IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i6p2596-d1361541.html
   My bibliography  Save this article

Ideology of Urban Road Transport Chaos and Accident Risk Management for Sustainable Transport Systems

Author

Listed:
  • Viacheslav Morozov

    (Department of Transport and Technological Systems, Industrial University of Tyumen, 625000 Tyumen, Russia)

  • Artur I. Petrov

    (Department of Road Transport Operation, Industrial University of Tyumen, 625000 Tyumen, Russia)

  • Vladimir Shepelev

    (Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia)

  • Mohammed Balfaqih

    (Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia)

Abstract

Transport systems are complex systems present in modern cities. The sustainability of all other urban systems depends on the sustainable functioning of urban transport. Various processes occur within transport systems. Road traffic is one of them. At the same time, road traffic is a rather complex process to manage, which is explained by the influence of many different internal and external environmental factors. The unpredictable and chaotic behavior of each vehicle in a traffic flow complicates predicting the transport situation and traffic management. This problem gave rise to several unsolved problems, including traffic congestion and road accident rates. The solution to these problems is connected with sustainably managing transport systems in terms of road traffic. However, numerous regularities between elements within the system should be understood in order to implement the management process. Unfortunately, the results of many previous studies often reflect only partial regularities and have limited functionality. Therefore, a new approach to urban traffic management is needed. As opposed to the existing solutions, the authors of this paper propose to implement management based on the regularities of changes in the chaos of the transport system. In this regard, the purpose of this research is to establish the relationship between road traffic chaos and road accident rates. The general methodological basis of this research is the system approach and its methods: analysis and synthesis. The theoretical studies were mostly based on the theories of chaos, dynamic systems, and traffic flows. The experimental studies were based on the theories of experimental design, probability, and mathematical statistics. To achieve this goal, a profound analysis covered studies on the sustainability of transport and dynamic systems, sociodynamics, and traffic. The authors proposed considering the relative entropy of lane occupancy at signal-controlled intersections as a measure for assessing traffic flow chaos and sustainability. Notably, as the main conclusions, the authors established regularities for the influence of entropy on the kinetic energy of traffic flows and injury risk. It also makes sense to emphasize that the initial data for the experiment were collected via real-time processing of video images using neural network technologies. These technologies will further allow for the implementation of traffic management and real-time forecasting of various events. Ultimately, the authors identified changes in injury risk depending on the level of road chaos. According to the authors, the obtained results can be used to improve the sustainability of urban transport systems. The research identified changes in injury risk depending on the level of road chaos, which could have significant implications for urban traffic management strategies.

Suggested Citation

  • Viacheslav Morozov & Artur I. Petrov & Vladimir Shepelev & Mohammed Balfaqih, 2024. "Ideology of Urban Road Transport Chaos and Accident Risk Management for Sustainable Transport Systems," Sustainability, MDPI, vol. 16(6), pages 1-32, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2596-:d:1361541
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/6/2596/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/6/2596/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alatas, Bilal & Akin, Erhan & Ozer, A. Bedri, 2009. "Chaos embedded particle swarm optimization algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 40(4), pages 1715-1734.
    2. 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.
    3. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    4. Abraham T. Narh & Neil Thorpe & Margaret C. Bell & Graeme A. Hill, 2016. "Do new sources of traffic data make the application of Chaos Theory to traffic management a realistic possibility?," Transport Reviews, Taylor & Francis Journals, vol. 36(5), pages 635-658, September.
    5. Andrew Dickerson & John Peirson & Roger Vickerman, 2000. "Road Accidents and Traffic Flows: An Econometric Investigation," Economica, London School of Economics and Political Science, vol. 67(265), pages 101-121, February.
    6. Quek, Wei-Liang & Chung, Ning Ning & Chew, Lock Yue, 2018. "An analysis on the traffic processing efficiency of a combination of serial and parallel bottlenecks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 491-502.
    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. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    2. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    3. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    4. Börjesson, Maria & Asplund, Disa & Hamilton, Carl, 2021. "Optimal kilometre tax for electric passenger cars," Working Papers 2021:3, Swedish National Road & Transport Research Institute (VTI).
    5. Golob, Thomas F. & Recker, Wilfred W., 2001. "Relationships Among Urban Freeway Accidents, Traffic Flow, Weather and Lighting Conditions," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2fh4x5hp, Institute of Transportation Studies, UC Berkeley.
    6. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    7. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.
    8. 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.
    9. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
    10. 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.
    11. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    12. Bingol, Harun & Alatas, Bilal, 2020. "Chaos based optics inspired optimization algorithms as global solution search approach," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    13. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    14. Pål Andreas Pedersen, 2001. "A Game Theoretical Approach to Road Safety," Studies in Economics 0105, School of Economics, University of Kent.
    15. Donya Rahmani & Saeed Heravi & Hossein Hassani & Mansi Ghodsi, 2016. "Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula," Papers 1605.02188, arXiv.org.
    16. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    17. 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.
    18. Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
    19. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    20. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.

    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:16:y:2024:i:6:p:2596-:d:1361541. 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.