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Smart City: Road Traffic Monitoring System Based on the Integration of IoT and ML

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Communication and Intelligent Systems (ICCIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 686))

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Abstract

One of the critical challenges generated by globalization is managing the traffic on the roads. The mishandling of road traffic because of its varying nature hinders efficient traffic flow, consumes time, and poses a risk to road safety. All these problems can be resolved with the aid of an efficient and trustworthy smart road traffic monitoring (SRTM) system. Even though a substantial amount of study has been done on road traffic management, still it remains an active topic of research. Evolving techniques like the Internet of things (IoT) and machine learning (ML) may help in the development of an efficient and robust system for monitoring traffic. Moreover, by integrating these techniques, decision-making mechanisms can be improved, and even urban evolution can be promoted. Therefore, the primary purpose of this paper is to study the role of the Internet of things (IoT) and machine learning (ML) in smart road traffic monitoring (SRTM) scenarios independently as well as when collaborating. Further, to gain a deeper understanding of the system, several IoT and ML frameworks for road traffic management are examined including the techniques used, outcomes, and their future work also. From the comparative analysis of the frameworks, it is seen that IoT and ML when used together in traffic management prove to be much more efficient. Moreover, this paper gives only the theoretic review of the state of traffic monitoring system and not any kind of practical implementation. So, in future, IoT and ML-aided efficient framework for road traffic monitoring will be designed and implemented.

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References

  1. Qian Y, Wu D, Bao W, Lorenz P (2019) The internet of things for smart cities: technologies and applications. IEEE Netw 33(2):4–5. https://doi.org/10.1109/MNET.2019.8675165

    Article  Google Scholar 

  2. Sarrab M, Pulparambil S, Awadalla M (2020) Development of an IoT based real-time traffic monitoring system for city governance. Glob Transitions 2:230–245. https://doi.org/10.1016/j.glt.2020.09.004

    Article  Google Scholar 

  3. Feltrin G, Popovic N, Wojtera M (2019) A sentinel node for event-driven structural monitoring of road bridges using wireless sensor networks. J Sens 2019. http://doi.org/10.1155/2019/8652527

  4. Zadobrischi E, Cosovanu LM, Dimian M (2020) Traffic flow density model and dynamic traffic congestion model simulation based on practice case with vehicle network and system traffic intelligent communication. Symmetry (Basel) 12(7). http://doi.org/10.3390/sym12071172

  5. Nuruddeen MI, Siyan P (2016) Analyzing factors responsible for road traffic accidents along Kano-Kaduna-Abuja dual carriageway Nigeria. J Econ Sustain Dev 7(12):156–163

    Google Scholar 

  6. Lilhore UK et al (2022) Design and implementation of an ML and IoT based adaptive traffic-management system for smart cities. Sensors 22(8). http://doi.org/10.3390/s22082908

  7. Lee WH, Chiu CY (2020) Design and implementation of a smart traffic signal control system for smart city applications. Sensors (Switzerland) 20(2). http://doi.org/10.3390/s20020508

  8. Bhat WA (2018) Is a data-capacity gap inevitable in big data storage? Computer (Long Beach Calif) 51(9):54–62. http://doi.org/10.1109/MC.2018.3620975

  9. Bhat WA (2018) Bridging data-capacity gap in big data storage. Futur Gener Comput Syst 87(2018):538–548. https://doi.org/10.1016/j.future.2017.12.066

    Article  Google Scholar 

  10. Cader A, Nafrees M, Mohamed A, Sujah A, Mansoor C (2021) Smart cities: emerging technologies and potential solutions to the cyber security threads. http://doi.org/10.1109/ICEECCOT52851.2021.9707994

  11. Kebbeh PS, Jain M, Gueye B (2020) SenseNet: IoT temperature measurement in railway networks for intelligent transport. In: IBASE-BF 2020—1st IEEE international conference on natural and engineering sciences for Sahel’s sustainable development—impact of big data application on society and environment, pp 1–8. http://doi.org/10.1109/IBASE-BF48578.2020.9069596

  12. Dhingra S, Madda RB, Patan R, Jiao P, Barri K, Alavi AH (2021) Internet of things-based fog and cloud computing technology for smart traffic monitoring. Internet of Things (Netherlands) 14. http://doi.org/10.1016/j.iot.2020.100175

