IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i17p7094-d406609.html
   My bibliography  Save this article

Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning

Author

Listed:
  • Shuanfeng Zhao

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Chao Wang

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Pei Wei

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Qingqing Zhao

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

At present, the recognition of vehicle flow is mainly achieved with an artificial statistical method or by intelligent recognition based on video. The artificial method requires a large amount of manpower and time, and the existing video-based vehicle flow recognition methods are only applicable to straight roads. Therefore, a deep recognition model (DERD) for urban road vehicle flow is proposed in this paper. Learning from the characteristic that the cosine distance between the feature vectors of the same target in different states is in a fixed range, we designed a deep feature network model (D-CNN) to extract the feature vectors of all vehicles in the traffic flow and to intelligently determine the real-time statistics of vehicle flow based on the change of distance between vectors. A detection and tracking model was built to ensure the stability of the feature vector extraction process and to obtain the behavior trajectory of the vehicle. Finally, we combined the behavior and the number of vehicle flows to achieve the deep recognition of vehicle flow. After testing with videos recorded in actual scenes, the experimental results showed that our method can intelligently achieve the deep recognition of urban road vehicle flow. Compared with the existing methods, our approach shows higher accuracy and faster real-time performance.

Suggested Citation

  • Shuanfeng Zhao & Chao Wang & Pei Wei & Qingqing Zhao, 2020. "Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7094-:d:406609
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/17/7094/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/17/7094/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Robert Socha & Bogusław Kogut, 2020. "Urban Video Surveillance as a Tool to Improve Security in Public Spaces," Sustainability, MDPI, vol. 12(15), pages 1-12, August.
    2. Wei Zhou & Wei Wang & Xuedong Hua & Yi Zhang, 2020. "Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
    3. Xu Sun & Kun Lin & Pengpeng Jiao & Huapu Lu, 2020. "The Dynamical Decision Model of Intersection Congestion Based on Risk Identification," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    4. Xiaolei Ma & Haiyang Yu & Yunpeng Wang & Yinhai Wang, 2015. "Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
    5. Wei Yu & Hua Bai & Jun Chen & Xingchen Yan, 2019. "Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro," Sustainability, MDPI, vol. 11(18), pages 1-19, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrzej Paszkiewicz & Bartosz Pawłowicz & Bartosz Trybus & Mateusz Salach, 2021. "Traffic Intersection Lane Control Using Radio Frequency Identification and 5G Communication," Energies, MDPI, vol. 14(23), pages 1-17, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
    2. Chuanwei Zhang & Xibo Xue & Peilin Qin & Lingling Dong, 2023. "Research on a Speed Guidance Strategy for Mine Vehicles in Three-Fork Roadways Based on Vehicle–Road Coordination," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
    3. Rania Wehbe & Isam Shahrour, 2021. "Assessment and Improvement of Anti-COVID-19 Measures in Higher Education Establishments," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
    4. Krzysztof Cebrat & Maciej Sobczyński, 2016. "Scaling Laws in City Growth: Setting Limitations with Self-Organizing Maps," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-11, December.
    5. Wei Yu & Xiaofei Ye & Jun Chen & Xingchen Yan & Tao Wang, 2020. "Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
    6. Xianwang Li & Zhongxiang Huang & Saihu Liu & Jinxin Wu & Yuxiang Zhang, 2023. "Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA)," Sustainability, MDPI, vol. 15(10), pages 1-30, May.
    7. Elżbieta Macioszek & Agata Kurek, 2020. "The Use of a Park and Ride System—A Case Study Based on the City of Cracow (Poland)," Energies, MDPI, vol. 13(13), pages 1-26, July.
    8. Maheshwari, Saurabh, 2020. "Network Sensor Error Quantification and Flow Reconstruction Using Deep Learning," Institute of Transportation Studies, Working Paper Series qt2qk093gx, Institute of Transportation Studies, UC Davis.
    9. Zhang, Yifan & Ng, S. Thomas, 2021. "Unveiling the rich-club phenomenon in urban mobility networks through the spatiotemporal characteristics of passenger flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    10. Wei Yu & Jun Chen & Xingchen Yan, 2019. "Space‒Time Evolution Analysis of the Nanjing Metro Network Based on a Complex Network," Sustainability, MDPI, vol. 11(2), pages 1-17, January.
    11. Shen, Hui & Lin, Jane, 2020. "Investigation of crowdshipping delivery trip production with real-world data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    12. Vishal Mandal & Abdul Rashid Mussah & Peng Jin & Yaw Adu-Gyamfi, 2020. "Artificial Intelligence-Enabled Traffic Monitoring System," Sustainability, MDPI, vol. 12(21), pages 1-21, November.
    13. Tuo Sun & Bo Sun & Zehao Jiang & Ruochen Hao & Jiemin Xie, 2021. "Traffic Flow Online Prediction Based on a Generative Adversarial Network with Multi-Source Data," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    14. Christian Martin Mützel & Joachim Scheiner, 2022. "Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data," Public Transport, Springer, vol. 14(2), pages 343-366, June.
    15. Muhammad Aqib & Rashid Mehmood & Ahmed Alzahrani & Iyad Katib & Aiiad Albeshri & Saleh M. Altowaijri, 2019. "Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs," Sustainability, MDPI, vol. 11(10), pages 1-33, May.
    16. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    17. Tang, Jinjun & Zhang, Shen & Zhang, Wenhui & Liu, Fang & Zhang, Weibin & Wang, Yinhai, 2016. "Statistical properties of urban mobility from location-based travel networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 694-707.
    18. Wang, Minjie & Yang, Su & Sun, Yi & Gao, Jun, 2017. "Discovering urban mobility patterns with PageRank based traffic modeling and prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 485(C), pages 23-34.
    19. Ming Li & Wei Yu & Jun Zhang, 2023. "Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    20. Kinga Kimic & Paulina Polko, 2022. "The Use of Urban Parks by Older Adults in the Context of Perceived Security," IJERPH, MDPI, vol. 19(7), pages 1-20, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7094-:d:406609. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.