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Journal Article

Citation

Khan T, Vivek AK, Mohapatra SS. Transp. Res. Rec. 2021; 2675(12): 1408-1421.

Copyright

(Copyright © 2021, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981211035761

PMID

unavailable

Abstract

Critical gap is a crucial factor for capacity estimation and safety evaluation at uncontrolled mid-block median openings (MBMOs). The U-turning vehicle makes a U-turn when a sufficient gap between two fast-moving vehicles is available in the approaching through traffic (ATT) stream. Even though, worldwide, temporal gaps are extensively used, the spatial gap is an important parameter that significantly affects minor street vehicles' safety (U-turning vehicles in this study). The present research, undertaken in India, focuses on estimating the temporal and spatial critical gap of U-turning vehicles at uncontrolled MBMOs. The collected data were analyzed for six different types of U-turning vehicles at varying approaching through traffic volume (ATTV). For critical gap estimation, four different critical gap estimation techniques, namely, modified Raff method (MRM), Ashworth method, binary logit model (BLM) method, and occupancy time (OT) method were employed, separately. From the analysis, temporal and spatial critical gaps were observed to vary between different types of vehicle, and it was also observed to vary with ATTV. The critical gap values of this study were found to be smaller than the critical values reported in developed countries, signifying the aggressive driving nature of drivers in developing countries. A detailed appraisal of the different critical gap estimation methods has been carried out. From this analysis, the OT method was found to provide the closest critical gap values with the published literature compared with other methods. Lastly, the findings from the present study will be useful for capacity estimation and safety evaluation at MBMO.


Language: en

Keywords

analysis; data and data science; modeling; operations; statistical methods; traffic flow; traffic flow theory and characteristics

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