论文标题
多任务学习用于稀疏流量预测
Multi-task Learning for Sparse Traffic Forecasting
论文作者
论文摘要
准确的交通预测对于提高智能运输系统的性能至关重要。以前的流量预测任务主要集中于小型和非隔离流量子系统，而Traffic4cast 2022竞赛则致力于探索整个城市的交通状态动态。只有一个小时的稀疏循环计数数据，任务是预测所有道路细分市场的拥塞类别，以及未来15分钟的超级发现的预期到达时间。循环计数器数据和高度不确定的实时交通状况的稀疏性使竞争具有挑战性。因此，我们提出了一个多任务学习网络，可以同时预测拥塞类别和每个路段的速度。具体而言，我们使用聚类和神经网络方法来学习循环计数器数据的动态特征。然后，我们以路段为节点构建图形，并根据图神经网络捕获道路段之间的空间依赖性。最后，我们通过多任务学习模块同时学习了三个措施，即交通拥堵类，速度值和音量类别。对于扩展的竞争，我们使用预测的速度来计算超级距离的预期到达时间。我们的方法在2022年流量4CAST竞赛提供的数据集上获得了出色的结果，源代码可在https://github.com/octopusli/neurips2022-traffic4cast上获得。
Accurate traffic prediction is crucial to improve the performance of intelligent transportation systems. Previous traffic prediction tasks mainly focus on small and non-isolated traffic subsystems, while the Traffic4cast 2022 competition is dedicated to exploring the traffic state dynamics of entire cities. Given one hour of sparse loop count data only, the task is to predict the congestion classes for all road segments and the expected times of arrival along super-segments 15 minutes into the future. The sparsity of loop counter data and highly uncertain real-time traffic conditions make the competition challenging. For this reason, we propose a multi-task learning network that can simultaneously predict the congestion classes and the speed of each road segment. Specifically, we use clustering and neural network methods to learn the dynamic features of loop counter data. Then, we construct a graph with road segments as nodes and capture the spatial dependence between road segments based on a Graph Neural Network. Finally, we learn three measures, namely the congestion class, the speed value and the volume class, simultaneously through a multi-task learning module. For the extended competition, we use the predicted speeds to calculate the expected times of arrival along super-segments. Our method achieved excellent results on the dataset provided by the Traffic4cast Competition 2022, source code is available at https://github.com/OctopusLi/NeurIPS2022-traffic4cast.
