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Optimal Traffic Route Finder System

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Reliability and Risk Assessment in Engineering

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

The main aim of this traffic route finder system is to reduce the number of re-computations for finding optimized route and alternate routes. This is to obtain less memory consumption and less wastage of resources that result in minimal response times. On the development of intelligent transport system (ITS), this increasing intensive on-demand of routing guidance system in the real time coincides with increasing growth of roads in the real world. This paper is about the values of the real-time traffic data obtained for arriving at an optimal vehicle routing solution within dynamic transportation networks. Our proposal is to implement an optimal vehicle routing algorithm in order to incorporate the dynamically changing traffic flows. Thus, we present a dynamic approach in selecting the paths for the implementation of our proposed algorithm for the effective road traffic transportations routing system by providing dynamically changing traffic flow of information and the historical data using GIS.

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References

  1. Bander J, White C (2002) A heuristic search approach for a nonstationary stochastic shortest path problem with terminal cost. Transp Sci 36:218–230

    Article  Google Scholar 

  2. Hashemi S, Mokarami S, Nasrabadi E (2010) Dynamic shortest path problems with time-varying costs. Optim Lett 4:147–156

    Article  MathSciNet  Google Scholar 

  3. Likhachev M, Ferguson D, Gordon G, Stentz A, Thrun S (2008) Anytime search in dynamic graphs. Artif Intell 172:1613–1643

    Article  MathSciNet  Google Scholar 

  4. Opasanon S, Miller-Hooks E (2006) Multicriteria adaptive paths in stochastic, time-varying networks. Eur J Oper Res 173:72–91

    Article  MathSciNet  Google Scholar 

  5. Zeng W (2009) Finding shortest paths on real road networks: the case for A*. Int J Geogr Inf Sci Taylor Fr 23:531–543

    Article  Google Scholar 

  6. Feldman R, Valdez-Flores C (2010) Applied probability and stochastic processes. Springer, Berlin

    Google Scholar 

  7. Sarkar A, Sahoo G, Sahoo UC (2012) Application of fuzzy logic in transport planning. Int J Soft Comput (IJSC) 3(2):1–21

    Article  Google Scholar 

  8. Sasikala KR, Petrou M, Kittler J (1996) Fuzzy classification with a GIS as an aid to decision making. University of Surrey, Guildford, Surrey GU2 5XH, UK

    Google Scholar 

  9. Viswarani CD, Vijayakumar D, Subbaraj L, Umashankar S, Kathirvelan J (2014) Optimization on shortest path finding for underground cable transmission lines routing using GIS. J Theor Appl Inf Technol 65(3):639–643

    Google Scholar 

  10. Cherkassky B, Goldberg A, Radzik T (1996) Shortest paths algorithms: theory and experimental evaluation. Math Program 73:129–174

    MathSciNet  MATH  Google Scholar 

  11. Dial R (1969) Algorithm 360: Shortest-path forest with topological ordering. In: Communications of the ACM, ACM, vol 12, pp 632–633

    Google Scholar 

  12. Kim S, Lewis M III, White C (2005) State space reduction for non stationary stochastic shortest path problems with real-time traffic information. IEEE Trans Intell Transp Syst 6:273–284

    Article  Google Scholar 

  13. Psaraftis H, Tsitsiklis J (1993) Dynamic shortest paths in acyclic networks with Markovian arc costs. Oper Res, JSTOR 41:91–101

    Article  MathSciNet  Google Scholar 

  14. Fan Y, Kalaba R, Moore I (2005) Shortest paths in stochastic networks with correlated link costs. Comput Math Appl 49:1549–1564

    Article  MathSciNet  Google Scholar 

  15. Delling D, Wagner D (2009) Time-dependent route planning. In: Robust and online large-scale optimization, Springer, Berlin, vol 5868, pp 207–230

    Google Scholar 

  16. Alazab A, Venkatraman S, Abawajy JL, Alazab M (2011) An optimal transportation routing approach using GIS-based dynamic traffic flows. In: International conference on information and financial engineering, IPEDR, vol 12

    Google Scholar 

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Correspondence to M. Monica Bhavani or A. Valarmathi .

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Monica Bhavani, M., Valarmathi, A. (2020). Optimal Traffic Route Finder System. In: Gupta, V., Varde, P., Kankar, P., Joshi, N. (eds) Reliability and Risk Assessment in Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-3746-2_4

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  • DOI: https://doi.org/10.1007/978-981-15-3746-2_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3745-5

  • Online ISBN: 978-981-15-3746-2

  • eBook Packages: EngineeringEngineering (R0)

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