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Reconciling spatiotemporal conjunction with digital twin for sequential travel time prediction and intelligent routing

Author

Listed:
  • Claire Y. T. Chen

    (Montpellier Business School)

  • Edward W. Sun

    (KEDGE Business School)

  • Yi-Bing Lin

    (National Yang Ming Chiao Tung University (NYCU)
    China Medical University)

Abstract

A traffic digital twin explicitly communicates the primary domain of the digital twin for traffic management and analysis in understanding and simulating traffic patterns, optimizing traffic flow, and addressing related challenges. It characterizes a virtual, computerized representation of the Intelligent Transportation System (ITS), where a digital twin mimics the real-world ITS by integrating real-time data, analysis and simulation to replicate and simulate the behavior, performance and dynamics of the transport system. This study proposes a novel deep learning algorithm, called the Bidirectional Anisometric Gated Recursive Unit (BDAGRU), which is designed for a digital twin to dynamically process current traffic information to predict near-future travel times and support route selection. After formulating the computational procedure using the stochastic gradient descent algorithm, we successfully perform several near-future sequence predictions (ranging from 15 to 150 min) with extensive multimodal (numerical and textual) data, especially under congested traffic conditions. We then determine the most efficient vehicle route by minimizing travel time under uncertainty, using a digital twin enhanced by the BDAGRU-driven deep learning method. Empirical analysis of extensive traffic data shows that our proposed model achieves two notable results: (1) significant improvement in the accuracy of travel time prediction over different time intervals compared to several traditional deep learning methods, and (2) competent determination of the best route with the shortest travel time, especially in scenarios with future uncertainties.

Suggested Citation

  • Claire Y. T. Chen & Edward W. Sun & Yi-Bing Lin, 2025. "Reconciling spatiotemporal conjunction with digital twin for sequential travel time prediction and intelligent routing," Annals of Operations Research, Springer, vol. 348(1), pages 671-716, May.
  • Handle: RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-024-05990-x
    DOI: 10.1007/s10479-024-05990-x
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    References listed on IDEAS

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    1. Emna Marrekchi & Walid Besbes & Diala Dhouib & Emrah Demir, 2021. "A review of recent advances in the operations research literature on the green routing problem and its variants," Annals of Operations Research, Springer, vol. 304(1), pages 529-574, September.
    2. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
    3. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    4. Vincent Charles & Ali Emrouznejad & Tatiana Gherman, 2023. "A critical analysis of the integration of blockchain and artificial intelligence for supply chain," Annals of Operations Research, Springer, vol. 327(1), pages 7-47, August.
    5. Guanghui Zhou & Chao Zhang & Zhi Li & Kai Ding & Chuang Wang, 2020. "Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1034-1051, February.
    6. Avraham, Edison & Raviv, Tal, 2020. "The data-driven time-dependent traveling salesperson problem," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 25-40.
    7. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2019. "Coherent quality management for big data systems: a dynamic approach for stochastic time consistency," Annals of Operations Research, Springer, vol. 277(1), pages 3-32, June.
    8. Jiang, Chenming & Bhat, Chandra R. & Lam, William H.K., 2020. "A bibliometric overview of Transportation Research Part B: Methodological in the past forty years (1979–2019)," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 268-291.
    9. Clarisse Dhaenens & Laetitia Jourdan, 2022. "Metaheuristics for data mining: survey and opportunities for big data," Annals of Operations Research, Springer, vol. 314(1), pages 117-140, July.
    10. Sajjad Rahmanzadeh & Mir Saman Pishvaee & Kannan Govindan, 2023. "Emergence of open supply chain management: the role of open innovation in the future smart industry using digital twin network," Annals of Operations Research, Springer, vol. 329(1), pages 979-1007, October.
    11. Gao, Hongyan & Liu, Fasheng, 2013. "Estimating freeway traffic measures from mobile phone location data," European Journal of Operational Research, Elsevier, vol. 229(1), pages 252-260.
    12. Santos, Maria João & Curcio, Eduardo & Amorim, Pedro & Carvalho, Margarida & Marques, Alexandra, 2021. "A bilevel approach for the collaborative transportation planning problem," International Journal of Production Economics, Elsevier, vol. 233(C).
    13. Corredera, Alberto & Ruiz, Carlos, 2023. "Prescriptive selection of machine learning hyperparameters with applications in power markets: Retailer’s optimal trading," European Journal of Operational Research, Elsevier, vol. 306(1), pages 370-388.
    14. Huber, Jakob & Müller, Sebastian & Fleischmann, Moritz & Stuckenschmidt, Heiner, 2019. "A data-driven newsvendor problem: From data to decision," European Journal of Operational Research, Elsevier, vol. 278(3), pages 904-915.
    15. Yao, Jia & Cheng, Ziyi & Chen, Anthony, 2023. "Bibliometric analysis and systematic literature review of the traffic paradoxes (1968–2022)," Transportation Research Part B: Methodological, Elsevier, vol. 177(C).
    16. Chen, Yi-Ting & Sun, Edward W. & Chang, Ming-Feng & Lin, Yi-Bing, 2021. "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0," International Journal of Production Economics, Elsevier, vol. 238(C).
    17. Wan-Ni Lai & Yi-Ting Chen & Edward W. Sun, 2021. "Comonotonicity and low volatility effect," Annals of Operations Research, Springer, vol. 299(1), pages 1057-1099, April.
    18. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    19. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    20. Nguyen, Win P.V. & Nof, Shimon Y., 2020. "Strategic lines of collaboration in response to disruption propagation (CRDP) through cyber-physical systems," International Journal of Production Economics, Elsevier, vol. 230(C).
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