Crash Risk Research Articles

Road rule enforcement and where to find it: An investigation of applications used to avoid detection when violating traffic rules

Introduction: One of the primary countermeasures in place to prevent road rule violations is legal enforcement, yet there are numerous applications that can undermine such efforts by notifying drivers of enforcement locations. However, the capabilities of these applications and how they can impact offending behavior is currently unknown. Method: Two studies were conducted to understand which of these applications are being used by drivers and how these applications are impacting road rule violations. Study 1 consisted of a content analysis that involved searching the Google Play Store and Apple iTunes Store for applications that could be used to avoid road rule violations using pre-determined keywords. Meanwhile, Study 2 consisted of 468 licensed Australian drivers (54.5% males) over the age of 17 years (Mage = 35 years) who completed a survey. Results: A total of 73 applications were identified for Study 1, with most of the applications displaying speed camera locations. It was found that applications that notify drivers of traffic enforcement locations are widely prevalent, can be used on a variety of interfaces and include numerous additional features. Study 2 found that those who use the applications were more willing to speed than those who do not use the applications, while there was no difference in phone use while driving between those who do and do not use the applications. Practical Applications: The findings have important implications for stakeholders, policy, and future research. For example, it is suggested that specific functions of these applications need to be regulated to reduce road rule violations and crash risk. Meanwhile, enforcement initiatives need to evolve at a faster rate to keep up to date with the changing technology that can undermine them.

Investigating The Role of Accounting Information Comparability in Mitigating Stock Price Crash Risk: Evidence from China’s Knowledge-Based Economy

Investigating The Role of Accounting Information Comparability in Mitigating Stock Price Crash Risk: Evidence from China’s Knowledge-Based Economy

From low resource slack to inflexibility: The share price effect of operational efficiency

This study examines the relationship between operational efficiency (OE) and stock price crash risk (SPCR). While high OE is arguably associated with better firm performance, it also increases vulnerability to disruptions and exposure to SPCR. Using a sample of US firms from 1997 to 2018, the study finds a positive relationship between OE and SPCR; additional results suggest that firm cash flow volatility serves as the channel through which this relationship is mediated. The study also finds evidence that high-OE firms tend to suppress bad news from investors, which exposes them to SPCR. Furthermore, the study shows that the positive relationship between OE and SPCR beyond the optimal level hurts firm value, highlighting the importance of balancing efficiency improvement efforts with business risk management. The results of this study have implications for managers and owners seeking to mitigate tail risk in firm investment decisions, and underscore the importance of maintaining business flexibility in the pursuit of efficiency.

Corporate social responsibility and stock-price crash risk: test of regulation mechanism based on institutional ownership and internal control

ABSTRACT Addressing financial risks draws attention from both government and academia. Current studies primarily focus on the impact of CSR on stock-price crash risk, overlooking the regulatory role of institutional ownership and internal control, and the mediating effect of analyst coverage. This paper investigates the relationship between CSR and stock price crash risk using data from A-share listed companies (2015–2021). Findings suggest that better CSR helps reduce stock-price crash risk. The mediating effect test shows that improved CSR attracts analysts’ attention and enhances information transparency, thus reducing stock-price crash risk. Institutional ownership, internal control and CSR have substitution relations in inhibiting stock-price crash risk. Further research shows CSR’s significant role in reducing stock-price crash risk for non-state-owned, eastern regions, and non-Big Four audited enterprises. This study enriches literature on stock-price crash risk and CSR and guide firms in reducing stock-price crash risk, promoting capital market development and enhancing enterprise evaluation systems.

Can a firm's hierarchical complexity affect its stock price behavior? Evidence from stock price crash risk

PurposeThis paper examines how a firm's hierarchical complexity, which is determined by the way it organizes its subsidiaries across the hierarchical levels, can impact its stock price crash risk.Design/methodology/approachThe authors employ a measure of hierarchical complexity that captures the depth and breadth of how subsidiaries are organized within a firm. This measure is calculated using information about firms' subsidiaries extracted from the Bureau van Dijk (BvD) database that allows the authors to construct each firm's hierarchical structure. The data sample includes 2,461 USA firms for the period from 2012 to 2017 (11,006 firm-year observations). Univariate tests and panel regression are used for the main analysis. Two-stage-least-squares (2SLS) instrumental variable regression and various other tests are employed for robustness check.FindingsThe results show a positive relationship between hierarchical complexity and stock price crash risk. This relationship is amplified in firms with a greater number of subsidiaries that are hierarchically distanced from the parent company as well as in firms with a greater number of foreign subsidiaries in countries with weaker rule of law.Originality/valueThis paper is the first to investigate the impact hierarchical complexity has on crash risk. The results highlight the role that a firm's organizational structure can have on asset pricing behavior.

