References
- 1. Brockmann, D., Hufnagel, L., & Geisel, T. (2006) The scaling laws of human travel. Nature, 439(7075), 462-465.
- 2. Afrin, T., & Yodo, N, (2020) A survey of road traffic congestion measures towards a sustainable and resilient transportation system, Sustainability, 12(11).10.3390/su12114660
- 3. Xu, T., Li, X., & Claramunt, C, (2017) Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories, Frontiers of Earth Science, 12(2), 253–263.
- 4. C Lana, I., Del Ser, J., Velez, M., & Vlahogianni, E. I, (2018) “Road Traffic Forecasting: Recent Advances and New Challenges”, IEEE Intelligent Transportation Systems Magazine, Vol. 10, No. 2, pp. 93–10910.1109/MITS.2018.2806634
- 5. Afrin, Tanzina, and Nita Yodo. (2020) A survey of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability, 4660.10.3390/su12114660
- 6. Wagner-Muns, I., Guardiola, I., Samaranayke, V. and Kayani, W. (2017) A functional data analysis approach to traffic volume forecasting. IEEE Transactions on Intelligent Transportation Systems, 19(3), 878–888.
- 7. Kamble, Shridevi Jeevan, and Manjunath R. Kounte (2021) SG-TSE: Segment-based Geographic Routing and Traffic Light Scheduling for EV Preemption based Negative Impact Reduction on Normal Traffic. International Journal of Advanced Computer Science and Applications, 12.12.10.14569/IJACSA.2021.0121236
- 8. Liu, Y. and Wu, H. (2017) Prediction of Road Traffic Congestion Based on Random Forest. 10th International Symposium on Computational Intelligence and Design (ISCID), 361-364.10.1109/ISCID.2017.216
- 9. Tedjopurnomo, D. A., Bao, Z., Zheng, B., Choudhury, F., & Qin, A. K, (2020) A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges. IEEE Transactions on Knowledge and Data Engineering.10.1109/TKDE.2020.3001195
- 10. Yin, X., Wu, G., Wei, J., Shen, Y., Qi, H., & Yin, B. (2020) A comprehensive survey on traffic prediction. arXiv preprint arXiv:2004.08555.
- 11. Nagy, A. M. and Simon, V. (2018) Survey on traffic prediction in smart cities. Pervasive and Mobile Computing, 50, 148–163.10.1016/j.pmcj.2018.07.004
- 12. Boukerche, A., & Wang, J. (2020) Machine Learning-based traffic prediction models for Intelligent Transportation Systems. Computer Networks, 181, 107530.10.1016/j.comnet.2020.107530
- 13. Li, Y., & Shahabi, C. (2018) A brief overview of machine learning methods for short-term traffic forecasting and future directions. Sigspatial Special, 10(1), 3-9.10.1145/3231541.3231544
- 14. Elfar, A., Talebpour, A., & Mahmassani, H. S. (2018) Machine learning approach to short-term traffic congestion prediction in a connected environment. Transportation Research Record, 2672(45), 185-195.10.1177/0361198118795010
- 15. Hosseinzadeh, A., & Safabakhsh, R. (2014) Learning vehicle motion patterns based on environment model and vehicle trajectories. Iranian Conference on Intelligent Systems (ICIS), 1-5. IEEE.10.1109/IranianCIS.2014.6802563
- 16. Lanka, S., & Jena, S. K. (2014) Analysis of GPS based vehicle trajectory data for road traffic congestion learning. Advanced Computing, Networking and Informatics, 2, 11-18. Springer, Cham.10.1007/978-3-319-07350-7_2
- 17. Chen, Z., Shen, H. T., & Zhou, X. (2011) Discovering popular routes from trajectories. In: 2011 IEEE 27th International Conference on Data Engineering, 900-911. IEEE.10.1109/ICDE.2011.5767890
- 18. Xu, L., Yue, Y., & Li, Q. (2013) Identifying urban traffic congestion pattern from historical floating car data. Procedia-Social and Behavioral Sciences, 96, 2084-2095.10.1016/j.sbspro.2013.08.235
- 19. Zhang, Y., & Zhang, Y. (2016) A comparative study of three multivariate short-term freeway traffic flow forecasting methods with missing data. Journal of Intelligent Transportation Systems, 20(3), 205-218.10.1080/15472450.2016.1147813
- 20. Tang, J., Liu, F., Zou, Y., Zhang, W., & Wang, Y. (2017) An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. IEEE Transactions on Intelligent Transportation Systems, 18(9), 2340-2350.10.1109/TITS.2016.2643005
- 21. Ran, B., Song, L., Zhang, J., Cheng, Y., & Tan, H. (2016) Using tensor completion method to achieving better coverage of traffic state estimation from sparse floating car data. PloS one, 11(7), e0157420.10.1371/journal.pone.0157420495783027448326
- 22. Zhang, K., Wu, L., Zhu, Z., & Deng, J. (2020) A multitask learning model for traffic flow and speed forecasting. IEEE Access, 8, 80707-80715.10.1109/ACCESS.2020.2990958
- 23. Tian, C., & Chan, W. K. (2021). Spatial-temporal attention wavenet: A deep learning framework for traffic prediction considering spatial-temporal dependencies. IET Intelligent Transport Systems, 15(4), 549-561.10.1049/itr2.12044
- 24. Akhtar, M., & Moridpour, S, (2021) A review of traffic congestion prediction using artificial intelligence. Journal of Advanced Transportation.10.1155/2021/8878011
