References
- Ahmed, M. M. A. W., & Monem, N. A. E. (2020). Sustainable and green transportation for better quality of life case study greater Cairo – Egypt. HBRC Journal, 16(1), 17–37. https://doi.org/10.1080/16874048.2020.1719340
- Arias, M. B., & Bae, S. (2016). Electric vehicle charging demand forecasting model based on big data technologies. Applied Energy, 183, 327–339. https://doi.org/10.1016/j.apenergy.2016.08.080
- Bae, S., & Kwasinski, A. (2012). Spatial and temporal model for electric vehicle rapid charging demand. 2012 IEEE Vehicle Power and Propulsion Conference, VPPC 2012, 3(1), 345–348. https://doi.org/10.1109/VPPC.2012.6422675
- Beeferman, D., & Berger, A. (2000). Agglomerative clustering of a search engine query log. Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 407–416. https://doi.org/10.1145/347090.347176
- Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. In Neural networks: Tricks of the trade (pp. 437–478). Springer.
- Box, G. E. P., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65(332), 1509–1526. https://doi.org/10.1080/01621459.1970.10481180
- Buzna, L., De Falco, P., Khormali, S., Proto, D., & Straka, M. (2019). Electric vehicle load forecasting: A comparison between time series and machine learning approaches. SyNERGY MED 2019 – 1st International Conference on Energy Transition in the Mediterranean Area, 1–5. https://doi.org/10.1109/SyNERGY-MED.2019.8764110
- Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics – Theory and Methods, 3(1), 1–27. https://doi.org/10.1080/03610927408827101
- Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–794.
- Chen, X., Li, Z., Yang, Y., Qi, L., & Ke, R. (2021). High-resolution vehicle trajectory extraction and denoising from aerial videos. IEEE Transactions on Intelligent Transportation Systems, 22(5), 3190–3202. https://doi.org/10.1109/TITS.2020.3003782
- Chen, X., (Michael), Zhou, L., & Li, L. (2019). Bayesian network for red-light-running prediction at signalized intersections. Journal of Intelligent Transportation Systems, 23(2), 120–132. https://doi.org/10.1080/15472450.2018.1486192
- Chen, X., Xu, X., Yang, Y., Wu, H., Tang, J., & Zhao, J. (2020). Augmented ship tracking under occlusion conditions from maritime surveillance videos. IEEE Access, 8, 42884–42897. https://doi.org/10.1109/ACCESS.2020.2978054
- Frey, H. C. (2020). Trends in transportation greenhouse gas emission. Advances in Carbon Management Technologies: Carbon Removal, Renewable and Nuclear Energy, 359, 359–375.
- Grabusts, P. (2011). The choice of metrics for clustering algorithms. Environment, Technologies, Resources. Proceedings of the International Scientific and Practical Conference, 2, 70–76.
- He, F., Wu, D., Yin, Y., & Guan, Y. (2013). Optimal deployment of public charging stations for plug-in hybrid electric vehicles. Transportation Research Part B: Methodological, 47(2013), 87–101. https://doi.org/10.1016/j.trb.2012.09.007
- International Energy Agency. (2014). Key world energy statistics. International Energy Agency.
- Keras Deep Learning Framework. (2020). https://keras.io/
- Li, G., & Zhang, X. P. (2012). Modeling of plug-in hybrid electric vehicle charging demand in probabilistic power flow calculations. IEEE Transactions on Smart Grid, 3(1), 492–499. https://doi.org/10.1109/TSG.2011.2172643
- Liu, H. C., & Lin, J. J. (2019). Associations of built environments with spatiotemporal patterns of public bicycle use. Journal of Transport Geography, 74(1), 299–312. https://doi.org/10.1016/j.jtrangeo.2018.12.010
- Liu, X. C., Taylor, J., Porter, R. J., & Wei, R. (2018). Using trajectory data to explore roadway characterization for bikeshare network. Journal of Intelligent Transportation Systems, 22(6), 530–546. https://doi.org/10.1080/15472450.2018.1444484
- Louie, H. M. (2017). Time-series modeling of aggregated electric vehicle charging station load. Electric Power Components and Systems, 45(14), 1498–1511. https://doi.org/10.1080/15325008.2017.1336583
- Luo, Z., Song, Y., Hu, Z., Xu, Z., Yang, X., & Zhan, K. (2011). Forecasting charging load of plug-in electric vehicles in China. IEEE Power and Energy Society General Meeting, 1–8. https://doi.org/10.1109/PES.2011.6039317
- Mann, A. K., & Kaur, N. (2013). Review paper on clustering techniques. Global Journal of Computer Science and Technology, 13(5), 1–7.
- Medsker, L., & Jain, L. C. (Eds.). (1999). Recurrent neural networks: Design and applications. CRC press.
