1. bookTom 23 (2022): Zeszyt 4 (November 2022)
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
1407-6179
Pierwsze wydanie
20 Mar 2000
Częstotliwość wydawania
4 razy w roku
Języki
Angielski
Otwarty dostęp

Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning

Data publikacji: 16 Nov 2022
Tom & Zeszyt: Tom 23 (2022) - Zeszyt 4 (November 2022)
Zakres stron: 293 - 310
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
1407-6179
Pierwsze wydanie
20 Mar 2000
Częstotliwość wydawania
4 razy w roku
Języki
Angielski

1. Brockmann, D., Hufnagel, L., & Geisel, T. (2006) The scaling laws of human travel. Nature, 439(7075), 462-465. Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

24. Akhtar, M., & Moridpour, S, (2021) A review of traffic congestion prediction using artificial intelligence. Journal of Advanced Transportation.10.1155/2021/8878011 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Otwórz DOISearch in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

41. Adewale, A. E., & Hadachi, A. (2020) Neural Networks Model for Travel Time Prediction Based on ODTravel Time Matrix. arXiv preprint arXiv:2004.04030. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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. Otwórz DOISearch in Google Scholar

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