Road Fatalities Research Articles

Accident Prevention for Vehicle Safety

Speed is one of the primary wellsprings of vehicle disasters. Many lives could have been saved on the off chance that emergency organizations had gotten the setback information and contacted them on time. This venture manages a mishap recognition framework. In case of a mishap, it involves different parts to call heroes for help. Effective programmed mishap identification with programmed warning of salvage administrations with data about the area of the mishap is a central prerequisite to saving significant living souls. The proposed framework manages mishap cautioning and recognition. Peruse the specific scope and longitude of the mishap vehicle and send this data to the closest crisis administrations. The motivation behind this undertaking is to perceive mishaps and ready salvage administrations on time. As of late the vast majority of the mishap happen because of laziness of drivers in vehicles and trucks. Yearly 1200 passings and 76000 harmed. This methodology incorporates examination of police detailed accident information, top to bottom nearby examinations promptly following an accident of the general driving populace. The present examination was intended to give additional data to traffic wellbeing and others could use in their endeavors to decrease the quantity of sleepy related accidents. than anywhere else in the world, including the more populous China. Calling road fatalities an "epidemic" that will become the world's fifth biggest killer by 2030, the report said while rich nations had been able to lower their death rates, these were sharply on the rise in the third world. It said 90% of deaths on the world's roads occur in low and middle-income countries (21.5 and 19.5 per lakh of population, respectively) though they have just 48% of all registered vehicles. The statistics for India are chilling. At least 13 people die every hour in road accidents in the country, the latest report of the National Crime Records Bureau reveals. However, road safety experts say the real numbers could be higher since many of these accident cases are not even reported. "There is no estimate of how many injured in road accidents die a few hours or days after the accident," points out RohitBaluja, member of the UN Road Safety Collaboration and Commission of Global Road Safety representing Asia. Based on the records, India will become the world number 1 in Road Deaths due to the poor record of average 13 die every hour, which is 1.14 lakh per year. This will make India to be the first place. This causative information about the accidents is the intent to develop the proposed technology as scientific traffic engineering wings to reduce the fatalities due to accidents. This proposed methodology is the automatic system which will provide the solution for identifying the accident location. Nowadays accidents are increasing at an alarming rate. Speed is the cause of most number of traffic accidents. In this project, we first applied Eclat algorithm Keywords— Eclat algorithm, clustering, classification,GPS to group the crime locations into 0 level, 1 level, 2 level accident tracking, Accident.

A reinforcement learning based autonomous vehicle control in diverse daytime and weather scenarios

Autonomous driving holds significant promise for substantially reducing road fatalities. Unlike traditional machine learning methods that have conventionally been applied to enhance the motion control of Autonomous Vehicles (AVs), recent attention has shifted toward the utilization of Deep Learning (DL) and Deep Reinforcement Learning (DRL) techniques. These advanced approaches have the potential to greatly improve AV vehicle control and empower vehicles to learn from their surroundings. However, the majority of existing research has concentrated on straightforward scenarios, often neglecting the intricate challenges posed by vulnerable road users such as pedestrians, cyclists, and motorcyclists, as well as the influence of varying weather conditions. In this study, we propose a novel model founded on DRL, specifically leveraging Deep-Q Networks (DQN), to effectively manage AVs in complex scenarios characterized by heavy traffic, diverse road users, and diverse weather conditions. Our approach involves training the model in diverse weather conditions, encompassing clear daytime and nighttime as well as challenging weather conditions like heavy rainfall during both the day and sunset. Through this comprehensive training, the AV becomes proficient in navigating safely through intersections and reaching its destination without any accidents. To rigorously evaluate and validate our proposed approach, extensive testing was conducted employing the CARLA simulator. The simulation results unequivocally demonstrate that our model not only reduces travel delays but also minimizes the occurrence of collisions, marking a significant step forward in achieving safer and more efficient autonomous driving.

