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A cloud‐based taxi trace mining framework for smart city
Author(s) -
Liu Jin,
Yu Xiao,
Xu Zheng,
Choo KimKwang Raymond,
Hong Liang,
Cui Xiaohui
Publication year - 2017
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2435
Subject(s) - trajectory , computer science , spark (programming language) , cluster analysis , data mining , scalability , overhead (engineering) , cloud computing , trace (psycholinguistics) , big data , field (mathematics) , smart city , similarity (geometry) , artificial intelligence , database , embedded system , linguistics , philosophy , physics , mathematics , astronomy , internet of things , pure mathematics , image (mathematics) , programming language , operating system
Summary As a well‐known field of big data applications, smart city takes advantage of massive data analysis to achieve efficient management and sustainable development in the current worldwide urbanization process. An important problem in smart city is how to discover frequent trajectory sequence pattern and cluster trajectory. To solve this problem, this paper proposes a cloud‐based taxi trajectory pattern mining and trajectory clustering framework for smart city. Our work mainly includes (1) preprocessing raw Global Positioning System trace by calling the Baidu API Geocoding; (2) proposing a distributed trajectory pattern mining (DTPM) algorithm based on Spark ; and (3) proposing a distributed trajectory clustering (DTC) algorithm based on Spark . The proposed DTPM algorithm and DTC algorithm can overcome the high input/output overhead and communication overhead by adopting in‐memory computation. In addition, the proposed DTPM algorithm can avoid generating redundant local trajectory patterns to significantly improve the overall performance. The proposed DTC algorithm can enhance the performance of trajectory similarity computation by transforming the trajectory similarity calculation into AND and OR operators. Experimental results indicate that DTPM algorithm and DTC algorithm can significantly improve the overall performance and scalability of trajectory pattern mining and trajectory clustering on massive taxi trace data. Copyright © 2016 John Wiley & Sons, Ltd.