  13. Yu W et al (2017) A survey on the edge computing for the internet of things. IEEE Access 6:6900–6919. http://doi.org/10.1109/ACCESS.2017.2778504

  14. Sittón-Candanedo I, Alonso RS, Rodríguez-González S, García Coria JA, De La Prieta F (2020) Edge computing architectures in Industry 4.0: a general survey and comparison. Adv Intell Syst Comput 950:121–131. http://doi.org/10.1007/978-3-030-20055-8_12

  15. Premsankar G, Di Francesco M, Taleb T (2018) Edge computing for the internet of things: a case study. IEEE Internet Things J 5(2):1275–1284. https://doi.org/10.1109/JIOT.2018.2805263

    Article  Google Scholar 

  16. Wan S, Ding S, Chen C (2022) Edge computing enabled video segmentation for real-time traffic monitoring in internet of vehicles. Pattern Recogn 121. http://doi.org/10.1016/j.patcog.2021.108146

  17. Li Z, Al Hassan R, Shahidehpour M, Bahramirad S, Khodaei A (2019) A hierarchical framework for intelligent traffic management in smart cities. IEEE Trans Smart Grid 10(1):691–701. http://doi.org/10.1109/TSG.2017.2750542

  18. Nizzad ARM et al (2021) Internet of things based automatic system for the traffic violation. http://doi.org/10.1109/ICEECCOT52851.2021.9708060

  19. Qiu J, Du L, Zhang D, Su S, Tian Z (2020) Nei-TTE: intelligent traffic time estimation based on fine-grained time derivation of road segments for smart city. IEEE Trans Ind Inf 16(4):2659–2666. https://doi.org/10.1109/TII.2019.2943906

    Article  Google Scholar 

  20. Verma P, Sood SK (2018) Cloud-centric IoT based disease diagnosis healthcare framework. J Parallel Distrib Comput 116:27–38. https://doi.org/10.1016/j.jpdc.2017.11.018

    Article  Google Scholar 

  21. Sood SK, Mahajan I (2018) A fog-based healthcare framework for Chikungunya. IEEE Internet Things J 5(2):794–801. https://doi.org/10.1109/JIOT.2017.2768407

    Article  Google Scholar 

  22. Suresh Kumar K, Radha Mani AS, Sundaresan S, Ananth Kumar T (2021) Modeling of VANET for future generation transportation system through edge/fog/cloud computing powered by 6G. Cloud IoT‐Based Veh Ad Hoc Netw 105–124. http://doi.org/10.1002/9781119761846.ch6

  23. Luhach AK, Jat DS, Hawari KB, Gao XZ, Lingras P (eds) (2020) Advanced informatics for computing research part 1. http://doi.org/10.1007/978-3-031-09469-9

  24. 15584-Article Text-55369-1-10-20210312.pdf

    Google Scholar 

  25. Zantalis F, Koulouras G, Karabetsos S, Kandris D (2019) A review of machine learning and IoT in smart transportation. Future Internet 11(4):1–23. https://doi.org/10.3390/FI11040094

    Article  Google Scholar 

  26. Rahman W, Islam R, Hasan A, Bithi NI, Hasan M (2020) Computer and intelligent waste management system using deep learning with IoT. J King Saud Univ Comput Inf Sci [Online]. Available: http://doi.org/10.1016/j.jksuci.2020.08.016

  27. Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2(3):1–21. https://doi.org/10.1007/s42979-021-00592-x

    Article  MathSciNet  Google Scholar 

  28. Ben Atitallah S, Driss M, Boulila W, Ben Ghezala H (2020) Leveraging deep learning and IoT big data analytics to support the smart cities development: review and future directions. Comput Sci Rev 38. http://doi.org/10.1016/j.cosrev.2020.100303

  29. Jia Y, Wu J, Ben-Akiva M, Seshadri R, Du Y (2017) Rainfall-integrated traffic speed prediction using deep learning method. IET Intel Transport Syst 11(9):531–536. https://doi.org/10.1049/iet-its.2016.0257

    Article  Google Scholar 

  30. Koesdwiady A, Soua R, Karray F (2016) Weather information in connected cars: a deep learning approach. IEEE Trans Veh Technol 65(12):9508–9517

    Google Scholar 

  31. Duan Y, Lv Y, Wang FY (2016) Travel time prediction with LSTM neural network. In: IEEE conference on intelligent transportation systems proceedings, ITSC, pp 1053–1058. http://doi.org/10.1109/ITSC.2016.7795686

