Retracted: Uncontrolled Intersection Operational Assessment under Mixed Traffic Conditions
This article has been corrected.
VIEW CORRECTIONPublication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 3
Abstract
The objective of this paper is to develop motorized level of service models using advanced heuristic techniques like functional networks, ordered probit analysis, and multivariate autoregressive spline for uncontrolled intersections under mixed traffic conditions. A total of 13 uncontrolled intersections from India are chosen and geometric, traffic, and roadside environmental data are collected using high-definition cameras. A novel influence area (INA) method for critical gap and follow-up time measurement is also developed. Over 7,000 effective uncontrolled intersection motorized driver responses are collected based on user satisfaction scores (1 = excellent to 6 = very bad). Eight variables with high significance () effects on perceived scores from Spearman’s correlation technique are modeled. Functional networks model showed greater efficiency as indicated by greater marginal ranking index of 11 (leading to lowest rank of first) among all the three level of service models proposed in this study. According to the sensitivity analysis, analogous to the proposed functional network model, volume to capacity ratio (highest effect), percentage of on-street parking (second highest effect), and service delay (third highest effect) showed superlative sensitivity toward level of service predictions at uncontrolled intersections compared to other parameters. The imperative outcomes of this research may help transport planners and traffic engineers to quantify operational evaluation of uncontrolled intersections and take crucial decisions toward their improvement.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the submitted article. Statistical Package for Social Sciences (SPSS) version 26.0.1, MATLAB 2017b, and Statistical Analysis Software 17.0.2 have been used for creating the AI models and validation of the model adjustment factors in this study, which is completely licensed (to the authors’ affiliated institute) and is thus not open source.
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 3 • March 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Apr 3, 2020
Accepted: Sep 28, 2020
Published online: Dec 26, 2020
Published in print: Mar 1, 2021
Discussion open until: May 26, 2021
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