Leveraging computer vision for predicting collision risks: a cross-sectional analysis of 2019–2021 fatal collisions in the USA

ObjectiveThe USA has higher rates of fatal motor vehicle collisions than most high-income countries. Previous studies examining the role of the built environment were generally limited to small geographic areas...

Analisis Jumlah Kecelakaan Lalu Lintas, Korban, dan kerugian di Provinsi Lampung Tahun 2021 Dengan Metode K-Means Cluster

Abstract: Provinsi Lampung is facing a serious challenge regarding the high rate of traffic accidents, triggered by the increasing number of vehicles and inadequate infrastructure. Low awareness of traffic regulations also serves as a major cause of accidents. This research employs the Cluster Analysis method within the Geographic Information System (GIS) to categorize traffic accident data in Lampung Province for the year 2021. The data encompasses the number of accidents, fatalities, serious injuries, minor injuries, and financial losses across 14 districts/cities. The cluster analysis results identify two main groups with distinct characteristics. The first cluster exhibits a low accident rate, while the second cluster shows a high accident rate, contributing significantly to the total number of accidents, fatalities, serious injuries, minor injuries, and financial losses. Recommendations for interventions are tailored to the characteristics of each cluster, emphasizing either fatal injury prevention or road infrastructure improvements. The findings of this research provide a foundation for designing more effective strategies to reduce accident risks and enhance traffic safety in Lampung Province Keywords: K-means, Accidents, Lampung Province

Forensic Assessment of Alcohol Intoxication in Cases of Fatal Road Traffic Accidents in Lithuania.

There is strong evidence that alcohol consumption is a significant risk factor for fatal road traffic accidents. It is estimated that the number of alcohol-related road accidents remains high in the past few years in Lithuania. This study aims to examine the prevalence of alcohol in blood samples collected from the autopsy results of road traffic accident victims. A retrospective study of 136 road traffic accident victims was performed in State Forensic Medicine Service of Lithuania in the period of 2013 to 2023. We analyzed blood alcohol concentration (BAC) in relation to sex, age, road user type, place and time of the day at death. 31% of the victims were under influence of alcohol at the time of death, with mean BAC 1.99 ± 0.92‰. The mean BAC was 2.16 ± 0.8‰ in male and 1.18 ± 1.12‰ in female group. By the type of road users, 23% of the pedestrians (mean BAC 2.45 ± 0.71‰), 32% of car drivers (mean BAC 2.13 ± 0.75‰), 41% of vehicle passengers (mean BAC of 1.73 ± 1.19‰), 37% of the motorcycle riders (mean BAC of 1.28 ± 0.53‰), 37% of the cyclists (mean BAC of 1.15 ± 0.75‰) were found to be intoxicated during the time of accident. Highest mean blood alcohol concentration was found during the night time hours (9 p. m. - 5 a. m.) 2.28 ± 0.91, comparing to in afternoon hours (12 p. m. - 5 p. m.) 1.49 ± 0.99, evening hours (5 p. m. - 9 p. m.) 2.10 ± 0.73 and morning hours (5 a. m. - 12 p. m.) 1.94 ± 1.00. The mean BAC in road traffic accidents during summer was 1.48 ± 0.71‰, spring 2.25 ± 0.76‰, autumn 2.12 ± 1‰, winter 2.42 ± 1‰. Alcohol consumption by road users is a significant contributing factor in road traffic accidents and their outcomes in Lithuania.

Scheduling optimisation of alcohol test sites

Drink driving is an infamous factor in road crashes and fatalities. Alcohol testing is a major countermeasure, and random breath tests (RBTs) deter tested drivers and passersby (observers who are not tested). We propose a genetic algorithm (GA)-based RBT scheduling optimisation method to achieve maximal deterrence of drink driving. The RBT schedule denotes the daily plan of where, when, and for how long tests should occur in the road network. The test results (positive and negative) and observing drivers are considered in the fitness function. The limited testing resource capacity is modeled by a number of constraints that consider the total duration of tests, the minimum and maximum duration of a single test site, and the total number of test sites during the day. Clustering of the alcohol-related crash data is used to estimate the matrix for drink driving on the scheduled day. The crash data and traffic flow data from Victoria, Australia are analysed and used to describe sober/drink driving. A detailed synthetic example is developed and a significant improvement with 150% more positive results and 59% more overall tests is observed using the proposed scheduling optimisation method.

