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Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges

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Abstract

Low latency in communication among the vehicles and RSUs, smooth traffic flow, and road safety are the major concerns of the Intelligent Transportation Systems. Vehicular Ad hoc Network (VANET) has gained attention from various research communities for such a matters. These systems need constant monitoring for proper functioning, opening the doors to apply Machine Learning algorithms on enormous data generated from different applications in VANET (for example, crowdsourcing, pollution control, environment monitoring, etc.). Machine Learning is an approach where the system automatically learns and improves itself based on previously processed data. These algorithms provide efficient supervised and unsupervised learning of these collected data, which effectively implements VANET’s objective. We highlighted the safety, communication, and traffic-related issues in VANET systems and their implementation in-feasibility and explored how machine learning algorithms can overcome these issues. Finally, we discussed future direction and challenges, along with a case study depicting a VANET based scenario.

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References

  1. Banerjee S, Odelu V, Das AK, Chattopadhyay S, Kumar N, Park Y, Tanwar S (2018) Design of an anonymity-preserving group formation based authentication protocol in global mobility networks. IEEE Access 6:20673–20693. https://doi.org/10.1109/ACCESS.2018.2827027

    Article  Google Scholar 

  2. Muhammad M, Safdar GA (2018) Survey on existing authentication issues for cellular-assisted v2x communication. Vehicular Communications 12:50–65

    Google Scholar 

  3. Bhatia J, Modi Y, Tanwar S, Bhavsar M Software defined vehicular networks: A comprehensive review. Int J Commun Syst 32(12):e4005

  4. Duan X, Liu Y, Wang X (2017) Sdn enabled 5g-vanet: Adaptive vehicle clustering and beamformed transmission for aggregated traffic. IEEE Commun Mag 55(7):120–127

    Google Scholar 

  5. Bhatia J, Govani R, Bhavsar M (2018) Software defined networking: From theory to practice. In: 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp 789–794, IEEE

  6. Sheikh MS, Liang J (2019) A comprehensive survey on vanet security services in traffic management system. Wirel Commun Mob Comput, 2019 2019:23. https://doi.org/10.1155/2019/2423915

    Google Scholar 

  7. Gupta R, Tanwar S, Kumar N, Tyagi S (2020) Blockchain-based security attack resilience schemes for autonomous vehicles in industry 4.0: A systematic review. Comput Electr Eng 86:106717. https://doi.org/10.1016/j.compeleceng.2020.106717. http://www.sciencedirect.com/science/article/pii/S0045790620305723

    Article  Google Scholar 

  8. Park H, Haghani A, Samuel S, Knodler MA (2018) Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accident Analysis & Prevention 112:39–49

    Google Scholar 

  9. Tanwar S, Tyagi S, Kumar S (2018) The role of internet of things and smart grid for the development of a smart city. In: Intelligent Communication and Computational Technologies, Springer, pp 23–33

  10. Mistry I, Tanwar S, Tyagi S, Kumar N (2020) Blockchain for 5g-enabled iot for industrial automation: A systematic review, solutions, and challenges. Mech Syst Signal Process 135:106382. https://doi.org/10.1016/j.ymssp.2019.106382. http://www.sciencedirect.com/science/article/pii/S088832701930603X

    Article  Google Scholar 

  11. Bhatia JB (2015) A dynamic model for load balancing in cloud infrastructure. Nirma University Journal of Engineering and Technology (NUJET) 4(1):15

    Google Scholar 

  12. Astarita V, Festa DC, Giofrè VP (2018) Mobile systems applied to traffic management and safety: a state of the art. Procedia computer science 134:407–414

    Google Scholar 

  13. Ashishdeep A, Bhatia J, Varma K (2016) Software process models for mobile application development: A review. Computer Science and Electronic Journal 7(1):150–153

    Google Scholar 

  14. Bhatia J, Mehta R, Bhavsar M (2018) Variants of software defined network (sdn) based load balancing in cloud computing: A quick review. In: Future Internet Technologies and Trends, pp 164–173, Cham, Springer International Publishing

