"This study analyzed the driver gap acceptance and rejection behavior during mandatory lane changes on a multilane freeway in congested and uncongested traffic conditions. During a lane change, drivers were more receptive to either the leading or the trailing gaps with vehicles in the target lane which governed the drivers' lane change and is termed as the governing gap. Drivers maneuvered till the governing gap was greater than the critical gap, accepted the gap and made a lane change. In this process, drivers reduced the non-governing gap to increase the length of the governing gap. The drivers as a result were found to be consistent with respect to the governing gap and inconsistent with respect to the non-governing gap. The governing gap, therefore, addresses the consistent driver behavior and avoids categorization of drivers as inconsistent. Critical gaps were estimated based on the consistent driver behavior using accepted and LRLA gaps, firstly, by categorizing the drivers based on the governing gap and the type of maneuver, and secondly, by categorizing the drivers based on the relative speeds. For a simple lane change model, categorization by governing gap and type of maneuver will be sufficient with a critical gap value distribution defined by empirical data for congested and uncongested traffic conditions. For a sophisticated lane change model, in addition to maneuver types, critical gaps estimated based on difference in relative speeds will help better replicate the realistic lane change behavior of drivers in case of congested traffic conditions"--Abstract, page iv.
Samaranayake, V. A.
Civil, Architectural and Environmental Engineering
M.S. in Civil Engineering
Missouri University of Science and Technology
ix, 44 pages
© 2012 Srinadh Kandada, All rights reserved.
Thesis - Open Access
Automobile driving -- Lane changing
Traffic flow -- Simulation methods
Print OCLC #
Electronic OCLC #
Link to Catalog Record
Kandada, Srinadh, "Driver mandatory lane change behavior: Use of governing gap in critical gap estimation" (2012). Masters Theses. 7442.