Optimization of urban intelligent transportation travel service system based on feature extraction and traffic prediction

[1] Hounsell, N. B., & Yap, Y. H. (2017). Hook turns as a solution to the right-turning traffic problem. Operations Research, 57(1-2), 73-75. Search in Google Scholar

[2] Tangermann, L., Vienneau, D., Hattendorf, J., Saucy, A., Nino Künzli, & Schffer, B., et al. (2022). The association of road traffic noise with problem behaviour in adolescents: a cohort study. Environmental Research, 207, 112645-. Search in Google Scholar

[3] Wu, Y. H., Zheng, M. H., He, W., Chen, Z. M., & Hua, B. (2019). Intelligent vehicle safety system based on beidou satellite navigation system. IET Intelligent Transport Systems, 13(6), 967-974. Search in Google Scholar

[4] Gao, F., Zhang, Q., Han, Z., & Yang, Y. (2021). Evolution test by improved genetic algorithm with application to performance limit evaluation of automatic parallel parking system. IET intelligent transport systems. (6), 15. Search in Google Scholar

[5] Chen, Z., Liang, Y., Wu, Y., & Sun, L. (2019). Research on comprehensive multi-infrastructure optimization in transportation asset management: the case of roads and bridges. Sustainability, 11(16), 4430. Search in Google Scholar

[6] Group, J. C. (2021). Aca supports updates to uniform traffic control devices manual. JCT coatingstech. (7), 18. Search in Google Scholar

[7] Xie, K., Ozbay, K., Kurkcu, A., & Yang, H. (2017). Analysis of traffic crashes involving pedestrians using big data: investigation of contributing factors and identification of hotspots. Risk Analysis, 37(2), 1459-1476. Search in Google Scholar

[8] Qu, X., Yang, M., Ji, J., Li, L., & Ran, B. (2021). Analyzing the safety impacts of variable speed limit control on aggregated driving behavior based on traffic big data. Journal of Advanced Transportation. Search in Google Scholar

[9] Kilpatrick, C. (2017). Big data drives new traffic models for urban planners. Engineering news-record, 279(6), 67-67. Search in Google Scholar

[10] Wang, Y., Yang, X., Liang, H., & Liu, Y. (2018). A review of the self-adaptive traffic signal control system based on future traffic environment. Journal of Advanced Transportation, 2018(PT.3), 1096123.1-1096123.12. Search in Google Scholar

[11] He, S., Chen, L., & Yue, M. (2018). Reliability analysis of driving behaviour in road traffic system considering synchronization of neural activity. NeuroQuantology, 16(4). Search in Google Scholar

[12] Wang, P., Liu, P., Wang, C., Wang, A., & Xia, X. (2020). Optimal dynamic investment allocation on construction of intelligent transportation infrastructure and road maintenance with environmental costs. Journal of Cleaner Production, 124786. Search in Google Scholar

[13] Zhang, Y., Lu, M., & Li, H. (2020). Urban traffic flow forecast based on fastgcrnn. Journal of Advanced Transportation, 2020(12), 1-9. Search in Google Scholar

[14] Feng, X., Ling, X., Zheng, H., Chen, Z., & Xu, Y. (2018). Adaptive multi-kernel svm with spatial-temporal correlation for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, PP. (99), 1-13. Search in Google Scholar

[15] Wang, Jia, Yang, Yang-lingzhi, Liu, & Shuai. (2018). Study of traffic characteristics based on internet web real-time traffic conditions by image identification technology. The imaging science journal. Search in Google Scholar

[16] Shepelev, V., Aliukov, S., Nikolskaya, K., & Shabiev, S. (2020). The capacity of the road network: data collection and statistical analysis of traffic characteristics. Energies, 13. Search in Google Scholar

[17] Yang, H. J. (2019). Xi ‘an intelligent transportation system construction platform research. Procedia Computer Science, 154, 181-185. Search in Google Scholar

[18] Chen, G., Cao, H., Aafaque, M., Chen, J., Ye, C., & Röhrbein, Florian, et al. (2018). Neuromorphic vision based multivehicle detection and tracking for intelligent transportation system. Journal of advanced transportation, 2018, 13. Search in Google Scholar

[19] Chang, J., Yao, W., & Li, X. (2017). The design of a context-aware service system in intelligent transportation system. International Journal of Distributed Sensor Networks, 13(10), 155014771773816. Search in Google Scholar

[20] Pan, X., Wang, H., He, Y., Xiong, W., & Zhou, W. (2017). Anomalous behavior detection method based on multidimensional trajectory characteristics. Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica, 38(4). Search in Google Scholar

[21] Li, D., Jahan, H., Huang, X., & Feng, Z. (2022). Human action recognition method based on historical point cloud trajectory characteristics. The visual computer. Search in Google Scholar

[22] Bang-Jian, Li, Quan-Bao, Wang, Deng-Ping, & Duan. (2018). Strain measurement errors with digital image correlation due to the savitzky-golay filter-based method. Measurement Science & Technology. Search in Google Scholar

[23] Kellman, Michael R.Rivest, Francois R.Pechacek, AlinaSohn, Lydia L.Lustig, Michael. (2018). Node-pore coded coincidence correction: coulter counters, code design, and sparse deconvolution. Nature reviews Cancer, 18(8). Search in Google Scholar

[24] Qing, X., & Niu, Y. (2018). Hourly day-ahead solar irradiance prediction using weather forecasts by lstm. Energy, 148(APR.1), 461-468. Search in Google Scholar

[25] Liu, J., Wang, G., Duan, L. Y., Abdiyeva, K., & Kot, A. C. (2018). Skeleton-based human action recognition with global context-aware attention lstm networks. IEEE Transactions on Image Processing, 27(99), 1586-1599. Search in Google Scholar

[26] Wu, Q., Guan, F., Lv, C., & Huang, Y. (2021). Ultra‐short‐term multi‐step wind power forecasting based on cnn‐lstm. IET Renewable Power Generation, 15(5). Search in Google Scholar

[27] Zhang, M., Li, W., & Du, Q. (2018). Diverse region-based cnn for hyperspectral image classification. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2623. Search in Google Scholar