论文标题
基于相似性的特征提取，用于大规模稀疏流量预测
Similarity-based Feature Extraction for Large-scale Sparse Traffic Forecasting
论文作者
论文摘要
短期流量预测是智能运输系统领域的广泛研究主题。但是，大多数现有的预测系统受到实时探测器数据的需求的限制，因为它们是时间序列预测问题。在这个问题上，Neurips 2022流量4cast挑战致力于通过公开可用的稀疏环数数据来预测全市交通状态。该技术报告介绍了我们第二名的获胜解决方案，以解决ETA预测的扩展挑战。我们使用多个最近的邻居（NN）过滤器提出了一种基于相似性的特征提取方法。提取基于相似性的功能，静态特征，节点流量和组合特征，以训练梯度提升决策树模型。在三个城市（包括伦敦，马德里和墨尔本）上的实验结果表明了我们方法的强烈预测性能，在旅行时间估计的任务中，这表现优于许多基于图形的神经网络的解决方案。源代码可在\ url {https://github.com/c-lyu/traffic4cast20222-tse}中获得。
Short-term traffic forecasting is an extensively studied topic in the field of intelligent transportation system. However, most existing forecasting systems are limited by the requirement of real-time probe vehicle data because of their formulation as a time series forecasting problem. Towards this issue, the NeurIPS 2022 Traffic4cast challenge is dedicated to predicting the citywide traffic states with publicly available sparse loop count data. This technical report introduces our second-place winning solution to the extended challenge of ETA prediction. We present a similarity-based feature extraction method using multiple nearest neighbor (NN) filters. Similarity-based features, static features, node flow features and combined features of segments are extracted for training the gradient boosting decision tree model. Experimental results on three cities (including London, Madrid and Melbourne) demonstrate the strong predictive performance of our approach, which outperforms a number of graph-neural-network-based solutions in the task of travel time estimation. The source code is available at \url{https://github.com/c-lyu/Traffic4Cast2022-TSE}.
