原文传递
Modeling Human Learning and Cognition Structure: Application to Driver Behavior in Dilemma Zone
| 题名： | Modeling Human Learning and Cognition Structure: Application to Driver Behavior in Dilemma Zone |
| 正文语种： | 英文 |
| 作者： | San Diego State Univ;Virginia Tech |
| 作者单位： | Sahar Ghanipoor Machiani;Montasir Abbas |
| 摘要： | In transportation studies, modeling human learning and decision-making processes plays a key role in developing realistic safety countermeasures and appropriate crash-mitigation strategies. In this study, a human learning model was created that captures the cognitive structure of human memory. The relationship between long-term and short-term memories was incorporated into a reinforcement learning technique to construct the human learning model. The model was then applied to dilemma zone data collected in a simulator study. Dilemma zone is an area of roadway ahead of the signalized intersection in which drivers have difficulty deciding whether to stop or proceed through at the onset of yellow. Driver choice behavior and learning process in dilemma zones was modeled, taking into account drivers' experiences at the previous intersections, and was compared to a pure machine learning model. The results of the model revealed lower and faster-merging errors when human learning was considered in training agents. The human learning model for dilemma zones presented here could be used to evaluate dilemma zone mitigation algorithms by considering their effects on driver agents. |
| 出版年： | 2016 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | Vol.142 |
| 期： | No.11 |
| 页码： | 04016057 |
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