Given traffic flow measurements for one highway, how to forecast its flow in future periods? Recent works in traffic forecast propose burdensome procedures by depending on additional data that is not always available, like traffic measurements from other roads linked to the one of interest, social media, trajectory and car accident data, geographical and socio-demographic attributes, driver behavior information and weather forecast. The most accurate algorithms force anyone to monitor an entire network of highways, even when there is a single highway of interest. This procedure is commonly unaffordable. How to obtain highly accurate results without using any additional data? We answer the question with AdaptFlow: a novel, adaptive algorithm able to accurately forecast traffic flow by individually monitoring highways that are connected to each other in a complex network using local flow measurements only. We performed experiments on large datasets from highways in UK and USA. Our AdaptFlow notably outperformed well-known related works on many settings. For example, it achieved 95.5% accuracy on average when forecasting the next 15 minutes flow of the UK highways, leading to an error rate that is 36% smaller than the one of the most accurate related work that does not use additional data.

给定一条高速公路的交通流量测量值，如何预测未来的流量？交通预测的最新工作通过依赖于并非总是可用的其他数据来提出繁琐的 程序，例如与感兴趣的道路相关的其他道路的交通测量，社交媒体，轨迹和车祸数据，地理和社会人口统计属性，驾驶员行为信息和天气预报。最精确的算法会迫使任何人监视整个高速公路网络，即使只有一条感兴趣的高速公路也是如此。此过程通常无法承受。如何在不使用任何其他数据的情况下获得高度准确的结果？我们用AdaptFlow回答问题：一种新颖的自适应算法，该算法能够通过仅监视本地网络中复杂网络中相互连接的高速公路来准确预测交通流量。我们对来自英国和美国高速公路的大型数据集进行了实验。在许多设置下，我们的AdaptFlow的性能明显优于知名的相关作品。例如，当预测英国高速公路的未来15分钟流量时，它平均达到95.5％的准确性，从而导致错误率比不使用其他数据的最准确的相关工作之一低36％。