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Enhancing Public Transit System Through AI and IoT

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  • Naveen Vemuri,
  • Venkata Manoj Tatikonda,
  • Naresh Thaneeru
Naveen Vemuri
Masters in Computer Science, Silicon Valley University, Bentonville, AR, USA
Venkata Manoj Tatikonda
Masters in Computer Science, Silicon Valley University, Bentonville, AR, USA
Naresh Thaneeru
Masters in Computer Applications, Kakatiya University, Bentonville, AR, USA
Published in : International Journal of Scientific Research and Management (IJSRM) , Vol. 12 No. 02 (2024)

Published 2024-02-22

Keywords

  • Transit System

Copyright (c) 2024 Naveen Vemuri, Venkata Manoj Tatikonda, Naresh Thaneeru

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The paper investigates the synergy between AI and IoT in revolutionizing public transit systems, emphasizing their role in addressing existing challenges and improving overall efficiency. It explores the application of IoT devices in creating a smart infrastructure that enables real-time data collection, monitoring, and management of transit operations.

The research delves into the realm of predictive maintenance, showcasing how AI algorithms, powered by IoT data, can anticipate potential issues in transit vehicles. By analyzing sensor data, transit authorities can proactively address maintenance needs, minimizing downtime, reducing repair costs, and ensuring a more reliable and sustainable public transit service.

Route optimization emerges as another crucial aspect of the study, highlighting how AI algorithms leverage historical and real-time data to recommend the most efficient transit routes. Factors such as traffic patterns, weather conditions, and passenger demand are considered to enhance overall system efficiency and reduce travel time for passengers.

The paper introduces the concept of dynamic scheduling, illustrating how AI-driven algorithms adapt transit schedules in real-time based on changing passenger needs and external factors. This dynamic approach aims to provide more responsive services, ultimately reducing wait times and improving overall user satisfaction.

Passenger information systems are explored as a pivotal component, illustrating how AI and IoT technologies enhance the passenger experience. Real-time communication through mobile apps, digital displays, and other channels ensures that passengers have accurate and timely information about arrival times, delays, and alternative routes, empowering them to make informed decisions.

The researchers also delve into fare optimization, examining how AI algorithms analyze data on passenger demographics, travel patterns, and economic factors to create fair and affordable fare structures. This approach aims to encourage ridership, increase revenue, and improve the financial sustainability of public transit systems.

The abstract presents a comprehensive overview of how the integration of AI and IoT technologies in public transit systems transforms urban mobility. The findings suggest that leveraging real-time data, predictive analytics, and dynamic solutions can significantly enhance the reliability, accessibility, and sustainability of public transit. As cities continue to explore innovative solutions, the abstract serves as a roadmap for developing smarter, user-friendly, and efficient urban transportation networks, ultimately contributing to improved quality of life for residents.

 

