Accident prediction using machine learning:analyzing weather conditions, and model performance

Abstract. The primary focus of this study was to investigate the impact of weather and road conditions on the severity of accidents and to determine the feasibility of machine learning models in accurately predicting the likelihood of such incidents. The research was centered on two key research questions. Firstly, the study examined the influence of weather and road conditions on accident severity and identified the most related factors contributing to accidents. We utilized an open-source accident dataset, which was preprocessed using techniques like variable selection, missing data elimination, and data balancing through the Synthetic Minority Over-sampling Technique (SMOTE). Chi-square statistical analysis was performed, suggesting that all weather-related variables are more or less associated with the severity of accidents. Visibility and temperature were found to be the most critical factors affecting the severity of road accidents. Hence, appropriate measures such as implementing effective fog dispersal systems, heatwave alerts, or improved road maintenance during extreme temperatures could help reduce accident severity. Secondly, the research evaluated the ability of machine learning models including decision trees, random forests, naive bayes, extreme gradient boost, and neural networks to predict accident likelihood. The models’ performance was gauged using metrics like accuracy, precision, recall, and F1 score. The Random Forest model emerged as the most reliable and accurate model for predicting accidents, with an overall accuracy of 98.53%. The Decision Tree model also showed high overall accuracy (95.33%), indicating its reliability. However, the Naive Bayes model showed the lowest accuracy (63.31%) and was deemed less reliable in this context. It is concluded that machine learning models can be effectively used to predict the likelihood of accidents, with models like Random Forest and Decision Tree proving the most effective. However, the effectiveness of each model may vary depending on the dataset and context, necessitating further testing and validation for real-world implementation. These findings not only provide insight into the factors affecting accident severity but also open a promising avenue in employing machine learning techniques for proactive accident prediction and mitigation. Future studies can aim to refine the models further and potentially integrate them into traffic management systems to enhance road safety

Factors Influencing the Pedestrian Injury Severity of Micromobility Crashes

[EN] The growth of micromobility transport in cities has created a new mobility paradigm, but this has also resulted in increased traffic conflicts and collisions. This research focuses on understanding the impacts of micromobility vehicles on pedestrian injury severity in urban areas of Spain between 2016 and 2021. The Random Forest classification model was used to identify the most significant factors and their combinations affecting pedestrian injury severity. To address the issue of unbalanced data, the synthetic minority oversampling technique was employed. The findings indicate that pedestrians' age, specifically those 70 years or older, is the most important variable in determining injury severity. Additionally, collisions at junctions or on weekends are associated with worse outcomes for pedestrians. The results highlight the combined influence of multiple factors, including offenses and distractions by micromobility users and pedestrians. These factors are more prevalent among younger micromobility users and those riding for leisure or on weekends. To enhance micromobility road safety and reduce pedestrian injuries, separating micromobility traffic from pedestrian areas is recommended, restricting micromobility vehicle use on sidewalks, providing training and information to micromobility users, conducting road safety campaigns, increasing enforcement measures, and incorporating buffer zones in bike lanes near on-street parking.This research is part of the research project PID2019-111744RB-I00, funded by MCIN/AEI/10.13039/501100011033. Likewise, this research has been partially funded by the European Union-Next GenerationEU (RD 289/2021), thanks to the granting of a "Margarita Salas" grant to Almudena Sanjurjo (UP2021-035), researcher at the Universidad Politecnica de Madrid (UPM), to carry out astay at the Universitat Politecnica de Valencia (UPV)Sanjurjo-De-No, A.; Pérez Zuriaga, AM.; García García, A. (2023). Factors Influencing the Pedestrian Injury Severity of Micromobility Crashes. Sustainability. 15(19):1-17. https://doi.org/10.3390/su151914348117151

Parametric and Non-Parametric Analyses for Pedestrian Crash Severity Prediction in Great Britain

The study aims to investigate the factors that are associated with fatal and severe vehicle– pedestrian crashes in Great Britain by developing four parametric models and five non-parametric tools to predict the crash severity. Even though the models have already been applied to model the pedestrian injury severity, a comparative analysis to assess the predictive power of such modeling techniques is limited. Hence, this study contributes to the road safety literature by comparing the models by their capabilities of identifying the significant explanatory variables, and by their performances in terms of the F-measure, the G-mean, and the area under curve. The analyses were carried out using data that refer to the vehicle–pedestrian crashes that occurred in the period of 2016–2018. The parametric models confirm their advantages in offering easy-to-interpret outputs and understandable relations between the dependent and independent variables, whereas the non-parametric tools exhibited higher classification accuracies, identified more explanatory variables, and provided insights into the interdependencies among the factors. The study results suggest that the combined use of parametric and non-parametric methods may effectively overcome the limits of each group of methods, with satisfactory prediction accuracies and the interpretation of the factors contributing to fatal and serious crashes. In the conclusion, several engineering, social, and management pedestrian safety countermeasures are recommended

