A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction

Ren, Honglei; Song, You; Wang, Jingwen; Hu, Yucheng; Lei, Jinzhi

doi:10.1109/itsc.2018.8569437

Cited by 140 publications

(66 citation statements)

References 12 publications

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“…Hence, AI can capture the spatial-temporal pattern of accidents in databases and identify patterns for which mitigation strategies can be designed. For example, a study [85] used deep recurrent neural network approach to predict the risk of traffic accidents by analysing the spatial and temporal patterns from a traffic accident database in Beijing, China. The results showed that this method was effective and can be applied to warn people around hazardous locations.…”

Section: Predictive Modelsmentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

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Section: Predictive Modelsmentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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“…For example, Chen et al (2016) have used a stack denoising AE (SDAE) to learn the hierarchical features of human mobility and their correlation with a traffic incident. In contrast, Ren et al (2017) and (Bao et al 2019) have implemented an LSTM model to evaluate risk, but Ren et al (2017) achieved better performance due to learning from more features.…”

Section: Traffic Incident Inferencementioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.

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“…Meanwhile, in Theofilatos's work [12], the idea of finite mixture logit models combined with Bayesian model was used to investigate accident likelihood and severity on urban arterials. Additionally, Ren et al [13] analyzed the spatial and temporal patterns of traffic accident frequency and proposed a deep learning approach to predict the risk of citywide traffic accident. Considering the spatial heterogeneity challenge in the data, the special deep learning method Hetero-ConvLSTM framework was proposed in work [14], which made reasonably accurate prediction results.…”

Section: Related Workmentioning

confidence: 99%

Traffic Accident Prediction Based on Multivariable Grey Model

Zhao

Liu

2020

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Owing to frequent traffic accidents and casualties nowadays, the ability to predict the number of traffic accidents in a period is significant for the transportation department to make decisions scientifically. However, owing to many variables affecting traffic accidents in the road traffic system, there are two critical challenges in traffic accident prediction. The first issue is how to evaluate the weight of each variable’s impact on the accident. The second issue is how to model the prediction process for multiple interrelated variables. Aiming to solve these two problems, we propose effective solutions to deal with traffic accident prediction. Firstly, for the first issue, we exploit the grey correlation analysis to measure the correlation of factors to accident occurrence. Then, for the second issue, we select the main factors by correlation analysis to establish a multivariable grey model—MGM(1,N) for prediction process modeling. Further, we explore the collinearity between variables and better optimize the predictive model. The experimental results show that our approach achieves best performance than four general-purpose comparative algorithms in traffic accident prediction task.

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A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction

Cited by 140 publications

References 12 publications

Applications of Artificial Intelligence in Transport: An Overview

Applications of Artificial Intelligence in Transport: An Overview

Applications of Deep Learning in Intelligent Transportation Systems

Traffic Accident Prediction Based on Multivariable Grey Model

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