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摘要: 为研究诱导模型的诱导效果, 用元胞自动机模型模拟车辆在路网中的行为, 仿真了不同诱导信息在不同交通量、不同受诱导率情况下对交通流的影响, 提出基于Agent的交通诱导模型, 模型采用Q-学习算法优化诱导信息, 可根据路网中交通流情况发布建议性诱导信息, 调节交通流分布。仿真结果表明: 影响诱导效果的主要因素为受诱导率和诱导信息, 基于Agent的交通诱导模型能有效均衡路网交通流, 且随着交通流的增加, 优势逐渐明显。在轻交通量情况下, 该模型较出行者自由选择路径模型略优; 但在重交通量情况下, 发布建议性的诱导信息比描述性诱导信息能减少12%平均行程时间。Abstract: To study the effect of traffic guidance model, cellular automata(CA)model was applied to simulate vehicles' behaviors in traffic network, and the impacts of different guidance informations on traffic flow were studied in different traffic volumes and different accepting guidance ratios.A traffic guidance model based on agent technology was built.In which Q-learning algorithm was used to optimize traffic guidance information, propositional information could be provided according to real time network traffic, traffic flow distribution was adjusted.Simulation result shows that the factors that impact guidance effect in network are accepting guidance ratio and guidance information, traffic guidance model based on agent can effectively balance network traffic flow, which is better to heavy traffic flow.In light traffic flow, the model has a little advantage compared with normal model, but in heavy traffic flow, propositional guidance information can decrease 12% average travel time compared with descriptive guidance information.
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Key words:
- traffic guidance /
- agent /
- microscopic traffic simulation /
- cellular automata /
- Q-learning algorithm
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图 3 Pacc为0.5时滞后性诱导信息对车辆的影响
Figure 3. Effect of lag guidance information on vehicles when Pacc is 0.5
图 4 Pacc为0.9时滞后性诱导信息对车辆的影响
Figure 4. Effect of lag guidance information on vehicles when Pacc is 0.9
图 6 可变诱导率下车辆的行驶时间
Figure 6. Vehicle travel times under different accepting guidance ratios
表 1 诱导效果比较
Table 1. Comparison of guidance effects
受诱导率 0.3 0.5 0.7 1.0 诱导效果 行程时间 车辆数 行程时间 车辆数 行程时间 车辆数 行程时间 车辆数 算例1 32.13 13.69 31.70 11.89 31.43 10.54 30.15 8.56 算例2 30.82 12.39 30.91 11.53 30.61 9.93 29.84 7.55 改进/% 4 9 2 3 2 4 7 11 
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表 2 不同交通量下受诱导率对诱导结果的影响
Table 2. Guidance effects of different accepting guidance ratios in different traffic volumes
交通流量/(veh·s-1) 车辆平均行程时间/s 改进/% Pacc为0.3 Pacc为0.5 Pacc为0.7 Pacc为1.0 0~0.3 31.94 30.82 30.30 30.06 5.0 0~0.5 39.54 37.16 35.79 34.45 12.8 0~0.7 48.83 45.93 42.40 39.62 18.0
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表 3 基于Agent诱导模型的诱导效果
Table 3. Effect of traffic guidance model based on agent
交通流量/(veh·s-1) 车辆平均行程时间/s 算例2条件 算例3条件可变受诱导率 Pacc为0.5 Pacc为1.0 0~0.3 30.82 30.06 30.50 0~0.5 37.16 34.45 35.52 0~0.7 45.93 39.62 40.28
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