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A novel method for optimal fuel consumption estimation and planning for transportation systems

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  • Wörz, Sascha
  • Bernhardt, Heinz

Abstract

With increasing public concern about the environment, liveability and sustainability have become important issues in minimal fuel consumption estimation for transportation systems. Microscopic fuel planning and emission models use vehicle speed and acceleration as inputs and are suitable for predicting the amount of fuel at the link level. However, the lack of microscopic traffic data limits the application of these models. A method is provided for acquiring microscopic information from macroscopic traffic data. The main approach is to reconstruct the state and vehicle group trajectories with an Expectation Maximization algorithm with nice convergence properties and then to apply Dijkstra‘s algorithm in order to find a transport route with minimal fuel consumption. Validation of the method shows that the estimated fuel consumption reflects the real fuel amount and hence, the route with minimal fuel consumption determined by Dijkstra‘s algorithm is actually suitable for optimal transport planning.

Suggested Citation

  • Wörz, Sascha & Bernhardt, Heinz, 2017. "A novel method for optimal fuel consumption estimation and planning for transportation systems," Energy, Elsevier, vol. 120(C), pages 565-572.
  • Handle: RePEc:eee:energy:v:120:y:2017:i:c:p:565-572
    DOI: 10.1016/j.energy.2016.11.110
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    References listed on IDEAS

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    1. Coifman, Benjamin, 2002. "Estimating travel times and vehicle trajectories on freeways using dual loop detectors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(4), pages 351-364, May.
    2. Safa, Majeed & Samarasinghe, Sandhya, 2013. "Modelling fuel consumption in wheat production using artificial neural networks," Energy, Elsevier, vol. 49(C), pages 337-343.
    3. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part II: Queueing at freeway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 289-303, August.
    4. Siami-Irdemoosa, Elnaz & Dindarloo, Saeid R., 2015. "Prediction of fuel consumption of mining dump trucks: A neural networks approach," Applied Energy, Elsevier, vol. 151(C), pages 77-84.
    5. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part I: General theory," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 281-287, August.
    6. Yeon, Jiyoun & Elefteriadou, Lily & Lawphongpanich, Siriphong, 2008. "Travel time estimation on a freeway using Discrete Time Markov Chains," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 325-338, May.
    7. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part III: Multi-destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 305-313, August.
    8. Zhang, Shaojun & Wu, Ye & Un, Puikei & Fu, Lixin & Hao, Jiming, 2016. "Modeling real-world fuel consumption and carbon dioxide emissions with high resolution for light-duty passenger vehicles in a traffic populated city," Energy, Elsevier, vol. 113(C), pages 461-471.
    9. Sun, Lu & Yang, Jun & Mahmassani, Hani, 2008. "Travel time estimation based on piecewise truncated quadratic speed trajectory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(1), pages 173-186, January.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Muhammad Ali & Muhammad Daud Kamal & Ali Tahir & Salman Atif, 2021. "Fuel Consumption Monitoring through COPERT Model—A Case Study for Urban Sustainability," Sustainability, MDPI, vol. 13(21), pages 1-12, October.
    2. Ji, Shaobo & Chen, Qiulin & Shu, Minglei & Tian, Guohong & Liao, Baoliang & Lv, Chengju & Li, Meng & Lan, Xin & Cheng, Yong, 2020. "Influence of operation management on fuel consumption of coach fleet," Energy, Elsevier, vol. 203(C).
    3. Hao Wang & Quan Liu & Hongyang Zhang & Yinlong Jin & Wenzhen Yu, 2022. "A Two-Stage Decision-Making Method Based on WebGIS for Bulk Material Transportation of Hydropower Construction," Energies, MDPI, vol. 15(5), pages 1-21, February.
    4. Kroyan, Yuri & Wojcieszyk, Michal & Kaario, Ossi & Larmi, Martti & Zenger, Kai, 2020. "Modeling the end-use performance of alternative fuels in light-duty vehicles," Energy, Elsevier, vol. 205(C).

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