Review of City-Wide 30 km/h Speed Limit Benefits in Europe

To date, more and more European cities are systematically working to expand the proportion of their street network with a speed limit of 30 km/h. This paper endeavored to assess the effectiveness of city-wide 30 km/h speed limits in Europe. In an effort to condense research outputs, a quantitative approach along with qualitative assessments were implemented. This study described the changes in safety, environment, energy, traffic, livability, and health before and after the phased implementation of city-wide 30 km/h speed limits. The systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Results from 40 different cities across Europe, including Brussels, Paris, and Zurich, indicated that reductions in speed limits improved road safety by decreasing the likelihood of crash risk and the severity of crashes that do occur. On average, the implementation of 30 km/h speed limits in European cities demonstrated a 23%, 37%, and 38% reduction in road crashes, fatalities, and injuries, respectively. Lower speed limits also yielded environmental benefits, with emissions decreasing on average by 18%, noise pollution levels by 2.5 dB, and fuel consumption by 7%, indicating enhanced fuel efficiency and reduced environmental impact. Encouraging citizens to embrace walking, cycling and utilizing public transit services can further contribute to a safer and environmentally sustainable urban environment.

Implementation of the new acts on driving under the influence of alcohol and clinical outcomes for patients severely injured in road traffic crashes

Objective Driving under the influence (DUI) of alcohol is a major risk factor for fatal road traffic injuries (RTIs) worldwide. This study aimed to investigate the relationship between the implementation of new acts on DUI of alcohol and the clinical outcomes of patients with severe RTIs in Korea. Methods This is a community-based cross-sectional study using a nationwide severe trauma registry in Korea. In 2018, 2 acts with the Yoon Chang-Ho Act (Yoon’s Act) were passed to strengthen the punishment for drunk driving fatal RTIs (first Yoon’s act) and lower the blood alcohol concentration limit to restrict driver’s licenses (second Yoon’s act). The first Yoon’s act was implemented on December 18, 2018, and the second Yoon’s act was implemented on June 25, 2019. The study periods were categorized as pre-Act-1, pre-Act-2, Act-1, and Act-2 according to the application of Yoon’s Act, and the study outcome was in-hospital mortality. Multivariable logistic regression analysis was conducted to estimate the relationship of the new acts and in-hospital mortality. Results Among a total of 20,376 patients with severe RTIs and 7,928 patients (drivers) with RTIs (hereafter drivers), the in-hospital mortality rates were 20.8% and 17.0%, and alcohol-related RTIs accounted for 9.7% and 8.1%, respectively. Severe RTIs tended to increase with each period (25.5 cases/day, 24.5 cases/day, 26.8 cases/day, and 30.4 cases/day, P for trend <.01). In-hospital mortality significantly decreased during the Act-2 period compared to the pre-Act-2 period for all patients with severe RTIs (adjusted odds ratio = 0.54, 95% confidence interval 0.43–0.67) and drivers with RTIs (adjusted odds ratio = 0.50, 95% confidence interval 0.34–0.73). Conclusions Implementation of the new acts on DUI of alcohol was associated with lower odds for in-hospital mortality for patients with severe RTIs. Further studies are needed to evaluate the long-term impact of the new acts on reducing alcohol-related RTIs.

