Crash Risk Research Articles

How do cyclists experience a context-aware prototype warning system? Assessing perceived safety, perception and riding behaviour changes through a field study

The number of bicycle crashes is increasing in many European countries. In the Netherlands, a country well-known for its high-quality cycling infrastructure and cycling culture, bicycle crashes are also increasing, especially for e-bike users. Smart bicycle technologies, such as safety warning support systems, could contribute to reducing crash risk for cyclists. However, perceived safety and trust in these technologies are determinant factors in accepting and using such technologies. This study investigated users’ perceived safety, perceived trust, and perceived performance with a context-aware prototype warning system to support cyclists in the real world. In addition, it investigated users’ riding behaviour changes when receiving warnings in high crash risk locations by collecting GPS data. The above were evaluated through a field trial experiment using three rides per participant, with the first one serving as a control ride and a follow-up questionnaire after each ride conducted in Enschede, the Netherlands, between April and May 2024, with a sample of 46 participants. Results show that participants’ perceived safety and trust increased after they tried out the prototype warning system. In addition, it was found that warning systems positively influence participants’ riding behaviour, since they reduced their speed. This study proves the potential benefits of smart bicycle technologies in improving cyclists’ safety.

Negative information hoarding in politically connected firms: The influence from the central environmental protection inspections

This study examines how managerial political connections affect corporate information environment in the context of central government environmental inspections. We find that politically connected firms are associated with higher crash risk than firms without such connections after central government inspections. In addition, we find that firms are more prone to crash risk when they are with achieved political connections (e.g. appointed as members of political advisory bodies), but not ascribed political connections (i.e. have work experience as government officials). Further analyses indicate that politically connected firms are more prone to crash risk after central government inspections due to increased reputational concerns. Overall, our study highlights that politically connected firms may face more challenges in managing negative information when there is intensified regulatory scrutiny, which exacerbates the risk profile of firms.

Enhancement and Validation of a Quantitative GC-MS Method for the Detection of ∆9-THC and THC-COOH in Postmortem Blood and Urine Samples

Cannabis is frequently detected in forensic investigations and is associated with an increased risk of fatal car crashes. This study aims to develop a method to detect and measure ∆9-tetrahydrocannabinol (∆9-THC) in blood and its metabolite, 11-nor-9-tetrahydrocannabinol-carboxylic acid (∆9-THC-COOH), in urine. The procedure employs two liquid-liquid extraction methods in conjunction with GC-MS in SIM mode. Both compounds were successfully processed, demonstrating the method's ease of use and efficiency. The method was validated for selectivity, identification capability, linearity, precision, limit of detection (LOD), limit of quantification (LOQ), and accuracy. Its effectiveness was further demonstrated by applying it to 30 authentic urine and blood samples from cannabis-related cases, establishing it as a valuable option for routine cannabinoid analysis in forensic toxicology labs.•The linearity range was 25–300 ng/mL for ∆9-THC in blood, and 50–300 ng/mL for ∆9-THC-COOH in urine, and calibration curves for both analytes showed R² values consistently above 0.989, confirming their linearity.•The LOD and LOQ for THC-COOH in hydrolyzed urine were 25 ng/mL and 50 ng/mL, respectively, and for THC in blood, they were 15 ng/mL and 25 ng/mL, respectively.•The variation coefficients were below 14%, and recoveries exceeded 81% for both compounds.

Demographic risk factors and injury severity scores in Substance-use behaviour related traffic crashes

Alcohol and drugs, including Tetrahydrocannabinol (THC), significantly impact roadway safety by impairing cognitive functions, coordination, and reaction times, leading to increased crash risk and severity. This study examines the prevalence of alcohol and drugs among 4,586 injured roadway users (drivers, riders, and passengers) in the U.S. from 2019 to 2021, utilizing an XGBoost model to identify key variables associated with Injury Severity Score (ISS) in substance-related traffic crashes, and highlighting influential factors such as injury location, demographic characteristics (age, race), safety compliance, and alcohol and drug presence. These risk factors were further analyzed through Cluster Correspondence Analysis (CCA) to reveal patterns and trends affecting injury severity across different demographic and behavioral groups. The findings reveal that 55.8% of the injured tested positive for substances, with cannabinoids being the most common, followed by alcohol, stimulants, and opioids. This study identified six core crash clusters, each with distinct characteristics, including older drivers, impaired young drivers, specific driver ethnicities, and motorcyclists. Key findings from clusters indicate that older drivers, despite high safety compliance and negative substance tests, faced crash risks potentially due to age-related limitations. Impaired young adult crashes arecharacterized by risky behavior, including alcohol and THC use combined with low safety compliance, while motorcyclists with high substance involvement and inconsistent helmet use, are identified as a high-risk group, frequently experiencing severe leg injuries. These insights underscore the need for comprehensive traffic safety policies targeting substance use and promoting effective safety measures to mitigate crash risks and improve road safety.

