An Application of Vector Autoregressive Model for Analyzing the Impact of Weather And Nearby Traffic Flow On The Traffic Volume
This incremental work addresses traffic congestion by improving prediction for transport network optimization.
The paper tackled traffic flow prediction at a road segment using nearby traffic and weather data, achieving an average RMSE of 565.0768111 with a VAR(36) model, though residuals were unstable and non-normal.
This paper aims to predict the traffic flow at one road segment based on nearby traffic volume and weather conditions. Our team also discover the impact of weather conditions and nearby traffic volume on the traffic flow at a target point. The analysis results will help solve the problem of traffic flow prediction and develop an optimal transport network with efficient traffic movement and minimal traffic congestion. Hourly historical weather and traffic flow data are selected to solve this problem. This paper uses model VAR(36) with time trend and constant to train the dataset and forecast. With an RMSE of 565.0768111 on average, the model is considered appropriate although some statistical tests implies that the residuals are unstable and non-normal. Also, this paper points out some variables that are not useful in forecasting, which helps simplify the data-collecting process when building the forecasting system.