Study of Various Materials for Automobile Bumper: A Review

India leads the world in annual road fatalities (230,000), despite being a top-ten car market. This discrepancy highlights the urgent need for stricter vehicle safety regulations. While concerns regarding an 8-15% increase in car prices due to stricter standards exist, the potential for global car exports and significant lives saved outweighs these costs. This review analyses the critical role car bumpers play in mitigating accident severity. By absorbing impact energy and protecting occupants and vehicles, bumpers directly address a crucial gap in current Indian automotive safety. Implementing stringent safety regulations, including robust bumper testing programs, can significantly reduce road fatalities and injuries, aligning India's car safety standards with global best practices and fostering a safer transportation ecosystem.

GRIDS: Personalized Guideline Recommendations while Driving Through a New City

Drive tourism has become increasingly popular in the past decade; however, driving through a new city is challenging because the road and traffic environments vary significantly across cities. A driver used to driving in one city may face severe difficulty in adapting to a different driving environment, leading to road fatalities. This article develops GRIDS , an explainable model for guidelines recommendation for inter-domain driving safety, which learns the driving rules behind the changing environment and recommends the necessary personalized guidelines to a driver while driving through a new city. We develop an explainable domain adaptation model to provide customized recommendations in terms of driving guidelines, broadly categorized into four major feature categories of a driving environment. A thorough evaluation over the CARLA driving simulator shows that the recommendations generated through GRIDS can help improve driving safety.

Towards Safer Roads: A Deep Learning Approach to Driver Distraction Detection in Four-wheeler Cars

The fact that there are more than 50 million cars sold annually and more than 1.3 million fatal motor vehicle accidents each year indicates the urgent need for stronger road safety regulations. Driver behaviour needs to be addressed, especially in emerging nations like India, which is responsible for 11% of all road fatalities worldwide. Diversion has been identified as the leading cause of 78% of accidents involving drivers. Distractions can take many different forms, from using a phone to interacting with others, and they greatly hinder road safety. This work aims to address this important problem by creating a highly effective machine learning (ML) model that employs computer vision techniques to classify various driver distractions in real-time. Utilising cutting-edge models, such as an ensemble of CNNs and convolutional neural networks (CNNs) like ResNet50. Our goal is to effectively identify and categorise distractions through the use of deep learning and picture recognition, allowing for proactive intervention to avert mishaps. Beyond classification accuracy, this study evaluates the model’s overall speed and scalability, which are critical for deployment on edge devices. We assess the practical practicality and wide-spread adoption of our approach by analysing performance parameters like inference time and resource utilisation

ANIMAL GAZING DETECTION USING CNN IN SMART AGRICULTURAL FIELD

Animal detection and warning systems play a critical role in safeguarding agricultural land from the intrusion of large animals, particularly ungulates, which pose threats to property and human safety. These systems are designed to identify and alert the presence of such animals before they encroach upon farming territories. Across different regions, the deployment of these systems aims to mitigate Animal Vehicle Collisions (AVCs) involving ungulate species, which can result in significant costs and hazards. However, the effectiveness of these systems may be hindered by the lack of regular monitoring for incidents of animal road fatalities. This paper explores the operational principles of animal detection systems, highlighting their reliance on machine learning technology to detect patterns indicative of animal presence. The detection process involves analyzing data collected from various sources to discern behavioral and environmental cues associated with animal activity. Challenges may arise from accurately distinguishing between different types of animals and background elements, necessitating the refinement of machine learning algorithms to minimize false positives triggered by environmental factors. Effective deployment of these systems requires comprehensive planning and integration into regional and national agricultural policies. By leveraging advancements in machine learning technology and data analytics, stakeholders can enhance the efficiency of these systems, thereby reducing the incidence of animal- related accidents and protecting agricultural resources. This paper underscores the importance of continuous monitoring and adaptation of animal detection systems to effectively mitigate risks and ensure the sustained protection of farming land and communities.

Post-lockdown burden of road injury involving hospitalisation in Victoria, Australia: A statewide, population-based time series analysis.