Empirical analysis of car-following behavior: Impacts of driver demographics, leading vehicle types, and speed limits on driver behavior and safety

Car-following behavior is the most fundamental and common driving behavior and is crucial for road safety and traffic efficiency. Traffic flow dynamics are greatly affected by this behavior, and driver-related factors in car-following behavior have been identified as a key cause of rear-end crashes. Despite extensive research on car-following behavior, a gap remains in understanding how this behavior manifests itself in culturally diverse driver populations and heterogeneous driving conditions. The aim of this study was to empirically investigate the effects of individual characteristics, leading vehicle types, posted speed limits and deceleration rates of the leading vehicle on car-following behavior. To this end, the car-following behavior of 61 participants was investigated in eight different scenarios involving a motorbike, sedan, SUV, and truck as the leading vehicle under two different speed limits, i.e., 50 km/h and 80 km/h in a driving simulator environment. The results showed that considerable variations in car-following behaviors exist depending on gender, age, driving experience, educational levels, and the type of leading vehicle, as well as speed limits and deceleration rates. The risk of rear-end crash was found to be higher at the lower speed limit and with a two-wheeler (motorbike) as the leading vehicle. Additionally, females were identified as a having higher crash risk than males. In summary, this study provides valuable insights that could be applied to enhance road safety, such as tailoring targeted training materials for high-risk groups and informing policy decisions. Incorporating these insights into model calibration can lead to more accurate and realistic simulations that capture the complexities of real-world driving scenarios.

Modeling Crash Risk on Roadway Networks Using Bayesian Regression Trees

Statistical modeling of vehicle crashes leads to a better understanding of how and why such crashes occur. Due to the irregular network structure of roadways, analyses are typically confined to a single roadway rather than considering the entire network collectively. Here, we present methodology to model crash risk of vehicle crashes on irregular roadway networks and estimate how that risk varies with road characteristics. We model vehicle crashes observed on a road network as a Poisson point pattern with a piecewise linear intensity surface. Further, we combine Bayesian additive regression trees (BART) and spatial data analysis to accurately explain the intensity surface allowing inference on the effect of road characteristics on crash risk. We illustrate the methodology using a dataset of vehicle crashes on Interstate highways in Utah.

Analysis of Influencing Factor for Rear-end Crash on Toll Road: An Examination Using Self-reported Questionnaires

Background Rear-end crashes on toll roads have been a significant issue in Indonesia. This type of crash exists and needs a mitigation strategy. However, limited research exists examining the mechanism and contributing factors of these crashes in Indonesia. This research aimed to describe how rear-end crashes have occurred on toll roads in Indonesia, describe associated risk factors, develop a model for assessing the risk of rear-end crashes on Indonesian toll roads and identify potential mitigation strategies. Materials and Methods A questionnaire was developed and assessed for validity and reliability. It was distributed (online) to 220 drivers who had experienced a rear-end crash or near-crash in the past year. Results The findings from this study showed that contributing factors are driver (55%), environment (33%), and vehicle (12%). Subject vehicles usually consisted of private vehicles, while the lead (struck) vehicles included private vehicles (91.8%), trucks (7.27%), and buses (0.93%). Discussion This study also found two phenomena of local unique behavior in Indonesian toll roads that lead to rear-end crashes, such as “Lane Hogger” and driving under the speed limit (trucks) that lead other vehicles to rear-end crashes. Conclusion Factors contributing to rear-crash risk on Indonesian toll roads were driving experience (ownership driving license 15-20 years), breaking driving rules, driving in the right lane, and using an MPV. The findings from this study can improve potential mitigation strategies, including developing focused driving rules, traffic signs on toll roads, and crash avoidance technology.