  32. Tang Y, Zhang C, Gu R, Li P, Yang B (2017) Vehicle detection and recognition for intelligent traffic surveillance system. Multimed Tools Appl 76(4):5817–5832. https://doi.org/10.1007/s11042-015-2520-x

    Article  Google Scholar 

  33. Zhao D, Member S, Chen Y, Lv L (2017) Attention for vehicle classification. IEEE 9(4):356–367

    Google Scholar 

  34. Ouyang Z, Niu J, Guizani M (2018) Improved vehicle steering pattern recognition by using selected sensor data. IEEE Trans Mob Comput 17(6):1383–1396

    Article  Google Scholar 

  35. Iyer LS (2021) AI enabled applications towards intelligent transportation. Transp Eng 5:100083. https://doi.org/10.1016/j.treng.2021.100083

    Article  Google Scholar 

  36. Jelínek J, Čejka J, Šedivý J (2022) Importance of the static infrastructure for dissemination of information within intelligent transportation systems. Commun Sci Lett Univ Žilina 24(2):E63–E73. https://doi.org/10.26552/COM.C.2022.2.E63-E73

    Article  Google Scholar 

  37. Cader A, Nafrees M (2022) Intelligent transportation system using smartphone. http://doi.org/10.1109/ICEECCOT52851.2021.9708053

  38. Bogaerts T, Masegosa AD, Angarita-Zapata JS, Onieva E, Hellinckx P (2020) A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data. Transp Res Part C Emerg Technol 112:62–77. https://doi.org/10.1016/j.trc.2020.01.010

    Article  Google Scholar 

  39. Zaki JF, Ali-Eldin A, Hussein SE, Saraya SF, Areed FF (2020) Traffic congestion prediction based on hidden Markov models and contrast measure. Ain Shams Eng J 11(3):535–551. https://doi.org/10.1016/j.asej.2019.10.006

    Article  Google Scholar 

  40. Alsaawy Y, Alkhodre A, Sen AA, Alshanqiti A, Bhat WA, Bahbouh NM (2022) A comprehensive and effective framework for traffic congestion problem based on the integration of IoT and data analytics. Appl Sci 12(4). http://doi.org/10.3390/app12042043

  41. Braz FJ et al (2022) Road traffic forecast based on meteorological information through deep learning methods. Sensors 22(12):1–19. https://doi.org/10.3390/s22124485

    Article  Google Scholar 

  42. Zhang W, Yu Y, Qi Y, Shu F, Wang Y (2019) Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transp A Transp Sci 15(2):1688–1711. https://doi.org/10.1080/23249935.2019.1637966

    Article  Google Scholar 

  43. Sahil, Sood SK (2021) Smart vehicular traffic management: an edge cloud centric IoT based framework. Internet of Things (Netherlands) 14. http://doi.org/10.1016/j.iot.2019.100140

  44. Zhao Z, Chen W, Wu X, Chen PCY, Liu J (2017) LSTM network: a deep learning approach for short-term traffic forecast. IET Intell Transp Syst 11(2):68–75. https://doi.org/10.1049/iet-its.2016.0208

    Article  Google Scholar 

  45. Vardhana M, Arunkumar N, Abdulhay E, Vishnuprasad PV (2019) Iot based real time traffic control using cloud computing. Cluster Comput 22(s1):2495–2504. https://doi.org/10.1007/s10586-018-2152-9

    Article  Google Scholar 

  46. Sun B, Sun T, Jiao P (2021) Spatio-temporal segmented traffic flow prediction with ANPRS data based on improved XGBoost. J Adv Transp 2021. http://doi.org/10.1155/2021/5559562

  47. Ozbayoglu M, Kucukayan G, Dogdu E (2016) A real-time autonomous highway accident detection model based on big data processing and computational intelligence. In: Proceedings—2016 IEEE international conference on big data, Big data 2016, pp 1807–1813. http://doi.org/10.1109/BigData.2016.7840798

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Correspondence to Komal Saini .

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Saini, K., Sharma, S. (2023). Smart City: Road Traffic Monitoring System Based on the Integration of IoT and ML. In: Sharma, H., Shrivastava, V., Bharti, K.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2022. Lecture Notes in Networks and Systems, vol 686. Springer, Singapore. https://doi.org/10.1007/978-981-99-2100-3_12

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