Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference
The Pennsylvania State University, USA（宾夕法尼亚州立大学，美国）；Tencent AI Lab（腾讯AI实验室）；Yantai University, China（烟台大学，中国）；University of Central Florida, USA（中佛罗里达大学，美国）；University of Science and Technology of China, China（中国科学技术大学，中国）；Intellifusion Inc.（Intellifusion公司，美国（注：根据常见翻译补充，原文本未明确国家，但机构名本身为Intellifusion Inc.））
Real-time traffic volume inference is key to an intelligent city. It is a challenging task because accurate traffic volumes on the roads can only be measured at certain locations where sensors are installed. Moreover, the traffic evolves over time due to the influences of weather, events, holidays, etc. Existing solutions to the traffic volume inference problem often rely on dense GPS trajectories, which inevitably fail to account for the vehicles which carry no GPS devices or have them turned off. Consequently, the results are biased to taxicabs because they are almost always online for GPS tracking. In this paper, we propose a novel framework for the citywide traffic volume inference using both dense GPS trajectories and incomplete trajectories captured by camera surveillance systems. Our approach employs a high-fidelity traffic simulator and deep reinforcement learning to recover full vehicle movements from the incomplete trajectories. In order to jointly model the recovered trajectories and dense GPS trajectories, we construct spatiotemporal graphs and use multi-view graph embedding to encode the multi-hop correlations between road segments into real-valued vectors. Finally, we infer the citywide traffic volumes by propagating the traffic values of monitored road segments to the unmonitored ones through masked pairwise similarities. Extensive experiments with two big regions in a provincial capital city in China verify the effectiveness of our approach.
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
Microsoft Research（微软研究院）；Southwest Jiaotong University（西南交通大学）；Xidian University（西安电子科技大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Data Science Lab, Hertie School, Berlin, Germany（海德堡学校数据科学实验室）；Centre for Sustainability, Hertie School, Berlin, Germany（海德堡学校可持续发展中心）；Department of Technology, Management and Economics, Intelligent Transportation Systems, Technical University of Denmark（技术、管理与经济系，智能交通系统，丹麦技术大学）