Investigating streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision

Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and deep learning techniques provides a cost-effective alternative for extracting streetscape features. However, prior studies often rely solely on semantic segmentation, overlooking distinctions in feature shapes and contours. This study addresses these limitations by combining semantic segmentation and object detection networks to comprehensively measure streetscape features from Baidu SVIs. Semantic segmentation identifies pixel-level proportions of features such as roads, sidewalks, buildings, fences, trees, and grass, while object detection captures discrete elements like vehicles, pedestrians, and traffic lights. Zero-inflated negative binomial regression models are employed to analyze the impact of these features on three crash types: vehicle-vehicle (VCV), vehicle–pedestrian (VCP), and single-vehicle crashes (SVC). Results show that incorporating streetscape features from combined deep learning methods significantly improves crash prediction. Vehicles have a significant impact on VCV and SVC crashes, whereas pedestrians predominantly affect VCP crashes. Road surfaces, sidewalks, and plants are associated with increased crash risks, while buildings and trees correlate with reduced vehicle crash frequencies. This study highlights the advantages of integrating semantic segmentation and object detection for streetscape analysis and underscores the critical role of environmental characteristics in road traffic crashes. The findings provide actionable insights for urban planning and traffic safety strategies.

Inhabiting influence of digital finance on stock price synchronicity

We examine how digital finance can influence stock price synchronicity in China for the period of 2011–2021. Results suggest that the digital finance index, coverage breadth, usage depth, and digitalization level can significantly reduce stock price synchronicity. The possible channels of this linkage are information opacity and stock price crash risk. We find that digital finance is effective in reducing stock price synchronicity only in state-owned enterprises and large firms. The results are robust and immune to reverse causality and endogeneity and have important implications for investors and policymakers.

Bank Financial Distress and Stock Price Crashes: A Quasi‐Experimental Approach

ABSTRACTUsing 118,292 US bank‐month observations, we examine the effects of short‐term changes in bank's financial distress on stock price crash risk. There is a significant positive association between short‐term changes in distress on stock price crash risk. The results remain consistent across alternative measures of distress and crash risk. We confirm robustness by employing additional tests for reverse causality and propensity score matching. We find opacity, proxied by discretionary loan‐loss provisions to be a potential channel through which increase in distress affects future crash risk. Our study underscores the critical association between increasing financial distress, loan‐loss reporting, and crash risk.

The effect of periodic vehicle inspection on road traffic crash risk

In the European Union (EU), periodic inspection of motor vehicles is mandatory. In Denmark, cars and vans are inspected for the first time four years after the first registration and subsequently every two years, while lorries and buses are inspected annually. Denmark follows the EU minimum requirements, but there have been considerations to increase the frequency of inspections. However, based on previous research, the positive effects on crash risk are questionable. The aim of this study was to investigate the association between time since the most recent inspection and crash risk using nationwide Danish registry data. The hypothesis is that crash risk increases proportional with time since latest inspection. Methodologically, we used a case-control design. In total, 72 089 cars, 11 289 vans, 3 101 lorries and 1 575 buses involved in crashes were included as cases. Five controls were randomly selected per case and matched by age, brand and total weight. The analyses were controlled for odometer reading and the number of brake defects at the latest inspection. The study found no association between periodic inspections and crash risk in separate analyses of each vehicle type. There were no specific effects of inspections of older vehicles aged 10 years or more. This study does not provide evidence for expanding the current Danish inspection programme with more frequent checks of vehicles. The study also indicates that vehicles that are subject to the inspection programme are in good condition such that defects have a small (not negligible) impact on crash risk.

Organization capital and stock price crash risk

This study explores the relationship between organization capital and stock price crash risk. Using a sample of publicly traded firms in China between 2010 and 2022, we find that firms with a high level of organization capital have higher future stock price crash risk. This positive relationship is more prominent for firms with low financing constraints, high profit retention ratios, and high levels of risk-taking behavior than for other firms. We also examine the potential mechanisms that moderate this positive relationship. Our results indicate that the positive association is less apparent among non-state-owned enterprises, firms in intensely religious environments, firms in which management holds shares, and firms with strong monitoring vigilance. Further analysis reveals that firms can reduce this positive association by increasing the frequency with which independent directors meet and increasing the diversity of the backgrounds of independent directors. Our findings are robust to various robustness tests, such as the instrumental variable approach, entropy balancing analysis, and alternative measures of organization capital and stock price crash risk.

