A Review and Analysis of IoT Enabled Smart Transportation Using Machine Learning Techniques

sayak mukhopadhyay; akshay kumar; janmejay gupta; anish bhatnagar; mvv prasad kantipudi; mangal singh

Outline

Acadlore takes over the publication of IJTDI from 2025 Vol. 9, No. 4. The preceding volumes were published under a CC BY 4.0 license by the previous owner, and displayed here as agreed between Acadlore and the previous owner. ✯ : This issue/volume is not published by Acadlore.

Open Access

Research article

A Review and Analysis of IoT Enabled Smart Transportation Using Machine Learning Techniques

sayak mukhopadhyay

,

akshay kumar

,

janmejay gupta

,

anish bhatnagar

,

mvv prasad kantipudi^*

,

mangal singh

Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU), 412115 Pune, India

International Journal of Transport Development and Integration

|

Volume 8, Issue 1, 2024

|

Pages 61-77

https://doi.org/10.18280/ijtdi.080106

Received: 09-10-2023,

Revised: 12-18-2023,

Accepted: 12-29-2023,

Available online: 03-30-2024

View Full Article|

Download PDF

Abstract:

This study represents the complex terrain of smart transport applications, focusing on the synergistic potential that emerges from the strategic confluence of Machine Learning (ML) and Internet of Things (IoT) methodologies. This review provides insight into how the dynamic nature and large volume of data created by IoT systems make them an excellent environment for the integration of ML approaches by exploring the interplay between these areas. Notably, a wide range of ML algorithms have been reviewed and suggested in the context of smart transportation, with a focus on critical areas such as route optimization, parking management, and accident detection/prevention. A crucial finding from this investigation is the noticeable gap in ML coverage throughout the range of smart lighting systems and parking applications. This highlights the need to refocus on these topics from an ML standpoint, opening the path for future investigation and innovation. This research tackles important topics including sustainability, cost-effectiveness, safety, and time efficiency, highlighting the fascinating possibilities of fusing IoT, ML, and smart mobility. Proactively preventing accidents, expedited parking reservations, cutting-edge street lighting, and accurate route suggestions are just a few benefits of the integration of these technologies. The study does, however, highlight the need for more research, particularly in unexplored areas like parking applications and smart lighting. By bridging these gaps and improving ML and IoT cooperation, smart transportation will be greatly improved and creative solutions for improved urban mobility will be offered.

Keywords: Smart city, Smart transportation, Internet of Things (IoT), Machine Learning applications

1. Introduction

The Internet of Things (IoT) is starting to alter the game by combining technological prowess with social effects to solve significant concerns. It functions as a vast, worldwide network that is ideal for meeting all of human requirements. Through the cooperation of digital and physical links, this complex system produces a world that provides several intelligent services. The most recent developments in information and communication technology have made all of this feasible (ICT). The term “Internet of Things” refers to the harmonic amalgamation of data streams gathered from a pantheon of separate things into a virtual terrain, all handled within the framework of the widespread digital infrastructure that underpins the Internet. As a result, every artefact, regardless of typology, that has the fundamental on/off binary and whose operation is inextricably linked to the enormous expanse of the internet ascends to the echelons of an IoT entity [1].

The integration of the Internet of Things (IoT) and transportation has recently emerged as a critical topic, with major consequences for urban planning, environmental sustainability, and social mobility. Several relevant issues highlight the need for a thorough analysis and evaluation of IoT-enabled smart transportation:

$\bullet$ Technological Advancement: The fast growth of IoT and Machine Learning (ML) technology demands frequent reviews to synthesize new breakthroughs, ensuring that academic and industrial practices stay current.

$\bullet$ Unexplored difficulties and potential: Despite substantial progress, there are still undiscovered difficulties and potential in bringing IoT to transportation. These include issues with data security, scalability, and interaction with existing infrastructure—all of which are critical for the actual deployment of smart transportation systems.

$\bullet$ Emergence of Smart Cities: The global proliferation of smart cities highlights the growing necessity for urban planners and politicians to understand how IoT might enhance transportation—a critical aspect of urban infrastructure.

$\bullet$ Environmental and Social Implications: Addressing transportation's environmental and social implications, such as emissions and accessibility, is critical. While IoT offers possible answers, they require extensive investigation and research.

This article aims to fill these gaps by providing an in-depth study and analysis of current IoT applications in smart transportation, with an emphasis on integration, obstacles, and prospective improvements. The goals are as follows:

$\bullet$ Present a current synthesis of the most recent research in IoT-enabled transportation, offering a condensed overview of current trends and developments.

