Intelligent Traffic Management: A Review of Challenges, Solutions, and Future Perspectives

1. Abbas, Z., Al-Shishtawy, A., Girdzijauskas, S., Vlassov, V. (2018) Short-term traffic prediction using long short-term memory neural networks. In: 2018 IEEE International Congress on Big Data (BigData Congress), 57-65.10.1109/BigDataCongress.2018.00015 Search in Google Scholar

2. Ahmad, F., Basit, A., Ahmad, H., Mahmud, S. A., Khan, G. M., & Yousaf, F. Z. (2013) Feasibility of deploying wireless sensor based road side solutions for intelligent transportation systems. In: 2013 International Conference on Connected Vehicles and Expo (ICCVE), 320-326.10.1109/ICCVE.2013.6799814 Search in Google Scholar

3. Ahmed, M.S., Cook, A.R. (1979) Analysis of freeway traffic time-series data by using Box-Jenkins techniques, No. 722. Search in Google Scholar

4. An, Y., Song, Q. and Zhao, X. (2011) Short-term traffic flow forecasting via echo state neural networks. In 2011 Seventh International Conference on Natural Computation, 2, 844-847.10.1109/ICNC.2011.6022154 Search in Google Scholar

5. Araújo, G. B., Queiroz, M. M., de LP Duarte-Figueiredo, F., Tostes, A. I., & Loureiro, A. A. (2014). Cartim: A proposal toward identification and minimization of vehicular traffic congestion for vanet. In: 2014 IEEE symposium on computers and communications (ISCC), 1-6.10.1109/ISCC.2014.6912491 Search in Google Scholar

6. Avatefipour, O., Sadry, F. (2018) Traffic management system using IoT technology-A comparative review. In: 2018 IEEE International Conference on Electro/Information Technology (EIT), 1041-1047.10.1109/EIT.2018.8500246 Search in Google Scholar

7. Barros, J., Araujo, M., Rossetti, R. J. (2015) Short-term real-time traffic prediction methods: A survey. In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 132-139.10.1109/MTITS.2015.7223248 Search in Google Scholar

8. Bell, M. G., Trozzi, V., Hosseinloo, S. H., Gentile, G., & Fonzone, A. (2012) Time-dependent Hyperstar algorithm for robust vehicle navigation. Transportation Research Part A: Policy and Practice, 46(5), 790-800.10.1016/j.tra.2012.02.002 Search in Google Scholar

9. Bernas, M., Płaczek, B., & Korski, W. (2018) Wireless Network with Bluetooth Low Energy Beacons for Vehicle Detection and Classification. In: International Conference on Computer Networks, 429-444. Springer, Cham.10.1007/978-3-319-92459-5_34 Search in Google Scholar

10. Bruno, R., & Nurchis, M. (2013) Robust and efficient data collection schemes for vehicular multimedia sensor networks. In: 2013 IEEE 14th International Symposium on” A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), 1-10. IEEE.10.1109/WoWMoM.2013.6583399 Search in Google Scholar

11. Cagara, D., Bazzan, A. L., & Scheuermann, B. (2014) Getting you faster to work: A genetic algorithm approach to the traffic assignment problem. In: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 105-106. ACM.10.1145/2598394.2598419 Search in Google Scholar

12. Chao, K. H., & Chen, P. Y. (2014) An intelligent traffic flow control system based on radio frequency identification and wireless sensor networks. International journal of distributed sensor networks, 10(5), 694545.10.1155/2014/694545 Search in Google Scholar

13. Chen, C. P., Zhou, J., & Zhao, W. (2012) A real-time vehicle navigation algorithm in sensor network environments. IEEE Transactions on Intelligent Transportation Systems, 13(4), 1657-1666.10.1109/TITS.2012.2201478 Search in Google Scholar

14. Chen, X., Chen, R. (2019) A Review on Traffic Prediction Methods for Intelligent Transportation System in Smart Cities. In: 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 1-5. IEEE.10.1109/CISP-BMEI48845.2019.8965742 Search in Google Scholar

15. Chhatpar, P., Doolani, N., Shahani, S., Priya, R.L. (2018) Machine learning solutions to vehicular traffic congestion. In: 2018 International Conference on Smart City and Emerging Technology (ICSCET), 1-4. IEEE.10.1109/ICSCET.2018.8537260 Search in Google Scholar

