IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v12y2021i4d10.1007_s13198-021-01071-5.html
   My bibliography  Save this article

Highway transportation optimization control system based on OD forecast information

Author

Listed:
  • Siru Chen

    (Shaanxi College of Communication Technology)

Abstract

In order to effectively solve the contradiction between supply and demand of road transportation and avoid waste of road facilities and construction resources, a scientific and systematic road network plan must be formulated. OD traffic flow reflects the distribution of traffic volume from the start point to the end point in the road network for a period of time. The purpose of this paper is to design and simulate a road transport optimization control system based on OD prediction information. This paper first converts the objective function of the variable speed multi-objective optimization model, and then determines the fitness function corresponding to each objective function. Then the linear normalization method is used to normalize the fitness function, and then the judgment matrix is established, and the weights are calculated by the square root method. Transform multi-objective optimization solutions into single-objective optimization solutions. Then the binary value of the decision variable speed limit value is encoded. Finally, crossover probability, mutation probability and genetic algorithm termination evolution algebra were determined. On this basis, the solution method of the optimal speed limit value is provided. After the variable speed limit was implemented for road transportation, the average speed value during peak hours increased from 82.0 to 85.9 km/h, an increase of 3.9 km/h; during the peak period, the vehicle speed increased from 83.3 to 85.6 km/h, an increase 2.3 km/h. The experimental data shows that the variable speed limit control strategy in the road transport optimization control system based on the OD prediction information has a significant effect on improving the driving efficiency of the fast lane.

Suggested Citation

  • Siru Chen, 2021. "Highway transportation optimization control system based on OD forecast information," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 748-756, August.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01071-5
    DOI: 10.1007/s13198-021-01071-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01071-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01071-5?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kaddoura, Ihab & Nagel, Kai, 2019. "Congestion pricing in a real-world oriented agent-based simulation context," Research in Transportation Economics, Elsevier, vol. 74(C), pages 40-51.
    2. Baozhen Yao & Bin Yu & Ping Hu & Junjie Gao & Mingheng Zhang, 2016. "An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot," Annals of Operations Research, Springer, vol. 242(2), pages 303-320, July.
    3. Chiou, Suh-Wen, 2018. "A traffic-responsive signal control to enhance road network resilience with hazmat transportation in multiple periods," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 105-118.
    4. Osorio, Carolina & Wang, Carter, 2017. "On the analytical approximation of joint aggregate queue-length distributions for traffic networks: A stationary finite capacity Markovian network approach," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 305-339.
    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. Gong, Manlin & Hu, Yucong & Chen, Zhiwei & Li, Xiaopeng, 2021. "Transfer-based customized modular bus system design with passenger-route assignment optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    2. Xingmin Wang & Zachary Jerome & Zihao Wang & Chenhao Zhang & Shengyin Shen & Vivek Vijaya Kumar & Fan Bai & Paul Krajewski & Danielle Deneau & Ahmad Jawad & Rachel Jones & Gary Piotrowicz & Henry X. L, 2024. "Traffic light optimization with low penetration rate vehicle trajectory data," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Wang, Hongping & Fang, Yi-Ping & Zio, Enrico, 2022. "Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Sahar Validi & Arijit Bhattacharya & P. J. Byrne, 2020. "Sustainable distribution system design: a two-phase DoE-guided meta-heuristic solution approach for a three-echelon bi-objective AHP-integrated location-routing model," Annals of Operations Research, Springer, vol. 290(1), pages 191-222, July.
    5. He, Brian Yueshuai & Zhou, Jinkai & Ma, Ziyi & Wang, Ding & Sha, Di & Lee, Mina & Chow, Joseph Y.J. & Ozbay, Kaan, 2021. "A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City," Transport Policy, Elsevier, vol. 101(C), pages 145-161.
    6. Zhang, Fang & Lu, Jian & Hu, Xiaojian & Meng, Qiang, 2023. "A stochastic dynamic network loading model for mixed traffic with autonomous and human-driven vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    7. Yu, Chunhui & Ma, Wanjing & Yang, Xiaoguang, 2020. "A time-slot based signal scheme model for fixed-time control at isolated intersections," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 176-192.
    8. Tariq Munir & Hussein Dia & Hadi Ghaderi, 2021. "A Systematic Review of the Role of Road Network Pricing in Shaping Sustainable Cities: Lessons Learned and Opportunities for a Post-Pandemic World," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    9. Flötteröd, G. & Osorio, C., 2017. "Stochastic network link transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 180-209.
    10. Thombre, Anurag & Agarwal, Amit, 2021. "A paradigm shift in urban mobility: Policy insights from travel before and after COVID-19 to seize the opportunity," Transport Policy, Elsevier, vol. 110(C), pages 335-353.
    11. Kannan Govindan, 2016. "Evolutionary algorithms for supply chain management," Annals of Operations Research, Springer, vol. 242(2), pages 195-206, July.
    12. Schröder, Daniel & Kirn, Lukas & Kinigadner, Julia & Loder, Allister & Blum, Philipp & Xu, Yihan & Lienkamp, Markus, 2023. "Ending the myth of mobility at zero costs: An external cost analysis," Research in Transportation Economics, Elsevier, vol. 97(C).
    13. Ben-Dor, Golan & Ogulenko, Aleksey & Klein, Ido & Ben-Elia, Eran & Benenson, Itzhak, 2024. "Simulation-based policy evaluation of monetary car driving disincentives in Jerusalem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    14. Lu, Xiao-Shan & Guo, Ren-Yong & Huang, Hai-Jun & Xu, Xiaoming & Chen, Jiajia, 2021. "Equilibrium analysis of parking for integrated daily commuting," Research in Transportation Economics, Elsevier, vol. 90(C).
    15. Kaddoura, Ihab & Bischoff, Joschka & Nagel, Kai, 2020. "Towards welfare optimal operation of innovative mobility concepts: External cost pricing in a world of shared autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 48-63.
    16. Yong Lin, 2023. "Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    17. Baozhen Yao & Qianqian Yan & Mengjie Zhang & Yunong Yang, 2017. "Improved artificial bee colony algorithm for vehicle routing problem with time windows," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-18, September.
    18. Jing Lu & Carolina Osorio, 2018. "A Probabilistic Traffic-Theoretic Network Loading Model Suitable for Large-Scale Network Analysis," Service Science, INFORMS, vol. 52(6), pages 1509-1530, December.
    19. Zheng, Fangfang & Jabari, Saif Eddin & Liu, Henry X. & Lin, DianChao, 2018. "Traffic state estimation using stochastic Lagrangian dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 143-165.
    20. Chiou, Suh-Wen, 2020. "A resilience-based signal control for a time-dependent road network with hazmat transportation," Reliability Engineering and System Safety, Elsevier, vol. 193(C).

    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:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01071-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.