$\bullet$ Identify and ponder on topics that have gotten scant attention in previous literature, such as the function of IoT in smart lighting and parking in the transportation industry.

$\bullet$ Provide Practical Insights: Provide insights and recommendations that are immediately useful to practitioners, policymakers, and field researchers.

$\bullet$ Set a Research Direction: Propose future research directions, focusing on areas where IoT and ML may further develop smart mobility.

2. Background Work

3. Smart Transportation Systems

4. Applications of Smart Transportation

5. Real-Life Examples of Smart Cities with Smart Transportation Around the Globe

6. Challenges and Future Work

7. Conclusion

This research looks on the successful use of Machine Learning (ML) and the Internet of Things (IoT) in smart transportation. It reveals numerous Machine Learning methods and their applications in areas such as improving traffic flow, smart parking, and accident detection systems utilizing IoT data. One important discovery is the identification of a huge gap in the use of ML, notably in smart lighting systems and parking applications. This disparity indicates the need for more study and development in these areas, giving chances for innovation. These developments have the potential to make cities more efficient, safe, and sustainable.

This study analyzes the successful application of Machine Learning (ML) and the Internet of Things (IoT) in smart transportation. It reveals several ML algorithms and their applications in domains like improving traffic flow, smart parking, and accident detection systems utilizing data from IoT. A noteworthy conclusion is the detection of a considerable gap in implementing ML, notably in smart lighting systems and parking applications. This gap emphasizes the need for greater research and development in these areas, giving chances for innovation. These developments might lead to more efficient, safe, and sustainable urban settings.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

[1] Oladimeji, D., Gupta, K., Kose, N. A., Gundogan, K., Ge, L.Q., Liang, F. (2023). Smart transportation: An overview of technologies and applications. Sensors, 23(8): 3880. [Crossref]

[2] Zantalis, F., Koulouras, G., Karabetsos, S., Kandris, D. (2019). A review of machine learning and IoT in smart transportation. Future Internet, 11(4): 94. [Crossref]

[3] Vangelista, L., Zanella, A., Zorzi, M. (2015). Long-range IoT technologies: The dawn of LoRaTM. In Future Access Enablers for Ubiquitous and Intelligent Infrastructures: First International Conference, FABULOUS 2015, Ohrid, Republic of Macedonia, pp. 51-58. [Crossref]

[4] Zhou, Z.L., Gupta, B. B., Gaurav, A., Li, Y.J., Lytras, M. D., Nedjah, N. (2022). An efficient and secure identity-based signature system for underwater green transport system. IEEE Transactions on Intelligent Transportation Systems, 23(9): 16161-16169. [Crossref]

[5] Liu, C.C., Ke, L. (2023). Cloud assisted internet of things intelligent transportation system and the traffic control system in the smart city. Journal of Control and Decision, 10(2): 174-187. [Crossref]

[6] Wu, Q., Huang, C.C., Wang, S.Y., Chiu, W.C., Chen, T. (2007). Robust parking space detection considering inter-space correlation. In 2007 IEEE International Conference on Multimedia and Expo, Beijing, China, pp. 659-662. [Crossref]

[7] Humayun, M., Almufareh, M. F., Jhanjhi, N. Z. (2022). Autonomous traffic system for emergency vehicles. Electronics, 11(4): 510. [Crossref]

[8] Ahmed, I., Zhang, Y.L., Jeon, G., Lin, W., Khosravi, M.M. R., Qi, L.Y. (2022). A blockchain-and artificial intelligence-enabled smart IoT framework for sustainable city. International Journal of Intelligent Systems, 37(9): 6493-6507. [Crossref]

[9] Swan, M. (2012). Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. Journal of Sensor and Actuator Networks, 1(3): 217-253. [Crossref]

[10] Kandogan, Y., Johnson, S.D. (2016). Role of economic and political freedom in the emergence of global middle class. International Business Review, 25(3): 711-725. [Crossref]

[11] Talari, S., Shafie-Khah, M., Siano, P., Loia, V., Tommasetti, A., Catalão, J.P. (2017). A review of smart cities based on the internet of things concept. Energies, 10(4): 421. [Crossref]

[12] Zhang, J.P., Wang, F.Y., Wang, K.F., Lin, W.H., Xu, X., Chen, C. (2011). Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 12(4): 1624-1639. [Crossref]