16. Choi, O., Kim, S., Jeong, J., Lee, H. W., & Chong, S. (2015) Delay-optimal data forwarding in vehicular sensor networks. IEEE transactions on vehicular technology, 65(8), 6389-6402. Search in Google Scholar

17. Chowdhury, M., Hasan, M., Safait, S. (2018) A novel approach to forecast traffic congestion using CMTF and machine learning (Doctoral dissertation, BRAC University).10.1109/ICIEV.2018.8640985 Search in Google Scholar

18. Chung, J., & Sohn, K. (2018) Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1670–1675.10.1109/TITS.2017.2732029 Search in Google Scholar

19. Collotta, M., Pau, G., Scatà, G., & Campisi, T. (2014) A dynamic traffic light management system based on wireless sensor networks for the reduction of the red-light running phenomenon. Transport and Telecommunication, 15(1), 1-11.10.2478/ttj-2014-0001 Search in Google Scholar

20. Cong, Z., De Schutter, B., & Babuška, R. (2013) Ant colony routing algorithm for freeway networks. Transportation Research Part C: Emerging Technologies, 37, 1-19.10.1016/j.trc.2013.09.008 Search in Google Scholar

21. Cordeschi, N., Amendola, D., Shojafar, M., Naranjo, P. G. V., & Baccarelli, E. (2015). Memory and memoryless optimal time-window controllers for secondary users in vehicular networks. In: Proceedings of the International Symposium on Performance Evaluation of Computer and Telecommunication Systems, 1-7. Society for Computer Simulation International.10.1109/SPECTS.2015.7285283 Search in Google Scholar

22. de Souza, A. M., Yokoyama, R. S., Botega, L. C., Meneguette, R. I., & Villas, L. A. (2015) Scorpion: A solution using cooperative rerouting to prevent congestion and improve traffic condition. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 497-503. IEEE.10.1109/CIT/IUCC/DASC/PICOM.2015.71 Search in Google Scholar

23. de Souza, A. M., Yokoyama, R. S., Maia, G., Loureiro, A., & Villas, L. (2016) Real-time path planning to prevent traffic jam through an intelligent transportation system. In: 2016 IEEE Symposium on Computers and Communication (ISCC), 726-731. IEEE.10.1109/ISCC.2016.7543822 Search in Google Scholar

24. de Souza, A. M., Brennand, C. A., Yokoyama, R. S., Donato, E. A., Madeira, E. R., Villas, L. A. (2017) Traffic management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed Sensor Networks, 13(4), p.1550147716683612.10.1177/1550147716683612 Search in Google Scholar

25. Dezani, H., Bassi, R. D., Marranghello, N., Gomes, L., Damiani, F., & Da Silva, I. N. (2014) Optimizing urban traffic flow using Genetic Algorithm with Petri net analysis as fitness function. Neurocomputing, 124, 162-167.10.1016/j.neucom.2013.07.015 Search in Google Scholar

26. Duan, Y., Lv, Y. and Wang, F.Y. (2016) Performance evaluation of the deep learning approach for traffic flow prediction at different times. In: 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), 223-227. IEEE.10.1109/SOLI.2016.7551691 Search in Google Scholar

27. Elleuch, W., Wali, A. and Alimi, A.M. (2016) Intelligent Traffic Congestion Prediction System Based on ANN and Decision Tree Using Big GPS Traces. In: International conference on intelligent systems design and applications, 478-487. Springer, Cham.10.1007/978-3-319-53480-0_47 Search in Google Scholar

28. Emo, A. K., Matthews, G., & Funke, G. J. (2016) The slow and the furious: Anger, stress and risky passing in simulated traffic congestion. Transportation research part F: traffic psychology and behaviour, 42, 1-14.10.1016/j.trf.2016.05.002 Search in Google Scholar

29. Fan, S. K. S., Su, C. J., Nien, H. T., Tsai, P. F., & Cheng, C. Y. (2018) Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection. Soft Computing, 22(17), 5707-5718.10.1007/s00500-017-2610-y Search in Google Scholar