Ever since COVID-19, short-term changes in transport injury patterns have been observed. The aim is to examine both the initial and the enduring impact of government lockdown and the pandemic on road injuries requiring hospitalisation and road fatalities. Time series analysis of Transport Accident Commission (TAC) claims involving hospitalisation and fatalities in Victoria, Australia, from July 2016 to May 2023, including lockdown (March 2020 to October 2020) and post-lockdown (November 2020 onwards). A total of 46 450 TAC claims were included. Average claims during the pre-pandemic period were 652/month. Lockdown restrictions were associated with a statistically significant fall in monthly claims (-255, 95% confidence interval [CI] = -315 to -194, P < 0.01). This was consistent across road users, days of the week, hours of the day, injury severity, sex and central versus rural locations. The post-lockdown period had a statistically significant reduction in monthly claims to 76% (95% CI = 67-84) of pre-pandemic levels (-158, 95% CI = -213 to -102, P <0.01). This was consistent across all subgroups except bicyclist injuries, which remained constant (-8, 95% CI = -16 to 0, P = 0.05). There was a significant upward trend in the fatality-to-claim ratio post-lockdown (0.001, 95% CI = 0-0.001, P <0.01). Road injury requiring hospitalisation decreased significantly during governmental lockdown and has returned to three-quarters of pre-pandemic levels (except bicyclist injuries that have remained constant), but there is an increasingly disproportionate number of fatalities. This represents a new baseline of injury burden for EDs and hospitals that manage trauma patients.

Pattern of Head Injury in Fatal Road Traffic Accident: Retrospective Study

Road traffic Accident have been reported to be a major cause of morbidity and mortality. Due to increasing modernization, road traffic accidents have become exponentially more frequent and dangerous. Mortality rate from road traffic injuries accounts 29.2 per 100000 people5. This study was conducted in the Department of Forensic Medicine, Silchar Medical College, Silchar, from 1st November 2021 to 31st October 2022. The aim of the study is the pattern of head injuries in fatal road traffic accident. Out of the 981 autopsy cases were conducted during the above-mentioned period, a total of 200 cases were found deaths due to head injuries. Out of 200 cases, common age group involved was from 40-50 years, the deceased were more from rural areas. Maximum cases during winter season. Commonest injuries were scalp abrasion, linear fracture of skull and subdural haemorrhage. Head injury has become a common cause of death and disability among Road Traffic Accident cases. This further shows the need of strict implementations of traffic rules. Positive policy initiatives such as mandatory helmet legislation need reliable and consistent data to support ongoing monitoring and enforcement of such initiatives.

Evaluation and comparison of administrative division-level road traffic safety indices of Egypt, England, Turkey, and the United States

Introduction: There is regional diversity inside countries regarding road safety indices (RSIs), and countries rarely have been compared based on these indicators. Thus, regional RSIs of England, the United States, Egypt, and Turkey were evaluated. Regional data were collected from the statistical center of each country. The adopted regional RSIs include road fatalities, health risk (HR) or fatalities per population, and traffic risk (TR) or fatalities per number of vehicles. The associations between variables were examined using correlation and regression analysis. The spatial distributions of subdivisions were evaluated using Moran's I, the local Moran index. Results: Considerable differences between the countries were observed, including differences in the spatial distribution of regions and associations between RSIs. Significant relationships were detected between road fatality, population, and the number of motor vehicles. Higher exposure rates mean higher fatalities in regions. A robust linear relationship between the HR and TR indices was identified in developed countries. There is a nonlinear and significant association between motorization rates and TR indices of regions, and fatality risk decreases as the motorization rate increases. There is a considerable gap between developed and developing countries regarding regional RSIs, and the transferability of road safety models from one country to another is challenging. Huge hotspots regarding RSIs were observed in Turkey and the United States. The locations of hot spots in terms of the risk indices were identical in the developed countries.