  15. Abdel-Halim IT, Fahmy H MA (2018) Prediction-based protocols for vehicular ad hoc networks: Survey and taxonomy. Comput Netw 130:34–50

    Google Scholar 

  16. Gupta R, Tanwar S, Tyagi S, Kumar N (2020) Machine learning models for secure data analytics: A taxonomy and threat model. Comput Commun 153:406–440. https://doi.org/10.1016/j.comcom.2020.02.008. http://www.sciencedirect.com/science/article/pii/S0140366419318493

    Article  Google Scholar 

  17. Thakkar P, Varma K, Ukani V, Mankad S, Tanwar S (2019) Combining user-based and item-based collaborative filtering using machine learning. In: Satapathy SC, Joshi A (eds) Information and Communication Technology for Intelligent Systems, pp 173–180, Singapore, Springer Singapore

  18. Li W, Zhang F, Zhang Y, Feng Z (2019) Adaptive sample weight for machine learning computer vision algorithms in v2x systems. IEEE Access 7:4676–4687

    Google Scholar 

  19. Gupta R, Tanwar S, Tyagi S, Kumar N (2019) Tactile internet and its applications in 5g era: A comprehensive review. Int J Commun Syst 32(14):e3981. https://doi.org/10.1002/dac.3981. https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.3981, https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.3981, e3981 dac.3981

    Article  Google Scholar 

  20. Sim GH, Klos S, Asadi A, Klein A, Hollick M (2018) An online context-aware machine learning algorithm for 5g mmwave vehicular communications. IEEE/ACM Trans Networking 26(6):2487–2500

    Google Scholar 

  21. Kumar N, Misra S, Rodrigues JoelJPC, Obaidat MS (2015) Coalition games for spatio-temporal big data in internet of vehicles environment: A comparative analysis. IEEE Internet Things J. 2(4):310–320

    Google Scholar 

  22. Kumar N, Iqbal R, Misra S, Rodrigues JoelJPC (2015) Bayesian coalition game for contention-aware reliable data forwarding in vehicular mobile cloud. Futur Gener Comput Syst 48:60– 72

    Google Scholar 

  23. Kumar N, Rodrigues JoelJPC, Chilamkurti N (2014) Bayesian coalition game as-a-service for content distribution in internet of vehicles. IEEE Internet Things J. 1(6):544–555

    Google Scholar 

  24. Iqbal R, Doctor F, More B, Mahmud S, Yousuf U (2018) Big data analytics : Computational intelligence techniques and application areas. / Iqbal, Rahat; Doctor, Faiyaz; More, Brian; Mahmud, Shahid; Yousuf, Usman. In: Technological Forecasting and Social Change, Vol. 153, 119253, 04.2020.

  25. Liang L, Ye H, Li GY (2019) Toward intelligent vehicular networks: A machine learning framework. IEEE Internet Things J. 6(1):124–135

    Google Scholar 

  26. Kibria MG, Nguyen K, Villardi GP, Zhao O, Ishizu K, Kojima F (2018) Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access 6:32328–32338

    Google Scholar 

  27. Tanwar S, Vora J, Tyagi S, Kumar N, Obaidat MS (2018) A systematic review on security issues in vehicular ad hoc network. Security and Privacy 1(5):e39. https://doi.org/10.1002/spy2.39. https://onlinelibrary.wiley.com/doi/pdf/10.1002/spy2.39, https://onlinelibrary.wiley.com/doi/abs/10.1002/spy2.39

    Article  Google Scholar 

  28. Hossain MA, Noor RM, Yau K-LA, Azzuhri SR, Z’aba MR, Ahmedy I (2020) Comprehensive survey of machine learning approaches in cognitive radio-based vehicular ad hoc networks. IEEE Access 8:78054–78108

    Google Scholar 

  29. Fotros M, Rezazadeh J, Sianaki OA (2020) A survey on vanets routing protocols for iot intelligent transportation systems. In: Workshops of the International Conference on Advanced Information Networking and Applications, pp 1097–1115, Springer

