论文标题
朝着动态的城市自行车使用预测用于电台网络重新配置的预测
Towards Dynamic Urban Bike Usage Prediction for Station Network Reconfiguration
论文作者
论文摘要
自行车共享已成为全球大都市居民的主要运输选择之一。通常以用户从一个车站捡起自行车并将其放在另一个车站的方式的方式进行操作。但是，自行车站并不是静态的，因为自行车站通常会重新配置以适应不断变化的需求或城市城市化。关键操作之一是评估候选位置并安装新站点以扩展自行车共享站网络。已经研究了传统的做法来预测现有的站点使用情况，而由于缺乏历史自行车的使用，评估新站点的挑战是极其挑战的。 为了填补这一空白，在这项工作中，我们提出了一种称为ATCOR的新颖有效的自行车站级预测算法，该算法可以预测现有和新站点的自行车使用情况（重新配置期间的候选位置）。为了解决缺乏历史数据问题，新站的虚拟历史用法是根据与周围现有站的相关性生成的，以进行ATCOR模型初始化。我们设计了以台站为中心的热图，这些图表的特征是以热图为中心的每个目标站点骑手之间的趋势与车站的邻近区域之间传播，从而使该模型能够捕获自行车站网络的可学习功能。捕获的功能进一步应用于新电台的自行车使用情况。我们对来自纽约市，芝加哥和洛杉矶在内的三个主要自行车共享系统的超过2300万次旅行进行的广泛实验研究表明，ATCOR在预测现有车站和未来车站方面的表现优于基准和最先进的模型。
Bike sharing has become one of the major choices of transportation for residents in metropolitan cities worldwide. A station-based bike sharing system is usually operated in the way that a user picks up a bike from one station, and drops it off at another. Bike stations are, however, not static, as the bike stations are often reconfigured to accommodate changing demands or city urbanization over time. One of the key operations is to evaluate candidate locations and install new stations to expand the bike sharing station network. Conventional practices have been studied to predict existing station usage, while evaluating new stations is highly challenging due to the lack of the historical bike usage. To fill this gap, in this work we propose a novel and efficient bike station-level prediction algorithm called AtCoR, which can predict the bike usage at both existing and new stations (candidate locations during reconfiguration). In order to address the lack of historical data issues, virtual historical usage of new stations is generated according to their correlations with the surrounding existing stations, for AtCoR model initialization. We have designed novel station-centered heatmaps which characterize for each target station centered at the heatmap the trend that riders travel between it and the station's neighboring regions, enabling the model to capture the learnable features of the bike station network. The captured features are further applied to the prediction of bike usage for new stations. Our extensive experiment study on more than 23 million trips from three major bike sharing systems in US, including New York City, Chicago and Los Angeles, shows that AtCoR outperforms baselines and state-of-art models in prediction of both existing and future stations.