30. Faye, S., Chaudet, C., & Demeure, I. (2012) A distributed algorithm for adaptive traffic lights control. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, 1572-1577. IEEE.10.1109/ITSC.2012.6338671 Search in Google Scholar

31. Friesen, M., Jacob, R., Grestoni, P., Mailey, T., Friesen, M. R., & McLeod, R. D. (2014) Vehicular traffic monitoring using bluetooth scanning over a wireless sensor network. Canadian Journal of Electrical and Computer Engineering, 37(3), 135-144.10.1109/CJECE.2014.2316416 Search in Google Scholar

32. Fu, R., Zhang, Z. and Li, L. (2016) November. Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), 324-328. IEEE.10.1109/YAC.2016.7804912 Search in Google Scholar

33. Gao, J., Leng, Z., Qin, Y., Ma, Z., Liu, X. (2013) Short-term traffic flow forecasting model based on wavelet neural network. In: 2013 25th Chinese control and decision conference (CCDC), 5081-5084. IEEE.10.1109/CCDC.2013.6561856 Search in Google Scholar

34. Ghosh, B., Basu, B., O’Mahony, M. (2009) Multivariate short-term traffic flow forecasting using time-series analysis. IEEE transactions on intelligent transportation systems, 10(2), 246-254.10.1109/TITS.2009.2021448 Search in Google Scholar

35. Ghosh, R.; Akula, A.; Kumar, S.; Sardana, H. (2015) Time–frequency analysis based robust vehicle detection using seismic sensor. J. Sound Vibr., 346, 424–434. Search in Google Scholar

36. Guo, F., Polak, J.W., Krishnan, R. (2010) Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions. In: 13th International IEEE Conference on Intelligent Transportation Systems, 1209-1214. IEEE.10.1109/ITSC.2010.5625291 Search in Google Scholar

37. Guorong, G., Yanping, L. (2010) Traffic flow forecasting based on PCA and wavelet neural network. In: 2010 International Conference of Information Science and Management Engineering, 1, 158-161. IEEE.10.1109/ISME.2010.10 Search in Google Scholar

38. Gupta, B., Awasthi, S., Gupta, R., Ram, L., Kumar, P., Prasad, B. R., & Agarwal, S. (2018) Taxi travel time prediction using ensemble-based random forest and gradient boosting model. In: Advances in Big Data and Cloud Computing, 63-78. Springer, Singapore.10.1007/978-981-10-7200-0_6 Search in Google Scholar

39. Haferkamp, M., Al-Askary, M., Dorn, D., Sliwa, B., Habel, L., Schreckenberg, M., & Wietfeld, C. (2017) Radio-based traffic flow detection and vehicle classification for future smart cities. In: 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 1-5. IEEE.10.1109/VTCSpring.2017.8108633 Search in Google Scholar

40. Hamed, M.M., Al-Masaeid, H.R., Said, Z.M.B. (1995) Short-term prediction of traffic volume in urban arterials. Journal of Transportation Engineering, 121(3), 249-254.10.1061/(ASCE)0733-947X(1995)121:3(249) Search in Google Scholar

41. Hamner, B. (2010) Predicting travel times with context-dependent random forests by modeling local and aggregate traffic flow. In: 2010 IEEE International Conference on Data Mining Workshops, 1357-1359. IEEE.10.1109/ICDMW.2010.128 Search in Google Scholar

42. Hong, W.C. (2011) Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing, 74(12-13), 2096-2107.10.1016/j.neucom.2010.12.032 Search in Google Scholar

43. Hou, Y., Edara, P., Sun, C. (2014) Traffic flow forecasting for urban work zones. IEEE transactions on intelligent transportation systems, 16(4), 1761-1770. Search in Google Scholar

44. Huang, W., Song, G., Hong, H., Xie, K. (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Transactions on Intelligent Transportation Systems, 15(5), 2191-2201.10.1109/TITS.2014.2311123 Search in Google Scholar

45. Jabbarpour, M. R., Jalooli, A., Shaghaghi, E., Noor, R. M., Rothkrantz, L., Khokhar, R. H., & Anuar, N. B. (2014) Ant-based vehicle congestion avoidance system using vehicular networks. Engineering Applications of Artificial Intelligence, 36, 303-319.10.1016/j.engappai.2014.08.001 Search in Google Scholar

