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Development of a delay model for unsignalized intersections applicable to traffic assignment

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  • Amir H. Shahpar
  • Hedayat Z. Aashtiani
  • Ardeshir Faghri

Abstract

This paper develops a model for estimating unsignalized intersection delays which can be applied to traffic assignment (TA) models. Current unsignalized intersection delay models have been developed mostly for operational purposes, and demand detailed geometric data and complicated procedures to estimate delay. These difficulties result in unsignalized intersection delays being ignored or assumed as a constant in TA models. Video and vehicle license plate number recognition methods are used to collect traffic volume data and to measure delays during peak and off-peak traffic periods at four unsignalized intersections in the city of Tehran, Iran. Data on geometric design elements are measured through field surveys. An empirical approach is used to develop a delay model as a function of influencing factors based on 5- and 15-min time intervals. The proposed model estimates delays on each approach based on total traffic volumes, rights-of-way of the subject approach and the intersection friction factor. The effect of conflicting traffic flows is considered implicitly by using the intersection friction factor. As a result, the developed delay model guarantees the convergence of TA solution methods. A comparison between delay models performed using different time intervals shows that the coefficients of determination, R -super-2, increases from 43.2% to 63.1% as the time interval increases from 5- to 15-min. The US Highway Capacity Manual (HCM) delay model (which is widely used in Iran) is validated using the field data and it is found that it overestimates delay, especially in the high delay ranges.

Suggested Citation

  • Amir H. Shahpar & Hedayat Z. Aashtiani & Ardeshir Faghri, 2011. "Development of a delay model for unsignalized intersections applicable to traffic assignment," Transportation Planning and Technology, Taylor & Francis Journals, vol. 34(5), pages 497-507, April.
  • Handle: RePEc:taf:transp:v:34:y:2011:i:5:p:497-507
    DOI: 10.1080/03081060.2011.586119
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    Cited by:

    1. Wenhao Li & Chengkun Liu & Tao Wang & Yanjie Ji, 2024. "An innovative supervised learning structure for trajectory reconstruction of sparse LPR data," Transportation, Springer, vol. 51(1), pages 73-97, February.

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