Skip to main content
SpringerLink
Log in

Sensor Based Smart Railway Accident Detection and Prevention System for Smart Cities Using Real Time Mobile Communication

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Recent advances in various technologies have introduced effective new systems that can be successfully deployed in various future rail operations and applications, including smart city security functions. Rail accidents are a major problem in the transportation industry in many countries around the world. There is an urgent need to install protective elements to prevent accidents. This research describes the design and implementation of the rail safety system for smart cities using real-time mobile communication. The proposed prototype system has two basic functions to operate, namely accident detection and accident prevention. The proposed system consists of several sensors, LTE module, micro-controller, motorized gate and various displays for traffic control. The detection of the train is tracked via round trip time of the ultrasonic sensor and a micro-controller is used together with a GPS module and an LTE module to detect accidents. Hence there is two-way communication between the train and the control room. The proposed system is implemented by controlling the automated doors according to the reading of the sensors. When the sensors detect the movement of the rail, they can indicate this in real time through a buzzer and the message, which will eventually close the doors installed at the level crossing. In addition, the coordinates of the rail are transmitted via the GPS module, the GPS module continuously monitors the location, speed and time of the car. Therefore, the stated goal is achieved through real-time two-way communication between the control room and the rail via LTE and GPS modules. In this research, a prototype is designed and tested in real time for various scenarios to demonstrate the effectiveness of the proposed system. In the end, this research provides the simulation results to substantiate our claim to the reliability and importance of the proposed system for the implementation in the smart cities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data Availibility

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Code Availability

The code developed during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. News. (2019). Emedia (vol. x, pp. 2–4). https://www.thenews.com.pk/print/514587-govt-taking-steps-to-upgrade-vulnerable-railway-level-crossings.

  2. UIC. (2019). Data from internet (vol. x, pp. 2–4). https://uic.org/img/pdf/worldwide-rail-transport-regional-share2017.pdf.

  3. Mowla, M. M., Ahmad, I., Habibi, D., & Phung, Q. V. (2017). A green communication model for 5G systems. IEEE Transactions on Green Communications and Networking, 1, 264–280.

    Article  Google Scholar 

  4. Thenews. (2019). Electronicmedia (vol. x, pp. 1–3). https://www.thenews.com.pk/print/397393-pakistan-railways-in-100-days-10-new-trains-16-deaths.

  5. Behnad, A., & Wang, X. (2017). Virtual small cells formation in 5G networks. IEEE Communications Letters, 21, 616–619.

    Article  Google Scholar 

  6. Report. (2019). Enews (vol. x, pp. 2–5). https://uic.org/com/uic-e-news/623/article/international-union-of-railways-uic-issues-yearly-report-2018-on-railwaypage=modalenews.

  7. Dzagbletey, P. A., & Jung, Y. (2018). Stacked microstrip linear array for millimeter-wave 5G baseband communication. IEEE Antennas and Wireless Propagation Letters, 17, 780–783.

    Article  Google Scholar 

  8. Li, X., Alam, K. M., & Wang, S. (2018). Trend analysis of Pakistan railways based on industry life cycle theory. Journal of Advanced Transportation, 2018, 1–11.

    Google Scholar 

  9. Al Wadhahi, N. T. S., Hussain, S. M., Yosof, K. M., Hussain, S. A., & Singh, A. V. (2018). Accidents detection and prevention system to reduce traffic hazards using IR sensors. In 2018 7th International conference on reliability, Infocom technologies and optimization (trends and future directions)(ICRITO) (pp. 737–741). IEEE.

  10. García, J. J., Hernandez, A., Urena, J., & Garcia, E. (2016). Fpga-based architecture for a multisensory barrier to enhance railway safety. IEEE Transactions on Instrumentation and Measurement, 65(6), 1352–1363.

    Article  Google Scholar 

  11. Morocho-Cayamcela, M. E., Lee, H., & Lim, W. (2019). Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions. IEEE Access, 7, 137184–137206.

    Article  Google Scholar 

  12. Amin, M. S., Jalil, J., & Reaz. (2012). Accident detection and reporting system using GPS, GPRS and GSM technology. In 2012 International conference on informatics, electronics and vision (ICIEV) (pp. 640–643). IEEE.

  13. Chettri, L., & Bera, R. (2020). A comprehensive survey on internet of things (IoT) toward 5G wireless systems. IEEE Internet of Things Journal, 7, 16–32.

