Artificial Intelligence in Road Traffic Accident Prediction

A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance

Today, one of the top concerns for governments is road safety. There are many safety features built into cars, yet traffic accidents still happen frequently and are unavoidable. To lessen the harm caused by traffic accidents, predicting their causes has become the primary goal. In this situation, it will be beneficial to examine the frequency of accidents so that we can use this information to further aid us in developing strategies to lessen them. From this, we can deduce the connections between traffic accidents, road conditions, and the impact of environmental factors on accident occurrence. In order to construct an accident prediction model, I used machine learning techniques, including the Decision Tree, Random Forest, and Logistic Regression. The development of safety measures and accident prediction will both benefit from these classification systems. Several elements, including weather, vehicle condition, road surface condition, and light condition, can be used to predict road accidents. Three dataset files—accidents, casualties, and vehicles are loaded into this dataset. This allows us to forecast the severity of accidents.

Aim: This study intends to assess drivers’ state of knowledge on the main causes and the best strategies capable of reducing road traffic accidents in the Bongo District of Ghana.
 Study Design: The study was a descriptive design.
 Place and Duration of Study: The study was conducted in the Bongo District of the Upper East Region of Ghana between July 2023 and August 2023.
 Methodology: The researcher conducted a survey involving 100 drivers who were randomly selected from a driver population of 120 in the Bongo District. The data were analyzed using Microsoft Excel [Version 19]. The results of the data analysis were presented as tables and charts.
 Results: The study found that the main causes of road traffic accidents emanate from human error such as low driving skills, excessive speeding, and poor vision of driver. Also, road conditions like bad road networks, no lane markings, and no speed limit signs were found as causal factors of road traffic accidents. Faulty vehicles with causative factors such as defective tyres, brakes, and lights were noted to be also responsible for road accidents. Nevertheless, the least factor was environmental conditions such as bad weather conditions and stray animals being enumerable as environmental conditions which cause road accidents. The study further found that the best strategies for curbing road traffic accidents were enforcement of traffic laws, education and training of drivers, obeying traffic rules by drivers, and maintaining vehicles well.
 Conclusion: The study, therefore, concluded that human factors play a central role in contributing to road accidents and curbing road traffic accidents. The research findings can be used to guide stakeholders in formulating and prioritizing interventions that are more human-centred for the curbing of road traffic accidents in the Bongo District of Ghana.

BackgroundThe M74 motorway extension, Glasgow, opened in June 2011. One justification for construction was an expectation that it would reduce road traffic accidents (RTAs) on local non-motorway roads. This study...

A review on neural network techniques for the prediction of road traffic accident severity

Objectives: To observe the road traffic accidents pattern across the states in India and to conclude whether the expenditure on road and transportation infrastructure is really justified with the traffic safety across the country. To decide the road safety policies across the states in India using backward elimination approach of regression model. Methods/Analysis: This study attempts to show statistical relations between road traffic accidents with predictive variables; number of vehicles registered, total length of roads, total number of road accidents and total expenditure on transportation infrastructure in the states of India. Time series data covering the period 2001 to 2013 was used on yearly road traffic accidents and other predictive variables for all 28 states of India. Findings: In recent decades, road accidents are becoming one of the most growing concerns in India. As mentioned in report of Ministry of Road Transport & Highways Government of India, road accidents impose significant costs which are about 3% GDP for India since 1999 to 2000. In 2004, India was 9th leading cause of death and expected to be 5th leading cause of death by 2030 worldwide. These facts are enough to catch attention of Government agencies and researchers towards safer traffic solutions. The results from the analysis shows few findings like expenditure on transportation infrastructure may not be an important predictive variable for road traffic accidents for pan India situations. Finally, if all the states of India partitioned in different clusters according to the behavior of states, the study states that there exists a strong positive correlation between road traffic accidents, number of vehicles registered and total length of roads. The relationship found to be statistically significant at 5% level of significance for each cluster of states. Applications/Improvements: The results obtained in study can assist transportation departments and Government bodies to implement better strategies across the country to ensure road traffic safety and reduce losses in terms of money and infrastructure costs.Keywords: Policies in India, Regression Analysis, Road Accidents, Transportation, Road Safety