A better start: board diversity matters in assessing stock price crash risk

PurposeThe determinants that contribute to reducing stock price crash risk have garnered attention from scholars and practitioners. However, our understanding of the relationship between board diversity and stock crash risk, as well as the contextual factors that influence this relationship, remains limited. To address this gap, this study aims to investigate how different attributes of board diversity affect stock price crash risk, particularly under conditions of higher performance hazard and ownership concentration.Design/methodology/approachUsing a two-stage least squares fixed-effects estimator, the authors analyze a panel data set of 1,792 firm-year observations across 282 firms listed on the KOSPI200 from 2010 to 2019.FindingsRelation-oriented diversity reduces future stock price crash risk, particularly when firms experience performance shortfalls and have concentrated ownership structures, but task-oriented diversity has no significant effects. The results imply that only relation-oriented diversity strengthens governance mechanisms by curtailing managerial bad news withholding behaviors, and the role of relation-oriented diversity in reducing stock crash risk becomes more crucial when firms have higher performance hazard and concentrated ownership.Originality/valueThis study makes crucial contributions as follows: the authors contribute to the stock crash risk literature by shifting the focus from how to when board diversity matters in assessing stock crash risk; the authors extend the board diversity research and enhance scholarly understanding of the effects of board diversity on corporate governance by highlighting that not all aspects of board diversity improve firm governance mechanisms; and the authors widen the lens from a single attribute to multiple attributes of diversity to reveal the effects of diversity on boards in assessing future crash risk.

Institution quality and stock price crash risk: a global perspective

PurposeThis study aims to provide empirical evidence on the relationship between formal institutions and stock price crash risk from a global perspective.Design/methodology/approachThis paper uses data of 35,468 firms globally over the years 1987–2019 and address the endogeneity issue by employing the Mundlak random effects estimator.FindingsThe authors find a significant negative impact of institution quality on stock price crash risk (i.e. better institutions reduce crash risk), after controlling for common determinants of crash risk such as leverage, return on asset, firm size, investment, etc. as well as macro factors such as GDP growth. This effect is robust to different measures of crash risk and sub-indicators of institutions quality. In addition, the authors also find this effect to be universally present in economies characterized by different levels of income.Originality/valueTo the best of the authors' knowledge, there's no known study that explores the potential causal relationship between institution quality and stock price crash risk. Therefore, the research topic in this study is original and can contribute significantly to the existing literature.

Digital transformation and risk of share price crash: Evidence from a new digital transformation index

Digital transformation has become a key initiative for firms’ sustainable development. Based on the annual reports of A-share listed companies, we use LDA model to construct a novel measure of firm digital transformation and examine its effect on the risk of stock price crash. We find that firm digital transformation significantly affects the risk of stock price crash. We then show that information asymmetry and firm performance are underlying mechanisms. Further, heterogeneity analyses show that the ownership and the degree of technology moderate our findings. This paper provides policy implications on the development of digital economy and capital markets.

The informal hierarchy of board and stock price crash risk

We examine the impact of the clarity of the informal board hierarchy on a firm's stock price crash risk. By analyzing data from Chinese A-shares between 2010 and 2021, we find that: First, there is a positive relationship between the clarity of the informal board hierarchy and stock price crash risk, suggesting that when the board hierarchy is less transparent, it contributes to a higher likelihood of stock price crashes. Second, the above effect is moderated by the level of independence of the supervisory board. When the supervisory board possesses greater autonomy in decision-making, it helps mitigate the above effect.

Banking competition and stock price crash risk

This paper examines whether an increase in bank competition affects firms' share price crash risk, using A-share listed companies in China from 2009 to 2021 as a research sample. The study finds that firms' share price crash risk decreases with the increase of banking competition; the mediation test finds that the increase of banking competition inhibits corporate non-compliance and increases the disclosure requirements of internal control information, reducing the share price crash risk.

