IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i19p8118-d422645.html
   My bibliography  Save this article

Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

Author

Listed:
  • Tu Peng

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

  • Xu Yang

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

  • Zi Xu

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

  • Yu Liang

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

Abstract

The sustainable development of mankind is a matter of concern to the whole world. Environmental pollution and haze diffusion have greatly affected the sustainable development of mankind. According to previous research, vehicle exhaust emissions are an important source of environmental pollution and haze diffusion. The sharp increase in the number of cars has also made the supply of energy increasingly tight. In this paper, we have explored the use of intelligent navigation technology based on data analysis to reduce the overall carbon emissions of vehicles on road networks. We have implemented a traffic flow prediction method using a genetic algorithm and particle-swarm-optimization-enhanced support vector regression, constructed a model for predicting vehicle exhaust emissions based on predicted road conditions and vehicle fuel consumption, and built our low-carbon-emission-oriented navigation algorithm based on a spatially optimized dynamic path planning algorithm. The results show that our method could help to significantly reduce the overall carbon emissions of vehicles on the road network, which means that our method could contribute to the construction of low-carbon-emission intelligent transportation systems and smart cities.

Suggested Citation

  • Tu Peng & Xu Yang & Zi Xu & Yu Liang, 2020. "Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods," Sustainability, MDPI, vol. 12(19), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:8118-:d:422645
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/19/8118/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/19/8118/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2012. "An adaptive large neighborhood search heuristic for the Pollution-Routing Problem," European Journal of Operational Research, Elsevier, vol. 223(2), pages 346-359.
    2. Miltiadis D. Lytras & Anna Visvizi, 2018. "Who Uses Smart City Services and What to Make of It: Toward Interdisciplinary Smart Cities Research," Sustainability, MDPI, vol. 10(6), pages 1-16, June.
    3. Miltiadis D. Lytras & Anna Visvizi & Akila Sarirete, 2019. "Clustering Smart City Services: Perceptions, Expectations, Responses," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    4. Nie, Yu (Marco) & Wu, Xing, 2009. "Shortest path problem considering on-time arrival probability," Transportation Research Part B: Methodological, Elsevier, vol. 43(6), pages 597-613, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    2. Nuri Cihat Onat & Galal M. Abdella & Murat Kucukvar & Adeeb A. Kutty & Munera Al‐Nuaimi & Gürkan Kumbaroğlu & Melih Bulu, 2021. "How eco‐efficient are electric vehicles across Europe? A regionalized life cycle assessment‐based eco‐efficiency analysis," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(5), pages 941-956, September.
    3. Wang, Huiwen & Yi, Wen & Zhen, Lu, 2024. "Optimal policy for scheduling automated guided vehicles in large-scale intelligent transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    4. Obada Asqool & Suhana Koting & Ahmad Saifizul, 2021. "Evaluation of Outlier Filtering Algorithms for Accurate Travel Time Measurement Incorporating Lane-Splitting Situations," Sustainability, MDPI, vol. 13(24), pages 1-23, December.

    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. Anna Visvizi & Shahira Assem Abdel-Razek & Roman Wosiek & Radosław Malik, 2021. "Conceptualizing Walking and Walkability in the Smart City through a Model Composite w 2 Smart City Utility Index," Energies, MDPI, vol. 14(23), pages 1-20, December.
    2. Alessandro Crivellari & Euro Beinat, 2020. "LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists," Sustainability, MDPI, vol. 12(1), pages 1-18, January.
    3. Radosław Malik & Anna Visvizi & Orlando Troisi & Mara Grimaldi, 2022. "Smart Services in Smart Cities: Insights from Science Mapping Analysis," Sustainability, MDPI, vol. 14(11), pages 1-16, May.
    4. Ibrahim Mutambik, 2023. "The Global Whitewashing of Smart Cities: Citizens’ Perspectives," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
    5. Maria Vincenza Ciasullo & Orlando Troisi & Mara Grimaldi & Daniele Leone, 2020. "Multi-level governance for sustainable innovation in smart communities: an ecosystems approach," International Entrepreneurship and Management Journal, Springer, vol. 16(4), pages 1167-1195, December.
    6. Mo, Pengli & Yao, Yu & D’Ariano, Andrea & Liu, Zhiyuan, 2023. "The vehicle routing problem with underground logistics: Formulation and algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    7. Jaroslav Burian & Karel Macků & Jarmila Zimmermannová & Barbora Kočvarová, 2018. "Spatio-Temporal Changes and Dependencies of Land Prices: A Case Study of the City of Olomouc," Sustainability, MDPI, vol. 10(12), pages 1-19, December.
    8. Yang, Lixing & Zhou, Xuesong, 2017. "Optimizing on-time arrival probability and percentile travel time for elementary path finding in time-dependent transportation networks: Linear mixed integer programming reformulations," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 68-91.
    9. Yanfang Zhang & Mushang Lee, 2019. "A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    10. Benoît Desmarchelier & Faridah Djellal & Faïz Gallouj, 2018. "Public Service Innovation Networks (PSINs): Collaborating for Innovation and Value Creation," Working Papers halshs-01934275, HAL.
    11. Yagcitekin, Bunyamin & Uzunoglu, Mehmet, 2016. "A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account," Applied Energy, Elsevier, vol. 167(C), pages 407-419.
    12. Johannes Stübinger & Lucas Schneider, 2020. "Understanding Smart City—A Data-Driven Literature Review," Sustainability, MDPI, vol. 12(20), pages 1-23, October.
    13. Lee, Jisun & Joung, Seulgi & Lee, Kyungsik, 2022. "A fully polynomial time approximation scheme for the probability maximizing shortest path problem," European Journal of Operational Research, Elsevier, vol. 300(1), pages 35-45.
    14. André Luis Azevedo Guedes & Jeferson Carvalho Alvarenga & Maurício Dos Santos Sgarbi Goulart & Martius Vicente Rodriguez y Rodriguez & Carlos Alberto Pereira Soares, 2018. "Smart Cities: The Main Drivers for Increasing the Intelligence of Cities," Sustainability, MDPI, vol. 10(9), pages 1-19, August.
    15. María Eugenia López-Pérez & María Eugenia Reyes-García & María Eugenia López-Sanz, 2023. "Smart Mobility and Smart Climate: An Illustrative Case in Seville, Spain," IJERPH, MDPI, vol. 20(2), pages 1-11, January.
    16. Benoît Desmarchelier & Faridah Djellal & Faïz Gallouj, 2019. "Towards a servitization of innovation networks: from traditional innovation networks to public service innovation networks for social innovation," Post-Print halshs-03177975, HAL.
    17. Barahimi, Amir Hossein & Eydi, Alireza & Aghaie, Abdolah, 2021. "Multi-modal urban transit network design considering reliability: multi-objective bi-level optimization," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    18. Fernando Ordóñez & Nicolás E. Stier-Moses, 2010. "Wardrop Equilibria with Risk-Averse Users," Transportation Science, INFORMS, vol. 44(1), pages 63-86, February.
    19. Kallestad, Jakob & Hasibi, Ramin & Hemmati, Ahmad & Sörensen, Kenneth, 2023. "A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 309(1), pages 446-468.
    20. Vadlamani, Satish & Hosseini, Seyedmohsen, 2014. "A novel heuristic approach for solving aircraft landing problem with single runway," Journal of Air Transport Management, Elsevier, vol. 40(C), pages 144-148.

    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:12:y:2020:i:19:p:8118-:d:422645. 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.