Incidence Of Crashes Research Articles

AVChain: Trusted Sharing of Autonomous Vehicle Crash Incident Data using Interoperating HyperLedger Fabric Networks and IPFS

Autonomous vehicles (AVs) are gaining in popularity over the years as a viable cab service apps as well as for personal use. However, incidents of crashes involving AVs continue to occur, adversely affecting their prospects for widespread acceptance by both end users and regulatory authorities. While such cases are routinely investigated, in the absence of a human to testify on what caused the crash, one has to rely solely on available data. It is therefore imperative that the data logged by AVs is accessible to the concerned parties in a trustworthy manner. In this paper, we present AVChain - a novel framework for using a permissioned blockchain like HyperLedger Fabric (HLF) to record and share AV data comprised of sensors, actuators, maps, planning algorithms and machine learning models so that the data stays immutable even in the face of cross blaming among involved parties. Since the data volume is extremely large, we appropriately compress and down sample the same before storing in a distributed file system, namely, IPFS (Inter-Planetary File System). The hashes of such IPFS data called Content Ids (CIDs) are committed to the HLF network for making them tamper proof. The HLF ledger can later be queried to obtain the CIDs, which are then further used to retrieve and un-compress the original data from IPFS. Effectiveness and usability of AVChain is demonstrated by generating autonomous vehicle data from CARLA, which is a widely used open source AV simulator. For sharing AV data across organizations like sensor and actuator suppliers, map service providers, machine learning model developers and law enforcement authorities, the Weaver tool has been used to make multiple HLF networks interoperate. We have also developed a web application to demonstrate the working of AVChain. Results of an extensive set of experiments establish the efficacy of our approach.

Effects of urbanisation and seasons on the relationship between frozen road conditions and road traffic injury: a longitudinal study of national emergency medical service data in South Korea

BackgroundFrozen road conditions could be a factor in road traffic injuries, and seasonality and urbanisation level are potential influencing factors. However, few studies have considered this relationship. Therefore, we examined...

Geo-parsing and Analysis of Road Traffic Crash Incidents for Data-Driven Emergency Response Planning

Geo-parsing and Analysis of Road Traffic Crash Incidents for Data-Driven Emergency Response Planning

EXPRESS: The Impact of App Crashes on Consumer Engagement

The authors develop and test a theoretical framework to examine the impact of app crashes on app engagement. The framework predicts that consumers increase engagement after encountering a single crash due to their need-for-closure and curiosity, yet reduce engagement after experiencing repeated and concentrated crashes, primarily because of frustration and perceived task unattainability; the recency of crashes moderates these effects. Field data analysis reveals that while a crash truncates a session and reduces content consumption, it increases page views in the following session. However, this increase in page views does not compensate for the loss during the crashed session. Frequent and more concentrated crashes curtail engagement. Three experiments in which crashes are exogenously manipulated in a different context support the validity and generalizability of these findings, confirm the proposed mediators, and demonstrate how to lessen the negative impact of repeated crashes with post-crash messages. The research adds new dimensions to the task pursuit literature and provides managers with a framework to quantify the economic impact of crashes, analyze content substitution behavior, and assess the bias of a transactional view of crash incidents. Additionally, it offers insights into targeted feature release to more tolerant users and strategic design of post-crash messages.

Commuter Cyclist Crash Incidence Rate on Cycling Infrastructure in Lyon and Marseille (France)

The aim of this study is to document the crash incidence rate for cyclists when cycling on five different types of cycling infrastructure in two cities in France. A questionnaire was carried out in 2021 with commuter cyclists in Lyon-Villeurbanne (n=780) and Marseille-Aix-en-Provence (n=404). Respondents provided data on their regular cycling trip and on crashes they have experienced. Using a Geographical Information System, the exposure of cyclists on each type of infrastructure was determined. By relating the number of crashes to the number of kilometres travelled, crash rates (per kilometre travelled) and the 95 percent confidence interval were estimated. This study shows that in the two cities studied, the crash incidence rate is significantly higher on contraflow cycle lanes than on the road. On the contrary, shared-use paths (greenways) are associated with a lower crash incidence rate compared to the road. Results for the other types of cycling infrastructure are unclear. Recommendations are discussed. Further studies are required to improve the design of cycling infrastructure and the broader transport policy to support safe cycling.

