Metro bus transit frequency regulation system for a smart city using an optimization algorithm

  • Authors

    • S. S.Govindaraj Madras Institute of Technology Campus, Anna University
    • J. Dhalia Sweetlin Madras Institute of Technology Campus, Anna University
    • A. Vignesh Madras Institute of Technology Campus, Anna University
    • J. Daphy Louis Lovenia Karunya Institute of Technology & Sciences
    • C. Roshini Madras Institute of Technology Campus, Anna University
    • P. Horsley Solomon SRMAAS College,Chennai
    2019-06-30
    https://doi.org/10.14419/ijet.v7i4.28118
  • Bus Allocation, Bus Transit System, Frequency Regulation, Nelder-Mead Optimization, Route Analysis.
  • Background:The increase in passengers with inadequate number of buses available results in an overcrowded bus. This is accompanied by a cramped experience for the passengers.

    Objectives:To facilitate the passengers and to ease their travel, by automating bus and route allocation.

    Methods:In this work, a Metro Bus Transit Frequency Regulation System Using Nelder-Mead Optimization Algorithm is presented. This system allows the Metropolitan Transport Corporation to allot buses in specified routes according to the demands and eases the ordeal that daily commuters face. Additionally, it aims to reduce the fuel expenses incurred by the Metropolitan Transport Corporation by optimizing the allocation of the number of buses in a particular route. It not only assuages the distresses of the passengers but also looks to reduce the woes of the workers of the Transport Corporation in allocation of buses. The vision of the proposed work is to produce a smart automated bus transit system which paves the way for a technologically improved city.

    Results:The simulation of the system resulted in 95% of requirements satisfaction by the passengers by using an average resource of 70% during the peak hours in the morning.

    Conclusions: From the results, it is inferred that developing a transit frequency regulation system will definitely ease the commutation of passengers and also reduces the fuel expenses. Indirectly the system can be used to reduce air pollution by reducing the number of private vehicles on road.

     

     


  • References

    1. [1] B. Dhivyabharathi, B. A. Kumar, L. Vanajakshi, Real time bus arrival time prediction system under Indian traffic condition, 2016 IEEE International Conference on Intelligent Transportation Engineering, Singapore (2016) 18-22.https://doi.org/10.1109/ICITE.2016.7581300.

      [2] A.Lakhouili, E. H. Essoufi, H. Medromi, Multiagent based model for urban traffic congestion measuring, 2015 5th World Congress on Information and Communication Technologies , Marrakech ( 2015) 73-77.https://doi.org/10.1109/WICT.2015.7489647.

      [3] D. K. Sharma, S. R. Ahuja, A first-come-first-serve bus-allocation scheme using ticket assignments,The Bell System Technical Journal 60 (7) (1981) 1257-1269.https://doi.org/10.1002/j.1538-7305.1981.tb00265.x.

      [4] A. Agrawal, P. Nagrath, Analysing and designing automated and dynamic bus route allocation, 2016 International Conference on Computational Techniques in Information and Communication Technologies, New Delhi (2016) 251-256.https://doi.org/10.1109/ICCTICT.2016.7514587.

      [5] Y. Fang, X. Hu, L.Wu, Y.Miao,A real-time scheduling method for a variable- route bus in a community. Advances in Intelligent Decision Technologies, Smart Innovation, Systems and Technologies, vol 4. Springer, Berlin, Heidelberg 239-247.https://doi.org/10.1007/978-3-642-14616-9_23.

      [6] S. An, X. Zhang, Real-time hybrid in-station bus dispatching strategy based on mixed integer programming. Information7(3) (2016) 43 1-12.https://doi.org/10.3390/info7030043.

      [7] S. Kim, M. E. Lewis, C. C. White, State space reduction for nonstationary stochastic shortest path problems with real-time traffic information, IEEE Transactions on Intelligent Transportation Systems, 6(3) (2005) 273-284.https://doi.org/10.1109/TITS.2005.853695.

      [8] P.Kirci, An optimization algorithm for a capacitated vehicle routing problem with time windows, Sadhana 41 (5) (2016) 519–529.

      [9] P. Schittekat, M. Sevaux, K. Sorensen, A mathematical formulation for a school bus routing problem, 2006 International Conference on Service Systems and Service Management, Troyes (2006)1552-1557.https://doi.org/10.1109/ICSSSM.2006.320767.

      [10] L. Spasovic, S. Chien, and C. Kelnhofer-Feeley, “A methodology for evaluating of school bus routing - a case study of Riverdale, New Jersey,†80th Annual Meeting, TRB, Washington D.C, (2001) 1-18.

      [11] S. Chandurkar, S. Mugade, S. Sinha, M. Misal, P. Borekar, Implementation of real time bus monitoring and passenger information system, International Journal of Scientific and Research Publications3(5) (2013) 1-5.

      [12] D. Barbucha, A multi-agent approach to the dynamic vehicle routing problem with time windows, Computational Collective Intelligence Technologies and Applications,Lecture Notes in Computer Science, vol. 8083, Springer, Berlin, Heidelberg, (2013) 467-476.https://doi.org/10.1007/978-3-642-40495-5_47.

      [13] M. Pavone, N. Bisnik, E. Frazzoli, V. Isler, A stochastic and dynamic vehicle routing problem with time windows and customer impatience, Mobile Network Applications, 14 (350) (2009)350-364.https://doi.org/10.1007/s11036-008-0101-1.

      [14] J. R. Hauser, An Efficient Model for Planning Bus Routes in Communities with Populations Between 20,000 and 250,000, Massachusetts Institute of Technology, Operations Research Center, (1973).

      [15] J. K. Rout, A. Dalmia, K. K. R. Choo, S. Bakshi, S. K. Jena, Revisiting Semi-Supervised Learning for Online Deceptive Review Detection, IEEE Access, 5 (2017)1319-1327.https://doi.org/10.1109/ACCESS.2017.2655032.

      [16] H Zhang, A. Zhou, G. Zhang, H. K. Singh, Accelerating MOEA/D by Nelder-Mead method, 2017 IEEE Congress on Evolutionary Computation, San Sebastian (2017) 976-983.

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    S.Govindaraj, S., Dhalia Sweetlin, J., Vignesh, A., Daphy Louis Lovenia, J., Roshini, C., & Horsley Solomon, P. (2019). Metro bus transit frequency regulation system for a smart city using an optimization algorithm. International Journal of Engineering & Technology, 7(4), 6523-6527. https://doi.org/10.14419/ijet.v7i4.28118