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Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization

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  • Li, Wan
  • Wang, Boyu
  • Sun, Ruixiao
  • Ai, Li
  • Lin, Zhenhong

Abstract

The study proposes a comprehensive Transportation Mobility (TransitMo) framework covering conceptual design, model formulation, optimization, simulation, and impact analysis of the transportation mobility system. TransitMo is composed of a transportation digital twin developed in Simulation of Urban MObility (SUMO) and an Intelligent Traffic Management and Control Center (ITMCC) that identifies the best ways to improve the movement of people within urban areas using various modes of transportation. This study encompasses advanced modeling techniques, algorithms, and strategic testing to optimize energy efficiency and mobility in a multimodal shared mobility network. TransitMo’s practical applications are exemplified through a city-scaled simulation network in Chattanooga, TN, employing demographic data to analyze historical traffic patterns and forecast future demands. Central to this methodology are three models: the User Preference Model (UP), the Energy Consumption Model (EC), and the System Optimization Model (SO). These models work in concert to iteratively devise the optimal travel incentives and minimize the total system cost in a real-time manner. Test results verified that the proposed adaptive incentive program and optimized bus scheduling can improve network performance by increasing public transit ridership.

Suggested Citation

  • Li, Wan & Wang, Boyu & Sun, Ruixiao & Ai, Li & Lin, Zhenhong, 2025. "Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization," Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225002294
    DOI: 10.1016/j.energy.2025.134587
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    References listed on IDEAS

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    1. Meng Li & Guowei Hua & Haijun Huang, 2018. "A Multi-Modal Route Choice Model with Ridesharing and Public Transit," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
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    3. Deng, Yuntian & Shao, Shiping & Mittal, Archak & Twumasi-Boakye, Richard & Fishelson, James & Gupta, Abhishek & Shroff, Ness B., 2022. "Incentive design and profit sharing in multi-modal transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 1-21.
    4. Nguyen, Tri K. & Hoang, Nam H. & Vu, Hai L., 2022. "A unified activity-based framework for one-way car-sharing services in multi-modal transportation networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
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    Cited by:

    1. Hu, Ziqiang & Wei, Yuguang & Li, Qi & Li, Chen, 2025. "Integrated optimization of storage node location and multi-commodities flow routes in multimodal transport networks with carbon emissions consideration," Energy, Elsevier, vol. 328(C).

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