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References

  1. Jevinger, Å., Zhao, C., Persson, J. A., & Davidsson, P. (2023). Artificial intelligence for improving public transport: a mapping study. Public Transport, 1-60.
  2. Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189.
  3. Solihati, K. D., & Indriyani, D. (2021, March). Managing Artificial Intelligence on Public Transportation (Case Study Jakarta City, Indonesia). In IOP Conference Series: Earth and Environmental Science (Vol. 717, No. 1, p. 012021). IOP Publishing.
  4. Chang, H. C., Okubo, T., Kobayashi, A., & Morimoto, A. (2022). Artificial Intelligence (AI) Applications Using Big Data and Survey Data for Exploring the Existence of the Potential Users of Public Transportation System. International Journal of Information & Management Sciences, 33(4).
  5. Mahor, V., Rawat, R., Kumar, A., Garg, B., & Pachlasiya, K. (2023). IoT and artificial intelligence techniques for public safety and security. In Smart urban computing applications (pp. 111-126). River Publishers.
  6. Singh, P., Elmi, Z., Lau, Y. Y., Borowska-Stefańska, M., Wiśniewski, S., & Dulebenets, M. A. (2022). Blockchain and AI technology convergence: Applications in transportation systems. Vehicular Communications, 100521.
  7. Henman, P. (2020). Improving public services using artificial intelligence: possibilities, pitfalls, governance. Asia Pacific Journal of Public Administration, 42(4), 209-221.
  8. Gaur, L., & Sahoo, B. M. (2022). Introduction to Explainable AI and Intelligent Transportation. In Explainable Artificial Intelligence for Intelligent Transportation Systems: Ethics and Applications (pp. 1-25). Cham: Springer International Publishing.
  9. Navarathna, P. J., & Malagi, V. P. (2018, December). Artificial intelligence in smart city analysis. In 2018 International conference on smart systems and inventive technology (ICSSIT) (pp. 44-47). IEEE.
  10. Mnyakin, M. (2023). Applications of AI, IoT, and Cloud Computing in Smart Transportation: A Review. Artificial Intelligence in Society, 3(1), 9-27.
  11. Guerrero-Ibáñez, J., Zeadally, S., & Contreras-Castillo, J. (2018). Sensor technologies for intelligent transportation systems. Sensors, 18(4), 1212.
  12. Al Shebli, K., Said, R. A., Taleb, N., Ghazal, T. M., Alshurideh, M. T., & Alzoubi, H. M. (2021, May). RTA’s employees’ perceptions toward the efficiency of artificial intelligence and big data utilization in providing smart services to the residents of Dubai. In The International Conference on Artificial Intelligence and Computer Vision (pp. 573-585). Cham: Springer International Publishing.
  13. Mhlanga, D. (2023). Artificial Intelligence and Machine Learning in Making Transport, Safer, Cleaner, More Reliable, and Efficient in Emerging Markets. FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals, 193-211.
  14. Pelleti, S., Bains, S., Bansal, A., Muda, I., Chowdhary, H., & Mahajan, Y. (2022, December). Management Information System based on Artificial Intelligence Technology. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) (pp. 2007-2012). IEEE.
  15. Samim, A. (2023). Analysis of Artificial Intelligence in Traffic Congestion and Management System. International Journal of Science and Research (IJSR), 12(1), 679-684.
  16. Fazelpour, F., Vafaeipour, M., Rahbari, O., & Rosen, M. A. (2014). Intelligent optimization to integrate a plug-in hybrid electric vehicle smart parking lot with renewable energy resources and enhance grid characteristics. Energy Conversion and Management, 77, 250-261.
  17. Abdulrazzaq Oraibi, W., Mohammadi-Ivatloo, B., Hosseini, S. H., & Abapour, M. (2023). Multi Microgrid Framework for Resilience Enhancement Considering Mobile Energy Storage Systems and Parking Lots. Applied Sciences, 13(3), 1285.
  18. Kour, S. P., Sharma, P., & Jalal, M. (2022, October). Artificial Intelligence in Transport-A Survey. In 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT) (pp. 1-6). IEEE.
  19. Heidari, A., Navimipour, N. J., & Unal, M. (2022). Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review. Sustainable Cities and Society, 104089.
  20. He, R., Molisch, A. F., Tufvesson, F., Zhong, Z., Ai, B., & Zhang, T. (2014). Vehicle-to-vehicle propagation models with large vehicle obstructions. IEEE Transactions on Intelligent Transportation Systems, 15(5), 2237-2248.
  21. Guerrero‐Ibañez, J., Contreras‐Castillo, J., & Zeadally, S. (2021). Deep learning support for intelligent transportation systems. Transactions on Emerging Telecommunications Technologies, 32(3), e4169.
  22. Madhavan, B. R. (2022). Traffic Flow Rate Determination & Parking Efficiency Improvement Using Artificial Intelligence/Computer Vision (Doctoral dissertation, University of Toledo).
  23. Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government information quarterly, 36(2), 358-367.
  24. Olugbade, S., Ojo, S., Imoize, A. L., Isabona, J., & Alaba, M. O. (2022). A review of artificial intelligence and machine learning for incident detectors in road transport systems. Mathematical and Computational Applications, 27(5), 77.
  25. Efthymiou, I. P., & Egleton, T. E. (2023). Artificial Intelligence for Sustainable Smart Cities. In Handbook of Research on Applications of AI, Digital Twin, and Internet of Things for Sustainable Development (pp. 1-11). IGI Global.
  26. Chen, X., Liu, Z., & Currie, G. (2016). Optimizing location and capacity of rail-based Park-and-Ride sites to increase public transport usage. Transportation Planning and Technology, 39(5), 507-526.
  27. Al-Turjman, F., Salama, R., & Altrjman, C. (2023). Overview of IoT Solutions for Sustainable Transportation Systems. NEU Journal for Artificial Intelligence and Internet of Things, 2(3).
  28. Jha, S. B., Jha, J. K., & Tiwari, M. K. (2019). A multi-objective meta-heuristic approach for transit network design and frequency setting problem in a bus transit system. Computers & Industrial Engineering, 130, 166-186.
