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Modelling passenger car equivalency at an urban midblock using stream speed as measure of equivalence

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  • Basu, Debasis
  • Maitra, Swati Roy
  • Maitra, Bhargab

Abstract

The effect of traffic volume and its composition on Passenger Car Equivalency (PCE) of different vehicle types in a mixed traffic stream is investigated taking an urban mid-block section as the case study. The reduction in stream speed caused by marginal increment in traffic volume by a vehicle type is compared with that of caused by an old technology car, for the estimation of PCE of that vehicle type. A Neural Network (NN) approach is explored for capturing the underlying non-linear effects of traffic volume and its composition level on the stream speed. It is found that PCE of a vehicle type varies in a non-linear manner with total traffic volume and compositional share of that vehicle type in the traffic stream. The speed model using NN technique alone could establish the variation of PCE with vehicle type, traffic volume and its composition.

Suggested Citation

  • Basu, Debasis & Maitra, Swati Roy & Maitra, Bhargab, 2006. "Modelling passenger car equivalency at an urban midblock using stream speed as measure of equivalence," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 34, pages 75-87.
    • Handle: RePEc:sot:journl:y:2006:i:34:p:75-87
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    File URL: http://hdl.handle.net/10077/5930
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    References listed on IDEAS

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    1. Dougherty, Mark S. & Cobbett, Mark R., 1997. "Short-term inter-urban traffic forecasts using neural networks," International Journal of Forecasting, Elsevier, vol. 13(1), pages 21-31, March.
    2. Webster, Nathan & Elefteriadou, Lily, 1999. "A simulation study of truck passenger car equivalents (PCE) on basic freeway sections," Transportation Research Part B: Methodological, Elsevier, vol. 33(5), pages 323-336, June.
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    Cited by:

    1. Yeung, Jian Sheng & Wong, Yiik Diew & Secadiningrat, Julius Raditya, 2015. "Lane-harmonised passenger car equivalents for heterogeneous expressway traffic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 361-370.
    2. Mathew, Tom V. & Ravishankar, K.V.R., 2012. "Neural network based vehicle-following model for mixed traffic conditions," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 52, pages 1-4.
    3. Mohapatra, Smruti Sourava & Bhuyan, P.K & Rao, K.V.Krishna, 2012. "Genetic algorithm fuzzy clustering using GPS data for defining level of service criteria of urban streets," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 52, pages 1-8.

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