46. Jenelius, E., & Koutsopoulos, H. N. (2017) Urban network travel time prediction based on a probabilistic principal component analysis model of probe data. IEEE Transactions on Intelligent Transportation Systems, 19(2), 436-445. Search in Google Scholar

47. Kang, D., Lv, Y., Chen, Y.Y. (2017) Short-term traffic flow prediction with LSTM recurrent neural network. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 1-6. IEEE.10.1109/ITSC.2017.8317872 Search in Google Scholar

48. Ke, R., Li, Z., Kim, S., Ash, J., Cui, Z., & Wang, Y. (2017) Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos. IEEE Transactions on Intelligent Transportation Systems, 18(4), 890–901.10.1109/TITS.2016.2595526 Search in Google Scholar

49. Khan, N. A. (2017) Real time predictive monitoring system for urban transport (Doctoral dissertation, Kingston University). Search in Google Scholar

50. Lee, H., Chowdhury, N. K., & Chang, J. (2008) A new travel time prediction method for intelligent transportation systems. In: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 473-483. Springer, Berlin, Heidelberg.10.1007/978-3-540-85563-7_61 Search in Google Scholar

51. Li, D.M., Liu, B.I.N. (2014) Modeling and prediction of highway traffic flow based on wavelet neural network. In: 2014 International Conference on Machine Learning and Cybernetics, 2, 675-679. IEEE.10.1109/ICMLC.2014.7009690 Search in Google Scholar

52. Liang, Z., Wakahara, Y. (2013) City traffic prediction based on real-time traffic information for intelligent transport systems. In: 2013 13th International Conference on ITS Telecommunications (ITST), 378-383. IEEE.10.1109/ITST.2013.6685576 Search in Google Scholar

53. Liao, S., Chen, J., Hou, J., Xiong, Q., Wen, J. (2018) Deep convolutional neural networks with random subspace learning for short-term traffic flow prediction with incomplete data. In: 2018 International Joint Conference on Neural Networks (IJCNN), 1-6. IEEE.10.1109/IJCNN.2018.8489536 Search in Google Scholar

54. Liu, W., & Wang, Z. (2010) Dynamic router real-time travel time prediction based on a road network. In: International Symposium on Information and Automation, 723-729. Springer, Berlin, Heidelberg.10.1007/978-3-642-19853-3_107 Search in Google Scholar

55. Liu, Y., & Li, S. (2011) A vehicle-logo location approach based on edge detection and projection. In: Proceedings of 2011 IEEE International Conference on Vehicular Electronics and Safety, 165-168. IEEE.10.1109/ICVES.2011.5983808 Search in Google Scholar

56. Liu, Y., Wang, Y., Yang, X., & Zhang, L. (2017). Short-term travel time prediction by deep learning: A comparison of different LSTM-DNN models. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 1-8. IEEE.10.1109/ITSC.2017.8317886 Search in Google Scholar

57. Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., Wang, Y. (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17(4), 818.10.3390/s17040818 Search in Google Scholar

58. Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y. (2015a) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54, 187-197.10.1016/j.trc.2015.03.014 Search in Google Scholar

59. Ma, X., Yu, H., Wang, Y., Wang, Y. (2015b) Large-scale transportation network congestion evolution prediction using deep learning theory. PloS one, 10(3), p.e0119044.10.1371/journal.pone.0119044 Search in Google Scholar

60. Meneguette, R. I., Geraldo Filho, P. R., Bittencourt, L. F., Ueyama, J., Krishnamachari, B., & Villas, L. A. (2015) Enhancing intelligence in inter-vehicle communications to detect and reduce congestion in urban centers. In: 2015 IEEE Symposium on Computers and Communication (ISCC), 1-6. IEEE.10.1109/ISCC.2015.8897528 Search in Google Scholar

61. Min, W., & Wynter, L. (2011) Real-time road traffic prediction with spatio-temporal correlations. Transportation Research Part C: Emerging Technologies, 19(4), 606-616.10.1016/j.trc.2010.10.002 Search in Google Scholar

