Large-scale planned special events in cities including concerts, football games and fairs can significantly impact urban mobility. The lack of reliable models for understanding and predicting mobility needs during urban events causes issues for mobility service users, providers as well as urban planners. In this article, we tackle the problem of building reliable supervised models for predicting the spatial and temporal impact of planned special events with respect to road traffic. We adopt a supervised machine learning approach to predict event impact from historical data and analyse effectiveness of a variety of features, covering, for instance, features of the events as well as mobility- and infrastructure-related features. Our evaluation results on real-world event data containing events from several venues in the Hannover region in Germany demonstrate that the proposed combinations of event-, mobility- and infrastructure-related features show the best performance and are able to accurately predict spatial and temporal impact on road traffic in the event context in this region. In particular, a comparison with both event-based and event-agnostic baselines shows superior capacity of our models to predict impact of planned special events on urban traffic.

中文翻译：

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跨镇交通-计划中的特殊事件对城市交通影响的监督预测

计划在城市举行的大型特殊事件，包括音乐会，足球比赛和集市，可能会严重影响城市的出行。缺乏可靠的模型来理解和预测城市事件期间的出行需求，给出行服务用户，提供者以及城市规划人员带来了麻烦。在本文中，我们解决了构建可靠的监督模型以预测计划中的特殊事件对道路交通的时空影响的问题。我们采用监督式机器学习方法，以根据历史数据预测事件的影响，并分析各种功能的有效性，例如，涵盖事件的功能以及与移动性和基础设施相关的功能。我们对包含来自德国汉诺威地区多个地点的事件的真实事件数据的评估结果表明，所建议的事件，移动性和基础设施相关功能的组合显示出最佳性能，并且能够准确预测时空影响在该地区事件中的道路交通 尤其是，与基于事件的基线和与事件不可知的基线进行比较后，我们的模型具有较强的预测计划中的特殊事件对城市交通影响的能力。