  30. Alrehan AM, Alhaidari FA (2019) Machine learning techniques to detect ddos attacks on vanet system: A survey. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp 1–6, IEEE

  31. Tong W, Hussain A, Bo WX, Maharjan S (2019) Artificial intelligence for vehicle-to-everything: A survey. IEEE Access 7:10823–10843

    Google Scholar 

  32. Ditcharoen A, Chhour B, Traikunwaranon T, Aphivongpanya N, Maneerat K, Ammarapala V (2018) Road traffic accidents severity factors: A review paper. In: 2018 5th International Conference on Business and Industrial Research (ICBIR), pp 339–343, IEEE

  33. Suhas S, Kalyan VV, Katti M, Prakash BVAjay, Naveena C (2017) A comprehensive review on traffic prediction for intelligent transport system. In: 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT), pp 138–143, IEEE

  34. Kavitha N, Chandrappa DN (2017) Comparative review on video based vehicular traffic data collection for intelligent transport system. In: 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT), pp 260–264, IEEE

  35. Jensen MB, Philipsen MP, Møgelmose A, Moeslund TB, Trivedi MM (2016) Vision for looking at traffic lights: Issues, survey, and perspectives. IEEE Trans Intell Transp Syst 17(7):1800– 1815

    Google Scholar 

  36. Barros J, Araujo M, Rossetti RosaldoJF (2015) Short-term real-time traffic prediction methods: a survey. In: Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015 International Conference on, pp 132–139, IEEE

  37. Glebe road its improvement. https://projects.arlingtonva.us/projects/intelligent-transportation-systems/, Accessed: 24-03-2019

  38. Tampa pilot. https://www.its.dot.gov/pilots/pilots_thea.htm/, Accessed: 24-03-2019

  39. New york city dot pilot. https://www.its.dot.gov/pilots/pilots_nycdot.htm/, Accessed: 24-03-2019

  40. Hamburg electric autonomous transportation. https://www.hamburg.de/pressearchiv-fhh/10120472/2017-12-20-bwvi-projekt-heat/, Accessed: 24-03-2019

  41. Cooperative its for mobility in european cities. https://cordis.europa.eu/project/rcn/196891/factsheet/en, Accessed: 24-03-2019

  42. Tanwar S, Bhatia Q, Patel P, Kumari A, Singh PK, Hong W (2020) Machine learning adoption in blockchain-based smart applications: The challenges, and a way forward. IEEE Access 8:474–488. https://doi.org/10.1109/ACCESS.2019.2961372

    Article  Google Scholar 

  43. Zhao H, Mao T, Duan J, Wang Y, Zhu H (2019) Fmcnn: A factorization machine combined neural network for driving safety prediction in vehicular communication. IEEE Access 7:11698–11706

    Google Scholar 

  44. Peng Z, Gao S, Li Z, Xiao B, Qian Y (2018) Vehicle safety improvement through deep learning and mobile sensing. IEEE Netw 32(4):28–33

    Google Scholar 

  45. Dairi A, Harrou F, Sun Y, Senouci M (2018) Obstacle detection for intelligent transportation systems using deep stacked autoencoder and k-nearest neighbor scheme. IEEE Sensors J 18(12):5122–5132

    Google Scholar 

  46. Dogru N, Subasi A (2018) Traffic accident detection using random forest classifier. In: Learning and Technology Conference (L&T), 2018 15th, pp 40–45, IEEE

  47. Zhao H, Mao T, Yu H, Zhang MK, Zhu H (2018) A driving risk prediction algorithm based on pca-bp neural network in vehicular communication. In: 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2, pp 164–169, IEEE

  48. Ye YY, Hao XL, Chen HJ (2018) Lane detection method based on lane structural analysis and cnns. IET Intell Transp Syst 12(6):513–520

    Google Scholar 

  49. Zhao H, Yu H, Mao T, Zhang M, Zhu H (2018) Vehicle accident risk prediction over adaboost from vanets. In: 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2, pp 39–43, IEEE