Crash Risk Among Young Drivers Diagnosed with Psychopathology

Young drivers’ crash rates are highly elevated. Despite the small age range, this population is extremely heterogeneous, with subgroups at greater risk than others. Real-world driving data were used to examine risks of young drivers diagnosed with psychiatric psychopathologies. 654 young drivers were assessed for up to 2yrs. Relative to healthy controls, drivers with any psychiatric diagnoses had higher crash/near-crash (CNC) rates (aIRR=1.49). Young drivers diagnosed with internalizing psychopathology (depression and/or anxiety) had 41% higher CNC rates compared to healthy controls. Young drivers with comorbid externalizing (ADD/ADHD/Tourette’s Syndrome) and internalizing psychopathologies had the highest crash and CNC rates compared to healthy controls (aIRR=2.60; aIRR=2.51, respectively). In conclusion, psychiatric disorders represent a specific vulnerability for crashes where young drivers with comorbid internalizing and externalizing disorders are at much greater risk to crash. Future research should examine possible variability or attenuation of risk associated with psychiatric treatment (e.g., medication or behavioral interventions).

CBT program to reduce recidivism risk for road crashes among adolescents and young adults: Results of a randomized controlled study and prospects

Road crashes are the first cause of mortality for young adults aged 18–25 years and the human factor contributes to 90–95% of events. The present study was carried out to determine the efficacity of the ECARR2 recurrence prevention program among adolescents and young adults at high risk of having a new traffic crash in the following months. A total of 288 participants having had a traffic crash that required going to the emergency room, at high risk of accident recurrence (ECARR≥5) were randomly allocated to either the intervention group (n = 144) or the control group (n = 144). Resultsrevealed that the risk of recurrence was highest during the first 6 months (66% of recurrences). In per-protocol analysis population, at 6 months after inclusion, the accident recurrence rate was 14.2% ± 3.3% in the intervention group, and 23.5% ± 4.0% in the control group. The intervention had an effect per se, independently of the other predictors (p = 0.020). This effect was mediated by the three interaction variables: BDI, Impulsive Behavior Scale lack of perseverance, and Orientation to Happiness engagement. It was therefore through these dimensions that the intervention had an effect. In conclusion, the ECARR score predicts the risk of recurrence, risk which is the highest during the first 6 months. Finally, results confirm the predictive validity of the ECARR scale. The ECARR score had an effect on the risk of recurrence regardless of group (p = 0.045) and was predictive of recurrence (p = 0.045).A brief psychological intervention such as ECARR2 program, offered to young people ar hight risk of having a new crash, just after the crash, seems to halve the risk of recurrence at 6 months. Future research should improve the brief psychological intervention and its access via a mobile application or few hours in high school or in a driving school given.

Treating Noise and Anomalies in Vehicle Trajectories From an Experiment With a Swarm of Drones

Unmanned aerial systems, known as “drones”, are relatively new in collecting traffic data. Data from drone videography can have potential applications for traffic research. Drones can record the vehicles from their aerial point-of-view and provide their naturalistic driving behavior. Processing raw data from drones to remove noise and anomalies is crucial to ensure that the data are fit for subsequent applications, e.g., the development of traffic flow or crash risk models. This study uses a part of the pNEUMA dataset, a large dataset with almost half a million trajectories captured by a swarm of drones over Athens, Greece. This novel dataset offers an opportunity to analyze the data attributes and treat the noise and outliers in the data. We use a combination of smoothing filters and Extreme Gradient Boosting with adaptive regularization to process the speed and acceleration profiles of the vehicle trajectories in the dataset. Our approach can help prospective data users treat this or similar trajectory datasets alternatively to applying manual thresholds and assist in accelerating research in microscopic traffic analysis.

Risk-ATTEND (Risk Anticipation Training to Enhance Novice Driving): Pilot Evaluation of a Risk Anticipation Training Program for Teen Drivers

Teen drivers have the highest crash risk when they start driving independently, and improving risk anticipation skills can help reduce this risk. Training can help teens acquire these skills faster and improve their overall safety record. The objective of this study was to evaluate an updated training program (based on RAPT and ACCEL) by introducing a more diverse set of training scenarios and leveraging deployment platforms that were better suited to modern devices. A pilot evaluation study was conducted with six teen drivers with less than six months of licensure to assess the updated training program (Risk-ATTEND). Risk-ATTEND was found to improve hazard anticipation-related gaze behaviors in teen drivers while also reducing workload. The research will aid in improving the training program’s usability, and web-based training will make the training program more accessible on modern devices.