Analyzing the Relationship Between User Feedback and Traffic Accidents Through Crowdsourced Data

Identifying road segments with a high crash incidence is essential for improving road safety. Conventional methods for detecting these segments rely on historical data from various sensors, which may inadequately capture rapidly changing road conditions and emerging hazards. To address these limitations, this study proposes leveraging crowdsourced data alongside historical traffic accident records to identify areas prone to crashes. By integrating real-time public observations and user feedback, the research hypothesizes that traffic accidents are more likely to occur in areas with frequent user-reported feedback. To evaluate this hypothesis, spatial autocorrelation and clustering analyses are conducted on both crowdsourced data and accident records. After defining hotspot areas based on user feedback and fatal accident records, a density analysis is performed on such hotspots. The results indicate that integrating crowdsourced data can complement traditional methods, providing a more dynamic and adaptive framework for identifying and mitigating road-related risks. Furthermore, this study demonstrates that crowdsourced data can serve as a strategic and sustainable resource for enhancing road safety and informing more effective road management practices.

Factors Affecting Single and Multivehicle Motorcycle Crashes: Insights from Day and Night Analysis Using XGBoost-SHAP Algorithm

This study aimed to identify and compare the risk factors associated with motorcycle crash severity during both daytime and nighttime, for single and multivehicle incidents in Thailand using 2021–2024 data. The research employed the XGBoost (Extreme Gradient Boosting) method for statistical analysis and extensively examined the temporal instability of risk factors. The results highlight the importance of features impacting the injury severity of roadway collisions across various conditions. For single motorcycle crashes, the key risk factors included speeding, early morning incidents, off-road events, and long holidays. In multivehicle crashes, rear-end collisions, interactions with large vehicles, and collisions involving other motorcycles or passenger cars were linked to increased injury severity. The findings indicate that the important factors associated with motorcyclist injury severity in roadway crashes vary depending on the type of crash and time of day. These insights are valuable for policymakers and relevant authorities in developing targeted interventions to enhance road safety and mitigate the incidence of severe and fatal motorcycle crashes.

Return to Driving Following Moderate-to-Severe Traumatic Brain Injury: A TBI Model System Longitudinal Investigation.

To examine longitudinal patterns of return to driving (RTD), driving habits, and crash rates associated with moderate-to-severe traumatic brain injury (TBI). Eight TBI Model System sites. Adults (N =334) with TBI that required inpatient acute rehabilitation with follow-up of 197 and 218 at 1 and 2years post-injury, respectively. Data collection at 2 years occurred almost exclusively during the pandemic, which may have affected results. Longitudinal and observational. Driving survey completed during rehabilitation and at phone follow-up 1 and 2years after injury. The rate of RTD was 65% at 1-year follow-up and 70% at 2-year follow-up. RTD at both follow-up time points was positively associated with family income. The frequency of driving and distance driven were diminished compared to before injury. Limitation of challenging driving situations (heavy traffic, bad weather, and at night) was reported at higher rates post-injury than before injury. Crash rates were 14.9% in the year prior to injury (excluding crashes that resulted in TBI), 9.9% in the first year post-injury, and 6% during the second year. RTD is common after TBI, although driving may be limited in terms of frequency, distance driven, and avoiding challenging situations compared to before injury. Incidence of crashes is higher than population-based statistics; however, those who sustain TBI may be at higher risk even prior to injury. Future work is needed to better identify characteristics that influence the likelihood of crashes post-TBI.

How Capital Market Liberalization Influences Stock Price Crash Risk: The Intermediate Effect of Corporate Accounting Clarity

The introduction of the Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect programs represents a pivotal juncture in the evolution of Chinas capital markets, signaling a significant move towards increased openness and integration. These initiatives have sparked considerable academic interest, underscoring their potential to transform the financial landscape. This research employs these programs as a quasi-natural experiment to investigate their profound impact on financial stability. Our analysis focuses on a comprehensive dataset encompassing A-share firms listed on both the Shanghai and Shenzhen Stock Exchanges over a thirteen-year period, from 2008 to 2021. Employing a robust difference-in-differences analytical approach, this study seeks to discern the effects of capital market liberalization on the incidence of stock price crashes. The empirical findings reveal a compelling association: greater openness in capital markets correlates significantly with a reduced risk of stock price crashes. This correlation is largely attributed to enhancements in corporate accounting transparency facilitated by these initiatives. Thus, these findings underscore the dual role of enhanced capital market openness in not only fostering stability in stock prices but also in mitigating market risks, safeguarding investor wealth, reducing systemic financial vulnerabilities, and fostering sustainable development across capital markets. This study contributes meaningfully to the existing literature on stock price crash risk by providing nuanced insights and actionable recommendations for policymakers and market participants alike, aiming to further bolster capital market openness and resilience.

