Volume 21 Issue 2
Aug.  2021
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(2): 243-255
LIN Yong-jie, HUANG Zi-lin, WU Pan, XU Lun-hui. RSSI positioning method of vehicles in tunnels based on semi-supervised extreme learning machine[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 243-255. doi: 10.19818/j.cnki.1671-1637.2021.02.021
Citation: LIN Yong-jie, HUANG Zi-lin, WU Pan, XU Lun-hui. RSSI positioning method of vehicles in tunnels based on semi-supervised extreme learning machine[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 243-255. doi: 10.19818/j.cnki.1671-1637.2021.02.021
shu

RSSI positioning method of vehicles in tunnels based on semi-supervised extreme learning machine

doi: 10.19818/j.cnki.1671-1637.2021.02.021

  • School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, Guangdong, China

Funds:

National Natural Science Foundation of China 61903145

Science and Technology Planning Project of Guangzhou 201803030045

Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province pdjh2020a0030

More Information
  • Author Bio:

    LIN Yong-jie(1987-), male, associate professor, PhD, linyjscut@scut.edu.cn

  • Corresponding author: HUANG Zi-lin(1995-), male, graduate student, ctjulian@mail.scut.edu.cn
  • Received Date: 2020-12-09
  • Publish Date: 2021-04-01
    Abstract
  • To improve the identification efficiency of highway tunnel emergencies and to realize the full-time monitoring of road traffic conditions, the problem of positioning connected automated vehicles based on the received signal strength indicator (RSSI) was studied based on a ubiquitous wireless sensor network on an intelligent road. Considering the continuous motion characteristics of vehicles in a tunnel, a semi-supervised extreme learning machine (SSELM) with a locally linear embedding (LLE) algorithm was proposed to achieve the RSSI fingerprint positioning. In the offline phase, the dimension reductions for a few RSSI sample datasets with their vehicle positions marked and for a mass of unmarked ones were conducted by using the LLE, and the low-dimensional manifolds corresponding to their high-dimensional data, which represented the target's location information, were recognized. The mapping relationship between the RSSI data and vehicle positions was fitted based on the SSELM. In the online phase, real-time collected RSSI data after manifold dimensionality reduction were put into the calibrated SSELM to estimate the positions of vehicles. The estimated position was smoothed using an unscented Kalman filter (UKF). Analysis result shows that compared with the existing semi-supervised learning algorithms, the proposed method can achieve better positioning performance regardless of the vehicle travel speed and deployed distances. Under a change in key variables, such as the proportion of marked data (reduced by 50%-90%), number of unmarked data (0-1 000), and deployed sensor distance (10-25 m), the proposed method still has the best positioning performance with a minimum average error of 3.09 m. In terms of computational complexity, when the marked data comprise 30% of the dataset (only 96 reference points), the average positioning error is 3.8 m and the training time reduces to 8.7 s. Therefore, the proposed SSELM with LLE algorithm can provide promising positioning performance for vehicles with different driving speeds in an environment with sparsely or densely deployed sensors. In addition, it has a shorter training time and lower dependence on sample size, which makes it an effective method for the auxiliary positioning of connected automated vehicles in tunnels. 2 tabs, 11 figs, 34 refs.

     

    • FullText(HTML)
  • loading
    • References(34)
  • [1]
    杜志刚, 倪玉丹, 杨理波, 等. 高速公路隧道曲线路段视线诱导设施有效性试验[J]. 交通运输工程学报, 2020, 20(1): 215-225. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202001021.htm

    DU Zhi-gang, NI Yu-dan, YANG Li-bo, et al. Effectiveness experiment of sight induction facilities of curve sections in highway tunnel[J]. Journal of Traffic and Transportation Engineering, 2020, 20(1): 215-225. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202001021.htm
    [2]
    赵跃峰, 张生瑞, 马壮林. 基于部分优势比的公路隧道交通事故严重程度分析模型[J]. 中国公路学报, 2018, 31(9): 159-166. doi: 10.3969/j.issn.1001-7372.2018.09.018

    ZHAO Yue-feng, ZHANG Sheng-rui, MA Zhuang-lin. Analysis of traffic accident severity on highway tunnels using the partial proportion odds model[J]. China Journal of Highway and Transport, 2018, 31(9): 159-166. (in Chinese) doi: 10.3969/j.issn.1001-7372.2018.09.018
    [3]
    黄合来, 罗启章, 彭韵颖, 等. 山区高速公路隧道群路段危化品运输风险评价体系研究[J]. 中南大学学报(自然科学版), 2018, 49(8): 2107-2114. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201808034.htm

    HUANG He-lai, LUO Qi-zhang, PENG Yun-ying, et al. Risk evaluation for hazardous chemicals transportation at mountainous freeway with tunnels groups[J]. Journal of Central South University (Science and Technology), 2018, 49(8): 2107-2114. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201808034.htm
    [4]
    MENG Qiang, QU Xiao-bo. Estimation of rear-end vehicle crash frequencies in urban road tunnels[J]. Accident Analysis and Prevention, 2012, 48: 254-263. doi: 10.1016/j.aap.2012.01.025
    [5]
    交通运输部综合规划司. 2019年交通运输行业发展统计公报[R]. 北京: 交通运输部综合规划司, 2020.

