Abstract
The objective of this paper is to develop and assess motorized level of service models using advanced artificial intelligence techniques like functional networks, multi-gene genetic programming and multi-variate auto-regressive spline for uncontrolled intersections under mixed traffic conditions. Thirteen intersections from India are chosen and geometric, traffic and roadside environmental data are collected using high-definition cameras. An innovative Influence for Gap Acceptance (INA) method for critical gap and follow-up-time measurement is also developed. About seven thousand effective uncontrolled intersection motorized driver responses are collected based on user satisfaction scores (1=excellent to 6=awfully bad). Eight variables with high significance effects on perceived scores from Spearman’s correlation technique are modelled. The proposed functional network model showed better efficiency with volume to capacity ratio, percentage of on-street parking and service delay showing supreme effects in level of service predictions. The imperative outcomes of this research may help transport planners and traffic engineers to quantify operational evaluation of uncontrolled intersections and take crucial decisions towards their improvement.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
