Intelligent Rescheduling Trains for Air Pollution Management
Commenced in January 2007
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Edition: International
Paper Count: 32804
Intelligent Rescheduling Trains for Air Pollution Management

Authors: Kainat Affrin, P. Reshma, G. Narendra Kumar

Abstract:

Optimization of timetable is the need of the day for the rescheduling and routing of trains in real time. Trains are scheduled in parallel with the road transport vehicles to the same destination. As the number of trains is restricted due to single track, customers usually opt for road transport to use frequently. The air pollution increases as the density of vehicles on road transport is increased. Use of an alternate mode of transport like train helps in reducing air-pollution. This paper mainly aims at attracting the passengers to Train transport by proper rescheduling of trains using hybrid of stop-skip algorithm and iterative convex programming algorithm. Rescheduling of train bi-directionally is achieved on a single track with dynamic dual time and varying stops. Introduction of more trains attract customers to use rail transport frequently, thereby decreasing the pollution. The results are simulated using Network Simulator (NS-2).

Keywords: Air pollution, routing protocol, network simulator, rescheduling.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130367

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References:


[1] Rail Transport and Environment Facts and Figures, the voice of European railways.
[2] Charles E. Perkins and Elizabeth M. Royer, “Ad-hoc On-Demand Distance Vector Routing”
[3] Mohammad T. Isaai and Madan G. Singh, “An Object-Oriented, Constraint-Based Heuristic for a Class of Passenger-Train Scheduling Problems: IEEE Transactions on Systems, Man and Cybernetic-Spart C: Applications and Reviews, Vol. 30, NO. 1, February 2000.
[4] Lijun Sun, Der-Horng Lee, Alex Erath, Xianfeng Huang, ”Using Smart Card Data to Extract Passengers Spatio-Temporal Density and Trains Trajectory of MRT System”, 2012.
[5] Neha Singh, Prof. Rajeshwar Lal Dua, Vinita Mathur, Network Simulator NS2-2.35, Volume 2, Issue 5, ISSN: 2277 128X, May 2012.
[6] Giacomo Zaninotto, Andrea DAriano, Dario Pacciarelli and Marco Pranzo, “Intelligent Decision Support for Scheduling and Rerouting Trains on an Italian Railway Line”, Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems, 2013.
[7] Shengfeng Xu, Gang Zhu, Chao Shen, Yan Le, ”Delay-Aware Fair Scheduling in Relay-Assisted High-Speed Railway Networks”, 8th International Conference on Communications and Networking in China (CHINACOM), 2013.
[8] Yixiang Yue, Song Han, Leishan Zhou, and Hesham A. Rakha, “Microscopic Resource Assignment Model and Lagrangian Relaxation Based Algorithm for Train Operation Scheduling in Railway Station”, Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems, 2013.
[9] G Jeevitha, N Magadevi, T Bharathi, V Gowtham, M Sivaramaganesh, “Safe And Secured Incorporation of Multi-Sensor Intelligent Traction System”, International Journal of Engineering Research, Vol.2, Issue 2, 2014.
[10] M. Sam’a, A. DAriano, A. Toli, D. Pacciarelli, “Metaheuristics for Real Time near Optimal Train Scheduling and Routing”, IEEE 18th International Conference on Intelligent Transportation Systems, 2015.
[11] Yihui Wang, Bin Ning, Tao Tang, Ton J. J. Van Den Boom, and Bart De Schutter, “Efficient Real-Time Train Scheduling for Urban Rail Transit Systems Using Iterative Convex Programming”, IEEE Transactions On Intelligent Transportation Systems, Vol. 16, NO. 6, December 2015.
[12] Aditya Dhatrak, Amruta Deshmukh and Rahul Dhadge, “Modified AODV Protocols: A Survey”, 2nd National Conference on Information and Communication Technology (NCICT).
[13] Prof Nitiket N Mhala and N K Choudhari, “An Implementation Possibilities For AODV Routing Protocol in Real World”. International Journal of Distributed and Parallel Systems (IJDPS), Vol.1, No.2.