  50. Reddy B, Kim Y-H, Yun S, Seo C, Jang J (2017) Real-time driver drowsiness detection for embedded system using model compression of deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 121–128

  51. Kim J, Kim J, Jang G-J, Lee M (2017) Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection. Neural Netw 87:109–121

    Google Scholar 

  52. Nguyen H, Cai C, Chen F (2017) Automatic classification of traffic incident’s severity using machine learning approaches. IET Intell Transp Syst 11(10):615–623

    Google Scholar 

  53. Yuan Y, Xiong Z, Wang Q (2017) An incremental framework for video-based traffic sign detection, tracking, and recognition. IEEE Trans Intell Transp Syst 18(7):1918–1929

    Google Scholar 

  54. Chandrasiri NP, Nawa K, Ishii A (2016) Driving skill classification in curve driving scenes using machine learning. Journal of Modern Transportation 24(3):196–206

    Google Scholar 

  55. Sun J, Sun J (2016) Real-time crash prediction on urban expressways: identification of key variables and a hybrid support vector machine model. IET intelligent transport systems 10(5):331–337

    Google Scholar 

  56. AlNajada H, Mahgoub I (2016) Autonomous vehicles safe-optimal trajectory selection based on big data analysis and predefined user preferences. In: Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual, pp 1–6, IEEE

  57. Birek L, Grzywaczewski A, Iqbal R, Doctor F, Chang V (2018) A novel big data analytics and intelligent technique to predict driver’s intent. Comput Ind 99:226–240

    Google Scholar 

  58. Šabanovič E, žuraulis V, Prentkovskis O, Skrickij V (2020) Identification of road-surface type using deep neural networks for friction coefficient estimation. Sensors 20(3):612

    Google Scholar 

  59. Verma B, Choudhary A (2018) Deep learning based real-time driver emotion monitoring. In: 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), 1–6, IEEE

  60. Gupta R, Kumari A, Tanwar S A taxonomy of blockchain envisioned edge-as-a-connected autonomous vehicles. Transactions on Emerging Telecommunications Technologies, n/a, n/a, e4009. https://doi.org/10.1002/ett.4009https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.4009, https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.4009

  61. Tanwar S, Tyagi S, Budhiraja I, Kumar N (2019) Tactile internet for autonomous vehicles: Latency and reliability analysis. IEEE Wirel Commun 26(4):66–72. https://doi.org/10.1109/MWC.2019.1800553

    Article  Google Scholar 

  62. Vachhani H, Obiadat MS, Thakkar A, Shah V, Sojitra R, Bhatia J, Tanwar S (2020) Machine learning based stock market analysis: A short survey. In: Raj JS, Bashar A, Ramson S RJ (eds) Innovative data communication technologies and application, pp 12–26, cham, springer international publishing

  63. K S, S.K. L, Khanna A, Tanwar S, Rodrigues JJPC, Roy NR (2019) Alzheimer detection using group grey wolf optimization based features with convolutional classifier. Computers & Electrical Engineering 77:230–243. https://doi.org/10.1016/j.compeleceng.2019.06.001. http://www.sciencedirect.com/science/article/pii/S0045790618325448

    Article  Google Scholar 

  64. Dave JR, Bhatia J (2013) Issues in static periodic broadcast in vanet. International Journal of Advances in Engineering & Technology 6(4):1712

    Google Scholar 

  65. Manvi SS, Tangade S (2017) A survey on authentication schemes in vanets for secured communication. Vehicular Communications 9:19–30

    Google Scholar 

  66. Bhatia J, Kakadia P, Bhavsar M, Tanwar S (2019) SDN Enabled Network Coding Based Secure Data Dissemination in VANET Environment. IEEE Internet Things J. 7(7):6078–6087. https://doi.org/10.1109/JIOT.2019.2956964

    Google Scholar 

  67. Karagiannis D, Argyriou A (2018) Jamming attack detection in a pair of rf communicating vehicles using unsupervised machine learning. Vehicular Communications 13:56–63

