Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model
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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/7341097 |
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| _version_ | 1841524664455659520 |
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| author | Sheng Dong Jibiao Zhou Shuichao Zhang |
| author_facet | Sheng Dong Jibiao Zhou Shuichao Zhang |
| author_sort | Sheng Dong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cb01a3a57c9f4142b245e22e23a47e63 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-cb01a3a57c9f4142b245e22e23a47e632025-02-03T05:47:43ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/73410977341097Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving ModelSheng Dong0Jibiao Zhou1Shuichao Zhang2School of Civil and Transportation Engineering, Ningbo University of Technology, Fenghua Rd. #201, Ningbo 315211, ChinaSchool of Civil and Transportation Engineering, Ningbo University of Technology, Fenghua Rd. #201, Ningbo 315211, ChinaSchool of Civil and Transportation Engineering, Ningbo University of Technology, Fenghua Rd. #201, Ningbo 315211, ChinaRapidly 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.http://dx.doi.org/10.1155/2019/7341097 |
| spellingShingle | Sheng Dong Jibiao Zhou Shuichao Zhang Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model Journal of Advanced Transportation |
| title | Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model |
| title_full | Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model |
| title_fullStr | Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model |
| title_full_unstemmed | Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model |
| title_short | Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model |
| title_sort | determining e bike drivers decision making mechanisms during signal change interval using the hidden markov driving model |
| url | http://dx.doi.org/10.1155/2019/7341097 |
| work_keys_str_mv | AT shengdong determiningebikedriversdecisionmakingmechanismsduringsignalchangeintervalusingthehiddenmarkovdrivingmodel AT jibiaozhou determiningebikedriversdecisionmakingmechanismsduringsignalchangeintervalusingthehiddenmarkovdrivingmodel AT shuichaozhang determiningebikedriversdecisionmakingmechanismsduringsignalchangeintervalusingthehiddenmarkovdrivingmodel |