Abstract
In this paper, considering different evolution behaviors in acceleration and deceleration of driving and the reinforcement learning, we propose a modified cellular automaton model in which the random acceleration behavior evolves with the driver's remembering to historic experience and the traffic condition around. Simulation results show that the proposed model can reveal the influence of driver's reinforcement learning on traffic and generate more realistic traffic phenomena.
| Original language | English |
|---|---|
| Pages (from-to) | 66-70 |
| Number of pages | 5 |
| Journal | Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) |
| Volume | 39 |
| Issue number | SUPPL. 2 |
| State | Published - Sep 2009 |
Keywords
- Cellular automaton model
- Engineering of communication and transportation
- Reinforcement learning effect
- Traffic flow
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