    Google Scholar 

  68. Lai WK, Lin M-T, Yang Y-H (2015) A machine learning system for routing decision-making in urban vehicular ad hoc networks. International Journal of Distributed Sensor Networks 11(3):374391

    Google Scholar 

  69. Polkowski Z, Vora J, Tanwar S, Tyagi S, Singh PK, Singh Y (2019) Machine learning-based software effort estimation: An analysis. In: 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp 1–6

  70. Khan S, Alam M, Fränzle M, Müllner N, Chen Y (2018) A traffic aware segment-based routing protocol for vanets in urban scenarios. Computers & Electrical Engineering 68:447–462

    Google Scholar 

  71. Chauhan K, Jani S, Thakkar D, Dave R, Bhatia J, Tanwar S, Obaidat MS (2020) Automated machine learning: The new wave of machine learning. In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp 205–212

  72. Slavik M, Mahgoub I (2011) Applying machine learning to the design of multi-hop broadcast protocols for vanet. In: 2011 7th International Wireless Communications and Mobile Computing Conference, pp 1742–1747, IEEE

  73. Patel VJ, Anuradha PG (2012) A review on routing overhead in broadcast based protocol on vanet. International Journal of Engineering and Innovative Technology (IJEIT) 2(5):109–113

    Google Scholar 

  74. Qi Q, Wang J, Ma Z, Sun H, Cao Y, Zhang L, Liao J (2019) Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach. IEEE Trans Veh Technol 68:4192–4203. https://doi.org/10.1109/TVT.2019.2894437

    Google Scholar 

  75. Ye H, Li GY (2018) Deep reinforcement learning for resource allocation in v2v communications. In: 2018 IEEE International Conference on Communications (ICC), pp 1–6, IEEE

  76. Tan LT, Hu RQ (2018) Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning. IEEE Trans Veh Technol 67(11):10190–10203

    Google Scholar 

  77. Sangare M, Banerjee S, Muhlethaler P, Bouzefrane S (2018) Predicting transmission success with support vector machine in vanets. In: 2018 IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN), pp 1–6, IEEE

  78. Cheng N, Lyu F, Chen J, Xu W, Zhou H, Zhang S, Shen X (2018) Big data driven vehicular networks. IEEE Netw, 99, pp 1–8

  79. He Y, Zhao N, Yin H (2018) Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach. IEEE Trans Veh Technol 67(1):44–55

    Google Scholar 

  80. Tuzi G, Medenica Z, Miucic R (2018) Using convolutional neural networks for distance estimation between dedicated short-range communications equipped vehicles. In: 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), pp 1–6, IEEE

  81. Lyu F, Cheng N, Zhou H, Xu W, Shi W, Chen J, Li M (2018) Dbcc: Leveraging link perception for distributed beacon congestion control in vanets. IEEE Internet Things J. 5(6):4237–4249

    Google Scholar 

  82. Nie L, Li Y, Kong X (2018) Spatio-temporal network traffic estimation and anomaly detection based on convolutional neural network in vehicular ad-hoc networks. IEEE Access 6:40168–40176

    Google Scholar 

  83. Pal R, Prakash A, Tripathi R, Singh D (2018) Analytical model for clustered vehicular ad hoc network analysis. ICT Express 4(3):160–164

    Google Scholar 

  84. Tang F, Mao B, Fadlullah ZM, Kato N, Akashi O, Inoue T, Mizutani K (2018) On removing routing protocol from future wireless networks: A real-time deep learning approach for intelligent traffic control. IEEE Wirel Commun 25(1):154–160

    Google Scholar 

  85. Sharma S, Kaul A (2018) Hybrid fuzzy multi-criteria decision making based multi cluster head dolphin swarm optimized ids for vanet. Vehicular Communications 12:23–38

    Google Scholar 

  86. Shams EA, Rizaner A, Ulusoy AH (2018) Trust aware support vector machine intrusion detection and prevention system in vehicular ad hoc networks. Computers & Security 78:245– 254

