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Efficient Map Matching and Discovery of Frequent and Dominant Movement Patterns in GPS Trajectory Data

Time: Wed 2020-12-16 13.00

Location: Videolänk via Zoom -, Du som saknar datorvana kan kontakta Gyözö Gidofalvi / Use this e-mail address if you need technical assistance, Stockholm (English)

Subject area: Geodesy and Geoinformatics, Geoinformatics

Doctoral student: Can Yang , Geoinformatik

Opponent: Contract Professor Mirco Nanni, University of Pisa

Supervisor: Professor Yifang Ban, Geoinformatik; Associate Professor Gyözö Gidofalvi, Geoinformatik; Docent Xiaoliang Ma, Transportplanering

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The wide deployment of Global Positioning System (GPS) sensors for movement data collection has enabled a wide range of applications in transportation and urban planning. Frequent and dominant movement patterns embedded in GPS trajectory data provide valuable knowledge including the spatial and temporal distribution of frequent routes selected by the tracked objects and the regular movement behavior in certain regions. Discovering frequent and dominant movement patterns embedded in GPS trajectory data needs to address several tasks including (1) matching noisy trajectories to the road network (referred as map matching), (2) extracting frequent and dominant movement patterns, and (3) retrieving the distribution of these patterns over user-specified attribute (e.g., timestamp, travel mode, etc.). These tasks confront several challenges in observation error, efficiency and large pattern search space.

To address those challenges, this thesis develops a set of algorithms and tools for efficient map matching and discovery of frequent and dominant movement patterns in GPS trajectory data. More specifically, two map matching algorithms are first developed, which improve the performance by precomputation and A-star search. Subsequently, a frequent route is extracted from map matched trajectories as a Contiguous Sequential Pattern (CSP). A novel CSP mining algorithm is developed by performing bidirectional pruning to efficiently search CSP and reduce redundancy in the result. After that, an efficient CSP comparison algorithm is developed to extend the bidirectional pruning to compare multiple sets of CSP. By comparing CSP mined from trajectories partitioned by a user-specified attribute, the distribution of frequent routes in the attribute space can be obtained. Finally, Regional Dominant Movement Pattern (RDMP) in trajectory data is discovered as regions where most of the objects follow a specific pattern. A novel movement feature descriptor called Directional Flow Image (DFI) is proposed to capture local directional movement information of trajectories in a multiple channel image and a convolutional neural network model is designed for DFI classification and RDMP detection.

Comprehensive experiments on both real-world and synthetic GPS datasets demonstrate the efficiency of the proposed algorithms as well as their superiority over state-of-the-art methods. The two map matching algorithms achieve considerable performance in matching densely sampled GPS data to small scale network and sparsely sampled GPS data to large scale network respectively. The CSP mining and comparison algorithms significantly outperform their competitors and effectively retrieve both spatial and temporal distribution of frequent routes. The RDMP detection method can robustly discover ten classes of commonly encountered RDMP in real-world trajectory data. The proposed methods in this thesis collectively provide an effective solution for answering sophisticated queries concerning frequent and dominant movement patterns in GPS trajectory data.