[13] Ray, P.P. (2016). A survey of IoT cloud platforms. Future Computing and Informatics Journal, 1(1-2): 35-46. [Crossref]

[14] Mehmood, Y., Ahmad, F., Yaqoob, I., Adnane, A., Imran, M., Guizani, S. (2017). Internet-of-things-based smart cities: Recent advances and challenges. IEEE Communications Magazine, 55(9): 16-24. [Crossref]

[15] Eisele, W.L., Fossett, T., Schrank, D.L., Farzaneh, M., Meier, P.J., Williams, S.P. (2014). Greenhouse gas emissions and urban congestion: Incorporation of carbon dioxide emissions and associated fuel consumption into Texas A&M Transportation Institute urban mobility report. Transportation Research Record, 2427(1): 73-82. [Crossref]

[16] Allen, H., Millard, K., Stonehill, M. (2013). A summary of the proceedings from the United Nations climate change conference in Doha Qatar and their significance for the land transport sector. In Proceedings of the Copenhagen: Bridging the Gap (BTG) Initiative, Doha, Qatar.

[17] Nasim, R., Kassler, A. (2012). Distributed architectures for intelligent transport systems: A survey. In Proceedings of the 2012 Second Symposium on Network Cloud Computing and Applications, London, pp. 130-136. [Crossref]

[18] McGregor, R.V., Eng, P., MacIver, A. (2003). Regional its architectures—From policy to project implementation. In Proceedings of the Transportation Factor 2003, Annual Conference and Exhibition of the Transportation Association of Canada, (Congres et Exposition Annuels de l’Association des transport du Canada) Transportation Association of Canada (TAC), St. John’s, NL, Canada, pp. 21-24.

[19] Guerrero-Ibanez, J.A., Zeadally, S., Contreras-Castillo, J. (2015). Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies. IEEE Wireless Communications, 22(6): 122-128. [Crossref]

[20] Mohammed, M., Khan, M.B., Bashier, E.B.M. (2016). Machine Learning: Algorithms and Applications. CRC Press.

[21] Huang, C.C., Wang, S.J. (2010). A hierarchical Bayesian generation framework for vacant parking space detection. IEEE Transactions on Circuits and Systems for Video Technology, 20(12): 1770-1785. [Crossref]

[22] Devi, S., Neetha, T. (2017). Machine Learning based traffic congestion prediction in a IoT based Smart City. International Research Journal of Engineering and Technology, 4(5): 3442-3445.

[23] Hou, Y., Edara, P., Sun, C. (2015). Traffic flow forecasting for urban work zones. IEEE Transactions on Intelligent Transportation Systems, 16(4): 1761-1770. [Crossref]

[24] Ghadge, M., Pandey, D., Kalbande, D. (2015). Machine learning approach for predicting bumps on road. In Proceedings of the 2015 IEEE International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Davangere, India, pp. 481-485. [Crossref]

[25] Ng, J.R., Wong, J.S., Goh, V.T., Yap, W.J., Yap, T.T.V., Ng, H. (2019). Identification of road surface conditions using IoT sensors and machine learning. In Computational Science and Technology: 5th ICCST 2018, Kota Kinabalu, Malaysia, pp. 259-268. [Crossref]

[26] Dogru, N., Subasi, A. (2018). Traffic accident detection using random forest classifier. In Proceedings of the 2018 15th Learning and Technology Conference (L&T), Jeddah, Saudi Arabia, pp. 40-45. [Crossref]

[27] Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., Vairo, C. (2017). Deep learning for decentralized parking lot occupancy detection. Expert Systems with Applications, 72: 327-334. [Crossref]

[28] Kanoh, H., Furukawa, T., Tsukahara, S., Hara, K., Nishi, H., Kurokawa, H. (2005). Short-term traffic prediction using fuzzy c-means and cellular automata in a wide-area road network. In Proceedings of the 2005 IEEE Intelligent Transportation Systems, Vienna, Austria, pp. 381-385. [Crossref]

[29] Almeida, P.R.D., Oliveira, L.S., Britto, A.S., Silva, E.J., Koerich, A.L. (2015). PKLot—A robust dataset for parking lot classification. Expert Systems with Applications, 42(11): 4937-4949. [Crossref]

[30] 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 of the 2016 IEEE International Conference on Big Data, Washington, DC, USA, pp. 1807-1813. [Crossref]

[31] Fusco, G., Colombaroni, C., Comelli, L., Isaenko, N. (2015). Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models. In Proceedings of the 2015 IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Budapest, Hungary, pp. 93-101. [Crossref]