62. Mohan, P., Padmanabhan, V.N., & Ramjee, R. (2008) Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM conference on Embedded network sensor systems, 323-336. ACM.10.1145/1460412.1460444 Search in Google Scholar

63. More, R., Mugal, A., Rajgure, S., Adhao, R.B., & Pachghare, V.K. (2016) Road traffic prediction and congestion control using Artificial Neural Networks. In: 2016 International Conference on Computing, Analytics and Security Trends (CAST), 52-57. IEEE.10.1109/CAST.2016.7914939 Search in Google Scholar

64. MTTM Movement-Travel Times Methodology (2018) URL: https://d3i4yxtzktqr9n.cloudfront.net/web-movement/static/pdfs/Movement-TravelTimesMethodology-76002ded22.pdf Search in Google Scholar

65. Na, Y., Guo, Y., Fu, Q., & Yan, Y. (2015) An acoustic traffic monitoring system: Design and implementation. In: 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 119-126. IEEE.10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.41 Search in Google Scholar

66. Oh, S., Byon, Y.J., Jang, K., & Yeo, H. (2018) Short-term travel-time prediction on highway: A review on model-based approach. KSCE Journal of Civil Engineering, 22(1), 298-310.10.1007/s12205-017-0535-8 Search in Google Scholar

67. Okutani, I., Stephanedes, Y.J. (1984) Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B: Methodological, 18(1), 1-11.10.1016/0191-2615(84)90002-X Search in Google Scholar

68. Padiath, A., Vanajakshi, L., Subramanian, S.C., Manda, H. (2009) October. Prediction of traffic density for congestion analysis under Indian traffic conditions. In: 2009 12th International IEEE Conference on Intelligent Transportation Systems, 1-6). IEEE.10.1109/ITSC.2009.5309716 Search in Google Scholar

69. Pamuła, T. (2018) Impact of data loss for prediction of traffic flow on an urban road using neural networks. IEEE Transactions on Intelligent Transportation Systems, 20(3), 1000-1009. Search in Google Scholar

70. Papageorgiou, M., Diakaki, C., Dinopoulou, V., Kotsialos, A., Wang, Y. (2003) Review of road traffic control strategies. In: Proceedings of the IEEE, 91(12), 2043-2067.10.1109/JPROC.2003.819610 Search in Google Scholar

71. Perez-Murueta, P., Gómez-Espinosa, A., Cardenas, C., & Gonzalez-Mendoza, M. (2019) Deep Learning System for Vehicular Re-Routing and Congestion Avoidance. Applied Sciences, 9(13), 2717.10.3390/app9132717 Search in Google Scholar

72. Pfleiderer, R.H., & Dieterich, M. (1995) New roads generate new traffic. World Transport Policy and Practice, 1(1), 29-31.10.1108/13527619510075657 Search in Google Scholar

73. Philip, A. M., Ramadurai, G., & Vanajakshi, L. (2018) Urban Arterial Travel Time Prediction Using Support Vector Regression. Transportation in Developing Economies, 4(1), 7.10.1007/s40890-018-0060-6 Search in Google Scholar

74. Polson, N. G., Sokolov, V. O. (2017) Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1-17.10.1016/j.trc.2017.02.024 Search in Google Scholar

75. Raj, J., Bahuleyan, H., Vanajakshi, L. D. (2016) Application of data mining techniques for traffic density estimation and prediction. Transportation Research Procedia, 17, 321-330.10.1016/j.trpro.2016.11.102 Search in Google Scholar

76. Rivas, J., Wunderlich, R., & Heinen, S. J. (2016) Road vibrations as a source to detect the presence and speed of vehicles. IEEE Sensors Journal, 17(2), 377-385. Search in Google Scholar

77. Saqib, M., Lee, C. (2010) Traffic control system using wireless sensor network. In: 2010 The 12th International Conference on Advanced Communication Technology (ICACT), 1, 352-357. IEEE. Search in Google Scholar

78. Sen, R., Sevani, V., Sharma, P., Koradia, Z., & Raman, B. (2009) Challenges in communication assisted road transportation systems for developing regions. NSDR’09. Search in Google Scholar

79. Shao, H., Soong, B.H. (2016) Traffic flow prediction with long short-term memory networks (LSTMs). In: 2016 IEEE Region 10 Conference (TENCON), 2986-2989. IEEE.10.1109/TENCON.2016.7848593 Search in Google Scholar