- 25. Kong, X., Xu, Z., Shen, G., Wang, J., Yang, Q. and B. Zhang. (2016) Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Generation Computer Systems, 61, 97–107.10.1016/j.future.2015.11.013
- 26. Zhao, J., Xu, F., Liu, W., Bai, J., & Luo, X. (2015) Travel time prediction based on pattern matching method. International Journal on Smart Sensing and Intelligent Systems, 8(1).10.21307/ijssis-2017-777
- 27. Servos, N., Liu, X., Teucke, M., & Freitag, M. (2019) Travel time prediction in a multimodal freight transport relation using machine learning algorithms. Logistics, 4(1), 1.10.3390/logistics4010001
- 28. He, P., Jiang, G., Lam, S. K., & Sun, Y. (2020) Learning heterogeneous traffic patterns for travel time prediction of bus journeys. Information Sciences, 512, 1394-1406.10.1016/j.ins.2019.10.073
- 29. Petersen, N. C., Rodrigues, F., & Pereira, F. C. (2019) Multi-output bus travel time prediction with convolutional LSTM neural network. Expert Systems with Applications, 120, 426-435.10.1016/j.eswa.2018.11.028
- 30. Wu, J., Wu, Q., Shen, J., & Cai, C. (2020) Towards attention-based convolutional long short-term memory for travel time prediction of bus journeys. Sensors, 20(12), 3354.10.3390/s20123354734909932545698
- 31. Zhang, W., Yu, Y., Qi, Y., Shu, F. and Y. Wang. (2019) Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transport metrica A: Transport Science, 15(2), 1688–1711.10.1080/23249935.2019.1637966
- 32. Wen, F., Zhang, G., Sun, L., Wang, X. and X. Xu. (2019) A hybrid temporal association rules mining method for traffic congestion prediction. Computers & Industrial Engineering, 130, 779–787.10.1016/j.cie.2019.03.020
- 33. He, Z., Qi, G., Lu, L. and Y. Chen. (2019) Network-wide identification of turn-level intersection congestion using only low-frequency probe vehicle data. Transportation Research Part C: Emerging Technologies, 108, 320–339.10.1016/j.trc.2019.10.001
- 34. Shridevi, Jeevan Kamble, Manjunath R. Kounte. (2022) A Survey on Emergency Vehicle Preemption Methods Based on Routing and Scheduling. International Journal of Computer Networks and Applications (IJCNA), 9(1), 60-71. DOI:10.22247/ijcna/2022/211623.
- 35. Zhao, H., Jizhe, X., Fan, L., Zhen, L. and L. Qingquan. (2019) A peak traffic Congestion prediction method based on bus driving time. Entropy, 21(7), 709.
- 36. Chen, Y. Z., Shen, S. F., Chen, T., & Yang, R. (2014) Path optimization study for vehicles evacuation based on Dijkstra algorithm. Procedia Engineering, 71, 159-165.10.1016/j.proeng.2014.04.023
- 37. Chen, Z., Jiang, Y., Sun, D. and X. Liu. (2020) Discrimination and prediction of traffic congestion states of urban road network based on spatio-temporal correlation. IEEE Access, 8, 3330–3342.10.1109/ACCESS.2019.2959125
- 38. Chen, C., Wang, H., Yuan, F., Jia, H., & Yao, B. (2020) Bus travel time prediction based on deep belief network with back-propagation. Neural Computing and Applications, 32(14), 10435-10449.10.1007/s00521-019-04579-x
- 39. Qi, G., Ceder, A. A., Zhang, Z., Guan, W., & Liu, D. (2021) New method for predicting long-term travel time of commercial vehicles to improve policy-making processes. Transportation Research Part A: Policy and Practice, 145, 132-152.10.1016/j.tra.2020.12.003
- 40. Sun, J., & Kim, J. (2021) Joint prediction of next location and travel time from urban vehicle trajectories using long short-term memory neural networks. Transportation Research Part C: Emerging Technologies, 128, 103114.10.1016/j.trc.2021.103114
- 41. Adewale, A. E., & Hadachi, A. (2020) Neural Networks Model for Travel Time Prediction Based on ODTravel Time Matrix. arXiv preprint arXiv:2004.04030.
- 42. Bogaerts, T., Masegosa, A. D., Angarita-Zapata, J. S., Onieva, E. and P. Hellinckx. (2020) A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data. Transportation Research Part C: Emerging Technologies, 112, 62–77.10.1016/j.trc.2020.01.010
- 43. Hou, Y., Chen, J., & Wen, S. (2021) The effect of the dataset on evaluating urban traffic prediction. Alexandria Engineering Journal, 60(1), 597-613.10.1016/j.aej.2020.09.038
- 44. Kamble, Shridevi Jeevan, and Manjunath R. Kounte. (2019) Routing and scheduling issues in vehicular ad-hoc networks. International Journal of Recent Technology and Engineering, 8.3: 4272-4283.10.35940/ijrte.C5168.098319
- 45. Joy, Helen K., Kounte, Manjunath R. (2022) Deep CNN Based Video Compression with Lung Ultrasound Sample. Journal of Applied Science and Engineering, 26(3), 313-321.
- 46. Naveen, Soumyalatha, Kounte, Manjunath R., Ahmed, Mohammed Riyaz. (2021) Low Latency Deep Learning Inference Model for Distributed Intelligent IoT Edge Clusters. IEEE Access, 9, 160607-160621. DOI: 10.1109/ACCESS.2021.3131396.