Evasive actions to prevent pedestrian collisions in varying space/time contexts in diverse urban and non-urban areas

This study aims to identify driver-safe evasive actions associated with pedestrian crash risk in diverse urban and non-urban areas. The research focuses on the integration of quantitative methods and granular naturalistic data to examine the impacts of different driving contexts on transportation system performance, safety, and reliability. The data is derived from real-life driving encounters between pedestrians and drivers in various settings, including urban areas (UAs), suburban areas (SUAs), marked crossing areas (MCAs), and unmarked crossing areas (UMCAs). By determining critical thresholds of spatial/temporal proximity-based safety surrogate techniques, vehicle–pedestrian conflicts are clustered through a K-means algorithm into different risk levels based on drivers' evasive actions in different areas. The results of the data analysis indicate that changing lanes is the key evasive action employed by drivers to avoid pedestrian crashes in SUAs and UMCAs, while in UAs and MCAs, drivers rely on soft evasive actions, such as deceleration. Moreover, critical thresholds for several Safety Surrogate Measures (SSMs) reveal similar conflict patterns between SUAs and UMCAs, as well as between UAs and MCAs. Furthermore, this study develops and delivers a pseudo-code algorithm that utilizes the critical thresholds of SSMs to provide tangible guidance on the appropriate evasive actions for drivers in different space/time contexts, aiming to prevent collisions with pedestrians. The developed research methodology as well as the outputs of this study could be potentially useful for the development of a driver support and assistance system in the future.

Risk analysis of China's financial market collapse based on cloud computing and machine learning algorithms

AbstractIn recent years, with the rapid advancement of hardware and software algorithms, information technology has penetrated every aspect of our lives, creating waves of digital transformation in enterprises represented by four underlying technologies, namely: ‘artificial intelligence, big data, cloud computing, and blockchain’. As a result, digital technology has emerged as the primary force behind industrial transformation and upgrading, as well as high‐quality development. Using the data of Shanghai and Shenzhen A‐share listed companies from 2007 to 2020, this paper empirically examines the impact of the digital transformation of listed companies on their stock price crash risk and its mechanism using a two‐way fixed effect model with time and individual effects. It can be seen from the research results that, overall, enterprise digital transformation can significantly reduce the risk of a stock price crash. Further analysis reveals that this risk‐inhibiting effect is more apparent for private enterprises and small and medium‐sized enterprises. In terms of impact mechanism, digital transformation can reduce the risk of a stock price crash by improving the quality of enterprise information internally, while the more analysts pay attention to an issue, the more obvious the role of digital transformation in reducing the risk of a stock price crash for enterprises externally. However, it is worth noting that if the stock price of an enterprise crashes, blindly implementing digital transformation may increase the crash risk. The paper illustrates the practical importance of digital technology from the perspective of enterprises in stabilizing the crash risk of stock prices and improving the performance of the capital market. Meanwhile, it contributes to theoretical research on the impact of digital transformation on microeconomic entities and provides practical knowledge and evidence for enterprises to accelerate digital transformation.

Impact assessment of professional drivers’ speed compliance and speed adaptation with posted speed limits in different driving environments and driving conditions

ABSTRACT This study analyzed the impact of driving environments (real-world and simulated world) and driving conditions (no time pressure and time pressure) on speed compliance and speed adaptation. Professional car drivers were recruited, and the data was collected in real-world and simulated world under no time pressure and time pressure driving conditions. The comparison results using Wilcoxon-signed rank test showed that speed compliance and speed adaptation were not consistently significant and were not in the same direction highlighting the influence of various factors like road features and driver characteristics. The generalized linear mixed model results showed that speed compliance was relatively better in simulated world (by 3.98 kmph) than real-world. Further, speed adaptation under time pressure was about 5.86 kmph lower during real-world as compared to simulated world. The findings from this study can provide new insights on road safety strategies and policy implications for limiting speeding-related crash risks.

