Differences Of Multiple Factors Affect Prediction of Traffic

Haoyang Li

doi:10.54097/ymv41f05

Authors

Haoyang Li

DOI:

https://doi.org/10.54097/ymv41f05

Keywords:

Traffic prediction; Machine learning; Traffic flows.

Abstract

Intelligent transportation system (ITS) helps to control traffic to protect pedestrian’s life and decrease the time cost of transportation which can help reduce the potential risk of accident. The most significant thing in ITS is the prediction of traffic flow. There already have many ways to predict the traffic flows. However, most of those method doesn’t realize that different factor has different effect and time cost. Time cost of a factor does not discuss in this paper. The purpose of this paper is comparing different factors that may affect traffic to find out which one affects traffic the most. Training models are used to compare the effect of those factors and relative error are used as accuracy. As a result of the study, time in a day affects traffic more in holiday, temperature, rain volume, snow volume, cloud cover, and the time in a day. This may mean that people more prefer to driving based on time not on weather.

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