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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
13.03.2025
DOI
https://doi.org/10.7307/ptt.v37i2.763
ISSUE
2025 (Vol 37), Issue 2
LICENSE
Copyright (c) 2025 Meiling HE, Guangrong MENG, Xiaohui WU, Xun HAN, Jiangyang FAN
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Road Traffic Accident Prediction Based on Multi-Source Data – A Systematic Review

Authors:Meiling HE, Guangrong MENG, Xiaohui WU, Xun HAN, Jiangyang FAN

Abstract

With the acceleration of urbanisation and the rapid increase in road traffic volume, the scientific prediction of traffic accidents has become crucial for improving road safety and enhancing traffic efficiency. However, traffic accident prediction is a complex and multifaceted problem that requires the comprehensive consideration of multiple factors, including people, vehicles, roads and the environment. This paper provides a detailed analysis of traffic accident prediction based on multi-source data. By thoroughly considering data sources, data processing and prediction methods, this paper introduces the various aspects of traffic accident prediction from different perspectives. It helps readers understand the characteristics of different data and methods, the process of accident prediction and the key technologies involved. At the end of the paper, the main challenges and future directions in road crash prediction research are summarised. For example, the lack of efficient data sharing between different departments and fields poses significant challenges to the integration of multi-source data. In the future, combining deep learning models with time-sensitive data, such as social media and vehicle network data, could effectively improve the accuracy of real-time accident prediction.

Keywords:multi-source data, road traffic accident, data processing, statistical learning, machine learning, deep learning

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