    Google Scholar 

  87. Ghaleb FA, Zainal A, Rassam MA, Mohammed F (2017) An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications. In: Application, Information and Network Security (AINS), 2017 IEEE Conference on, pp 13–18, IEEE

  88. Zhang J, Ren M, Labiod H, Khoukhi L (2017) Link duration prediction in vanets via adaboost. In: GLOBECOM 2017-2017 IEEE Global Communications Conference, pp 1–6, IEEE

  89. Roscher K, Nitsche T, Knorr R (2017) Know thy neighbor-a data-driven approach to neighborhood estimation in vanets. In: Vehicular Technology Conference (VTC-Fall), 2017 IEEE 86th, 1–5, IEEE

  90. Zhao L, Li Y, Meng C, Gong C, Tang X (2016) A svm based routing scheme in vanets. In: Communications and Information Technologies (ISCIT), 2016 16th International Symposium on, pp 380–383, IEEE

  91. Taherkhani N, Pierre S (2016) Centralized and localized data congestion control strategy for vehicular ad hoc networks using a machine learning clustering algorithm. IEEE Trans Intell Transp Syst 17 (11):3275–3285

    Google Scholar 

  92. Wahab OA, Mourad A, Otrok H, Bentahar J (2016) Ceap: Svm-based intelligent detection model for clustered vehicular ad hoc networks. Expert Syst Appl 50:40–54

    Google Scholar 

  93. Bhatia JB (2019) Design and development of a framework for reliable data dissemination in vanet environment using software defined networking and cloud

  94. Magaia N, Sheng Z (2019) Refiov: a novel reputation framework for information-centric vehicular applications. IEEE Trans Veh Technol 68(2):1810–1823

    Google Scholar 

  95. Li Q, Wang F, Wang J, Li W (2019) Lstm-based sql injection detection method for intelligent transportation system. IEEE Trans Veh Technol 68(5):4182–4191. https://doi.org/10.1109/TVT.2019.2893675

    Google Scholar 

  96. Portugal-Poma LP, Marcondes CesarAC, Senger H, Arantes L (2014) Applying machine learning to reduce overhead in dtn vehicular networks. In: 2014 Brazilian Symposium on Computer Networks and Distributed Systems, pp 94–102, IEEE

  97. Gao H, Liu C, Li Y, Yang X (2020) V2vr: Reliable hybrid-network-oriented v2v data transmission and routing considering rsus and connectivity probability. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.2983835

  98. Tanwar S, Tyagi S, Kumar N, Obaidat MS (2019) La-mhr: Learning automata based multilevel heterogeneous routing for opportunistic shared spectrum access to enhance lifetime of wsn. IEEE Syst J 13(1):313–323. https://doi.org/10.1109/JSYST.2018.2818618

    Article  Google Scholar 

  99. Mungra D, Agrawal A, Sharma P, Tanwar S, Obaidat MS (2020) Pratit: a cnn-based emotion recognition system using histogram equalization and data augmentation. Multimedia Tools and Applications 79 (3):2285–2307

    Google Scholar 

  100. Khaliq KA, Raza SM, Chughtai O, Qayyum A, Pannek J (2018) Experimental validation of an accident detection and management application in vehicular environment. Computers & Electrical Engineering 71:137–150

    Google Scholar 

  101. Jo D, Yu B, Jeon H, Sohn K (2019) Image-to-image learning to predict traffic speeds by considering area-wide spatio-temporal dependencies. IEEE Trans Veh Technol 68(2):1188–1197

    Google Scholar 

  102. Raj J, Bahuleyan H, Vanajakshi LD (2016) Application of data mining techniques for traffic density estimation and prediction. Transportation Research Procedia 17:321–330

    Google Scholar 

  103. Cheng Z, Lu J, Li Y (2018) Freeway crash risks evaluation by variable speed limit strategy using real-world traffic flow data. Accident Analysis & Prevention 119:176–187

    Google Scholar 

  104. Wang Y, Cao J, Li W, Gu T, Shi W (2017) Exploring traffic congestion correlation from multiple data sources. Pervasive and Mobile Computing 41:470–483