    Article  Google Scholar 

  14. Ahmed, A., Noor, K. R., Imteaj, A., & Rahman, T. (2018). Unmanned multiple railway gates controlling and bi-directional train tracking with alarming system using principles of IoT. In 2018 International conference on innovations in science, engineering and technology (ICISET) (pp. 486–491). IEEE.

  15. Zhang, L., Zhao, H., Hou, S., Zhao, Z., Xu, H., Wu, X., et al. (2019). A survey on 5G millimeter wave communications for UAV-assisted wireless networks. IEEE Access, 7, 117460–117504.

    Article  Google Scholar 

  16. Amarnatha, R. E., et al. (2017). A secure railway crossing system using IoT. 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA). (Vol. 2, pp. 196–199). IEEE

  17. Ahmad, W. S. H. M. W., Radzi, N. A. M., Samidi, F. S., Ismail, A., Abdullah, F., Jamaludin, M. Z., & Zakaria, M. N. (2020). 5G technology: Towards dynamic spectrum sharing using cognitive radio networks. IEEE Access, 8, 14460–14488.

    Article  Google Scholar 

  18. Chen, R., Long, W.-X., Mao, G., & Li, C. (2018). Development trends of mobile communication systems for railways. IEEE Communications Surveys & Tutorials, 20(4), 3131–3141.

    Article  Google Scholar 

  19. Ghosh, A., Maeder, A., Baker, M., & Chandramouli, D. (2019). 5G evolution: A view on 5G cellular technology beyond 3GPP release 15. IEEE Access, 7, 127639–127651.

    Article  Google Scholar 

  20. Oo, H. M., San Hlaing, N. N., & Oo, T. T. (2019). Four IR sensor based automatic control of railway gate using microcontroller. IEEE.

    Google Scholar 

  21. Wu, C., Wang, C., Sheng, J., & Wang, Y. (2019). Cooperative learning for spectrum management in railway cognitive radio network. IEEE Transactions on Vehicular Technology, 68, 5809–5819.

    Article  Google Scholar 

  22. Liu, Y., Wang, C., Huang, J., Sun, J., & Zhang, W. (2019). Novel 3-D nonstationary mmwave massive MIMO channel models for 5G high-speed train wireless communications. IEEE Transactions on Vehicular Technology, 68, 2077–2086.

    Article  Google Scholar 

  23. Liu, Y., Li, C., Xia, X., Quan, X., Liu, D., Xu, Q., et al. (2019). Multiband user equipment prototype hardware design for 5G communications in sub-6-GHz band. IEEE Transactions on Microwave Theory and Techniques, 67, 2916–2927.

    Article  Google Scholar 

  24. Sim, G. H., Klos, S., Asadi, A., Klein, A., & Hollick, M. (2018). An online context-aware machine learning algorithm for 5G mmwave vehicular communications. IEEE/ACM Transactions on Networking, 26, 2487–2500.

    Article  Google Scholar 

  25. Myint, S. H., Yu, K., & Sato, T. (2019). Modeling and analysis of error process in 5G wireless communication using two-state Markov chain. IEEE Access, 7, 26391–26401.

    Article  Google Scholar 

  26. Jen, Y. Y., & Chang, S. K. J. (2016). Information and communication technologies for enhanced emergency management in Taiwan high speed rail. In 2016 IEEE international conference on intelligent rail transportation (ICIRT) (pp. 67–74). IEEE.

  27. Habibi, M. A., Nasimi, M., Han, B., & Schotten, H. D. (2019). A comprehensive survey of ran architectures toward 5G mobile communication system. IEEE Access, 7, 70371–70421.

    Article  Google Scholar 

  28. Djanali, S., Huda, M., & Shiddiqi, A. M. (2014). Location finder using augmented reality for railways assistance. In 2014 2nd international conference on information and communication technology (ICoICT) (pp. 487–492). IEEE.

  29. Oh, S.-C., Kim, G.-D., Jeong, W.-T., & Park, Y.-T. (2008). Vision-based object detection for passenger’s safety in railway platform. In 2008 International conference on control, automation and systems (pp. 2134–2137). IEEE.

  30. Hodge, V. J., O’Keefe, S., Weeks, M., & Moulds, A. (2014). Wireless sensor networks for condition monitoring in the railway industry: A survey. IEEE Transactions on Intelligent Transportation Systems, 16(3), 1088–1106.