    Department of Comprehensive Planning, Ministry of Transport. Transportation development statistics bulletin in 2019[R]. Beijing: Department of Comprehensive Planning, Ministry of Transport, 2020. (in Chinese)
    [6]
    马壮林, 邵春福, 胡大伟, 等. 高速公路交通事故起数时空分析模型[J]. 交通运输工程学报, 2012, 12(2): 93-99. doi: 10.3969/j.issn.1671-1637.2012.02.015

    MA Zhuang-lin, SHAO Chun-fu, HU Da-wei, et al. Temporal-spatial analysis model of traffic accident frequency on expressway[J]. Journal of Traffic and Transportation Engineering, 2012, 12(2): 93-99. (in Chinese) doi: 10.3969/j.issn.1671-1637.2012.02.015
    [7]
    林杉, 许宏科, 刘占文, 等. 公路隧道突发事件CBR-RBR交通控制方法[J]. 交通运输工程学报, 2011, 11(4): 108-113. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201104016.htm

    LIN Shan, XU Hong-ke, LIU Zhan-wen, et al. Traffic control method of highway tunnel emergency based on CBR and RBR[J]. Journal of Traffic and Transportation Engineering, 2011, 11(4): 108-113. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201104016.htm
    [8]
    SONG Xiang, LI Xu, TANG Wen-cheng, et al. RFID/in-vehicle sensors-integrated vehicle positioning strategy utilizing LSSVM and federated UKF in a tunnel[J]. The Journal of Navigation, 2016, 69(4): 845-68. doi: 10.1017/S0373463315000946
    [9]
    COSTA E. Simulation of the effects of different urban environments on GPS performance using digital elevation models and building databases[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(3): 819-29. doi: 10.1109/TITS.2011.2122258
    [10]
    LEE J E, LIM H S, JEONG S H, et al. Enhanced iron-tunnel recognition for automotive radars[J]. IEEE Transactions on Vehicular Technology, 2015, 65(6): 4412-4418.
    [11]
    SONG Xiang, LI Xu, TANG Wen-cheng, et al. A hybrid positioning strategy for vehicles in a tunnel based on RFID and in-vehicle sensors[J]. Sensors, 2014, 14(12): 23095-23118. doi: 10.3390/s141223095
    [12]
    DONG Li-li, QIN Li, XU Wen-hai, et al. Detection method for vehicles in tunnels based on surveillance images[C]//IEEE. 4th International Conference on Transportation Information and Safety (ICTIS). New York: IEEE, 2017: 836-843.
    [13]
    徐志刚, 李金龙, 赵祥模, 等. 智能公路发展现状与关键技术[J]. 中国公路学报, 2019, 32(8): 1-24. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201908002.htm

    XU Zhi-gang, LI Jin-long, ZHAO Xiang-mo, et al. A review on intelligent road and its related key technologies[J]. China Journal of Highway and Transport, 2019, 32(8): 1-24. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201908002.htm
    [14]
    LI Chang-le, LUO Qu-yuan, MAO Guo-qiang, et al. Vehicle-mounted base station for connected and autonomous vehicles: opportunities and challenges[J]. IEEE Wireless Communications, 2019, 26(4): 30-36. doi: 10.1109/MWC.2019.1800541
    [15]
    CRUZ S, ABRUDAN T, XIAO Z, et al. Neighbor-aided localization in vehicular networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(10): 2693-2702. doi: 10.1109/TITS.2017.2655146
    [16]
    PARKER R, VALAEE S. Vehicular node localization using received-signal-strength indicator[J]. IEEE Transactions on Vehicular Technology, 2007, 56(6): 3371-3380. doi: 10.1109/TVT.2007.907687
    [17]
    FASCISTA A, CICCARESE G, COLUCCIA A, et al. Angle of arrival-based cooperative positioning for smart vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(9): 2880-2892. doi: 10.1109/TITS.2017.2769488
    [18]
    CHAN Y T, TSUI W Y, SO H C, et al. Time-of-arrival based localization under NLOS conditions[J]. IEEE Transactions on Vehicular Technology, 2006, 55(1): 17-24. doi: 10.1109/TVT.2005.861207
    [19]
    YANG Kai, AN Jian-ping, BU Xian-yuan, et al. Constrained total least-squares location algorithm using time-difference-of-arrival measurements[J]. IEEE Transactions on Vehicular Technology, 2009, 59(3): 1558-1562. http://ieeexplore.ieee.org/document/5342476
    [20]
    CHEN C H, LEE C A, LO C C. Vehicle localization and velocity estimation based on mobile phone sensing[J]. IEEE Access, 2016, 4: 803-817. doi: 10.1109/ACCESS.2016.2530806
    [21]
    HUANG Zi-lin, ZHU Xian-yuan, LIN Yong-jie, et al. A novel WiFi-oriented RSSI signal processing method for tracking low-speed pedestrians[C]//IEEE. 5th International Conference on Transportation Information and Safety (ICTIS). New York: IEEE, 2019: 1018-1023.
    [22]
    HUANG Zi-lin, XU Lun-hui, LIN Yong-jie. Multi-stage pedestrian positioning using filtered WiFi scanner data in an urban road environment[J]. Sensors, 2020, 20(11): 3259. doi: 10.3390/s20113259
    [23]
    WANG Bang, ZHOU Sheng-liang, LIU Wen-yu, et al. Indoor localization based on curve fitting and location search using received signal strength[J]. IEEE Transactions on Industrial Electronics, 2015, 62(1): 572-582. doi: 10.1109/TIE.2014.2327595
    [24]
    夏颖, 马琳, 张中兆, 等. 基于半监督流形学习的WLAN室内定位算法[J]. 系统工程与电子技术, 2014, 36(7): 1422-1427. https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201407032.htm