- Muehlegger, E., & Rapson, D. (2018). Subsidizing mass adoption of electric vehicles: Quasi-experimental evidence from California. National Bureau of Economic Research. https://doi.org/10.3386/w25359
- Nitish, S., Geoffrey, H., Alex, K., Ilya, S., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958. https://doi.org/10.1016/0370-2693(93)90272-J
- Pagany, R., Marquardt, A., & Zink, R. (2019). Electric charging demand location Model-A userand destination-based locating approach for electric vehicle charging stations. Sustainability (Sustainability), 11(8), 2301. https://doi.org/10.3390/su11082301
- PlugShare EV Charging Map. (2020). https://www.plugshare.com/
- Sepp, H., & Jurgen, S. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
- Speidel, S., & Bräunl, T. (2014). Driving and charging patterns of electric vehicles for energy usage. Renewable and Sustainable Energy Reviews, 40, 97–110. https://doi.org/10.1016/j.rser.2014.07.177
- Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems, 4, 3104–3112.
- Taleongpong, P., Hu, S., Jiang, Z., Wu, C., Popo-Ola, S., & Han, K. (2020). Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network. Journal of Intelligent Transportation Systems, 1–28. https://doi.org/10.1080/15472450.2020.1858822
- Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ, 35(8), 48–90. https://doi.org/10.7287/peerj.preprints.3190v2
- Wang, S., Cao, J., Chen, H., Peng, H., & Huang, Z. (2020). SeqST-GAN: Seq2Seq generative adversarial nets for multi-step urban crowd flow prediction. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6(4), 1–24.
- Wang, M., Lin, X., & Yu, L. (2019). Comprehensive evaluation of green transportation in Chongqing main urban area based on sustainable development theory. Systems Science & Control Engineering, 7(1), 369–378. https://doi.org/10.1080/21642583.2019.1681032
- Wang, N., Pan, H., & Zheng, W. (2017). Assessment of the incentives on electric vehicle promotion in China. Transportation Research Part A: Policy and Practice, 101(2017), 177–189. https://doi.org/10.1016/j.tra.2017.04.037
- Wang, N., Tang, L., & Pan, H. (2017). Effectiveness of policy incentives on electric vehicle acceptance in China: A discrete choice analysis. Transportation Research Part A: Policy and Practice, 105, 210–218. https://doi.org/10.1016/j.tra.2017.08.009
- Wu, J., Cheng, L., Chu, S., Xia, N., & Li, M. (2020). A green view index for urban transportation: How much greenery do we view while moving around in cities? International Journal of Sustainable Transportation, 14(12), 972–989. https://doi.org/10.1080/15568318.2019.1672001
- Xing, Q., Chen, Z., Zhang, Z., Huang, X., Leng, Z., Sun, K., Chen, Y., & Wang, H. (2019). Charging demand forecasting model for electric vehicles based on online ride-hailing trip data. IEEE Access, 7, 137390–137409. https://doi.org/10.1109/ACCESS.2019.2940597
- Xu, M., Meng, Q., Liu, K., & Yamamoto, T. (2017). Joint charging mode and location choice model for battery electric vehicle users. Transportation Research Part B: Methodological, 103, 68–86. https://doi.org/10.1016/j.trb.2017.03.004
- Xydas, E. S., Marmaras, C. E., Cipcigan, L. M., Hassan, A. S., & Jenkins, N. (2013). Forecasting electric vehicle charging demand using support vector machines. Proceedings of the Universities Power Engineering Conference, 1–6. https://doi.org/10.1109/UPEC.2013.6714942
- Yi, Z., Liu, X. C., Wei, R., & Grubesic, T. H. (2021). Snowplow truck performance assessment and feature importance analysis using machine-learning techniques. Journal of Transportation Engineering, Part A: Systems, 147(2), 04020160. https://doi.org/10.1061/JTEPBS.0000486
- Zhang, Z., Li, M., Lin, X., Wang, Y., & He, F. (2019). Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies. Transportation Research Part C: Emerging Technologies, 105, 297–322. https://doi.org/10.1016/j.trc.2019.05.039
- Zhang, Y., Lin, D., & Liu, X. C. (2019). Biking islands in cities: An analysis combining bike trajectory and percolation theory. Journal of Transport Geography, 80, 102497. https://doi.org/10.1016/j.jtrangeo.2019.102497
- Zheng, X., Streimikiene, D., Balezentis, T., Mardani, A., Cavallaro, F., & Liao, H. (2019). A review of greenhouse gas emission profiles, dynamics, and climate change mitigation efforts across the key climate change players. Journal of Cleaner Production, 234, 1113–1133. https://doi.org/10.1016/j.jclepro.2019.06.140
- Zhou, Y. (2019). Analysis program 2019-EV sales tracking from Argonne national laboratory.
- Zhu, J., Yang, Z., Guo, Y., Zhang, J., & Yang, H. (2019). Short-term load forecasting for electric vehicle charging stations based on deep learning approaches. Applied Sciences (Sciences), 9(9), 1723. https://doi.org/10.3390/app9091723
- Zhuang, D., Hao, S., Lee, D.-H., & Jin, J. G. (2020). From compound word to metropolitan station: Semantic similarity analysis using smart card data. Transportation Research Part C: Emerging Technologies, 114, 322–337. https://doi.org/10.1016/j.trc.2020.02.017