- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | ZHAO Dongbao, FENG Linlin, DENG Yue, CAO Lianhai. Real-time Online Compression Method for Vehicle Trajectory Data Based on Smart Phone Sensors[J]. Journal of Southwest Jiaotong University, 2022, 57(1): 1-10. doi: 10.3969/j.issn.0258-2724.20210136
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Popularization of various portable mobile devices with positioning function produces massive spatial-temporal trajectory data of moving objects, and the huge data scale has brought severe challenges to trajectory data management and analysis. Therefore, a spatial-temporal trajectory data compression algorithm based on smart phone sensors is proposed. The algorithm recognizes the turning behavior and speed change behavior of the vehicle by monitoring and analyzing the data change law of the linear acceleration sensor and direction sensor built in the smartphone, and requests GPS sensor positioning to record the corresponding trajectory feature points according to the pattern recognition result, so as to realize real-time online compression of vehicle trajectory. The proposed algorithm is compared with the representative trajectory compression algorithms characterized by feature point extraction. The results indicate that it is slightly weaker than the representative trajectory compression algorithms in compression accuracy, its spatial-temporal distance deviation is 0.4 meters more than that of the online NOPW (normalopening window) algorithm on average, and its spatial distance deviation is 0.6 meters more that of the online NOPW algorithm on average. The real-time performance of the proposed algorithm is strong, and the feature points can be obtained in the current second, the calculation efficiency of the proposed algorithm is high, and the calculation time consumption is reduced by about 30% compared with the DP (douglas-peucker) algorithm, which reduces the amount of network transmission data; It only requests positioning and sampling at key feature points, the compression results is able to adapt to changes in road conditions to some extent, thus it reduces the storage space of the mobile phone, and decreases the power consumption of the mobile phone.
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