Analyzing the Suitability of Vehicle Telematics Data as a Surrogate Safety Measure for Short-Term Crashes

Traffic safety analysis relies heavily on the comprehensiveness of traffic crash data. However, crash occurrences are rare events. Researchers have sought alternative measures closely linked to crash occurrences, known as surrogate safety measures, to address this challenge. The advent of vehicle telematics has introduced a valuable source of data, consisting of time-stamped positional information of vehicles, facilitated through telecommunication networks. Wejo Data Services Inc. collects vehicle telematics data through collaboration with various automotive original equipment manufacturers (OEMs) to furnish connected vehicle data along with driver event variables, such as braking and acceleration. This study seeks to investigate the relationship between hard-braking events and the incidence of total crashes and peak period crashes across different facility types using analysis of variance and negative binomial models. The results show a strong correlation between hard braking and crash frequency. The ratio of hard-braking events to crashes exhibits notable variations across peak hours, off-peak periods, and nighttime. Further, the coefficient of hard braking is significantly higher on freeways compared with non-freeways. The model performance of different short period models improved better than the extent of improvement in total crash model after hard braking was introduced as an explanatory variable. The marginal effects of hard braking on crash frequency on freeways are consistently higher than those in non-freeway segments.

Factors and Challenges in Road Crash Incidents: Basis for Enhanced Interventions

Factors and Challenges in Road Crash Incidents: Basis for Enhanced Interventions

Enhancing Aviation Safety through AI-Driven Mental Health Management for Pilots and Air Traffic Controllers.

This article provides an overview of the mental health challenges faced by pilots and air traffic controllers (ATCs), whose stressful professional lives may negatively impact global flight safety and security. The adverse effects of mental health disorders on their flight performance pose a particular safety risk, especially in sudden unexpected startle situations. Therefore, the early detection, prediction and prevention of mental health deterioration in pilots and ATCs, particularly among those at high risk, are crucial to minimize potential air crash incidents caused by human factors. Recent research in artificial intelligence (AI) demonstrates the potential of machine and deep learning, edge and cloud computing, virtual reality and wearable multimodal physiological sensors for monitoring and predicting mental health disorders. Longitudinal monitoring and analysis of pilots' and ATCs physiological, cognitive and behavioral states could help predict individuals at risk of undisclosed or emerging mental health disorders. Utilizing AI tools and methodologies to identify and select these individuals for preventive mental health training and interventions could be a promising and effective approach to preventing potential air crash accidents attributed to human factors and related mental health problems. Based on these insights, the article advocates for the design of a multidisciplinary mental healthcare ecosystem in modern aviation using AI tools and technologies, to foster more efficient and effective mental health management, thereby enhancing flight safety and security standards. This proposed ecosystem requires the collaboration of multidisciplinary experts, including psychologists, neuroscientists, physiologists, psychiatrists, etc. to address these challenges in modern aviation.

Modeling of Risk Factors for Vulnerable Road Users’ Crashes on National Highways Passing through Medium-sized Indian Cities

The safety of vulnerable road users, such as pedestrians, bicyclists and motorised twowheelers, on national highways passing through urban areas has been a significant concern in developing countries like India. This study analyzes Visakhapatnam police crash data (2014-2017) to identify high-risk road segments to vulnerable road users. Establishing negative binomial models helps analyze risk variables for fatal and injury crashes concerning several susceptible road users on mid-block sections. Further, this study focuses on site-specific information collected from road-safety audits, speed, average daily traffic, length of road section and percentage of two-wheelers, also considered to fully help understand the likelihood of a crash. The statistical analysis of this paper identified the risk variables, like length of road segment, presence of service roads, land-use type (i.e., commercial/mixed land use), number of curves and average daily traffic associated with the incidence of fatal and injury crashes of vulnerable road users in Visakhapatnam city, India. The conclusions highlight the crucial safety measures that transportation planners and policymakers must take to create a more secure environment for road users. Keywords: Vulnerable road users, Mid-block sections, Negative binomial models, Crash, Count-data models, Safety measures.

Fatal Traffic Risks With a Total Solar Eclipse in the US

This case-control study describes the incidence of fatal traffic crashes in the US during the 2017 total solar eclipse.