[32] Yu, J., Chang, G.L., Ho, H.W., Liu, Y. (2008). Variation based online travel time prediction using clustered neural networks. In Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China, pp. 85-90. [Crossref]

[33] Kulkarni, A., Mhalgi, N., Gurnani, S., Giri, N. (2014). Pothole detection system using machine learning on Android. International Journal of Emerging Technology and Advanced Engineering, 4(7): 360-364

[34] Yang, J.C., Han, Y.R., Wang, Y.F., Jiang, B., Lv, Z.H., Song, H.B. (2017). Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city. Future Generation Computer Systems, 108: 976-986. [Crossref]

[35] Lannez, S., Foggia, G., Muscholl, M., Passelergue, J.C., Lebosse, C., Mercier, K. (2013). Nice Grid: Smart grid pilot demonstrating innovative distribution network operation. In 2013 IEEE Grenoble Conference, Grenoble, France, pp. 1-5. [Crossref]

[36] Sheth, A. (2018). History of machine learning. https://medium.com/bloombench/history-of-machine-learning- 7c9dc67857a5, accessed on 13 August 2022.

[37] Ang, L.M., Seng, K.P., Ijemaru, G. K., Zungeru, A.M. (2018). Deployment of IoV for smart cities: Applications, architecture, and challenges. IEEE Access, 7: 6473-6492.

[38] Kostrzewski M, Marczewska M, Uden L. The Internet of Vehicles and Sustainability—Reflections on Environmental, Social, and Corporate Governance. Energies, 16(7): 3208. [Crossref]

[39] Pinelis, M. (2013). Automotive sensors and electronics: Trends and developments in 2013. In Proceedings of the Automotive Sensors and Electronics Expo; Detroit, MI, USA.

[40] Sumorek A., Buczaj, M. (2012). New elements in vehicle communication “media oriented systems transport” protocol. Teka. Commission of Motorization Energetics Agriculture, 12(1): 275-279.

[41] Gohar, A., Nencioni, G. (2021). The role of 5G technologies in a smart city: The case for intelligent transportation system. Sustainability, 13(9): 5188. [Crossref]

[42] Camacho, F., Cárdenas, C., Muñoz, D. (2018). Emerging technologies and research challenges for intelligent transportation systems: 5G, HetNets, and SDN. International Journal on Interactive Design and Manufacturing (IJIDeM), 12: 327-335. [Crossref]

[43] Teece, D.J. (2017). 5G Mobile: Disrupting the Automotive Sector. UC Berkeley, Berkeley, CA, USA.

[44] Celesti, A., Galletta, A., Carnevale, L., Fazio, M., Ĺay-Ekuakille, A., Villari, M. (2018) An IoT cloud system for traffic monitoring and vehicular accidents prevention based on mobile sensor data processing. IEEE Sensors Journal, 18(12): 4795-4802. [Crossref]

[45] Jimenez, J.A. (2018). Smart transportation systems. In: McClellan, S., Jimenez, J., Koutitas, G. (eds) Smart Cities. Springer, Cham. [Crossref]

[46] Gupta, M., Benson, J., Patwa, F., Sandhu, R. (2020). Secure V2V and V2I communication in intelligent transportation using cloudlets. IEEE Transactions on Services Computing, 15(4): 1912-1925. [Crossref]

[47] Singh, A., Kumar, M., Rishi, R., Madan, D.K. (2011). A relative study of MANET and VANET: Its applications, broadcasting approaches and challenging issues. In Proceedings of the International Conference on Computer Science and Information Technology, Bangalore, India, pp. 627-632. [Crossref]

[48] Al-Dweik, A., Muresan, R., Mayhew, M., Lieberman, M. (2017). IoT-based multifunctional scalable real-time enhanced road side unit for intelligent transportation systems. In Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, Ontario, Canada, pp. 1-6. [Crossref]

[49] Barth, D. (2009). The bright side of sitting in traffic: Crowdsourcing Road congestion data. https://googleblog.blogspot.com/2009/08/bright-side-of-sitting- in-traffic.html, accessed on 22 August 2022.

