Predictive modelling

Random forest

World Wide Web

Engineering

Transport engineering

Injury prevention

Real-time data

Human factors and ergonomics

Support vector machine

Traffic flow (computer networking)

Computer security

China

Data mining

Beijing

Computer science

Medical emergency

Occupational safety and health

Environmental health

Poison control

Political science

Medicine

Crowdsourcing

Artificial neural network

Traffic congestion

Intelligent transportation system

Machine learning

Pathology

Accident Analysis & Prevention

Traffic accident management is a critical issue for advanced intelligent traffic management. The increasingly abundant crowdsourcing data and floating car data provide new support for improving traffic accident management. This paper investigates the methods to predict the complicated behavior of traffic flow evolution after traffic accidents using crowdsourcing data. Based on the available data source, the traffic condition is divided into four levels by congestion delay index: severely congested, congested, slow moving and uncongested. Four types of accidents are consequently defined based on the occurrence of each level. A hierarchical scheme is designed for identifying the most congested level and sequentially predicting duration of each level. The proposed model is validated using traffic accident data in 2017 from an anonymous source in Beijing, China by embedding three machine learning algorithms, random forest (RF), support vector machine (SVM) and neural network (NN), in the scheme. The results show NN outperforms the other two models when the assessment is conducted in absolute differences. Meanwhile, RF has a slightly better performance than SVM, especially when predicting the short-period congestion of severely congested level at the first time. By continuously updating the traffic condition information, significant improvement in accuracy can be acquired regardless of the exact model used. This study shows that emerging crowdsourcing data can be used in a real-time analysis of traffic accidents and the proposed model is effective to analyze such data.

Real-time traffic accidents post-impact prediction: Based on crowdsourcing data