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.

随着经济实力和科学技术的不断发展，智能交通系统的建设已成为许多城市的新发展方向。完整，准确的交通数据集可以提高交通预测的准确性，并促进城市ITS的建设。现有的大多数交通数据集都是在高速公路上收集的，它们只是单向道路数据。很少分析天气对交通预测的影响，同时缺少更多的交通辅助信息。使用此类数据集进行实验可能会导致结果不准确和令人信服，这对研究城市道路预测参考值意义不大。在本文中，我们有动机开发一个新的数据集来评估城市交通预测。我们的数据集（西安道路交通）收集了308条城市道路数据，其中包括双向道路数据，天气数据，行驶角度和拥堵程度。西安道路交通可以为城市道路状态预测和智能交通城市建设提供帮助。我们使用当前更流行的机器学习模型进行实验。实验还证明，我们的数据集比其他数据集的预测结果更准确，更有说服力。