Short-time traffic flow forecast with weather characteristics

Mingzhou, Wang; Tian, Si; Chen, Chao; Zhong, Jiandan

doi:10.1109/ccns50731.2020.00039

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…This article is informed by a meticulous review and analysis of a vast body of relevant material from the past three years. Machine-learning models developed by various scholars, such as Zeroual et al [2], Ahmed et al [3], Liu [4], Tahir and Rashid [5], Sajan and Kumar [6], and Zhou et al [7], have been explored in depth. We employed a diverse array of machine-learning models in our experiments.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Machine-learning Models for Analyzing Weather and Traffic Accident Correlations

Xuan,

Yang

2023

JICCE

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Owing to advancements in intelligent transportation systems (ITS) and artificial-intelligence technologies, various machinelearning models can be employed to simulate and predict the number of traffic accidents under different weather conditions. Furthermore, we can analyze the relationship between weather and traffic accidents, allowing us to assess whether the current weather conditions are suitable for travel, which can significantly reduce the risk of traffic accidents. In this study, we analyzed 30000 traffic flow data points collected by traffic cameras at nearby intersections in Washington, D.C., USA from October 2012 to May 2017, using Pearson's heat map. We then predicted, analyzed, and compared the performance of the correlation between continuous features by applying several machine-learning algorithms commonly used in ITS, including random forest, decision tree, gradient-boosting regression, and support vector regression. The experimental results indicated that the gradient-boosting regression machine-learning model had the best performance.

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