80. Shen, W., Wynter, L. (2012) Real-time road traffic fusion and prediction with GPS and fixed-sensor data. In: 2012 15th International Conference on Information Fusion, 1468-1475. IEEE. Search in Google Scholar

81. Shojafar, M., Cordeschi, N., Amendola, D., & Baccarelli, E. (2015) Energy-saving adaptive computing and traffic engineering for real-time-service data centers. In: 2015 IEEE International Conference on Communication Workshop (ICCW), 1800-1806. IEEE.10.1109/ICCW.2015.7247442 Search in Google Scholar

82. Singh, G., Bansal, D., & Sofat, S. (2014) Intelligent Transportation System for Developing Countries-A Survey. International Journal of Computer Applications, 85(3).10.5120/14824-3058 Search in Google Scholar

83. Sładkowski, A., & Pamuła, W. eds. (2016) Intelligent transportation systems-problems and perspectives, Vol. 303. Springer International Publishing.10.1007/978-3-319-19150-8 Search in Google Scholar

84. Smith, B.L., Demetsky, M. J. (1994) Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 2, 1706-1709. IEEE. Search in Google Scholar

85. Soua, R., Koesdwiady, A., Karray, F. (2016) Big-data-generated traffic flow prediction using deep learning and dempster-shafer theory. In: 2016 International joint conference on neural networks (IJCNN), 3195-3202. IEEE.10.1109/IJCNN.2016.7727607 Search in Google Scholar

86. Teodorović, D., & Dell’Orco, M. (2008) Mitigating traffic congestion: solving the ride-matching problem by bee colony optimization. Transportation Planning and Technology, 31(2), 135-152.10.1080/03081060801948027 Search in Google Scholar

87. Tian, X. (2018) Research on Travel Time Prediction under Internet of Vehicles. In: 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 38-40. IEEE.10.1109/ICITBS.2018.00017 Search in Google Scholar

88. Underwood, R.T. (1990) Traffic management: an introduction. Search in Google Scholar

89. Van Arem, B., Kirby, H.R., Van Der Vlist, M.J., Whittaker, J.C. (1997) Recent advances and applications in the field of short-term traffic forecasting. International journal of forecasting, 13(1), 1-12. Search in Google Scholar

90. Van Der Voort, M., Dougherty, M., Watson, S. (1996) Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C: Emerging Technologies, 4(5), 307-318.10.1016/S0968-090X(97)82903-8 Search in Google Scholar

91. Wang Z., Li J., Fang M., & Li, Y. (2015) A multimetric ant colony optimization algorithm for dynamic path planning in vehicular networks. Int J Distrib Sens Netw, 11(10).10.1155/2015/271067 Search in Google Scholar

92. Wang, Y., Papageorgiou, M., Messmer, A. (2008) Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing. Transportation Research Part A: Policy and Practice, 42(10), 1340-1358. Search in Google Scholar

93. Wang, Z., Yu, Y., Ju, D. (2018) Analysis and Prediction of Urban Traffic Congestion Based on Big Data. International Journal on Data Science and Technology, 4(3), p.100.10.11648/j.ijdst.20180403.14 Search in Google Scholar

94. Wedde, H.F., & Senge, S. (2013) BeeJamA: A distributed, self-adaptive vehicle routing guidance approach. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1882-1895.10.1109/TITS.2013.2269713 Search in Google Scholar

95. Wen, F., Gen, M., & Yu, X. (2011) A new multiobjective genetic algorithm for route selection. IEEJ Transactions on Electronics, Information and Systems, 131(3), 619-625.10.1541/ieejeiss.131.619 Search in Google Scholar

96. Williams, B. M., Hoel, L. A. (2003) Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of transportation engineering, 129(6), 664-672.10.1061/(ASCE)0733-947X(2003)129:6(664) Search in Google Scholar

97. Williams, B.M. (2001) Multivariate vehicular traffic flow prediction: evaluation of ARIMAX modeling. Transportation Research Record, 1776(1), 194-200.10.3141/1776-25 Search in Google Scholar