  29. Berryhill, J., Heang, K. K., Clogher, R., & McBride, K. (2019). Hello, World: Artificial intelligence and its use in the public sector.
  30. Weligamage, H. D., Wijesekara, S. M., Chathwara, M. D. S., Kavinda, H. I., Amarasena, N., & Gamage, N. (2022, October). An Approach of Enhancing the Quality of Public Transportation Service in Sri Lanka using IoT. In 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 0311-0316). IEEE.
  31. Nikitas, A., Michalakopoulou, K., Njoya, E. T., & Karampatzakis, D. (2020). Artificial intelligence, transport and the smart city: Definitions and dimensions of a new mobility era. Sustainability, 12(7), 2789.
  32. Shaheen, M., Arshad, M., & Iqbal, O. (2020). Role and Key Applications of Artificial Intelligence & Machine Learning in Transportation. European Journal of Technology, 4(1), 47-59.
  33. Herath, H. M. K. K. M. B., & Mittal, M. (2022). Adoption of artificial intelligence in smart cities: A comprehensive review. International Journal of Information Management Data Insights, 2(1), 100076.
  34. Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications policy, 44(6), 101976.
  35. Daganzo, C. F. (2005). Improving city mobility through gridlock control: an approach and some ideas.
  36. Shladover, S. E. (2018). Connected and automated vehicle systems: Introduction and overview. Journal of Intelligent Transportation Systems, 22(3), 190-200.
  37. Russo, F., & Comi, A. (2021). Sustainable urban delivery: the learning process of path costs enhanced by information and communication technologies. Sustainability, 13(23), 13103.
  38. Mozur, P. (2018). Inside China's dystopian dreams: AI, shame and lots of cameras. International New York Times, NA-NA.
  39. Rammohan, A. (2023). Revolutionizing Intelligent Transportation Systems with Cellular Vehicle-to-Everything (C-V2X) technology: Current trends, use cases, emerging technologies, standardization bodies, industry analytics and future directions. Vehicular Communications, 100638.
  40. Zhang, J., Wang, F. Y., Wang, K., 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.
  41. Hager, G. D., Drobnis, A., Fang, F., Ghani, R., Greenwald, A., Lyons, T., ... & Tambe, M. (2019). Artificial intelligence for social good. arXiv preprint arXiv:1901.05406.
  42. Deveci, M., Mishra, A. R., Gokasar, I., Rani, P., Pamucar, D., & Özcan, E. (2022). A decision support system for assessing and prioritizing sustainable urban transportation in metaverse. IEEE Transactions on Fuzzy Systems, 31(2), 475-484.
  43. Pani, A., & Mourya, S. A REVIEW ON SMART AND INTELLIGENT E-GOVERNANCE.
  44. Kharche, A., Badholia, S., & Upadhyay, R. K. (2024). Implementation of blockchain technology in integrated IoT networks for constructing scalable ITS systems in India. Blockchain: Research and Applications, 100188.
  45. Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), 1473.
  46. Alotaibi, O., & Potoglou, D. (2018). Introducing public transport and relevant strategies in Riyadh City, Saudi Arabia: A stakeholders’ perspective. Urban, Planning and Transport Research, 6(1), 35-53.
  47. Qin, X., Ke, J., Wang, X., Tang, Y., & Yang, H. (2022). Demand management for smart transportation: A review. Multimodal Transportation, 1(4), 100038.
  48. Morrison, S. A., Winston, C., Bailey, E. E., & Kahn, A. E. (1989). Enhancing the performance of the deregulated air transportation system. Brookings Papers on Economic Activity. Microeconomics, 1989, 61-123.
  49. Lee, H., Chatterjee, I., & Cho, G. (2023, August). Enhancing Parking Facility of Container Drayage in Seaports: A Study on Integrating Computer Vision and AI. In 2023 IEEE 6th International Conference on Knowledge Innovation and Invention (ICKII) (pp. 384-387). IEEE.
  50. Chan, N. D., & Shaheen, S. A. (2012). Ridesharing in North America: Past, present, and future. Transport reviews, 32(1), 93-112.
  51. Topolski, M., Topolska, K., Janicki, M., & Kolanek, C. (2016). The management of urban parking LOTS. Archives of Transport System Telematics, 9.
  52. Alsamhi, S. H., Ma, O., Ansari, M. S., & Almalki, F. A. (2019). Survey on collaborative smart drones and internet of things for improving smartness of smart cities. Ieee Access, 7, 128125-128152.
  53. Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019). A review of machine learning and IoT in smart transportation. Future Internet, 11(4), 94.
  54. Ma, Y., Wang, Z., Yang, H., & Yang, L. (2020). Artificial intelligence applications in the development of autonomous vehicles: A survey. IEEE/CAA Journal of Automatica Sinica, 7(2), 315-329.
  55. Sriratnasari, S. R., Wang, G., Kaburuan, E. R., & Jayadi, R. (2019, August). Integrated Smart Transportation using IoT at DKI Jakarta. In 2019 International Conference on Information Management and Technology (ICIMTech) (Vol. 1, pp. 531-536). IEEE.
  56. Singh, S. K., Rathore, S., & Park, J. H. (2020). Blockiotintelligence: A blockchain-enabled intelligent IoT architecture with artificial intelligence. Future Generation Computer Systems, 110, 721-743.
  57. Tyagi, A. K., & Sreenath, N. (2022). Management and Impact of COVID-19 on Intelligent Transportation System. In Intelligent Transportation Systems: Theory and Practice (pp. 305-325). Singapore: Springer Nature Singapore.
  58. Zhang, S., Chen, J., Lyu, F., Cheng, N., Shi, W., & Shen, X. (2018). Vehicular communication networks in the automated driving era. IEEE Communications Magazine, 56(9), 26-32.
  59. Saravanan, M., Devipriya, R., Sakthivel, K., Sujith, J. G., Saminathan, A., & Vijesh, S. (2022, December). Optimized Load Balancing and Routing Using Machine Learning Approach in Intelligent Transportation Systems: A Survey. In International Conference on Hybrid Intelligent Systems (pp. 929-939). Cham: Springer Nature Switzerland.