[50] Afidis, P., Teixeira, J., Bahmankhah, B., Macedo, E., Coelho, M.C., Bandeira, J. (2017). Exploring crowd-sourcing information to predict traffic-related impacts. In Proceedings of the 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Milan, Italy, pp. 1-6. [Crossref]

[51] Distefano, S., Merlino, G., Puliafito, A., Cerotti, D., Dautov, R. (2017). Crowdsourcing and stigmergic approaches for (Swarm) intelligent transportation systems. In Proceedings of the International Conference on Human Centered Computing, Kazan, Russia, pp. 616-626. [Crossref]

[52] Fan, X.Y., Liu, J.C., Wang, Z., Jiang, Y., Liu, X. (2017). Crowdsourced road navigation: Concept, design, and implementation. EEE Communications Magazine, 55(6): 126-128. [Crossref]

[53] Tubaishat, M., Shang, Y., Shi, H.C. (2007). Adaptive traffic light control with wireless sensor networks. In Proceedings of the 2007 4th IEEE Consumer Communications and Networking Conference, Las Vegas, NV, USA, pp. 187-191.

[54] Cha, Y.J.; Choi, W.; Büyüköztürk, O. (2017). Deep learning-based crack damage detection using convolu- tional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5): 361-378. [Crossref]

[55] Gopalakrishnan, K., Gholami, H., Vidyadharan, A., Choudhary, A., Agrawal, A. (2018). Crack damage detection in unmanned aerial vehicle images of civil infrastructure using pre-trained deep learning model. International Journal for Traffic and Transport Engineering, 8(1): 1-14. [Crossref]

[56] Kokilavani, M.; Malathi, A. (2017). Smart street lighting system using IoT. Journal of Advanced Research in Applied Science and Technology, 3: 8-11.

[57] Jia, G.Y., Han, G.J., Li, A.H., Du, J.X. (2018). SSL: Smart street lamp based on fog computing for smarter cities. IEEE Transactions on Industrial Informatics, 14(11): 4995-5004. [Crossref]

[58] Tripathy, A.K., Mishra, A.K., Das, T.K. (2017). Smart lighting: Intelligent and weather adaptive lighting in street lights using IoT. In Proceedings of the 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India, pp. 1236-1239. [Crossref]

[59] Neyestani, N., Damavandi, M.Y., Shafie-khah, M., Catalão, J.P.S. (2015). Modeling the PEV traffic pattern in an urban environment with parking lots and charging stations. In Proceedings of the 2015 IEEE Eindhoven PowerTech, Eindhoven, Netherlands, pp. 1-6. [Crossref]

[60] Yazdani-Damavandi, M., Moghaddam, M.P., Haghifam, M.R., Shafie-khah, M., Catalão, J.P.S. (2015). Modeling operational behavior of plug-in electric vehicles’ parking lot in multienergy systems. IEEE Transactions on Smart Grid, 7(1): 124-135. [Crossref]

[61] Neyestani, N., Damavandi, M.Y., Shafie-Khah, M., Contreras, J.; Catalão, J.P.S. (2015). Allocation of plug-in vehicles’ parking lots in distribution systems considering network-constrained objectives. IEEE Transactions on Power Systems, 30(5): 2643-2656. [Crossref]

[62] Amsterdam Smart City. (2017). https://amsterdamsmartcity.com/, accessed on 24 February 2017.

[63] Hancke, G.P., Silva, B.D.C.e., Hancke, Jr.G.P. The role of advanced sensing in smart cities. Sensors, 13(1): 393-425. [Crossref]

[64] Chang, I.C., Tai, H.T., Yeh, F.H., Hsieh, D.L., Chang, S.H. (2013). A VANET-based A* route planning algorithm for travelling time-and energy-efficient GPS navigation app. International Journal of Distributed Sensor Networks, 9(7): 794521. [Crossref]

[65] Global Infrastructure Hub (2020). Vehicle to vehicle (V2V) connectivity. https://www.gihub.org/infrastructuretechnology-use-cases/case-studies/vehicle-to-vehicle-v2v-connectivity/, accessed on 10 July 2022.

[66] Servei de Premsa (2017). El Web de la Ciutat de Barcelona. http://ajuntament. barcelona.cat/premsa/tag/smart-city/, accessed on 24 February 2017.