The immediate effects of vision-zero corridor upgrades on pedestrian crashes in New York: A before-and-after spatial point process approach

The long-term effects of the Vision-Zero (VZ) approach in Scandinavia are well documented. In contrast, information regarding the immediate effects of VZ at the starting phase upon gradual implementation is scarce. Taking New York City as the case study, we analyzed both the local and global effects of the Vision-Zero gradual implementation on pedestrian crashes in the early stage of implementation starting from 2014. The data analysis comprised 8,165 pedestrian injury crashes. Using location data, the crashes were matched to VZ infrastructure improvement location, start and completion dates. The experimental design included a treatment and two types of control conditions, and we controlled for well-known covariates including traffic exposure, land use, and risk-prone areas. We estimated a Geyer Saturation model and kernel density function for modeling the effect of Vision-Zero on crash intensity and dispersion two years before and after the implementation of Vision-Zero. The results reveal a significant global decrease of 6.1 % (p = 0.004) in pedestrian crash incidence in the treated sections compared with the control group two years after the treatment, and a greater dispersion of pedestrian injuries following the policy implementation.

Ambulance Traffic Crashes in Japan: Characteristics of Casualties and Efforts to Improve Ambulance Safety

Background An ambulance traffic crash not only leads to injuries among emergency medical service (EMS) professionals but also injures patients or their companions during transportation. We aimed to describe the incidence of ambulance crashes, seating location, seatbelt use for casualties (ie, both fatal and nonfatal injuries), ambulance safety efforts, and to identify factors affecting the number of ambulance crashes in Japan. Methods We conducted a nationwide survey of all fire departments in Japan. The survey queried each fire department about the number of ambulance crashes between January 1, 2017, and December 31, 2019, the number of casualties, their locations, and seatbelt usage. Additionally, the survey collected information on fire department characteristics, including the number of ambulance dispatches, and their safety efforts including emergency vehicle operation training and seatbelt policies. We used regression methods including a zero-inflated negative binomial model to identify factors associated with the number of crashes. Results Among the 726 fire departments in Japan, 553 (76.2%) responded to the survey, reporting a total of 11,901,210 ambulance dispatches with 1,659 ambulance crashes (13.9 for every 100,000 ambulance dispatches) that resulted in a total of 130 casualties during the 3-year study period (1.1 in every 100,000 dispatches). Among the rear cabin occupants, seatbelt use was limited for both EMS professionals (n = 3/29, 10.3%) and patients/companions (n = 3/26, 11.5%). Only 46.7% of the fire departments had an internal policy regarding seatbelt use. About three-fourths of fire departments (76.3%) conducted emergency vehicle operation training internally. The output of the regression model revealed that fire departments that conduct internal emergency vehicle operation training had fewer ambulance crashes compared to those that do not (odds of being an excessive zero −2.20, 95% CI: −3.6 to −0.8). Conclusion Two-thirds of fire departments experienced at least one crash during the study period. The majority of rear cabin occupants who were injured in ambulance crashes were not wearing a seatbelt. Although efforts to ascertain seatbelt compliance were limited, Japanese fire departments have attempted a variety of methods to reduce ambulance crashes including internal emergency vehicle operation training, which was associated with fewer ambulance crashes.

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

AbstractContemporary spatial statistics studies often underestimate the complexity of road networks, thereby inhibiting the strategic development of effective interventions for car accidents. In response to this limitation, the primary objective of this study is to enhance the spatiotemporal analysis of urban crash data. We introduce an innovative spatial‐temporal weight matrix (STWM) for this purpose. The STWM integrates external covariates, including road network topological measurements and economic variables, offering a more comprehensive view of the spatiotemporal dependence of road accidents. To evaluate the functionality of the presented STWM, random effect eigenvector spatial filtering analysis is employed on Boston's traffic accident data from January to March 2016. The STWM improves analysis, surpassing distance‐based SWM with a lower residual standard error of 0.209 and a higher adjusted R2 of 0.417. Furthermore, the study emphasizes the influence of road length on crash incidents, spatially and temporally, with random standard errors of 0.002 for spatial effects and 0.026 for non‐spatial effects. This is particularly evident in the north and center of the study area during specific periods. This information can help decision‐makers develop more effective urban development models and reduce future crash risks.