Exploring the Factors Influencing Car Drivers' Perception and Crash Probability with Food Delivery Riders

Motorcycle food delivery riders (MFDR) face significant crash risks and some of these crashes are attributed to other vehicle drivers. This paper assesses drivers’ perceptions and observed riding behaviors of MFDRs, through a survey, and analyses driver factors that increase MFDR crash risk. Drivers indicated that MFDRs exhibited reckless riding (tailgating, filtering, and weaving), and drivers’ demographics, driving characteristics, and personality traits anger affected these observations. Personality trait anger influenced fault identified in a crash and motorcycle riding perception while driver demographics had a minor effect on the perceptions. Drivers’ motorcycle riding experience affected the understanding of MFDR behaviors and perception. Driver’s age, employment, motorcycle riding experience, violations, emotional state while driving (hurry), observed MFDR violations factor score, and motorcycle riding perception affected crash probability. The results could be used to develop strategies to promote traffic safety in mixed environments by providing motorcycle riding behavior awareness and improving driver training.

Dynamic inferences of crash risks in freeway merging zones: a spatio-temporal deep learning model

Freeway merging zones are critical for freeway operations and management due to potential crashes arising from complex vehicle merging behaviours. This paper investigates the application of a spatio-temporal deep learning model to infer crash risks in the zones. We first introduce a crash risk index based on Time-To-Collision and vehicle merging patterns. An innovation is that the developed spatio-temporal transformer model can analyze the evolving risk index. This model effectively captures dynamic risk features through a multi-head attention mechanism within its spatio-temporal learning components. Numerical experiments on nine inference tasks with varying spatial resolutions show improved performance of the model with lower resolution. Moreover, the ST-Transformer model is benchmarked against three advanced deep learning models, which consistently demonstrates its superiority in capturing spatio-temporal dependence in risk sequences. This investigation significantly contributes to a richer understanding of proactive traffic safety, providing valuable insights for advanced freeway management and driver assistance systems.

Spatial analysis of daytime and nighttime crash severity on horizontal curves of mountainous rural highways: A case study in Northern Iran

According to statistical analysis, many road crashes, especially on rural two-lane undivided (RTU) highways passing through mountainous areas, occur on horizontal curves. Spatial analysis of these crashes, while incorporating the influence of lighting conditions, can significantly improve safety on rural highways. This study proposes a method based on spatial clustering combined with a hybrid approach of random forest and binary logistic regression modeling framework to examine the severity of crashes occurring on RTU horizontal curves in mountainous areas. In contrast to prior studies that primarily focused on either statistical modeling or spatial analysis in isolation, this research integrates these methodologies to provide a more comprehensive understanding of the factors influencing crash severity. To assess the influence of various factors on crash severity, this study applied the proposed approach to all curves and crash-prone curves identified through spatial autocorrelation analysis, considering four separate datasets, four models defined by temporal conditions (daytime/nighttime), and spatial aspects (all curves/crash-prone curves) (RB1: daytime crashes on all curves, RB2: nighttime crashes on all curves, RB3: daytime crashes on crash-prone curves, and RB4: nighttime crashes on crash-prone curves). This innovative approach distinguishes this study from previous research by enabling a detailed investigation into how various crash factors manifest differently under distinct spatio-temporal conditions, thus facilitating a nuanced analysis of their interplay. Based on the analysis, collision type, collision angle, and collision reason emerged as significant factors influencing crash severity across all models, regardless of lighting conditions and spatial aspects. Additionally, head-on and rear-end collisions were found to increase the risk of severe injury crashes. Furthermore, the modeling results identified geometric features of curves, such as type, radius, and length, as critical factors in identifying crash-prone curves. These findings advance existing research by highlighting the significant role of spatial factors and lighting conditions in determining crash severity, while the comprehensive evaluation of both all curves and crash-prone curves through spatial analysis enhances the understanding of the interaction between spatial attributes and crash dynamics. This study provides valuable insights for the engineering community, offering practical applications in safety interventions, including targeted signage, curve design modifications, and driver education programs, which can help mitigate crash severity on RTU horizontal curves in mountainous areas.

An Exploratory Assessment of Driver Injury Severities in Large-Truck Crashes Involving Fatigued and Non-Fatigued Driving

