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Multi-Criteria Decision-Making for Sustainable Transport: A Case Study on Traffic Flow Prediction Using Spatial–Temporal Traffic Sequence

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Sustainability

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 333))

Abstract

Traffic flow on highways exhibits a dynamic phenomenon in different operational settings. The concept of sustainability in transportation engineering is elucidated using multi-criteria decision-making analysis (MCDA), a discipline of Operation Research (OR) which is in a wide range of applications and practices in real-time traffic engineering. The principle criteria considered in the study of short-term prediction of traffic flow rate are spatial and temporal information. The spatial–temporal components of physical traffic flow are the parametric measures. Exploring the intrinsic relationship between these measures helps in realizing the dynamics of traffic flow rate. Hence, the objective of this chapter is to elucidate the significance of MCDA considering the spatial and temporal measures of traffic flow rate. Also, this chapter presents a case study on the formulation of algorithm for the prediction of traffic flow rate on highways. Experimental results are reported with an estimation of time complexity of algorithms.

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Authors and Affiliations

  1. Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, Tamil Nadu, India

    Jayanthi Ganapathy

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  1. Jayanthi Ganapathy
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Correspondence to Jayanthi Ganapathy .

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Editors and Affiliations

  1. Ingenium Research Group, University of Castilla-La Mancha, Ciudad Real, Spain

    Fausto Pedro García Márquez

  2. LeBow College of Business, Drexel University, Philadelphia, PA, USA

    Benjamin Lev

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Ganapathy, J. (2023). Multi-Criteria Decision-Making for Sustainable Transport: A Case Study on Traffic Flow Prediction Using Spatial–Temporal Traffic Sequence. In: García Márquez, F.P., Lev, B. (eds) Sustainability. International Series in Operations Research & Management Science, vol 333. Springer, Cham. https://doi.org/10.1007/978-3-031-16620-4_9

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  • DOI: https://doi.org/10.1007/978-3-031-16620-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16619-8

  • Online ISBN: 978-3-031-16620-4

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