access Road by improving visibility and reducing the risk of road crashes caused by low visibility. Generally, the significance of streetlights is needed in Mega Dike Access Road in order to address the issue of low visibility for drivers and improve overall road safety in the area.

Common institutional ownership and stock price crash risk

AbstractThis paper presents new evidence on the economic benefits arising from common institutional ownership. We find a negative and significant effect of common institutional ownership on stock price crash risk. This effect is robust to a battery of robustness checks and is causal according to some identification tests, including difference‐in‐differences analyses on financial institution mergers. We find evidence that the negative effect is attributable to the monitoring role of common institutional owners—a role that is enabled by common owners' lower information processing cost and greater monitoring incentives owing to governance externalities. We also find that common owners negatively influence crash risk through constraining bad news hoarding and that common owners are more likely to force CEO turnover when a firm has higher crash risk. Overall, our results suggest that common institutional shareholders play a unique and effective monitoring role that fends off stock price crashes.

Machine Learning Approach to Enhance Highway Railroad Grade Crossing Safety by Analyzing Crash Data and Identifying Hotspot Crash Locations

Safe railway operation is vital for public safety, the environment, and property. Concurrent with climbing amounts of rail traffic on the Canadian rail network are increases in the last decade in the annual crash counts for derailment, collision, and highway railroad grade crossings (HRGCs). HRGCs are important spatial areas of the rail network, and the development of community areas near railway tracks increases the risk of HRGC crashes between highway vehicles and moving trains, resulting in consequences varying from property damage to injuries and fatalities. This research aims to identify major factors that cause HRGC crashes and affect the severity of associated casualties. Using these causal factors and ensemble algorithms, machine learning models were developed to analyze HRGC crashes and the severity of associated casualties between 2001 and 2022 in Canada. Furthermore, spatial autocorrelation and optimized hotspot analysis tools from ArcGIS software were used to identify hotspot locations of HRGC crashes. The optimized hotspot analysis shows the clustering of HRGC crashes around major Canadian cities. The analysis of cluster characteristics supports the results obtained for causal factors of HRGC crashes. These research outcomes help one to better understand the major causal factors and hotspot locations of HRGC crashes and assist authorities in implementing countermeasures to improve the safety of HRGCs across the rail network.

Assessing driver behavior in work zones: A discretized duration approach to predict speeding

Higher speeds in work zones have been linked to an increased likelihood of crashes and more severe crash outcomes. To enhance safety, speed limits are often reduced in work zones, aiming to create a steady flow of traffic and safer traffic operations such as merging and flagging. However, this speed reduction can also lead to abrupt speed changes, resulting from sudden braking or acceleration, increasing the risk of crashes. This disruption in speed and flow results increases the likelihood of rear-end crashes. Ensuring driver compliance with the reduced speed limits and traffic flow operations is challenging as work zones may cause frustration and lead to more instances of speeding. Therefore, proactively predicting speeding events in work zones can be crucial for the safety of both workers and road users, as it enables the implementation of speed enforcement measures to maintain and improve driver compliance in advance. In this study, we employ the duration-based prediction framework to forecast speeding occurrences in work zones. The model is used to identify significant predictors of speeding including visibility, number of lanes, posted speed limit, segment length, coefficient of variation in speed, and travel time index. Among these variables, the number of lanes, posted speed limit, and coefficient of variation of speed are positively associated with speeding. On the other hand, visibility, segment length, and travel time index are negatively associated with speeding. Results show the model’s predictive accuracy is higher for speeding events with shorter durations between consecutive occurrences. The model predicted speeding within 61% of the actual epoch when speeding events within 5 h of one another were considered for validation. This indicates that the model is more effective for road segments and work zones where speeding occurs more frequently. The prediction framework can be a great asset for agencies to improve work zone safety in real-time by enabling them to proactively implement effective work zone enforcement measures to control speeding and to stay prepared, preventing potential hazards.

Motorcyclists Preferred Lane Position on Federal Roads in Malaysia

Motorcycle fatalities comprised of 60% of the total fatalities in Malaysia. Weaving between traffic and speeding are among the contributors to the high motorcycle fatalities and there is not much understanding of the. This study was conducted with the objective to determine the lane preference of motorcyclists within various road cross-section and speed of motorcyclists when using the lanes. The lane usage was recorded with video camera and video was playback to count the count of motorcyclists using their respective lane. As for speed of motorcyclists, spot speed measurement was conducted covertly to avoid affecting motorcyclists’ behaviour. A total of 11 locations with different cross-section designs throughout the Malaysia was identified for observation. The empirical results show that more than 95% of the motorcyclists travelled on the non-exclusive motorcycle lane (NEML) when the NEML is presence at 2 lane single carriageway section. Meanwhile, paved shoulder about 2.2-2.3m wide was found to be used by majority of motorcyclists (75%). Apart from obtaining the lateral travel position of the motorcyclists, the spot speed data were also collected. The speed results revealed that motorcyclists travelled statistical significantly slower on NEML or Paved shoulder (PS) than on main travel lane. The findings of this study conclude that NEML and paved shoulder can affect motorcyclists to ride slower and thus enhance the safety of motorcyclists in term of speed exposure. In addition, the NEML and PS separate motorcyclists from other motorized vehicles which could reduce the risk of crashes of motorcyclists with another vehicle. Even though provision of NEML incurs high costs, however in long term, NEML would prove to be one of the most efficient allocation resources in reducing motorcycle crashes. However, further studies need to be carried out on the use of paved shoulder by motorcyclists.