
@article{ref1,
title="A driver behavior-based lane-changing model for urban arterial streets",
journal="Transportation science",
year="2014",
author="Elefteriadou, Lily and Sun, Daniel",
volume="48",
number="2",
pages="184-205",
abstract="Lane-changing algorithms have attracted increased attention during recent years. However, limited research has been conducted to address the probability of changing lanes as a function of driver characteristics and lane-changing scenarios. This study contributes to the development of a comprehensive framework for modeling drivers' lane-changing maneuver on arterials by using driver behavior-related data. Focus group studies and &quot;in-vehicle&quot; driving tests were performed to investigate the effects of driver type under various lane changes on urban arterials and to collect microscopic vehicular data. With these field collected values, a model was developed to estimate the probability of changing lanes under various lane-changing scenarios and to estimate the corresponding gap acceptance characteristics. The lane-changing probability for each scenario was modeled as a function of the factors identified from the focus group discussions, as well as the driver types. In the gap acceptance modeling, a sequence of &quot;hand-shaking negotiations&quot; was introduced to describe vehicle interactions that may occur during lane-changing maneuvers. The proposed lane-changing model was implemented in the CORSIM (CORrider SIMulation) micro-simulator. The simulation capabilities of the newly developed model were compared to the original lane-changing algorithm in CORSIM and to the field observations. The validation results indicated that the new model better replicates the observed traffic under various levels of flow. Keywords : focus group study; gap acceptance; lane changing; vehicle interactions; microscopic simulation; CORSIM (CORrider SIMulation)<p /> <p>Language: en</p>",
language="en",
issn="0041-1655",
doi="10.1287/trsc.1120.0435",
url="http://dx.doi.org/10.1287/trsc.1120.0435"
}