98. Won, M., Zhang, S., & Son, S. H. (2017) WiTraffic: Low-cost and non-intrusive traffic monitoring system using WiFi. In: 2017 26th International Conference on Computer Communication and Networks (ICCCN), 1-9. IEEE.10.1109/ICCCN.2017.8038380 Search in Google Scholar

99. Wu, Y., Tan, H. (2016) Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv preprint arXiv:1612.01022. Search in Google Scholar

100. Xie, Y., Zhang, Y., Ye, Z. (2007) Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition. Computer-Aided Civil and Infrastructure Engineering, 22(5), -based 326-334.10.1111/j.1467-8667.2007.00489.x Search in Google Scholar

101. Xu, D., Shi, Y. (2017) A combined model of random forest and multilayer perceptron to forecast expressway traffic flow. In: 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), 448-451. IEEE. Search in Google Scholar

102. Xu, T., Li, X., & Claramunt, C. (2018) Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories. Frontiers of earth science, 12(2), 253-263.10.1007/s11707-016-0634-8 Search in Google Scholar

103. Yang, H.F., Dillon, T.S., Chen, Y.P.P. (2016) Optimized structure of the traffic flow forecasting model with a deep learning approach. IEEE transactions on neural networks and learning systems, 28(10), 2371-2381. Search in Google Scholar

104. Yu, B., Wang, H., Shan, W., & Yao, B. (2018) Prediction of bus travel time using random forests based on near neighbors. Computer Aided Civil and Infrastructure Engineering, 33(4), 333-350.10.1111/mice.12315 Search in Google Scholar

105. Yu, B., Yin, H., Zhu, Z. (2017) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875. Search in Google Scholar

106. Yu, H., & Lu, F. (2012) A multi-modal route planning approach with an improved genetic algorithm. Advances in Geo-Spatial Information Science, 193. Search in Google Scholar

107. Zargari, S. A., Siabil, S. Z., Alavi, A. H., Gandomi, A. H. (2012) A computational intelligence-based approach for short-term traffic flow prediction. Expert Systems, 29(2), 124-142. Search in Google Scholar

108. Zhang, D. and Kabuka, M.R., 2018. Combining weather condition data to predict traffic flow: a GRU-based deep learning approach. IET Intelligent Transport Systems, 12(7), pp.578-585.10.1049/iet-its.2017.0313 Search in Google Scholar

109. Zhang, J., Zheng, Y., Sun, J., Qi, D. (2019) Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Transactions on Knowledge and Data Engineering, 32(3), 468-478. Search in Google Scholar

110. Zhang, S., Wu, G., Costeira, J. P., & Moura, J. M. (2017) Fcn-rlstm: Deep spatio-temporal neural networks for vehicle counting in city cameras. In: Proceedings of the IEEE international conference on computer vision, 3667-3676. Search in Google Scholar

111. Zhang, Y., Huang, G. (2018) Traffic flow prediction model based on deep belief network and genetic algorithm. IET Intelligent Transport Systems, 12(6), 533-541.10.1049/iet-its.2017.0199 Search in Google Scholar

112. Zhao, J., Gao, Y., Tang, J., Zhu, L., & Ma, J. (2018) Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and-Nearest Neighbor Pattern Matching Method. Journal of Advanced Transportation.10.1155/2018/5721058 Search in Google Scholar

113. Zhao, Z., Chen, W., Wu, X., Chen, P.C., Liu, J. (2017) LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, 11(2), 68-75.10.1049/iet-its.2016.0208 Search in Google Scholar

114. Zhou, J., Chen, C.P., Chen, L., & Zhao, W. (2013) A user-customizable urban traffic information collection method based on wireless sensor networks. IEEE Transactions on Intelligent Transportation Systems, 14(3), 1119-1128.10.1109/TITS.2013.2252902 Search in Google Scholar

115. Zonoozi, A., Kim, J. J., Li, X. L., & Cong, G. (2018) Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns. In: IJCAI, 3732-3738. Search in Google Scholar

116. Zou, Z., Gao, P., Yao, C. (2018) City-Level Traffic Flow Prediction via LSTM Networks. In: Proceedings of the 2nd International Conference on Advances in Image Processing, 149-153.10.1145/3239576.3239623 Search in Google Scholar