Joint prediction of road-traffic and parking occupancy over a city with representation learning
Joint prediction of road-traffic and parking occupancy over a city with representation learning
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通过表征学习联合预测城市道路交通和停车占用率
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发表时间：
2016
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通讯作者：
Ludovic Denoyer
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作者：
Ali Ziat;Bertrand Leroy;Nicolas Baskiotis;Ludovic Denoyer
随着旅程规划服务开始包括真实的时间交通预测特征以便计算沿着旅程的更准确的路线选择沿着，自适应交通控制系统也可以从该预测中受益以便最小化交通拥堵。但是，这两个专用于最终用户和道路交通管理当局的系统也可以从其他信息中受益，特别是从停车可用性预测中受益，因为巡航停车位代表了城市交通的重要部分：当寻找停车位时，驾驶员必须猜测要去哪里，如果他们猜错了，可能会面临很长的距离来找到下一个位置，导致相当多的时间损失和交通拥堵的恶化。我们专注于交通和停车可用性的同时预测。我们的方法依赖于机器学习技术，更确切地说，依赖于表示学习方法：每条道路和停车场由一个共同的大维度空间中的向量表示，该空间捕获了关于所观察到的现象的结构和动力学信息。因此，这样的模型能够联合捕获停车和交通之间的时空相关性，从而产生高性能的预测系统。我们在大里昂（法国）城区的实验结果表明，与最先进的方法相比，我们的方法是有效的。
As journey planning services begins to include real time traffic forecast features in order to compute more accurate routing along the journey, adaptive traffic control systems can also benefit from this prediction so as to minimize traffic congestion. But these two systems dedicated to end user and road traffic management authorities could also benefits from other information, and particularly from parking availability prediction since cruising for parking spot represents a significant part of urban traffic: when looking for a parking, drivers must guess where to go, and if they are wrong, may face long distances to find the next location, resulting in considerable time loss and a worsening of traffic congestion. We focus on the simultaneous prediction of traffic and parking availability. Our approach relay on machine learning techniques and more precisely on representation learning methods: each road and car-park is represented by a vector in a common large dimensional space which captures both structural and dynamical information about the observed phenomenon. Such a model is thus able to jointly capture the spatio-temporal correlations between parking and traffic resulting in a high performance prediction system. The results of our experiments on the Grand Lyon (France) urban area show the effectiveness of our approach compared to state of the art methods.