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Title: | Delay models for Roundabouts under mixed Traffic flow Conditions in Developing Countries |
Authors: | Patnaik, Ashish Kumar Agarwal, Akshay Bhuyan, Prasanta Kumar Kumar, D. Prasanth |
Keywords: | Roundabout Delay Mixed traffic Multivariate regression (MVR) Sensitivity analysis |
Issue Date: | Nov-2017 |
Citation: | 4th International conference on transportation research group of India (CTRG 2017), Bombay, India, 17 - 20 December, 2017 |
Abstract: | The prime focus of this study is to develop empirical roundabout entry delay models for cars, two wheelers, bicycles & heavy vehicles as a function of traffic flow and geometric variables. Multivariate regression (MVR) analysis is employed to develop the above four delay models under mixed traffic flow conditions. Required data were collected from 8 roundabouts located in five cities of India. The M/M/1 delay model is validated under mixed traffic flow conditions and found that it is showing high variability for high delay ranges. The Co-efficient of determination (R2) and Nash-Sutcliffe co-efficient (E) are found to be (0.97, 0.89), (0.91, 0.91), (0.85, 0.84) & (0.82, 0.81) for cars, bicycles, two wheelers & heavy vehicles delay models respectively. The independent variables such as weaving length (), circulating flow ( ), Weaving width (Ww), Entry width ( ), diameter of central island (D) and entry Flow ( ) are found to be statistically significant at 95% confidence level. In sensitivity analysis, entry flow variable (Ef) is found to be highest contributing variable such as the percentage (%) of sharing are 32.46 and 31.71 in cars and bicycle models respectively. Also Weaving width (Ww) and diameter of central island (D) are contributing 41.52 %, 33.37% in two wheelers and heavy vehicle delay models respectively. |
Description: | Copyright of this document belongs to proceedings publisher. |
URI: | http://hdl.handle.net/2080/2896 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2017_CTRG_AKPatnaik_Delay models.pdf | Conference Paper | 222.68 kB | Adobe PDF | View/Open |
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