Exploring nighttime pedestrian crash patterns at intersection and segments: Findings from the machine learning algorithm

Introduction: Pedestrian safety at nighttime is an ongoing critical traffic safety concern. Although poor visibility is primarily associated with nighttime pedestrian crashes, other contributing factors such as humans, vehicles, roadways, and environmental factors interact with each other to cause a crash. Additionally, the pattern of nighttime pedestrian crashes differs significantly according to the intersection and segment location, which requires further exploration. Data: This study applied Association Rules Mining (ARM), a rule-based machine learning method, to reveal the association of nighttime pedestrian crash risk factors according to the intersection and segment locations using 2,505 nighttime pedestrian fatal and injury crashes in Louisiana (2015–2019). Results and conclusions: Based on the generated rules, the results show that nighttime pedestrian crashes at the intersection are associated with right-turn vehicle movement, older drivers (>64 years) at the high-speed intersection, senior pedestrians (>64 years) in rainy weather conditions, violation by pedestrian age group ‘<15 years’, and alcohol-intoxicated pedestrian violation in business/industrial areas. Additionally, ‘careless operation’ at the intersection is associated with alcohol-involved drivers. Most of the nighttime pedestrian crashes at segments are associated with roadways with no physical separation and the absence of streetlights. Driver alcohol involvement and their physical condition (inattentive/distracted) are also associated with pedestrian crashes associated at the segment location at night. Other segment pedestrian crashes are linked to the interstate in dark conditions, open country locations, and high-speed roadways. Additionally, the crash site investigation identified several critical pedestrian safety concerns including the lack of crosswalk facilities, high driveway density, and pedestrian behavioral patterns (e.g., crossing at roadway segments close to the intersection location). Practical Applications: The findings of this study can be used for selecting the appropriate countermeasures based on a case-by-case basis. The exposure patterns can be used in educational campaigns to strategically reduce nighttime pedestrian crashes.

Media Tone and Stock Price Crash Risk: Evidence from China

Following the 2008 financial crisis, multiple studies have contributed to the research on stock price crashes. However, most of the studies on stock price crashes are from the corporate management perspective, focusing on factors such as the board’s character, the CEO’s power, the brand’s capital, and ESG performance. Few studies have taken external information, such as media coverage, into consideration. Meanwhile, in the era of 5G, internet media has witnessed exponential growth, heavily enhancing the speed of information transmission; this could possibly impact the future risk associated with stock price crashes. From this perspective, our study extends the coverage by investigating the relationship between internet media coverage and the potential risk of stock price crashes. Using a comprehensive dataset of the Chinese stock market from 2008 to 2021, we found that the optimistic (pessimistic) tones of internet media were positively (negatively) correlated with the future risk of crashes. These findings remained firm after accounting for winsorization, corporate governance control, firm fixed effects, and instrumental variable analysis. Further analyses showed that media tone impacts were more pronounced for firms with higher analyst coverage. Our study indicates that investors, especially retail investors, who are more easily influenced by internet media, should be more cautious about the increasingly favorable internet coverage of listed companies, which could result in a heightened future risk of stock price crashes. Moreover, regulators should inform investors when listed companies are experiencing more favorable internet coverage to minimize potential stock market fluctuations and investment losses for investors.

An Econometric Analysis of Weather Effects on Roadway Crash Severity in Bangladesh: Evidence from the Dhaka Metropolitan Area

This study identifies how weather-related factors affect crash severity and the number of fatalities from roadway crashes. We use pooled cross-section data for weather-related variables from the National Aeronautics and Space Administration (NASA) and crash data from the Dhaka Metropolitan Police (DMP). The novelty of our paper compared to existing research is the use of weather-related factors as regressors in a multinomial logit model. We use crash severity as the dependent variable. The results show that a unit increase in the specific humidity is associated with a change in the odds of fatal crashes by a factor of 0.2195, keeping other contributory factors constant. This research also shows that an increase in temperature is associated with an increase in the odds of a fatal accident. Moreover, it is observed that a one-unit increase in precipitation results in a 1.1151-unit increase in the odds of the risk of fatal crashes compared to that of non-fatal crashes. After a detailed inspection, wind speed was discovered to be an insignificant weather parameter with regard to accident severity. Furthermore, the number of fatalities is displayed graphically in a time series to thoroughly examine the fatality trend’s relationship with the monthly averaged weather variables. It is expected that the findings of this research will provide policy makers with insights into the weather-related causes of crash severity and assist in the execution of necessary measures to decrease unexpected and avoidable losses on Bangladesh’s roads.