Latest revision as of 16:07, 12 February 2021

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.

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The different versions of the original document can be found in:

• http://dx.doi.org/10.1155/2019/7341097 under the license http://creativecommons.org/licenses/by/4.0/

• https://doi.org/10.1155/2019/7341097 under the license http://creativecommons.org/licenses/by/4.0

• http://downloads.hindawi.com/journals/jat/2019/7341097.pdf,

http://downloads.hindawi.com/journals/jat/2019/7341097.xml,

http://dx.doi.org/10.1155/2019/7341097 under the license cc-by

• http://dx.doi.org/10.1155/2019/7341097,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2019/7341097,

http://downloads.hindawi.com/journals/jat/2019/7341097.pdf,

https://academic.microsoft.com/#/detail/2927112542