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《Journal of Geo-Information Science》 2018-05
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A Map Reduce-based Method for Parallel Calculation of Bus Passenger Origin and Destination from Massive Transit Data

WU Qunyong;SU Keyun;ZOU Zhijie;National & Local Joint Engineering Research Center of Geo-spatial Information Technology, Fuzhou University;Key Laboratory of Spatial Data Mining & Information Sharing of MOE;  
Bus passengers' origin and destinations(OD) can truly reflect travel characteristics and demands of residents, which is an important basic data for bus system evaluation, scheduling and route optimization, with significantly practical value in urban planning. Existing OD estimation methods are mostly applied to a small amount of bus data, which cannot directly and rapidly calculate mass transit passenger OD. In order to solve these problems, a parallel method for calculation of massive transit passengers' origin and destinations based on Map Reduce is investigated. Firstly, database migration tool was applied to transfer massive bus data stored in relational database to HBase. Secondly, Map Reduce parallel computing framework was introduced to divide the IC card data into multiple Map tasks in the light of region numbers in HBase to calculate origins. The origins are grouped and stored into HDFS by user in the Reduce function. Thirdly, the destinations are estimated by origins in parallel which are divided into multiple Map tasks according to block numbers stored in HDFS. According to the travel record of each passenger, destinations can be accurately calculated by the means of public transit chain method and history similarity. In the end, taking IC card data and GPS bus data in Xiamen from June 13 to 26,2015 as the example, which has 295 bus lines, 16 879 661 bus records, and 14 410 058 complete OD pairs which accounted for 78.9% of IC card data. Comparing with the traditional method, the computational efficiency has substantially improved. The results illustrate that the parallel method can not only calculate bus passenger OD accurately, but also has higher computational efficiency.
【Fund】: 国家自然科学基金项目(41471333);; 中央引导地方科技发展专项项目(2017L3012)~~
【CateGory Index】: U491.17
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