Impact of alcohol driving-while-impaired license suspension duration on future alcohol-related license events and motor vehicle crash involvement in North Carolina, 2007 to 2016

Background/PurposeLicense suspensions are a strategy to address alcohol-impaired driving behavior and recidivism following an alcohol driving while impaired (alcohol-DWI) conviction. Little is known about the specific impacts of conviction-related suspensions on safety outcomes and given recent fluctuations in alcohol-impaired driving behavior, crashes, and suspension trends, updated and focused assessments of this intervention are necessary. This study aimed to 1) examine the association between type of recent alcohol-DWI suspension and having a secondary alcohol-related license outcome and/or future crash event in North Carolina (NC) between 2007 and 2016; and 2) assess potential modification of these associations by race/ethnicity. MethodsWe used linked NC licensing data, NC crash data, and county-level contextual data from a variety of data sources. We compared individuals ages 21 to 64 who sustained initial (1-year) versus repeat (4-year) suspensions for alcohol-related license and crash involvement outcomes. We estimated unadjusted and adjusted hazard ratios (aHRs) using Cox proportional hazards models and produced Kaplan-Meier (KM) survival curves using a three-year follow-up period. After observing statistically significant modification by race/ethnicity, we calculated stratified aHRs for each outcome (Black and White subgroups only, as other subgroups had low numbers of outcomes). Results122,002 individuals sustained at least one alcohol-DWI conviction suspension (117,244 initial, 4,758 repeat). Adjusted KM survival curves indicated that within three years of the index suspension, the predicted risks of having a license outcome and crash outcome were about 8 % and 15 %, respectively, among individuals with an initial suspension and 5 % and 10 %, respectively, among individuals with a repeat suspension. After adjusting for potential confounding, we found that compared to those with an initial suspension, those with repeat suspensions had a lower incidence of future license (aHR: 0.49; 95 % CI: 0.42, 0.57) and crash outcomes (aHR: 0.67; 95 % CI: 0.60, 0.75). Among Black individuals, license outcome incidence was 162 % lower among repeat versus initial index suspension groups (aHR: 0.38; 95 % CI: 0.26, 0.55), while for White individuals, the incidence was 87 % lower (aHR: 0.54; 95 % CI: 0.45, 0.64). Similarly, crash incidence for repeat versus initial suspensions among Black individuals was 56 % lower (aHR: 0.64; 95 % CI: 0.50, 0.83), while only 39 % lower among White individuals (aHR: 0.72; 95 % CI: 0.63, 0.81). ConclusionsDecreased incidence of both license and crash outcomes were observed among repeat versus initial index suspensions. The magnitude of these differences varied by race/ethnicity, with larger decreases in incidence among Black compared to White individuals. Future research should examine the underlying mechanisms leading to alcohol-impaired driving behavior, convictions, recidivism, and crashes from a holistic social-ecological perspective so that interventions are designed to both improve road safety and maximize other critical public health outcomes, such as access to essential needs and services (e.g., healthcare and employment).

Fatal crashes and rare events logistic regression: an exploratory empirical study.

Fatal road accidents are statistically rare, posing challenges for accurate estimation through the classic logit model (LM). This study seeks to validate the efficacy of a rare events logistic model (RELM) in enhancing the precision of fatal crash estimations. Both LM and RELM were employed to examine the relationship between pertinent risk factors and the incidence of fatal crashes. Crash-injury datasets sourced from Hillsborough County, Florida served as the empirical basis for evaluating the performance metrics of both LM and RELM. The analysis revealed that RELM yielded more accurate predictions of fatal crashes compared to LM. Receiver operating characteristic (ROC) curves were constructed, and the area under the curve (AUC) for each model was computed to offer a comparative performance assessment. The empirical evidence notably favored RELM over LM as substantiated by superior AUC values. The study offers empirical validation that RELM is demonstrably more proficient in predicting fatal crashes than the LM, thereby recommending its application for nuanced traffic safety analytics.

Investigating effective factors on rural crash severity at marginal areas around cities in Iran: a partial proportional odds modelling approach

Over the past decade, rural crashes have been responsible for an average of 65% of crash-induced casualties in Iran. Evidence from prior studies reveals that a significant number of these rural crashes occur at marginal areas around cities. Thus, Exclusive crash severity models should be developed to identify the factors associated with higher injury and fatality probabilities in these areas. In this study, a partial proportional odds (PPO) model was formulated using the rural crash data collected from roads leading to the city of Isfahan. The PPO model holds the ordinal nature of crash observations and allows for different influences of independent variables on various crash severity levels. Insights derived from the results reveal that factors such as vehicle traffic maintaining an average speed exceeding 95 km/h, the occurrence of multi-vehicle crashes, the incidence of overturn-type crashes, the at-fault vehicle being a truck/trailer and at-fault or not-at-fault vehicle being a motorcycle, increase the likelihood of more severe rural crashes. Conversely, a foreign vehicle being at-fault, and the driver of the at-fault vehicle aged between 30 and 40 years, tend to diminish the occurrence of severe crashes at marginal areas around cities.