Skip to content
2000
Volume 1, Issue 1
  • ISSN: 2772-316X
  • E-ISSN: 2772-3178

Abstract

Background

Since the second half of 2021, the prices of natural gas, coal and oil have soared, but at the same time, the Russia-Ukraine conflict is likely to become a catalyst for Europe and the world to accelerate the green and low-carbon transformation of energy, prompting countries to accelerate investment in renewable energy, improve energy security and achieve energy independence and the energy crisis started in Europe and eventually spread around the world. Under the new circumstances, the global green and low-carbon energy transition is imperative. The International Energy Agency released the “2023 Global Electric Vehicle Outlook” report, which showed that global electric vehicle sales will grow by 35% in 2023 from the previous year to 14 million units, increasing the total share of the overall vehicle market to 18%. Replacing traditional fossil fuels with low energy consumption and low pollution has become a trend in the automotive industry.

Objective

Therefore, this paper studies the travel distribution pattern of electric vehicles in Dalian city, which paves the way for the future development of the electric vehicle industry.

Methods

First of all, this paper predicted the number of electric vehicles in Dalian in the next five years. Next, the gravity model and double-constraint gravity model were used to predict and analyze the travel generation, attraction and distribution of each traffic district. The gravity model is based on the concept of gravity in physics, this model can simulate the travel attraction between transportation communities. The dual constraint gravity model is an extension of the gravity model, taking into account the impact of factors other than distance on traffic distribution. For example, land type, land intensity utilization coefficient, . Finally, taking Shahekou District of Dalian city as an example, this paper made an empirical analysis of the travel distribution of electric vehicles in Shahekou District.

Results

This article fully considers the impact of land use types on residents' travel. Residential land is an important factor affecting travel volume, while public facility land is an important factor affecting attraction volume. For areas with high travel attractions, it is necessary to consider building more charging facilities around them to solve the problem of difficult charging. The distribution results showed that the amounts of travel in each traffic community were not much different, but their attraction volumes were greatly different.

Conclusion

After understanding the distribution of electric vehicle traffic in various residential areas, it is possible to arrange the planning of public charging facilities more reasonably. The research provides practical guidance for the transportation planning of electric vehicles in such urban cities as Dalian city.

Loading

Article metrics loading...

/content/journals/css/10.2174/012772316X270709231208101954
2023-12-22
2025-09-04
Loading full text...

Full text loading...

References

  1. ZhouJ. ZhangT. HuP. New energy vehicle development forecast based on grey forecast.Electronic World202032
    [Google Scholar]
  2. Jiao, Yanqing Research on the prediction of diffusion drivers and market ownership of new energy vehicles.Automotive Pract. Technol.2020175
    [Google Scholar]
  3. ZengMing ZengFanxiao Zhu, Xiaoli Forecast of electric vehicle ownership in China based on Bass model. China Elect. Power,2013
    [Google Scholar]
  4. WangRuimiao. ChenTao. LiuYongxiang. The elastic coefficient method and the thousand-person ownership method predict the number of electric vehicles.Agricult. Equipm. Vehicle Eng.2011064043
    [Google Scholar]
  5. WangR. prediction of the private ownership of new energy vehicles in Beijing based on the system dynamics model.Inner. Mongol. Stat.202014
    [Google Scholar]
  6. LeeY. KimC. ShinJ. A hybrid electric vehicle market penetration model to identify the best policy mix: A consumer ownership cycle approach.Appl. Energy201618443849910.1016/j.apenergy.2016.10.038
    [Google Scholar]
  7. RietmannN. HüglerB. LievenT. Forecasting the trajectory of electric vehicle sales and the consequences for worldwide CO2 emissions.J. Clean. Prod.202026112103810.1016/j.jclepro.2020.121038
    [Google Scholar]
  8. CpA. MlB. SeC. Forecasted datasets of electric vehicle consumption on the electricity grid of Spain.Data Brief202031105823
    [Google Scholar]
  9. AbdelbakyM. PeetersJ.R. DuflouJ.R. Forecasting the development trend of low e mission vehicle technologies: Based on patet data.Technol. Forecast. Soc. Change20211664120651
    [Google Scholar]
  10. LeeJ.H. HardmanS.J. TalG. Who is buying electric vehicles in California? Characterising early adopter heterogeneity and forecasting market diffusion.Energy Res. Soc. Sci.20195521822610.1016/j.erss.2019.05.011
    [Google Scholar]
  11. BlackA. The chicago area transportation study: A case study of rational planning.J. Plann. Educ. Res.1990101273710.1177/0739456X9001000105
    [Google Scholar]
  12. ShaoC. Traffic planning principle. Version 2.China Railway Press2014
    [Google Scholar]
  13. Study on the prediction method and model of urban rail transit.Lanzhou Jiaotong University2015
    [Google Scholar]
  14. OpenshawS. Building Fuzzy Spatial Interaction Models.Advances in Spatial Science199710.1007/978‑3‑662‑03499‑6_18
    [Google Scholar]
  15. SchnciderJ. A Urban planning and the American family.Stetson L. Rev2007
    [Google Scholar]
  16. The application of the maximum entropy model in the prediction of traffic distribution.Transpor. Syst. Eng. Inform.2005515
    [Google Scholar]
  17. JiangS. ChengL. KongL. An Production Forecast Model Based on Accumulative Method.Meteorological & Environmental Sciences2017
    [Google Scholar]
  18. MikkonenK. LuomaM. The parameters of the gravity model are changing – how and why?J. Transp. Geogr.19997427728310.1016/S0966‑6923(99)00024‑1
    [Google Scholar]
  19. SunX. PeiY. Discussion on the prediction methods of traffic generation and distribution in planning new urban areas.International Academic Conference In The Field Of Transportation2006
    [Google Scholar]
  20. Luo, Zhizhong Urban traffic demand analysis based on land use. Xi 'an: Chang' an University., 2006
  21. MiH. ZhouW. ShiW. Correction of the gravity model of population migration and its application.Population Study200933499104
    [Google Scholar]
  22. GuoX. The generalized gravity model of travel distribution prediction of the entropy parameter of land structure.Traffic Inform. Safety201432417
    [Google Scholar]
  23. ZhouK. Urban transportation demand prediction based on land use. Nan’jing.Nanjing Agricultural University2017
    [Google Scholar]
  24. Tan, Xiaoyu Research on the relationship between land use and attraction in transportation communities.Logist. Technol.20124410.3969/j.issn.1005‑152X.2012.04.031
    [Google Scholar]
  25. Dalian Municipal People’s Government Website2018Available from: https://www.dl.gov.cn/
  26. Global EV Outlook 2023: Catching up with climate ambitions.2023Available from: https://iea.blob.core.windows.net/assets/dacf14d2-eabc-498a-8263-9f97fd5dc327/GEVO2023.pdf
/content/journals/css/10.2174/012772316X270709231208101954
Loading
/content/journals/css/10.2174/012772316X270709231208101954
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test