A Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction

2021 Fiscal Year Final Research Report

• Project/Area Number: 20K19859
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 61030:Intelligent informatics-related
• Research Institution: The University of Tokyo
• Principal Investigator: JIANG Renhe 東京大学, 情報基盤センター, 助教 (20865266)
• Project Period (FY): 2020-04-01 – 2022-03-31
• Keywords: Smart City / Spatiotemporal Data / Deep Learning / Transportation / Urban Mobility / Crowd Flow / Benchmark

Outline of Final Research Achievements

Predicting the density and flow of the crowd or traffic at a citywide level becomes possible by using the big data and cutting-edge AI technologies. It has been a very significant research topic with high social impact, which can be widely applied to emergency management, traffic regulation, and urban planning. By meshing a large urban area to a number of fine-grained mesh-grids, citywide crowd and traffic information in a continuous time period can be represented like a video, where each timestamp can be seen as one video frame. Based on this idea, a series of methods have been proposed to address video-like prediction for citywide crowd and traffic. Through this research, we build a standard benchmark for such kind of urban computing problems based on multiple open datasets. The research achievement was awarded by CIKM 2021 Best Resource Paper Runner-Up.

Free Research Field

知能情報学

Academic Significance and Societal Importance of the Research Achievements

近年、IoT（Internet of Things：モノのインターネット）、ビッグデータ、人工知能技術の急速な発展に伴い、スマートシティは新しい科学技術分野として各国の学術界、産業界および各国政府から非常に重視されている。そこで、人を中核としたスマートシティの実現に最も重要な研究課題と技術は、都市規模の人流・交通流の知覚、分析、シミュレーション、予測である。本研究提案をスマートシティ構築のためのキーテクノロジーと位置づけ、Japan Society 5.0の実現に大きく貢献し、画像処理と自然言語処理以外の人工知能・データサイエンス研究を大きく発展させることを期待するものである。