Conference proceeding
ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification
KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, pp.1306-1315
01/01/2020
DOI: 10.1145/3394486.3403183
Abstract
Given the historical movement trajectories of a set of individual human agents (e.g., pedestrians, taxi drivers) and a set of new trajectories claimed to be generated by a specific agent, the Human Mobility Signature Identification (HuMID) problem aims at validating if the incoming trajectories were indeed generated by the claimed agent. This problem is important in many real-world applications such as driver verification in ride-sharing services, risk analysis for auto insurance companies, and criminal identification. Prior work on identifying human mobility behaviors requires additional data from other sources besides the trajectories, e.g., sensor readings in the vehicle for driving behavior identification. However, these data might not be universally available and is costly to obtain. To deal with this challenge, in this work, we make the first attempt to match identities of human agents only from the observed location trajectory data by proposing a novel and efficient framework named Spatio-temporal Siamese Networks (ST-SiameseNet). For each human agent, we extract a set of profile and online features from his/her trajectories. We train ST-SiameseNet to predict the mobility signature similarity between each pair of agents, where each agent is represented by his/her trajectories and the extracted features. Experimental results on a real-world taxi trajectory dataset show that our proposed ST-SiamesNet can achieve an F-1 score of 0.8508, which significantly outperforms the state-of-the-art techniques.
Details
- Title: Subtitle
- ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification
- Creators
- Huimin Ren - Worcester Polytech Inst, Worcester, MA 01609 USAMenghai Pan - Worcester Polytechnic InstituteYanhua Li - Worcester Polytechnic InstituteXun Zhou - University of IowaJun Luo - Lenovo
- Resource Type
- Conference proceeding
- Publication Details
- KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, pp.1306-1315
- Publisher
- Assoc Computing Machinery
- DOI
- 10.1145/3394486.3403183
- Number of pages
- 10
- Grant note
- SAFER-SIM University Transportation Center IIS-1942680; CNS-1657350; CMMI-1831140 / NSF; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2020
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380489602771
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