@article{ref1,
title="Determining e-bike drivers' decision-making mechanisms during signal change interval using the hidden Markov driving model",
journal="Journal of advanced transportation",
year="2019",
author="Dong, Sheng and Zhou, Jibiao and Zhang, Shuichao",
volume="2019",
number="",
pages="e7341097-e7341097",
abstract="Rapidly increasing e-bike use in China has resulted in new traffic problems including rising accident rates at intersections related to e-bike drivers' decision-making during multiple signal phases. Traditional one-step decision models (such as GHM) lack randomness and cannot adequately model e-bike drivers' complex behavior. Therefore, this study used a Hidden Markov Driving Model (HMDM) to analyze e-bike drivers' decision-making process based on high-resolution trajectory data. Video data were collected at three intersections in Shanghai and processed for use in the HMDM model. Five decision types (pass, stop, stop-pass, pass-stop, and multiple) composed of speed and acceleration/deceleration information were defined and used to analyze the impact of flashing green signals on e-bike drivers' behavior and decision-making processes. Approximately 40% of drivers made multiple decisions during the flashing green and yellow signal phases, in contrast to the traditional GHM model assumption that drivers only make one decision. Distance from stop-line had the most obvious influence on the number of decisions. The use of flashing green signals nearly eliminated the dilemma zone for e-bike drivers but enlarged the option zone, inducing more stop/pass decisions. HMDM can be applied to improve the accuracy of traffic simulation, the fine design of traffic signals, the stability analysis of traffic control schemes, and so on.<p /> <p>Language: en</p>",
language="en",
issn="0197-6729",
doi="10.1155/2019/7341097",
url="http://dx.doi.org/10.1155/2019/7341097"
}