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首页> 外文会议>International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering >Analysis of influencing factors on urban traffic congestion and prediction of congestion time based on spatiotemporal big data
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Analysis of influencing factors on urban traffic congestion and prediction of congestion time based on spatiotemporal big data

机译：基于时空大数据的城市交通拥挤影响因素分析及拥堵时间预测

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In recent years, with the acceleration of urbanization, the number of motor vehicles in China has increased dramatically. Traffic congestion and urban disease are increasingly prominent, which to some extent limits the development of the national economic level. In this study, we take the five urban areas in the center of Chengdu as an example, use Python crawler to mine the data that can help to study the influencing factors of urban traffic congestion from the 1.4 billion GPS records of taxis in Chengdu, the road traffic flow and POI data crawled on the open platform of AMAP, and then establish the principal component analysis, grey correlation analysis and neural network prediction model to find out the impact of traffic congestion and the main factors of the problem. Finally, we found that seven indicators, such as time period, the number of traffic lights, road width and traffic volume, have significant impact on road traffic congestion, and predicted traffic congestion through BP neural network.
机译：近年来，随着城市化进程的加快，中国的机动车数量急剧增加。交通拥堵和城市疾病日益突出，这在一定程度上限制了国民经济水平的发展。在本研究中，我们以成都市中心的五个市区为例，使用Python搜寻器来挖掘数据，这些数据可以帮助我们从成都14亿辆出租车的GPS记录中研究城市交通拥堵的影响因素，道路交通流量和POI数据在AMAP的开放平台上爬网，然后建立主成分分析，灰色关联分析和神经网络预测模型，以找出交通拥堵的影响和问题的主要因素。最后，我们发现时间周期，交通信号灯数量，道路宽度和交通量等七个指标对道路交通拥堵有显着影响，并通过BP神经网络预测了交通拥堵。

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