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Estimation of Vehicular Speed and Passenger Car Equivalent Under Mixed Traffic Condition Using Artificial Neural Network

  • Research Article - Civil Engineering
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Abstract

Development of speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining the individual vehicle speed. Estimation of passenger car equivalent (PCE) which is essential in converting the mixed traffic volume into its equivalent homogeneous also requires the speed information of individual vehicle categories at varying traffic conditions on the road. The present research was carried out to model the individual vehicle speed and to study the effects of traffic volume and its composition on individual speed and PCE in the context of urban mixed traffic. Traffic data on classified traffic volume and speed information were collected at six-lane divided arterial mid-block road sections in New Delhi, India. The methodology of artificial neural network was adopted to develop a volume-based speed prediction model for individual vehicle category. Validation results showed a great deal of agreement between the predicted and the observed speeds. Then, the sensitivity analysis was performed utilizing the model developed in order to examine the effects of traffic volume and its composition on individual speed and corresponding PCE.

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Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    Subhadip Biswas & Indrajit Ghosh

  2. CSIR-Central Road Research Institute, New Delhi, 110025, India

    Satish Chandra

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  1. Subhadip Biswas
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  2. Satish Chandra
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  3. Indrajit Ghosh
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Correspondence to Subhadip Biswas.

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Biswas, S., Chandra, S. & Ghosh, I. Estimation of Vehicular Speed and Passenger Car Equivalent Under Mixed Traffic Condition Using Artificial Neural Network. Arab J Sci Eng 42, 4099–4110 (2017). https://doi.org/10.1007/s13369-017-2597-9

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  • DOI: https://doi.org/10.1007/s13369-017-2597-9

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