Fatigue poses a persistent safety challenge for long-haul truck drivers, significantly elevating the risk of highway crashes and compromising daily work performance. This study investigates the factors influencing driver injury severity in single-large truck crashes due to fatigued driving, compared with non-fatigued driving (normal driving). Using Florida crash data from 2011 to 2019 (inclusive), covering both fatigue and non-fatigue-related crashes, this analysis employs random parameters logit models to account for potential unobserved heterogeneity in means and variances. A comprehensive array of factors affecting driver injury severity, including spatial and temporal characteristics, vehicle and traffic attributes, roadway conditions, and driver-specific characteristics, are examined. Strikingly, different parameter estimates emerge for fatigue and non-fatigue-related crashes, indicating fundamental dissimilarities in unobserved heterogeneity in large truck crash data. The estimated model results unveil distinct marginal effects between fatigued and non-fatigued driving, particularly concerning severe injury crashes, suggesting significant variation in driver behavior based on fatigue levels. These findings contribute substantially to the growing literature on the intrinsic disparities between fatigued and non-fatigued driving behaviors. Moreover, this research underscores the potential ramifications of these distinctions on the safety performance of commercial trucks, highway safety technologies, and policy-related safety countermeasures. Understanding the unique characteristics of fatigued driving can inform targeted interventions to bolster safety within the commercial trucking industry and mitigate the impact of severe crashes resulting from fatigued driving.

Drivers’ long-term crash risks associated with being ticketed for speeding

Introduction: This research analyzes the relationship between police-issued tickets for speeding and the crash risk of those drivers, in New Zealand, between 2015–2019. Method: The main data are constructed through data-matching license details of crash outcomes with all officer-issued tickets for speeding between 2015–2016 (N = 534,935). The sub-group of drivers that accumulate tickets is compared to a coarsened exact matched set of drivers of the same age. Results: There is a strong relationship between the number of tickets a person has in a two-year period (2015–16) and the likelihood of a crash outcome (2017–2019). However, the accumulation of tickets is not the best predictor of crash likelihood. A combination of the excess in speed and the accumulation of tickets increases the relative odds of a subsequent crash. These results are discussed considering the threshold at which New Zealand criminalizes alcohol-relating offending (notionally 4.2 times the base rate crash risk). The same rate of elevated crash risk exists when a driver has one ticket for being 10 km/h over the speed limit and has another speeding ticket within two years.

CEO Connectedness and Firm Transparency

ABSTRACTOur research reveals that CEO connections with Audit Committee directors, established through past employment, education, or social organization memberships, significantly impact firm transparency. These connections increase the likelihood of firms issuing less transparent and readable financial reports. Furthermore, these connections are linked to decreased long‐term firm value and increased crash risk. Our findings underscore the crucial role of CEO connectedness in corporate disclosure transparency and firm value. We employed multiple methodologies to address endogeneity concerns. Our results remain robust.

An advanced real-time crash prediction framework for combined hard shoulder running and variable speed limits system using transformer

One of the important applications of real-time crash prediction analysis lies in the field of proactive traffic management, where instantaneous crash risk evaluation and dynamic decision-making are prerequisites. This research proposes an integrated and advanced real-time crash risk prediction framework for Variable Speed Limits (VSL) and Hard Shoulder Running (HSR) implemented freeways considering their operational periods. Statistical methods are utilized to identify the significant crash contributing factors (related to traffic, roadway geometry, and weather conditions) and explain their relationships with crashes. Time-Embedded Transformer models are proposed to predict the likelihood of real-time crash events. The sensitivity and false alarm rate of the proposed AM peak model are found to be 0.76 and 0.27, respectively, whereas the values are 0.78 and 0.24, respectively for the PM peak model. Additionally, the results indicate substantial improvements in model prediction performance (i.e., an increment of sensitivity values by 7.04% and 8.33% in the AM and PM models, respectively) after incorporating the general safety condition of a freeway segment as an input feature while estimating real-time crash prediction models. Practitioners and policymakers could apply this method to obtain a more accurate real-time crash likelihood estimation, identify important crash precursors, dynamically update algorithms, and enhance safety aspects while operating traffic management strategies on freeways.

Environmental corporate social responsibility and stock price crash risk: The role of environmental performance and ISO 14001

This paper examines the impact of environmental corporate social responsibility (CSR) on stock price crash risk. Using a sample of publicly listed Chinese firms for the period 2010 to 2020, we find that environmental performance negatively affects crash risk. ISO 14001 certification, however, does not exert a significant effect. The negative relationship between environmental performance and crash risk is mediated by the visibility of the firm, and moderated by the certification of ISO 14001, respectively. In addition, the documented relationship is more evident for firms belonging to heavily polluting industries, non-SOEs and firms with greater media coverage and higher market competitive status. Our results remain consistent after controlling for endogeneity concerns and a batch of robustness tests. This study provides new insights into the CSR literature by highlighting the risk-mitigating role of environmental CSR in firms’ future stock price crashes.