Trajectory Data Mining Dr. Yu Zheng Lead Researcher, Microsoft Research Chair Professor at Shanghai Jiao Tong University Editor-in-Chief of ACM Trans. Intelligent Systems and Technology

Spatial Trajectories Yu Zheng. Trajectory Data Mining: An Overview. ACM Transactions on Intelligent Systems and Technology. 2015, vol. 6, issue 3.Trajectory Data Mining: An Overview

Sources of Trajectory Data Human mobility – Active recording Travel logs Sport analysis Check-ins … – Passive recording Credit card transactions Public transit records Mobile phone signal, Wi-Fi… …. Yu Zheng. Trajectory Data Mining: An Overview. ACM Transactions on Intelligent Systems and Technology. 2015, vol. 6, issue 3.Trajectory Data Mining: An Overview

Sources of Trajectory Data Mobility of transportation vehicles – Taxis, buses, trucks,… – Air planes, ferries, cruise,… Mobility of Animals – Migration: Birds, zebra, tiger Mobility of natural phenomena – Hurricane, tornado,…

Paradigm of Trajectory Data Mining Yu Zheng. Trajectory Data Mining: An Overview. ACM Transactions on Intelligent Systems and Technology. 2015, vol. 6, issue 3.Trajectory Data Mining: An Overview

Public Trajectory Datasets GeoLife Trajectory Dataset [159]: – a GPS trajectory dataset from Microsoft Research GeoLife project [153], collected by 182 users from April 2007 to August The dataset has been used to estimate the similarity between users [54], which enables friend and location recommendations [154][155]. T-Drive Taxi Trajectories [160]: – A sample of trajectories from Microsoft Research T-Drive project [123], generated by over 10,000 taxicabs in a week of 2008 in Beijing. GPS Trajectory with Transportation Labels [161]: – Each trajectory has a set of transpor­tation mode labels, such as driving, taking a bus, riding a bike and walking. Check-in Data from Location-based Social Networks [162]: – The dataset consists of the check-in data generated by over 49,000 users in New York City and 31,000 users in Los Angeles as well as the social structure of the users. Each check-in includes a venue ID, the category of the venue, a timestamp, and a user ID. Hurricane trajectories [163]: – This dataset is provided by the National Hurricane Service (NHS), containing 1,740 trajectories of Atlantic Hurricanes (formally defined as tropical cyclone) from 1851 to NHS also provides annotations of typical hurric­ane tracks for each month throughout the annual hurricane season that spans from June to November. The Greek truck trajectories [164]: – This dataset contains 1,100 trajectories from 50 different trucks delivering concrete around Athens, Greece. It was used to evaluate trajectory pattern mining task in [34]. Movebank animal tracking data [165]: – Movebank is a free, online database of animal tracking data, helping animal tracking researchers to manage, share, protect, analyze, and archive their data.

Important References Yu Zheng. Trajectory Data Mining: An Overview. ACM Transactions on Intelligent Systems and Technology, vol. 6, issue 3, 2015.Trajectory Data Mining: An Overview Yu Zheng. Methodologies for Cross-Domain Data Fusion: An Overview. IEEE Transactions on Big Data, vol. 1, no Methodologies for Cross-Domain Data Fusion: An Overview Yu Zheng, Licia Capra, Ouri Wolfson, Hai Yang. Urban Computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, vol. 5, issue 3, pp. 38, 2014Urban Computing: concepts, methodologies, and applications Yu Zheng, Xiaofang Zhou. Computing with Spatial Trajectories. Springer ISBN: Computing with Spatial Trajectories Yu Zheng, Xing Xie, Wei-Ying Ma, GeoLife: A Collaborative Social Networking Service among User, location and trajectory. IEEE Data Engineering Bulletin. 33, 2, 2010, pp GeoLife: A Collaborative Social Networking Service among User, location and trajectory

Thanks! Yu Zheng Homepage Yu Zheng. Trajectory Data Mining: An Overview.Trajectory Data Mining: An Overview ACM Transactions on Intelligent Systems and Technology. 2015, vol. 6, issue 3.