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Kriging based saturation flow models for traffic conditions in Indian cities

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  • Saha, Arpita
  • Chakraborty, Souvik
  • Chandra, Satish
  • Ghosh, Indrajit

Abstract

Saturation flow is crucial for designing the signal cycle and the corresponding green phase at an intersection. Therefore, it is important to compute saturation flow with a higher order of accuracy. Highway capacity manual (HCM) has provided a saturation flow model for homogeneous traffic condition with strict lane discipline. However, traffic in many parts of the world is highly heterogeneous and hence, it is necessary to develop saturation flow models for such situations. To this end, this paper presents saturation flow models for heterogeneous traffic conditions. To be specific, four saturation flow models based on Kriging variants have been proposed. Unlike available saturation flow models, the proposed models consider the effect of both geometric and traffic characteristics. Field data from fifteen intersections located in five different cities of India – Delhi, Patiala, Chandigarh, Mumbai and Panchkula – are used for developing and validating the models. Results obtained using the proposed models have been compared with other saturation flow models available in the literature. It is observed that the proposed models yield superior results outperforming existing saturation flow models.

Suggested Citation

  • Saha, Arpita & Chakraborty, Souvik & Chandra, Satish & Ghosh, Indrajit, 2018. "Kriging based saturation flow models for traffic conditions in Indian cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 38-51.
    • Handle: RePEc:eee:transa:v:118:y:2018:i:c:p:38-51
    DOI: 10.1016/j.tra.2018.08.037
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    References listed on IDEAS

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    2. Taghreed Alghamdi & Khalid Elgazzar & Taysseer Sharaf, 2021. "Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling," Future Internet, MDPI, vol. 13(9), pages 1-18, August.

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