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All issues Volume 412 (2023) E3S Web Conf., 412 (2023) 01082 References
Open Access
Issue
E3S Web Conf.
Volume 412, 2023
International Conference on Innovation in Modern Applied Science, Environment, Energy and Earth Studies (ICIES’11 2023)
Article Number 01082
Number of page(s) 11
DOI https://doi.org/10.1051/e3sconf/202341201082
Published online 17 August 2023
  1. IPCC. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York: Cambridge University Press; 2014. [Google Scholar]
  2. IEA. CO2 Emissions from Fuel Combustion 2020: Highlights. Paris: International Energy Agency; 2020. [Google Scholar]
  3. WHO. Ambient (outdoor) air pollution. Geneva: World Health Organization; 2021. [Google Scholar]
  4. Cervero R. Transport infrastructure and sustainable development. Energy Policy. 1994;22(10):825-834. [Google Scholar]
  5. Litman T. Sustainable transportation: Definition and context. Victoria, BC: Victoria Transport Policy Institute; 2021. [Google Scholar]
  6. Banister, D. (2019). Transport and climate change: a review. Journal of Transport Geography, 78, 33-46. [Google Scholar]
  7. Li, X., Zhang, C., & Lu, J. (2018). A big data analytics approach to reducing carbon footprint in transportation systems. Transportation Research Part D: Transport and Environment, 61, 244-257. [CrossRef] [Google Scholar]
  8. Jia, X., Ma, Z., Wang, Y., & Liu, X. (2019). Research on traffic prediction model based on machine learning algorithm. IEEE Access, 7, 73423-73430. [Google Scholar]
  9. Wang, Z., Wu, J., Yu, B., & Feng, Z. (2018). A big data approach to real-time traffic forecasting using mobile phone signaling data. Transportation Research Part C: Emerging Technologies, 89, 45-63. [Google Scholar]
  10. Wang, S., Zhang, Y., & Jin, X. (2020). Optimization of urban public transportation based on big data analysis. Journal of Ambient Intelligence and Humanized Computing, 11(6), 2459-2467. [Google Scholar]
  11. Chen, C., Wang, Y., & Li, Z. (2019). Big data in transportation: a review. Transportation Research Part C: Emerging Technologies, 98, 331-348. [Google Scholar]
  12. Tsai, Y. H., & Chen, C. Y. (2019). Optimizing real-time transportation planning using big data analytics. Sustainability, 11(14), 3778. [CrossRef] [Google Scholar]
  13. Qin, X., Hu, Y., Xie, X., & Tang, T. (2018). Intelligent transportation systems based on big data analytics: A survey. IEEE Access, 6, 58962-58976. [Google Scholar]
  14. Hall, R. W. (2018). Real-time transportation monitoring and management. CRC Press. [Google Scholar]
  15. Lu, J., & Wang, S. (2017). Intelligent transportation systems: Smart and green infrastructure design. CRC Press. [Google Scholar]
  16. Kassou, M. et al., Digital transformation in flow planning: The case of container terminals at a smart port, Journal of Theoretical and Applied Information Technologythis link is disabled, 2021, 99(9), pp. 1966–1976 [Google Scholar]
  17. Zhang, Y., & Chow, A. H. (2017). Big data analytics for intelligent transportation systems. In Handbook of Big Data Analytics (pp. 491-518). Springer. [Google Scholar]
  18. Calabrese, F., Diao, M., Lorenzo, G. D., & Ferreira, J.Jr (2015). Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies, 58, 252-264. [Google Scholar]
  19. Tsai, Y. H., & Chen, C. Y. (2019). Optimizing real-time transportation planning using big data analytics. Sustainability, 11(14), 3778. [CrossRef] [Google Scholar]
  20. Qin, X., Hu, Y., Xie, X., & Tang, T. (2018). Intelligent transportation systems based on big data analytics: A survey. IEEE Access, 6, 58962-58976. [Google Scholar]
  21. Zhu, X., Lv, Y., & Zeng, Q. (2018). Big data analytics for intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1488-1508. [Google Scholar]
  22. Zhang, Y., Yuan, L., Wang, F., & Zhu, Z. (2019). A machine learning approach for real-time traffic prediction and optimization. Transportation Research Part C: Emerging Technologies, 100, 18-32. [Google Scholar]
  23. Ahmed, M., Rehmani, M. H., & Shiraz, M. (2020). Traffic flow clustering and routing optimization using deep learning and ant colony algorithm. Journal of Network and Computer Applications, 165, 102690. [Google Scholar]
  24. Li, Y., Zhang, M., Li, M., & Yu, Y. (2022). Real-time Traffic Congestion Detection and Prediction Based on Deep Learning. IEEE Access, 10, 26081-26090. [Google Scholar]
  25. Wang, C., Wang, Y., & Zhou, Z. (2022). Real-Time Vehicle Routing Problem with Time Windows and Traffic Congestion Considering Electric Vehicles. Journal of Advanced Transportation, 2022, 1-16. [Google Scholar]
  26. Liu, D., Liu, C., Wang, F., Chen, Y., & Li, K. (2022). Real-Time Intelligent Traffic Signal Control Considering Congestion Prediction. Journal of Advanced Transportation, 2022, 1-13. [Google Scholar]
  27. Xu, L., Ma, D., Wang, S., & Yu, Z. (2023). Real-Time Congestion Prediction and Intelligent Traffic Signal Control with Deep Reinforcement Learning. Transportation Research Part C: Emerging Technologies, 138, 30-46. [Google Scholar]

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