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A Method for the Optimization of Daily Activity Chains Including Electric Vehicles

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  • Dimitrios Rizopoulos

    (Department of Transport Technology and Economics (KUKG), Faculty of Transportation Engineering and Vehicle Engineering (KJK), Budapest University of Technology and Economics (BME), 1111 Budapest, Hungary)

  • Domokos Esztergár-Kiss

    (Department of Transport Technology and Economics (KUKG), Faculty of Transportation Engineering and Vehicle Engineering (KJK), Budapest University of Technology and Economics (BME), 1111 Budapest, Hungary)

Abstract

The focus of this article is to introduce a method for the optimization of daily activity chains of travelers who use Electric Vehicles (EVs) in an urban environment. An approach has been developed based on activity-based modeling and the Genetic Algorithm (GA) framework to calculate a suitable schedule of activities, taking into account the locations of activities, modes of transport, and the time of attendance to each activity. The priorities of the travelers concerning the spatial and temporal flexibility were considered, as well as the constraints that are related to the limited range of the EVs, the availability of Charging Stations (CS), and the elevation of the road network. In order to model real travel behavior, two charging scenarios were realized. In the first case, the traveler stays in the EV at the CS, and in the second case, the traveler leaves the EV to charge at the CS while conducting another activity at a nearby location. Through a series of tests on synthetic activity chain data, we proved the suitability of the method elaborated for addressing the needs of travelers and being utilized as an optimization method for a modern Intelligent Transportation System (ITS).

Suggested Citation

  • Dimitrios Rizopoulos & Domokos Esztergár-Kiss, 2020. "A Method for the Optimization of Daily Activity Chains Including Electric Vehicles," Energies, MDPI, vol. 13(4), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:906-:d:321932
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    References listed on IDEAS

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    Cited by:

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    3. Jairo Ortega & Sarbast Moslem & Juan Palaguachi & Martin Ortega & Tiziana Campisi & Vincenza Torrisi, 2021. "An Integrated Multi Criteria Decision Making Model for Evaluating Park-and-Ride Facility Location Issue: A Case Study for Cuenca City in Ecuador," Sustainability, MDPI, vol. 13(13), pages 1-16, July.
    4. Tiande Mo & Yu Li & Kin-tak Lau & Chi Kin Poon & Yinghong Wu & Yang Luo, 2022. "Trends and Emerging Technologies for the Development of Electric Vehicles," Energies, MDPI, vol. 15(17), pages 1-34, August.
    5. Jairo Ortega & Sarbast Moslem & János Tóth & Tamás Péter & Juan Palaguachi & Mario Paguay, 2020. "Using Best Worst Method for Sustainable Park and Ride Facility Location," Sustainability, MDPI, vol. 12(23), pages 1-18, December.
    6. Patrick Manser & Tom Haering & Tim Hillel & Janody Pougala & Rico Krueger & Michel Bierlaire, 2024. "Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling," Transportation, Springer, vol. 51(2), pages 501-528, April.
    7. Luis B. Elvas & Joao C Ferreira, 2021. "Intelligent Transportation Systems for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-9, September.
    8. Marija Zima-Bockarjova & Antans Sauhats & Lubov Petrichenko & Roman Petrichenko, 2020. "Charging and Discharging Scheduling for Electrical Vehicles Using a Shapley-Value Approach," Energies, MDPI, vol. 13(5), pages 1-21, March.

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