Fatal road traffic accident and medico-legal aspects in Lomé (Togo)

Objectives: know the circumstances and causes of death related to traffic accidents which have been the subject of forensic expertise in Togo. Methods: It was a case series study on fatal road traffic accident victims who benefited from forensic expertise from 2017 to 2021 (5 years) in the pathological anatomy department of Lomé in Togo. Result: A total of 248 forensic expertise files were collected, representing an annual frequency of 49.6 cases. The sex ratio (M/F) was 3.1. The ages of the victims ranged from 1 to 84 years with an average age of 39.2±4.3 years. The circumstances were dominated by motorcycle collisions (101 cases; 40.7%) followed by motorcycle- vehicle (67 cases; 27.0%). Speeding was reported as the leading cause of accidents in 179 cases (72.2%). The victims were mainly passengers and motorcycle drivers in 115 cases (46.4%). The anatomical lesions were grouped into four groups: polytrauma (152 cases, 61.3%), severe head trauma isolated (55 cases, 22.2%), thoracic contusion (23 cases, 9.3%), and abdominopelvic contusion with perineal lesion (18 cases, 7.2%). Conclusion: Raising awareness and applying road safety principles among motorcycle and car drivers will help stop this threat. Government at all levels should implement strong policies to reduce the speed of vehicles on the roads. Objectifs : connaître les circonstances et les causes des décès liés aux accidents de la circulation qui ont fait l'objet d'une expertise médico-légale au Togo. Méthodes : Il s'agissait d'une étude de série de cas sur les victimes d'accidents mortels de la circulation ayant bénéficié d'une expertise médico-légale de 2017 à 2021 (5 ans) dans le service d'anatomie pathologique de Lomé au Togo. Résultats : deux cent quarante-huit (248) dossiers d'expertise médicolégale ont été recueillis, soit une fréquence annuelle de 49,6 cas. Le sex-ratio (H/F) était de 3,1. L'âge des victimes variait de 1 à 84 ans avec une moyenne de 39,2±4,3 ans. Les circonstances étaient dominés par les collisions entre motocyclettes (101 cas ; 40,7%) suivies par les collisions entre motocyclettes et véhicules (67 cas ; 27,0%). L'excès de vitesse était la cause principale des accidents dans 179 cas (72,2%). Les victimes étaient principalement des passagers et des conducteurs de moto dans 115 cas (46,4 %). Les lésions anatomiques ont été regroupées en quatre groupes : polytraumatisme (152 cas, 61,3%), traumatisme crânien sévère isolé (55 cas, 22,2%), contusion thoracique (23 cas, 9,3%) et contusion abdomino-pelvienne avec lésion périnéale (18 cas, 7,2%). Conclusion : La sensibilisation et l'application des principes de sécurité routière parmi les conducteurs de motocyclettes et de voitures contribueront à mettre un terme à cette menace. Le gouvernement à tous les niveaux devrait mettre en œuvre des politiques fortes pour réduire la vitesse des véhicules sur les routes.

Pattern and distribution of injuries associated with road traffic accident in Haldwani (Nainital) - An autopsy based study

Indiais one of the fastest growing economies. There is a rapid inflation of vehicles on the road that range from motorcycles to heavy vehicles which also shows an active surge in the fatal road accidents. This study is focussed to find out the pattern and distribution of injuries in fatal road traffic accident cases in Kumaon region of Uttarakhand as to plan at the time of policy making and traffic law implementation.: The present study was conducted in the department of Forensic Medicine &amp; Toxicology of Government Medical College, Haldwani from January 1 2019 to December 31 2019, on total 110 cases of RTA brought to mortuary for medico-legal post mortem examination.: Maximum number of cases sustained head and neck injuries (42 out of 110 externally) followed by extremities injury (28 out of 110). Total 38 cases out of 110 sustained head and neck injuries internally, followed by abdominal injuries (28 cases). Abrasions (30 cases) and laceration (29 cases) were commonest type of external injury noted in RTA cases followed by contusion (22 cases) and fracture (19 cases). Most common cause of death among RTA victims was head injury (40 out of 110 cases) followed by shock and haemorrhage (37 out of 110 cases).