[67] Michiorri, A., Girard, R., Kariniotakis, G., Lebossé, C., Albou, S. (2012). A local energy management system for solar integration and improved security of supply: The Nice Grid project. In 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Berlin, Germany, pp. 1-6. [Crossref]

[68] Al Mamun, M.A., Puspo, J.A., Das, A.K. (2017). An intelligent smartphone based approach using IoT for ensuring safe driving. In Proceedings of the 2017 IEEE International Conference on Electrical Engineering and Computer Science (ICECOS), Palembang, Indonesia, pp. 217-223. [Crossref]

[69] Shukla, S., Balachandran, K., Sumitha, V. (2016). A framework for smart transportation using Big Data. In Proceedings of the 2016 International Conference on ICT in Business Industry & Government (ICTBIG), Indore, India, pp. 1-3. [Crossref]

[70] Daniel, S., Doran, M.A. (2013). geoSmartCity: Geomatics contribution to the smart city. In Proceedings of the 14th Annual International Conference on Digital Government Research, New York, USA, pp. 65-71. [Crossref]

[71] Azgomi, H.F., Jamshidi, M. (2018). A brief survey on smart community and smart transportation. In Proceedings of the 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece, pp. 932-939. [Crossref]

[72] Pop, M.D., Pandey, J., Ramasamy, V. (2020). Future networks 2030: Challenges in intelligent transportation systems. In Proceedings of the 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, pp. 898-902. [Crossref]

[73] Datta, S.K., Da Costa, R.P.F., Härri, J., Bonnet, C. (2016). Integrating connected vehicles in Internet of Things ecosystems: Challenges and solutions. In Proceedings of the 2016 IEEE 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Coimbra, Portugal, pp. 1-6. [Crossref]

[74] Narayanan, S., Chaniotakis, E., Antoniou, C. (2020). Shared autonomous vehicle services: A comprehensive review. Transportation Research Part C: Emerging Technologies, 111: 255–293.

[75] Lee, S., Yoon, D., Ghosh, A. (2008). Intelligent parking lot application using wireless sensor networks. In Proceedings of the 2008 International Symposium on Collaborative Technologies and Systems, Irvine, CA, USA, pp. 48-57. [Crossref]

[76] Munoz-Organero, M., Ruiz-Blaquez, R., Sa´nchez-Ferna´ndez, 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. Computers, Environment, Urban Systems, 68: 1-8. [Crossref]

[77] Ajay, P., Nagaraj, B., Pillai, B. M., Suthakorn, J., Bradha, M. (2022). Intelligent ecofriendly transport management system based on IoT in urban areas. Environment, Development and Sustainability, pp. 1-8. [Crossref]

[78] Wu, Q., Huang, C., Wang, S.Y, Chiu, W.C., Chen, T. (2007). Robust parking space detection considering inter-space correlation. In 2007 IEEE International Conference on Multimedia and Expo, Beijing, China, pp. 659-662. [Crossref]

[79] Sastre, R., Gil Jimenez, P., Acevedo, F., Maldonado Bascon, S. (2007). Computer algebra algorithms applied to computer vision in a parking management system. In 2007 IEEE International Symposium on Industrial Electronics, Vigo, Spain, pp. 1675-1680. [Crossref]

[80] Bong, D.B.L., Ting, K.C., Lai, K.C. (2008). Integrated approach in the design of car park occupancy information system. IAENG International Journal of Computer Science, 35(1): 1-8.

[81] Huang, C.C., Tai, Y.S., Wang, S.J. (2013). Vacant parking space detection based on plane-based Bayesian hierarchical framework. IEEE Transactions on Circuits and Systems for Video Technology, 23(9): 1598-1610. [Crossref]

[82] Ichihashi, H., Notsu, A., Honda, K., Katada, T., Fujiyoshi, M. (2009). Vacant parking space detector for outdoor parking lot by using surveillance camera and FCM classifier. In 2009 IEEE International Conference on Fuzzy Systems, Jeju, Korea (South), pp. 127-134. [Crossref]

[83] Strickland, E. (2011). Cisco bets on South Korean smart city. IEEE Spectrum, 48(8): 11-12. [Crossref]

[84] Gangwani, D., Gangwani, P. (2021). Applications of machine learning and artificial intelligence in intelligent transportation system: A review. In: Choudhary, A., Agrawal, A.P., Logeswaran, R., Unhelkar, B. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 778. Springer, Singapore. [Crossref]

Cite this:

APA Style

IEEE Style

BibTex Style

MLA Style

Chicago Style

GB-T-7714-2015

Mukhopadhyay, S., Kumar, A., Gupta, J., Bhatnagar, A., Kantipudi, M. V. V. P., & Singh, M. (2024). A Review and Analysis of IoT Enabled Smart Transportation Using Machine Learning Techniques. Int. J. Transp. Dev. Integr., 8(1), 61-77. https://doi.org/10.18280/ijtdi.080106

pdf

Citations