    Google Scholar 

  105. Sen R, Raman B (2012) Intelligent transport systems for indian cities. In: Presented as part of the 6th USENIX/ACM Workshop on Networked Systems for Developing Regions

  106. Bhattacharya P, Tanwar S, Bodke U, Tyagi S, Kumar N (2019) Bindaas: Blockchain-based deep-learning as-a-service in healthcare 4.0 applications. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/TNSE.22.019.296193

  107. Yau K-LA, Qadir J, Khoo HL, Ling MH, Komisarczuk P (2017) A survey on reinforcement learning models and algorithms for traffic signal control. ACM Computing Surveys (CSUR) 50(3):34

    Google Scholar 

  108. Liebig T, Piatkowski N, Bockermann C, Morik K (2017) Dynamic route planning with real-time traffic predictions. Inf Syst 64:258–265

    Google Scholar 

  109. Kumar N, Chilamkurti N, Park JH (2013) Alca: agent learning–based clustering algorithm in vehicular ad hoc networks. Personal and ubiquitous computing 17(8):1683–1692

    Google Scholar 

  110. Liang X, Du X, Wang G, Han Z (2019) A deep reinforcement learning network for traffic light cycle control. IEEE Trans Veh Technol 68(2):1243–1253. https://doi.org/10.1109/TVT.2018.2890726

    Google Scholar 

  111. Li J, Luo G, Cheng N, Yuan Q, Wu Z, Gao S, Liu Z (2019) An end-to-end load balancer based on deep learning for vehicular network traffic control. IEEE Internet Things J. 6(1):953–966

    Google Scholar 

  112. Guo R, Keshavamurthy S, Oguchi K (2018) Simultaneous object detection and association in connected vehicle platform. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp 840–845, IEEE

  113. Tomar AS, Singh M, Sharma G, Arya KV (2018) Traffic management using logistic regression with fuzzy logic. Procedia Computer Science 132:451–460

    Google Scholar 

  114. Li D, Deng L, Cai Z, Franks B, Yao X (2018) Intelligent transportation system in macao based on deep self-coding learning. IEEE Transactions on Industrial Informatics 14(7):3253–3260

    Google Scholar 

  115. Wu Y, Tan H, Qin L, Ran B, Jiang Z (2018) A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies 90:166–180

    Google Scholar 

  116. Wang Q, Wan J, Li X (2018) Robust hierarchical deep learning for vehicular management. IEEE Trans Veh Technol 68(5):4148–4156. https://doi.org/10.1109/TVT.2018.2883046

    Google Scholar 

  117. Kurniawan J, Syahra SensaGS, Dewa CK, et al. (2018) Traffic congestion detection: Learning from cctv monitoring images using convolutional neural network. Procedia computer science 144:291–297

    Google Scholar 

  118. Cheng S, Lu F, Peng P, Wu S (2018) Short-term traffic forecasting: An adaptive st-knn model that considers spatial heterogeneity. Comput Environ Urban Syst 71:186–198

    Google Scholar 

  119. Munoz-Organero M, Ruiz-Blaquez R, Sánchez-Fernández L (2018) Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on gps traces while driving. Comput Environ Urban Syst 68:1–8

    Google Scholar 

  120. Chen W, An J, Li R, Fu L, Xie G, Bhuiyan M ZA, Li K (2018) A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features. Futur Gener Comput Syst 89:78–88

    Google Scholar 

  121. Jeon H-S, Kum D-S, Jeong W-Y (2018) Traffic scene prediction via deep learning: Introduction of multi-channel occupancy grid map as a scene representation. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp 1496–1501, IEEE

  122. ElHatri C, Boumhidi J (2018) Fuzzy deep learning based urban traffic incident detection. Cogn Syst Res 50:206–213

    Google Scholar 

  123. Rahimipour S, Moeinfar R, Hashemi SM (2018) Traffic prediction using a self-adjusted evolutionary neural network. J. Mod. Transport. 27:306–316. https://doi.org/10.1007/s40534-018-0179-5