    XIA Ying, MA Lin, ZHANG Zhong-zhao, et al. WLAN indoor positioning algorithm based on semi-supervised manifold learning[J]. Systems Engineering and Electronics, 2014, 36(7): 1422-1427. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201407032.htm
    [25]
    KASANTIKUL K, XIU Chun-di, YANG Dong-kai, et al. An enhanced technique for indoor navigation system based on WiFi-RSSI[C]//IEEE. 2015 Seventh International Conference on Ubiquitous and Future Networks. New York: IEEE, 2015: 513-518.
    [26]
    HUANG Guang-bin, ZHU Qin-yu, SIEW C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1/2/3): 489-501. http://www.sciencedirect.com/science/article/pii/S0925231206000385
    [27]
    HUANG Gao, SONG Shi-ji, GUPTA J N D, et al. Semi-supervised and unsupervised extreme learning machines[J]. IEEE Transactions on Cybernetics, 2014, 44(12): 2405-2417. doi: 10.1109/TCYB.2014.2307349
    [28]
    ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290: 2323-2326. doi: 10.1126/science.290.5500.2323
    [29]
    JONDHALE S R, DESHPANDE R S. Kalman filtering framework-based real time target tracking in wireless sensor networks using generalized regression neural networks[J]. IEEE Sensors Journal, 2018, 19(1): 224-233. http://ieeexplore.ieee.org/document/8478766
    [30]
    FANG S H, LIN T N. Principal component localization in indoor WLAN environments[J]. IEEE Transactions on Mobile Computing, 2011, 11(1): 100-110. http://ieeexplore.ieee.org/document/5710948
    [31]
    蒋育康, 郭爱煌, 艾渤, 等. 城市轨道交通隧道环境下大规模MIMO信道建模[J]. 铁道学报, 2018, 40(11): 84-90. doi: 10.3969/j.issn.1001-8360.2018.11.012

    JIANG Yu-kang, GUO Ai-huang, AI Bo, et al. Massive MIMO channel modeling for tunnel of urban rail transit[J]. Journal of the China Railway Society, 2018, 40(11): 84-90. (in Chinese) doi: 10.3969/j.issn.1001-8360.2018.11.012
    [32]
    SHIH Y C, HSU Y Y, CHEN C H, et al. Adaptive attenuation factor model for localization in wireless sensor networks[J]. International Journal of Pervasive Computing and Communications, 2008, 4(3): 257-267. doi: 10.1108/17427370810911621
    [33]
    YU Feng, JIANG Ming-hua, LIANG Jing, et al. 5G WiFi signal-based indoor localization system using cluster k-nearest neighbor algorithm[J]. International Journal of Distributed Sensor Networks, 2014, 10(12): 247525-1-12. doi: 10.1155/2014/247525
    [34]
    王金甲, 田佩佩. 半监督极限学习机用于Android手机活动识别的研究[J]. 高技术通讯, 2017, 27(3): 228-236. doi: 10.3772/j.issn.1002-0470.2017.03.005

    WANG Jin-jia, TIAN Pei-pei. Study of applying semi-supervised extreme learning machines to activity recognition using Android phones[J]. Chinese High Technology Letters, 2017, 27(3): 228-236. (in Chinese) doi: 10.3772/j.issn.1002-0470.2017.03.005
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (685) PDF downloads(95) Cited by()
    Proportional views
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return