An Injury Severity Prediction-Driven Accident Prevention System

Traffic accidents are inevitable events that occur unexpectedly and unintentionally. Therefore, analyzing traffic data is essential to prevent fatal accidents. Traffic data analysis provided insights into significant factors and driver behavioral patterns causing accidents. Combining these patterns and the prediction model into an accident prevention system can assist in reducing and preventing traffic accidents. This study applied various machine learning models, including neural network, ordinal regression, decision tree, support vector machines, and logistic regression to have a robust prediction model in injury severity. The trained model provides timely and accurate predictions on accident occurrence and injury severity using real-world traffic accident datasets. We proposed an informative negative data generator using feature weights derived from multinomial logit regression to balance the non-fatal accident data. Our aim is to resolve the bias that happens in the favor of the majority class as well as performance improvement. We evaluated the overall and class-level performance of the machine learning models based on accuracy and mean squared error scores. Three hidden layered neural networks outperformed the other models with 0.254 ± 0.038 and 0.173 ± 0.016 MSE scores for two different datasets. A neural network, which provides more accurate and reliable results, should be integrated into the accident prevention system

Addressing smart city challenges utilizing machine learning: vehicular crash and public transportation fuel consumption prediction

According to the United Nations Department of Economic and Social Affairs, 64% of the developing world and 86% of the developed world will be urbanized by 2050. This presents both new challenges and wonderful opportunities. Thanks to the fast, steady growth of technologies such as the Internet of Things (IoT), and Internet of People, the process of collecting the data required to solve the challenges that urbanization brings forth has been alleviated; thus, improving the quality of life for the citizens of urban environments. This thesis focuses on solutions to two of the challenges facing urbanized areas: vehicular crashes and public transportation fuel consumption by utilizing innovative machine learning models. These solutions can assure the safety of citizens, assist with urban planning, emission reduction, smart city development, etc

A Deep Learning Approach for Real-time Crash Risk Prediction at Urban Arterials

Real-time crash risk prediction aims to predict the crash probabilities within a short time period, it is expected to play a crucial role in the advanced traffic management system. However, most of the existing studies only focused on freeways rather than urban arterials because of the complicated traffic environment of the arterials. This thesis proposes a long short-term memory convolutional neural network (LSTM-CNN) to predict the real-time crash risk at arterials. The advantage of this model is it can benefit from both LSTM and CNN. Specifically, LSTM captures the long-term dependency of the data while CNN extracts the time-invariant features. Four urban arterials in Orlando, FL are selected to conduct a case study. Different types of data are utilized to predict the crash risk, including traffic data, signal timing data, and weather data. Various data preparation techniques are applied also. In addition, the synthetic minority over-sampling technique (SMOTE) is used for oversampling the crash cases to address the data imbalance issue. The LSTM-CNN is fine-tuned on the training data and validated on the test data via different metrics. In the end, five other benchmarks models are also developed for model comparison, including Bayesian Logistics Regression, XGBoost, LSTM, CNN, and Sequential LSTM-CNN. Experimental results suggest that the proposed LSTM-CNN outperforms others in terms of Area Under the Curve (AUC) value, sensitivity, and false alarm rate. The findings of this thesis indicate the promising performance of using LSTM-CNN to predict real-time crash risk at arterials

