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| Research on Parking Space Occupancy Recognition Based on MobileNet and Intelligent Parking Guidance Strategy |
| GU Si-si, SUN Xiao-fei, WANG Miao, YU Jia-qing |
| CCCC HIGHWAY CONSULTANTS CO., LTD., Beijing 100088 China |
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Abstract In order to solve the problem that parking space is hard to find in expressway service area, and provide accurate parking space information and effective parking guidance, a strategy of parking space occupancy recognition based on MobileNet and intelligent parking guidance is proposed. On the basis of detailed analysis of parking status and existing problems in expressway service area, this paper puts forward the classification strategy of intelligent parking guidance in service area, and builds the technical route of parking space recognition based on AI vision. Firstly, taking the high-grade video and low-grade video in the service area as the detection data sources, the lightweight MobileNet classification model is used to analyze the parking space occupancy in real time, so as to provide accurate and reliable information of the spare and occupied parking space in the parking area; Secondly, through the three-grade guidance screen, the three-grade parking guidance of main line preview guidance, entrance total capacity guidance and parking space guidance by vehicle type is realized, so that travelers can have a comprehensive understanding of parking space occupancy in the service area in advance. In order to verify the effectiveness of intelligent parking guidance classification strategy, a highway service area in North China is selected for field verification. The results show that the recognition accuracy of parking space occupancy recognition model based on MobileNet is 98.0% under sufficient illumination during the day and 90.0% at night; During peak holidays, the total travel time of vehicles due to congestion and waiting in service areas is reduced by about 7%, which can save 20% ~ 30% of the time for finding parking spaces compared with the time for finding parking spaces in traditional service areas. Therefore, the proposed parking space occupancy recognition based on MobileNet and intelligent parking guidance strategy can significantly improve the practical problems in the service area, meet the parking demand of the public to the maximum extent, relieve the parking pressure during peak hours, and improve the traffic capacity of the service area.
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Received: 02 January 2022
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| Fund:Supported by the Pilot Sub-task of CCCC's Transportation Power Construction (No.ZJJTQG-RW3-6-1) |
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[1] WU Kai-hua, ZHENG Hao, XIE Fei. Strategy Study of Establishing City Image in Expressway Service Area[J]. Highway, 2018, 63(11):199-203. (in Chinese)
[2] WANG Shu. Research of Comprehensive Evaluation of User-oriented Expressway Service Area[J]. Journal of Highway and Transportation Research and Development, 2016, 33(2):125-129. (in Chinese)
[3] WANG Shu, XU Ying-jun. Calculation of Reasonable Scale of Parking Spaces in Expressway Service Area Based on Queuing Theory[J]. Journal of Highway and Transportation Research and Development, 2016, 33(3):116-119. (in Chinese)
[4] TIAN Jia. Study on the Operation Index of Expressway Service Area[D]. Beijing:Beijing Jiaotong University, 2018. (in Chinese)
[5] CHEN Xing-wen, TUO Yong-li, YUAN Shuai, et al. Research on Optimization and Promotion of Existing Service Area of Expressway[J]. Highway, 2019, 66(2):219-224. (in Chinese)
[6] LI Yun, XIA Jing-jing, ZHANG Ya-jing, et al. Layout and Traffic Organization Design of Freeway Service Transferring Stations[J]. Highway, 2019, 44(1):70-75. (in Chinese)
[7] ZHANG Nan-nan, ZHANG Xiao. Design and Implementation of Intelligent Parking Lot System Based on Internet of Things technology[J]. Modern Electronics Technique, 2020, 43(12):38-41. (in Chinese)
[8] XU Hong, YANG Shun-xin, YANG Zhe. Research on VMS Information Release Content of Expressway in Shanxi Province under the Event State[J]. China ITS Journal, 2018, (3):88-91. (in Chinese)
[9] MA Fei, WU Qun-qi, WANG Lian. Impact of Multisource Guidance Information on Path Change of Drivers[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(5):61-66. (in Chinese)
[10] CHEN Da-di, TAN Wei, MO Xin-qiang. Construction Scheme of Intelligent Parking Guidance System in Express-way Service Area[J]. Western China Communications Science & Technology, 2020, (6):141-142, 153. (in Chinese)
[11] YING Jie, CHEN Wen, YANG Hai-ma, et al. Research on Parking Space Recognition and Counting Algorithm Based on Affine Transformation and Template Matching[J]. Application Research of Computers, 2022, 39(3):919-924. (in Chinese)
[12] AN Xu-xiao, DENG Hong-min, SHI Xing-yu. Parking Lot Space Detection Method Based on Mini Convolutional Neural Network[J]. Journal of Computer Applications2018, 38(4). 935-938. (in Chinese)
[13] WANG Dong-hui. Research on Information Perception Method of Intelligent Parking Lot Based on AI Vision[D]. Jilin:Jilin University. 2021. (in Chinese)
[14] HAN Chong, WANG Jun-li, WU Yu-xi, et al. A Review of Deep Learning Models Based on Neuroevolution[J]. Acta Electronica Sinica, 2021, 49(2):372-379.
[15] SANDLER M, HOWARD A, ZHU M L, et al. MobileNetv2:Inverted Residuals and Linear Bottlenecks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, IEEE, 2018:4510-4520.
[16] GAO Shu-ping, ZHAO Qing-yuan, QI Xiao-gang, et al. Research on the Improved Image Classification Method of MobileNet[J]. CAAI Transactions on Intelligent Systems, 2021, 6(1):11-20. (in Chinese)
[17] YANG Guan-ci, YANG Jing, LI Shao-bo, et al. Modified CNN Algorithm Based on Dropout and ADAM Optimizer[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition) 2018, 46(7):122-127. (in Chinese)
[18] FAN Xiang-yu, ZHANG Cong. Research on Recommendation System Model Based on Adam[J]. Journal of Wuhan Polytechnic University, 2020, 39(1):62-67. (in Chinese) |
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2022, Vol. 16 