Prediction of rear-seat belt use: Application of extended theory of planned behavior

Objectives Rear-seat belts have been shown to significantly reduce the severity of road vehicle collisions and fatalities. However, their use by rear-seat passengers is significantly less than that by front-seat passengers. Thus, the psychological factors underlying individuals’ decision to wear a seat belt in the rear seat require further investigation. Methods An extended theory of planned behavior (eTPB) was used to examine individuals’ behavior of wearing a rear-seat belt. An online survey was conducted and a total of 515 valid questionnaires were collected in China. Results While attitude, descriptive norms, and law enforcement all have a significant effect on individuals’ intention to wear a seat belt in the rear, subjective norms and perceived behavioral control do not. Individuals’ attitudes toward wearing a seat belt in the rear seat are significantly influenced by law enforcement and behavioral intention, but not by perceived behavioral control. The mediation effect analysis reveals that law enforcement has the greatest overall effect on behavior, followed by attitude and descriptive norms. Conclusions The results of this paper contribute to more effective recommendations to improve the use of rear seat belts and to safeguard rear seat passengers.

A comparison of traditional road safety assessment methods and the newly developed 'road safety deserts' approach

Road fatalities were labelled a pandemic as early as 1973 (BMJ 1973). The number of road fatalities reached 1.35 million in 2016. Currently over 3 500 people perish every day on the world's roads. South Africa has one of the highest road fatality rates in the world, with a fatality rate of 25.9 deaths per 100 000 population (WHO 2018). In order to understand and improve the road safety situation of a region, effective road safety assessments must be carried out. This paper presents a comparison of four different road safety assessment approaches, both traditional and novel, and serves as a proof of concept for the 'road safety desert' methodology, a new technique adapted from the 'transit desert' concept. This new approach to road safety assessment explores the possibility of geo-coded supply and demand comparisons to identify 'road safety deserts' - areas that have a comparatively higher road safety risk. This paper shows that there are several unique and effective ways to assess road safety, and that each approach incorporates different characteristics within their methodologies. It is recommended that road safety analysis is conducted using a multitude of methods, so as to improve understanding and intervention selection.

Exploring the Influence of Drivers’ Visual Surroundings on Speeding Behavior

Despite awareness campaigns and legal consequences, speeding is a significant cause of road accidents and fatalities globally. To combat this issue, understanding the impact of a driver's visual surroundings is crucial in designing roadways that discourage speeding. This study investigates the influence of visual surroundings on drivers in 15 US cities using 3,407,253 driver view images from Lytx, covering 4,264 miles of roadways. By segmenting and analyzing these images along with vehicle-related variables, the study examines factors affecting speeding behavior. After filtering the images, to ensure an accurate representation of the driver’s view, 1,340,035 driver view images were used for analysis. Statistical models, including hurdle beta and bivariate probit models with random driver effects as well as Machine Learning’s eXtreme Gradient Boosting (XGBoost), were employed to estimate speeding behavior. The results indicate that factors within the driver's visual environment, weather conditions, and driver heterogeneity significantly impact speeding. Speeding behavior also varies across geographic locations, even within the same city, suggesting a connection between local context and speeding. The study highlights the importance of the driver's environment, showing that more open spaces encourage speeding, while areas with trees and buildings are associated with reduced speeding. Notably, this research differs from previous studies by utilizing real-time data from dash cameras, providing a dynamic and accurate representation of the driver's visual surroundings. This approach enhances the reliability of the findings and empowers transportation engineers and planners to make informed decisions when designing roadways and implementing interventions to address effectively excessive speeding. In addition to examining speeding behavior, the study also analyzes time-headway, a key factor affecting safety and risky driver behavior, to explore its relationship with speeding. The findings offer valuable insights into the factors influencing speeding and the driver's visual environment. These insights can inform efforts to create environments that discourage speeding (and close car following) and ultimately reduce severe accidents caused by excessive speed (and tailgating).