The effect of the dataset on evaluating urban traffic prediction

With the continuous development of economic strength and science and technology, the construction of Intelligent Transportation System(ITS) has become a new development direction in many cities. A complete and accurate traffic dataset can improve the accuracy of traffic prediction and promote the construction of ITS in cities. Most of the existing traffic datasets are collected on highways, and they are only one-way road data. There is little analysis of the impact of weather on traffic prediction, and more traffic auxiliary information is lacking at the same time. The use of such datasets for experiments can lead to inaccurate and unconvincing results, which is of little significance for the study of urban road prediction reference. In this paper, we are motivated to develop a new dataset for the evaluation of Metropolitan Traffic Prediction. Our dataset(XiAn Road Traffic) collected 308 urban road data and included two-way road data, weather data, driving angles, and congestion levels. XiAn Road Traffic can provide help for urban road state prediction and intelligent transportation city construction. We use the current more popular machine learning model for experiments. It is also proved by experiments that our dataset is more accurate and persuasive than the prediction results of other datasets. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

Automated summary

This paper introduces a new urban traffic prediction dataset-XiAn Road Traffic, which includes two-way road data, weather data, driving angles, and congestion levels. Experimental results show that this dataset is more accurate and persuasive than the prediction results of other datasets.