    Article  Google Scholar 

  31. Hwang, K., Cho, J., Park, J., Har, D., & Ahn, S. (2018). Ferrite position identification system operating with wireless power transfer for intelligent train position detection. IEEE Transactions on Intelligent Transportation Systems, 20(1), 374–382.

    Article  Google Scholar 

  32. Pak. (2019). Pakistan rail (vol. x, pp. 2–3). pakrail.gov.pk.

  33. Liyanage, C., Dias, N., Amaratunga, D., & Haigh, R. (2017). Current context of transport sector in south Asia: Recommendations towards a sustainable transportation system. Built Environment Project and Asset Management, 7(5), 490–505.

    Article  Google Scholar 

  34. Goyal, S., Bedi, P., Kumar, J., et al. (2022). Realtime accident detection and alarm generation system over IoT. In Multimedia technologies in the internet of things environment (vol. 2, pp. 105–126). Springer.

  35. Ramírez-Moreno, M. A., Keshtkar, S., Padilla-Reyes, D. A., Ramos-López, E., García-Martínez, M., Hernández-Luna, M. C., et al. (2021). Sensors for sustainable smart cities: A review. Applied Sciences, 11(17), 8198.

    Article  Google Scholar 

  36. Telang, S., Chel, A., Nemade, A., & Kaushik, G. (2021). Intelligent transport system for a smart city. In Security and privacy applications for smart city development (pp. 171–187). Springer.

  37. Iqbal, Z., Khan, M. I., Hussain, S., & Habib, A. (2021). An efficient traffic incident detection and classification framework by leveraging the efficacy of model stacking. Complexity, 2021, 1–17.

    Article  Google Scholar 

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

    Article  Google Scholar 

  39. Chehri, A., Sharma, T., Debaque, B., Duclos, N., & Fortier, P. (2022). Transport systems for smarter cities, a practical case applied to traffic management in the city of Montreal. In Sustainability in energy and buildings 2021 (pp. 255–266). Springer.

  40. Rao, P. M., & Deebak, B. (2022). Security and privacy issues in smart cities/industries: Technologies, applications, and challenges. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-03707-1.

    Article  Google Scholar 

  41. Shankaran, R. S., & Rajendran, L. (2022). Intelligent transport systems and traffic management in smart cities (pp. 133–180). CRC Press.

    Google Scholar 

  42. Shikdar, T. A., Rashid, M. M., Ayub, F. B., & Faisal, S. (2022). Development of automated protection and monitoring system for poor railway infrastructure. In Proceedings of third international conference on communication, computing and electronics systems (pp. 527–546). Springer.

  43. Bulgariu, C.-L. (2022). The attractiveness of the railway transport system-mobility and the correlation with safety and security. In International conference “new technologies, development and applications” (pp. 687–694). Springer.

  44. Zhang, S., Lee, W.-K., & Pong, P. (2013). Train detection by magnetic field sensing. Sensors and Materials, 25(6), 423–436.

    Google Scholar 

  45. Prasad, R. J. C., Reddy, P. A., & Praneetha, Y. S. (2016). GSM based automatic railway gate control system with real-time monitoring. International Journal of Science, Engineering and Technology Research (IJSETR), 5(3), 740–745.

  46. Bharath Kumar Reddy, G., Sharath Kumar Reddy, G., Ravinder Reddy, R., & Navya, G. (2018). Safeguard of railway crossing using IoT. Journal of Telecommunications System & Management. https://doi.org/10.4172/2167-0919.1000165.

    Article  Google Scholar 

Download references

Funding

There is no funding sources for this research.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock, Pakistan

    Ali Mustafa, Ozain Rasheed, Shahzad Rehman & Farman Ullah

  2. Department of Computer Systems Engineering, UET, Peshawar, Pakistan

    Salman Ahmed

Authors
  1. Ali Mustafa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ozain Rasheed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shahzad Rehman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Farman Ullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Salman Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Mustafa.

Ethics declarations

Conflict of interest

There is no conflict of interest between the authors for this research.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mustafa, A., Rasheed, O., Rehman, S. et al. Sensor Based Smart Railway Accident Detection and Prevention System for Smart Cities Using Real Time Mobile Communication. Wireless Pers Commun 128, 1133–1152 (2023). https://doi.org/10.1007/s11277-022-09992-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09992-5

Keywords

Search

Navigation