Efeito das características do veículo na segurança, consumos e emissões

Engenharia MecânicaNos últimos anos, o número de vítimas de acidentes de tráfego por milhões de habitantes em Portugal tem sido mais elevado do que a média da União Europeia. Ao nível nacional torna-se premente uma melhor compreensão dos dados de acidentes e sobre o efeito do veículo na gravidade do mesmo. O objetivo principal desta investigação consistiu no desenvolvimento de modelos de previsão da gravidade do acidente, para o caso de um único veículo envolvido e para caso de uma colisão, envolvendo dois veículos. Além disso, esta investigação compreendeu o desenvolvimento de uma análise integrada para avaliar o desempenho do veículo em termos de segurança, eficiência energética e emissões de poluentes. Os dados de acidentes foram recolhidos junto da Guarda Nacional Republicana Portuguesa, na área metropolitana do Porto para o período de 2006-2010. Um total de 1,374 acidentes foram recolhidos, 500 acidentes envolvendo um único veículo e 874 colisões. Para a análise da segurança, foram utilizados modelos de regressão logística. Para os acidentes envolvendo um único veículo, o efeito das características do veículo no risco de feridos graves e/ou mortos (variável resposta definida como binária) foi explorado. Para as colisões envolvendo dois veículos foram criadas duas variáveis binárias adicionais: uma para prever a probabilidade de feridos graves e/ou mortos num dos veículos (designado como veículo V1) e outra para prever a probabilidade de feridos graves e/ou mortos no outro veículo envolvido (designado como veículo V2). Para ultrapassar o desafio e limitações relativas ao tamanho da amostra e desigualdade entre os casos analisados (apenas 5.1% de acidentes graves), foi desenvolvida uma metodologia com base numa estratégia de reamostragem e foram utilizadas 10 amostras geradas de forma aleatória e estratificada para a validação dos modelos. Durante a fase de modelação, foi analisado o efeito das características do veículo, como o peso, a cilindrada, a distância entre eixos e a idade do veículo. Para a análise do consumo de combustível e das emissões, foi aplicada a metodologia CORINAIR. Posteriormente, os dados das emissões foram modelados de forma a serem ajustados a regressões lineares. Finalmente, foi desenvolvido um indicador de análise integrada (denominado “SEG”) que proporciona um método de classificação para avaliar o desempenho do veículo ao nível da segurança rodoviária, consumos e emissões de poluentes.Face aos resultados obtidos, para os acidentes envolvendo um único veículo, o modelo de previsão do risco de gravidade identificou a idade e a cilindrada do veículo como estatisticamente significativas para a previsão de ocorrência de feridos graves e/ou mortos, ao nível de significância de 5%. A exatidão do modelo foi de 58.0% (desvio padrão (D.P.) 3.1). Para as colisões envolvendo dois veículos, ao prever a probabilidade de feridos graves e/ou mortos no veículo V1, a cilindrada do veículo oposto (veículo V2) aumentou o risco para os ocupantes do veículo V1, ao nível de significância de 10%. O modelo para prever o risco de gravidade no veículo V1 revelou um bom desempenho, com uma exatidão de 61.2% (D.P. 2.4). Ao prever a probabilidade de feridos graves e/ou mortos no veículo V2, a cilindrada do veículo V1 aumentou o risco para os ocupantes do veículo V2, ao nível de significância de 5%. O modelo para prever o risco de gravidade no veículo V2 também revelou um desempenho satisfatório, com uma exatidão de 40.5% (D.P. 2.1). Os resultados do indicador integrado SEG revelaram que os veículos mais recentes apresentam uma melhor classificação para os três domínios: segurança, consumo e emissões. Esta investigação demonstra que não existe conflito entre a componente da segurança, a eficiência energética e emissões relativamente ao desempenho dos veículos.During the last years, the number of fatalities per million inhabitants in Portugal has always been higher than the average in the European Union. Therefore, at national level, there is a need for a more effective understanding of crash data and vehicles effects on crash severity. This research examined the effects of vehicle characteristics on severity risk, fuel use and emissions. The main goal of this research was to develop models for crash severity prediction in single vehicle-crashes and two-vehicle collisions. Furthermore, this research aimed at developing an integrated analysis to evaluate vehicle’s safety, fuel efficiency and emission performances. Crash data were collected from the Portuguese Police Republican National Guard records for the Porto metropolitan area, for the period 2006-2010. A total of 1,374 crashes were collected, 500 single-vehicle crashes and 874 two-vehicle collisions. For the safety analysis, logistic regressions were used. For single-vehicle crashes, the effect of vehicle characteristics to predict the probability of a serious injury and/or killed in vehicle occupants (designed as binary target) was explored. For two-vehicle collisions, additional binary targets were designed: one target to predict the probability of a serious injury and/or killed in vehicle V1) and another target to predict the probability of a serious injury and/or killed in vehicle V2). To overcome the challenge imposed by sample size and high imbalanced data (only 5.1% were severe crashes), research methodology was developed based on a resampling strategy and 10 stratified random samples were used for validation. During the modeling stage, the effect of vehicle characteristics, such as weight, engine size, wheelbase and age of vehicle were analyzed. For the vehicle’s fuel efficiency and emissions analysis, pollutants were estimated using CORINAIR methodology. Following, emissions data were fit into linear regression models. Finally, an integrated analysis indicator (entitled “SEG”) that provides rating classification for the evaluation of vehicle’s safety, fuel efficiency and emission performances, was developed. Regarding these results, for single-vehicle crashes, injury severity prediction model identified age of the vehicle and engine size as statistically significant, at 5% level. Model performance accuracy rate was 58.0% (S.D. 3.1). For two-vehicle collisions, when predicting injury severity in vehicle V1, the engine size of the opponent vehicle (vehicle V2) increased the risk for the occupants of the subject vehicle (vehicle V1), at 10% level. Injury severity prediction model for vehicle V1 revealed a good performance with a mean prediction accuracy rate of 61.2% (S.D. 2.4). When predicting injury severity for the other vehicle involved (vehicle V2), the engine size of the opponent vehicle (vehicle V1) increased the risk for the occupants of vehicle V2, at 5% level. Injury severity prediction model for vehicle V2 achieved a mean prediction accuracy rate of 40.5% (S.D. 2.1). The results of the integrated analysis indicator, SEG, revealed that recent vehicle achieved better rating simultaneously for all the three domains: safety, fuel efficiency and emissions performances. Newer vehicles showed a better overall safety rating, were more fuel efficient (less CO2 emissions) and reduced emissions (more environmental friendly). This research relevance showed that there is no trade-off between safety, fuel efficiency and emissions

Developing an advanced collision risk model for autonomous vehicles

Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.