    Google Scholar 

  124. Cheng J, Wu W, Cao J, Li K (2017) Fuzzy group-based intersection control via vehicular networks for smart transportations. IEEE Transactions on Industrial Informatics 13(2):751– 758

    Google Scholar 

  125. Raza A, Zhong M (2017) Hybrid lane-based short-term urban traffic speed forecasting: A genetic approach. In: Transportation Information and Safety (ICTIS), 2017 4th International Conference on, pp 271–279, IEEE

  126. Yang H-F, Dillon TS, Chen Y-PP (2017) 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

    Google Scholar 

  127. Du X, Zhang H, VanNguyen H, Han Z (2017) Stacked lstm deep learning model for traffic prediction in vehicle-to-vehicle communication. In: Vehicular Technology Conference (VTC-Fall), 2017 IEEE 86th, pp 1–5, IEEE

  128. Koesdwiady A, Soua R, Karray F (2016) Improving traffic flow prediction with weather information in connected cars: a deep learning approach. IEEE Trans Veh Technol 65(12):9508–9517

    Google Scholar 

  129. Li L, Lv Y, Wang F-Y (2016) Traffic signal timing via deep reinforcement learning. IEEE/CAA Journal of Automatica Sinica 3(3):247–254

    MathSciNet  Google Scholar 

  130. Yu X, Xiong S, He Y, Wong WE, Zhao Y (2016) Research on campus traffic congestion detection using bp neural network and markov model. Journal of information security and applications 31:54–60

    Google Scholar 

  131. Kumar K, Parida M, Katiyar VK (2013) Short term traffic flow prediction for a non urban highway using artificial neural network. Procedia-Social and Behavioral Sciences 104:755–764

    Google Scholar 

  132. Bhatia J, Dave R, Bhayani H, Tanwar S, Nayyar A (2020) SDN-based real-time urban traffic analysis in VANET environment. Comput Commun 149:162–175

    Google Scholar 

  133. Kumari A, Gupta R, Tanwar S, Kumar N (2020) Blockchain and ai amalgamation for energy cloud management: Challenges, solutions, and future directions. Journal of Parallel and Distributed Computing 143:148–166. https://doi.org/10.1016/j.jpdc.2020.05.004. http://www.sciencedirect.com/science/article/pii/S074373152030277X

    Article  Google Scholar 

  134. Kuang L, Yan X, Tan X, Li S, Yang X (2019) Predicting taxi demand based on 3d convolutional neural network and multi-task learning. Remote Sens 11(11):1265

    Google Scholar 

  135. Ziebinski A, Cupek R, Grzechca D, Chruszczyk L (2017) Review of Advanced Driver Assistance Systems (ADAS). In: AIP Conference Proceedings, 1906, p 120002, AIP Publishing LLC

  136. Jiménez F, Naranjo JE, Anaya JJ, García F, Ponz A, Armingol JM (2016) Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14:2245–2254

    Google Scholar 

  137. Martinez CM, Heucke M, Wang F-Y, Gao B, Cao D (2017) Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey. IEEE Trans Intell Transp Syst 19(3):666–676

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Vishwakarma Government Engineering College, Gujarat, India

    Sahil Khatri, Hrishikesh Vachhani, Shalin Shah & Jitendra Bhatia

  2. Department of Computer Science and Engineering, Indian Institute of Infrastructure Technology Research And Management (IITRAM), Gujarat, India

    Manish Chaturvedi

  3. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Gujarat, India

    Sudeep Tanwar

  4. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala India. Department of Computer Science and Information Engineering, Asia University, Taiwan. King Abdul Aziz University, Jeddah, Saudi Arabia

    Neeraj Kumar

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  1. Sahil Khatri
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  2. Hrishikesh Vachhani
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  4. Jitendra Bhatia
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Correspondence to Neeraj Kumar.

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Khatri, S., Vachhani, H., Shah, S. et al. Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges. Peer-to-Peer Netw. Appl. 14, 1778–1805 (2021). https://doi.org/10.1007/s12083-020-00993-4

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