IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i13p6996-d579412.html
   My bibliography  Save this article

Optimization Model of Regional Traffic Signs for Inducement at Road Works

Author

Listed:
  • Lianzhen Wang

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Han Zhang

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Lingyun Shi

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Qingling He

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Huizhi Xu

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

Abstract

A variety of pipelines are distributed under urban roads. The upgrading of pipelines is bound to occupy certain road resources, compress the driving space of motor vehicles for a long time, aggravate the traffic congestion in the construction section, and then affect the traffic operation of the whole region. A reasonable layout of traffic signs for inducement to guide the traffic flow in the area where the construction section is located is conducive to promoting a balanced distribution of traffic flow in the regional road network, so as to achieve the reduction of automobile exhaust emissions and the sustainable development of traffic. In this paper, the layout optimization method of regional traffic signs for inducement is proposed. Taking the maximum amount of guidance information that the regional traffic signs can provide as the objective function, and taking the traffic volume, the characteristics of intersection nodes and the standard deviation of road saturation as the independent variables, the layout optimization model of guidance facilities is constructed, which can optimize the layout of traffic guidance signs in the area affected by the construction section, and achieve the goal that the minimum number of facilities can provide the maximum amount of guidance information. The results of the case study show that among the 64 alternative locations where traffic guidance signs can be set in the study area, eight optimal locations are finally determined as the setting points of guidance facilities through this model, and the effective increment of guidance information is the largest at this time. The model proposed in this paper can be used for reference to promote the sustainable development of traffic in the area where the construction section is located.

Suggested Citation

  • Lianzhen Wang & Han Zhang & Lingyun Shi & Qingling He & Huizhi Xu, 2021. "Optimization Model of Regional Traffic Signs for Inducement at Road Works," Sustainability, MDPI, vol. 13(13), pages 1-14, June.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:6996-:d:579412
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/13/6996/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/13/6996/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xinqiang Chen & Jinquan Lu & Jiansen Zhao & Zhijian Qu & Yongsheng Yang & Jiangfeng Xian, 2020. "Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    2. Wu, Bing & Tang, Yuheng & Yan, Xinping & Guedes Soares, Carlos, 2021. "Bayesian Network modelling for safety management of electric vehicles transported in RoPax ships," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    3. Bing Wu & Junhui Zhang & Tsz Leung Yip & C. Guedes Soares, 2021. "A quantitative decision-making model for emergency response to oil spill from ships," Maritime Policy & Management, Taylor & Francis Journals, vol. 48(3), pages 299-315, April.
    4. Zhong, Shiquan & Zhou, Lizhen & Ma, Shoufeng & Jia, Ning, 2012. "Effects of different factors on drivers’ guidance compliance behaviors under road condition information shown on VMS," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(9), pages 1490-1505.
    5. Paz, Alexander & Peeta, Srinivas, 2009. "Information-based network control strategies consistent with estimated driver behavior," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 73-96, January.
    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. Qin Zeng & Yun Chen & Xiazhong Zheng & Shiyu He & Donghui Li & Benwu Nie, 2023. "Optimization of Underground Cavern Sign Group Layout Using Eye-Tracking Technology," Sustainability, MDPI, vol. 15(16), pages 1-32, August.

    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. Yu, Qing & Teixeira, Ângelo Palos & Liu, Kezhong & Rong, Hao & Guedes Soares, Carlos, 2021. "An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Mariano Gallo & Mario Marinelli, 2020. "Sustainable Mobility: A Review of Possible Actions and Policies," Sustainability, MDPI, vol. 12(18), pages 1-39, September.
    3. Xue, Jie & Yip, Tsz Leung & Wu, Bing & Wu, Chaozhong & van Gelder, P.H.A.J.M., 2021. "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China," Renewable Energy, Elsevier, vol. 172(C), pages 897-917.
    4. Hailin Zheng & Qinyou Hu & Chun Yang & Jinhai Chen & Qiang Mei, 2021. "Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    5. Wang, Xinjian & Liu, Zhengjiang & Loughney, Sean & Yang, Zaili & Wang, Yanfu & Wang, Jin, 2022. "Numerical analysis and staircase layout optimisation for a Ro-Ro passenger ship during emergency evacuation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Zhai, Linbo & Yang, Yong & Song, Shudian & Ma, Shuyue & Zhu, Xiumin & Yang, Feng, 2021. "Self-supervision Spatiotemporal Part-Whole Convolutional Neural Network for Traffic Prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    7. Poulopoulou, Maria & Spyropoulou, Ioanna, 2019. "Active traffic management in urban areas: Is it effective for professional drivers? The case of variable message signs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 412-423.
    8. Qiang Luo & Xiaodong Zang & Xu Cai & Huawei Gong & Jie Yuan & Junheng Yang, 2021. "Vehicle Lane-Changing Safety Pre-Warning Model under the Environment of the Vehicle Networking," Sustainability, MDPI, vol. 13(9), pages 1-16, May.
    9. 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.
    10. Sheu, Jiuh-Biing & Wu, Hsi-Jen, 2015. "Driver perception uncertainty in perceived relative speed and reaction time in car following – A quantum optical flow perspective," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 257-274.
    11. Yang Shao & Zhongbin Luo & Huan Wu & Xueyan Han & Binghong Pan & Shangru Liu & Christian G. Claudel, 2020. "Evaluation of Two Improved Schemes at Non-Aligned Intersections Affected by a Work Zone with an Entropy Method," Sustainability, MDPI, vol. 12(14), pages 1-24, July.
    12. Gao, Dawei & Huang, Kai & Zhu, Yongsheng & Zhu, Linbo & Yan, Ke & Ren, Zhijun & Guedes Soares, C., 2024. "Semi-supervised small sample fault diagnosis under a wide range of speed variation conditions based on uncertainty analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Assemi, Behrang & Baker, Douglas & Paz, Alexander, 2020. "Searching for on-street parking: An empirical investigation of the factors influencing cruise time," Transport Policy, Elsevier, vol. 97(C), pages 186-196.
    14. Paz, Alexander & Peeta, Srinivas, 2009. "On-line calibration of behavior parameters for behavior-consistent route guidance," Transportation Research Part B: Methodological, Elsevier, vol. 43(4), pages 403-421, May.
    15. Quy Nguyen-Phuoc, Duy & An Ngoc Nguyen, Nguyen & Nguyen, Minh Hieu & Ngoc Thi Nguyen, Ly & Oviedo-Trespalacios, Oscar, 2022. "Factors influencing road safety compliance among food delivery riders: An extension of the job demands-resources (JD-R) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 541-556.
    16. Yu-Ting Hsu & Srinivas Peeta, 2013. "An aggregate approach to model evacuee behavior for no-notice evacuation operations," Transportation, Springer, vol. 40(3), pages 671-696, May.
    17. Lyuchao Liao & Zhiyuan Hu & Chih-Yu Hsu & Jinya Su, 2023. "Fourier Graph Convolution Network for Time Series Prediction," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    18. Timothy C. Matisziw, 2019. "Maximizing Expected Coverage of Flow and Opportunity for Diversion in Networked Systems," Networks and Spatial Economics, Springer, vol. 19(1), pages 199-218, March.
    19. 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.
    20. Pankaj Gupta & Mukesh Kumar Mehlawat & Faizan Ahemad, 2023. "Selection of renewable energy sources: a novel VIKOR approach in an intuitionistic fuzzy linguistic environment," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(4), pages 3429-3467, April.

    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:13:y:2021:i:13:p:6996-:d:579412. 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.