Road accidents are still a prominent urban issue, worsened by a growing population and uncertain weather patterns. Conventional accident prediction models use static historical data, which does not enable them to be responsive to real-time traffic patterns. This research is responding to the demand for predictive models that are adaptive, smart, and can facilitate smart city infrastructure through real-time data integration. Machine learning models—Multilayer Perceptron, Gradient Boosting, Random Forest, Support Vector Machine, and K-Nearest Neighbors—were tested with both historical and real-time traffic data. The models were optimized and trained on varied datasets to improve prediction accuracy. Of these, Gradient Boosting recorded the highest accuracy at 88.1%, followed by Random Forest at 83.73%, showing the power of ensemble learning techniques in predicting accidents. This research emphasizes the significance of real-time data integration for accident prediction and prevention. Merging environmental elements like weather conditions and traffic congestion improves prediction quality, allowing proactive prevention of accidents. By leveraging these models, cities can shift towards data-based road management, infrastructure planning, and congestion control. The results show that real-time models of accident prediction have the potential to enhance urban safety, opening the door to smarter and more efficient traffic management systems.

Purpose: The main target of this paper was to examine the significance of Artificial Intelligence and Machine Learning and their effect on the transportation business.
 Methodology: This hypothesis was a survey of the significant machine learning calculations and their applications in the field of big data. This paper try to attempt to exhibit the need to remove significant data from the huge measure of enormous information as traffic data available in this day and age and recorded diverse machine learning strategies that can be utilized to separate this information needed to encourage better dynamic for transportation applications.
 Findings: This paper present an investigation of the different Artificial Intelligence (AI) methods that have been actualized to improve Intelligent Transportation Systems (ITS). Specifically, this paper assembled them into three main territories relying upon the main field where they were applied: Vehicle control, Traffic control and prediction, and Road security and accident prediction. The aftereffects of this examination uncover that the mix of various AI methodologies is by all accounts promising, particularly to oversee and investigate the huge measure of data created in transportation

Do higher fuel prices help reduce road traffic accidents?

ABSTRACTObjective: It is estimated that road traffic accidents are globally responsible for approximately 1.2 million deaths and 20 to 50 million injuries. About 70% of traffic incidences (TIs) occur in developing countries and among countries with high TI rates; Iran is the first. The aim of this study was to measure the association between being responsible for a traffic accident and some vehicle, human; and environmental related factors in Yasuj, a city with a high incidence of road traffic injuries and deaths in Iran.Methods: This is a time-, date-, and place-matched case–control study conducted in 2015 using all traffic accidents registered and investigated by police during 2012. In total, 194 drivers were considered the at-fault driver in a traffic accident and the 194 drivers in the same collisions were included in the analysis.Results: Based on the results from multivariate conditional logistic regression, significant associations between vehicle maneuver (ORTurn to right or left/Moving forward = 11.10, 95% confidence interval [CI], 1.77–69.58, P = .01) and age (odds ratio [OR] = 1.11, 95% CI, 1.004–1.22, P = .04) and the chance of being an at-fault driver were found.Conclusion: Driver behavior–related interventions including training and law enforcement seem to be more effective in reducing road traffic accidents in Iran.

Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules’ mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using case-based reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.

Background. Fatal road traffic accidents constitute a significant public health problem. Aim. The aim of the study was to analyze road traffic accidents in Sana’a city, Yemen, during 2013–2015. Methods. The required information and data of accidents were collected, respectively, from police reported road accident statistics, Sana’a city. 11684 cases of road traffic accidents during period 2013–2015 were studied. Results. 840 people were killed and 9760 people were injured in RTAs during 2013–2015 in Sana’a city. The accident rates per 100000 of the population were 234.8 in 2013, 180.3 in 2014, and 92.2 in 2015. Mortality rates per 100000 of the population were 15.17 in 2013, 12.2 in 2014, and 8.9 in 2015. High speed was the single most important factor responsible for accidents, deaths, and injuries accounting for 38.1% of RTAs followed by drivers’ fault accounting for 18.9% of the total RTAs. The most common age group involved was ≥18 years. Males accounted for 83.1% of killed and injured persons. Most frequent victims of road traffic accidents were vehicles motor (48.9%) followed by pedestrians (38.1%). More than half of RTAs occurred on the straight road. Conclusion. RTAs are a major source of public health concern in Sana’a city, Yemen. Preventive measures to reduce the burden of mortality and morbidity due to road accidents should be introduced.

