IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v16y2020i2p1550147720903620.html
   My bibliography  Save this article

Tracking and climbing behavior recognition of heavy-duty trucks on roadways

Author

Listed:
  • Lei Tang
  • Jingchi Jia
  • Zongtao Duan
  • Jingyu Ma
  • Xin Wang
  • Weiwei Kong

Abstract

The tracking and behavior recognition of heavy-duty trucks on roadways are keys for the development of automated heavy-duty trucks and an advanced driver assistance system. The spatiotemporal information of trucks from trajectory tracking and motions learnt from behavior analysis can be employed to predict possible driving risks and generate safe motion to avoid roadway accidents. This article presents a unified tracking and behavior recognition algorithm that can model the mobility of heavy-duty trucks on long inclined roadways. Random noise within the sampled elevation data is addressed by time-based segmentation to extract time-continuous samples at geographical locations. A Kalman filter is first used to distinguish error offsets from random noise and to estimate the distribution of truck elevations for different time intervals. A Markov chain Monte Carlo model is then applied to classify truck behaviors based on the change in elevation between two geographical locations. A heavy-duty truck mobility (HVMove) model is constructed based on the map information to apply the roadway geometry to the tracking and behavior recognition algorithm. We develop an extended Metropolis–Hastings algorithm to tune the parameters of the HVMove model. The proposed model is verified and evaluated through extensive experiments based on a real-world trajectory dataset covering sections of an expressway and national and provincial highways. From the experimental results, we conclude that the HVMove model provides sufficient accuracy and efficiency for automated heavy-duty trucks and advanced driver assistance system applications. In addition, HVMove can generate maps with the elevation information marked automatically.

Suggested Citation

  • Lei Tang & Jingchi Jia & Zongtao Duan & Jingyu Ma & Xin Wang & Weiwei Kong, 2020. "Tracking and climbing behavior recognition of heavy-duty trucks on roadways," International Journal of Distributed Sensor Networks, , vol. 16(2), pages 15501477209, February.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:2:p:1550147720903620
    DOI: 10.1177/1550147720903620
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147720903620
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147720903620?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Denos C. Gazis & Robert Herman & Richard W. Rothery, 1961. "Nonlinear Follow-the-Leader Models of Traffic Flow," Operations Research, INFORMS, vol. 9(4), pages 545-567, August.
    2. Sun, Li & Zhao, Lindu & Hou, Jing, 2015. "Optimization of postal express line network under mixed driving pattern of trucks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 147-169.
    Full references (including those not matched with items on IDEAS)

    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. Nakata, Makoto & Yamauchi, Atsuo & Tanimoto, Jun & Hagishima, Aya, 2010. "Dilemma game structure hidden in traffic flow at a bottleneck due to a 2 into 1 lane junction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(23), pages 5353-5361.
    2. McCrea, Jennifer & Moutari, Salissou, 2010. "A hybrid macroscopic-based model for traffic flow in road networks," European Journal of Operational Research, Elsevier, vol. 207(2), pages 676-684, December.
    3. Li, Xiaopeng & Wang, Xin & Ouyang, Yanfeng, 2012. "Prediction and field validation of traffic oscillation propagation under nonlinear car-following laws," Transportation Research Part B: Methodological, Elsevier, vol. 46(3), pages 409-423.
    4. Wang, Xiao & Jiang, Rui & Li, Li & Lin, Yi-Lun & Wang, Fei-Yue, 2019. "Long memory is important: A test study on deep-learning based car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 786-795.
    5. Gunpinar, Serkan & Centeno, Grisselle, 2016. "An integer programming approach to the bloodmobile routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 86(C), pages 94-115.
    6. Coifman, Benjamin & Ponnu, Balaji, 2020. "Adjacent lane dependencies modulating wave velocity on congested freeways-An empirical study," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 84-99.
    7. Chandle Chae & Youngho Kim, 2023. "Investigation of Following Vehicles’ Driving Patterns Using Spectral Analysis Techniques," Sustainability, MDPI, vol. 15(13), pages 1-15, July.
    8. Zhang, H.M. & Kim, T., 2005. "A car-following theory for multiphase vehicular traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 39(5), pages 385-399, June.
    9. Georgia Perakis & Guillaume Roels, 2006. "An Analytical Model for Traffic Delays and the Dynamic User Equilibrium Problem," Operations Research, INFORMS, vol. 54(6), pages 1151-1171, December.
    10. Rehborn, Hubert & Klenov, Sergey L. & Palmer, Jochen, 2011. "An empirical study of common traffic congestion features based on traffic data measured in the USA, the UK, and Germany," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4466-4485.
    11. Kaur, Ramanpreet & Sharma, Sapna, 2018. "Modeling and simulation of driver’s anticipation effect in a two lane system on curved road with slope," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 110-120.
    12. Yifan Pan & Yongjiang Wang & Baobin Miao & Rongjun Cheng, 2022. "Stabilization Strategy of a Novel Car-Following Model with Time Delay and Memory Effect of the Driver," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    13. Xiangyang Cao & Bingzhong Zhou & Qiang Tang & Jiaqi Li & Donghui Shi, 2018. "Urban Wasteful Transport and Its Estimation Methods," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
    14. Hongxing Zhao & Ruichun He & Xiaoyan Jia, 2019. "Estimation and Analysis of Vehicle Exhaust Emissions at Signalized Intersections Using a Car-Following Model," Sustainability, MDPI, vol. 11(14), pages 1-25, July.
    15. Kai Nagel & Peter Wagner & Richard Woesler, 2003. "Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling," Operations Research, INFORMS, vol. 51(5), pages 681-710, October.
    16. Daiheng Ni & John D. Leonard & Chaoqun Jia & Jianqiang Wang, 2016. "Vehicle Longitudinal Control and Traffic Stream Modeling," Transportation Science, INFORMS, vol. 50(3), pages 1016-1031, August.
    17. Zhang, Xiaoyan & Jarrett, David F., 1997. "Stability analysis of the classical car-following model," Transportation Research Part B: Methodological, Elsevier, vol. 31(6), pages 441-462, November.
    18. Tian, Junfang & Li, Guangyu & Treiber, Martin & Jiang, Rui & Jia, Ning & Ma, Shoufeng, 2016. "Cellular automaton model simulating spatiotemporal patterns, phase transitions and concave growth pattern of oscillations in traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 560-575.
    19. Cheng, Qixiu & Lin, Yuqian & Zhou, Xuesong (Simon) & Liu, Zhiyuan, 2024. "Analytical formulation for explaining the variations in traffic states: A fundamental diagram modeling perspective with stochastic parameters," European Journal of Operational Research, Elsevier, vol. 312(1), pages 182-197.
    20. Fang, Yaling & Shi, Zhongke, 2015. "Chaos analysis and delayed-feedback control in a discrete dynamic coupled map traffic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 422(C), pages 40-46.

    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:sae:intdis:v:16:y:2020:i:2:p:1550147720903620. 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: SAGE Publications (email available below). General contact details of provider: .

    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.