Method for Fast Map Construction Based on GPS Data and Compressed Grid Algorithm

Jian Zhang, Shuai Ling, Ping Wang, Xiaoyang Hu and Lu Liu
Additional contact information
Jian Zhang: School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China
Shuai Ling: College of Management and Economics, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300192, China
Ping Wang: College of Management and Economics, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300192, China
Xiaoyang Hu: College of Management and Economics, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300192, China
Lu Liu: School of Geographic and Environmental Sciences, Tianjin Normal University, No. 393, Binshui Road, Xiqing District, Tianjin 300382, China

Land, 2021, vol. 10, issue 12, 1-17

Abstract: Electronic maps play an important role in the field of urban traffic management, but the interface functions provided by map service agencies are limited, and commercial maps are generally expensive. Furthermore, the map generation algorithms based on the Global Positioning System (GPS) data can be very complex and take up a lot of storage space, which limits their application to specific practical problems, such as the real-time update of area maps, temporary road control, emergency route planning, and other scenarios. In order to solve this problem, an intuitive, extensible, and flexible method of constructing urban road maps is proposed. Using the Othello-coordinated method, the representation of the unit grid cell was redesigned. Through this method, the disadvantages of the raster map’s large storage space and computing resource requirements are compensated for during processing, improving the topological expression ability of the raster map and the speed with which the construction of the map is realized. The application potential of the proposed method is demonstrated by the evaluation of public transport service and road network resilience. In our experiments, the optimization efficiency of storage space was up to 99.914%, and the calculation accuracy of bus coverage was about 99.86%.

Keywords: digital grid map; map compression; map splicing; bus coverage; resilient traffic (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
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