Road traffic accidents are the world’s number one cause of death among young people between 16 and 24. Bangladesh has one of the highest fatality rate in road accidents in the world. Moreover, Dhaka, the capital of Bangladesh, is the most vulnerable city both in terms of total number of accidents and accident rates. A total number of 2,720 accidents occurred within 2007-2011. This has caused a total of 1,481 numbers of pedestrian fatal accidents with 1,562 pedestrian fatal casualties. At this backdrop, road safety problem has become one of the major issues for the transport regulators and traffic law enforcers in Dhaka. The objective of this research is to identify the most accident-prone roads, the victims as well the in-depth reasons for traffic accident in Dhaka Metropolitan Area. In this article, the road safety situation and traffic accident trends are discussed elaborately. It also discusses some contemporary issues and priorities in addressing the road safety problems. Based on the findings of this study, some recommendations related to Engineering, Enforcement, Educational and Emergency Response aspects are made. This kind of research will help generating new ideas to find out the parameters for reducing road traffic accidents in greater Dhaka City. Vol. 6, December 2013, pp. 103-118

Risk management and industrial maintenance have evolved into proactive approaches supported by emerging technologies. This article explores the integration of mathematical models and artificial intelligence (AI) techniques in the prediction and prevention of industrial accidents. Through systematic review, predictive modeling, and case study analysis, significant improvements in operational efficiency and occupational safety are evidenced. It is concluded that AI and quantitative models are key tools in the digital transformation of industrial maintenance.

Road traffic accidents represent an acute global issue, with approximately 1.19 million fatalities annually, demanding innovative solutions beyond traditional statistical approaches like Poisson and Negative Binomial regression models. This study presents a comprehensive theoretical framework that synergizes Artificial Intelligence (AI), fuzzy logic, and Systemic Safety Science through the Haddon Matrix to revolutionize accident prediction and prevention strategies. The study proposes a framework that employs advanced AI techniques, including Bayesian Long Short-Term Memory (LSTM) networks for temporal risk assessment, Graph Neural Networks (GNNs) for spatial relationship modeling, and XGBoost for feature importance analysis, which can process diverse data sources such as traffic patterns, weather conditions, and vehicle telemetry. Complementing these AI components, the chapter proposes a Mamdani-type fuzzy inference system that can translate predictive outputs into actionable safety measures like adaptive speed controls and collision warnings, effectively handling real-world uncertainties through interpretable linguistic rules. The Haddon Matrix provides the structural foundation, systematically organizing interventions across host (driver-related), agent (vehicle-related), and environmental factors throughout pre-crash, crash, and post-crash phases. The proposed MATLAB/Simulink-based implementation architecture incorporates data pipelines from Kaggle and HighD datasets, hybrid AI-fuzzy decision modules, and continuous feedback mechanisms for model refinement at five-second intervals. Notable innovations include enhanced explainability through Shapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques, adaptive learning capabilities for evolving traffic scenarios, and compliance with ISO 26262/5469 safety standards. While the framework demonstrates significant theoretical promise for intelligent transportation systems, it acknowledges practical challenges, including data privacy concerns, sensor reliability issues, and stringent real-time processing requirements with sub-500ms latency thresholds. Future research directions highlight the potential of edge computing, digital twin technology, and regulatory sandbox environments to bridge the gap between theoretical conception and practical deployment, ultimately aiming to create safer, more responsive transportation ecosystems that harmonize computational precision with a human-centric control paradigm.