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摘要: 在传统经验式公路隧道交通控制方法基础上, 利用人工智能中的知识表示方法和基于规则的专家系统, 提出一种范例推理与规则推理相结合的公路隧道突发事件交通控制方法, 建立了突发事件下公路隧道车辆诱导预案生成及管理系统。针对某平行分离式隧道, 使用面向对象的范例表示方法, 将已有范例表示为包含范例属性信息、控制方案信息和结果信息的三元组形式, 并给出了计算范例相似度的方法。使用IF-THEN的产生式规则表示方法, 结合公路隧道突发事件的类别属性和阻塞车道数属性, 给出了产生单点交通事故车辆分流控制策略的推理规则。仿真结果表明: 与传统范例推理方法相比, 在范例数量不足条件下, 该交通控制方法可缩短预案生成时间22%, 方法有效。Abstract: On the basis of traditional traffic control methods concluded by using experiences in highway tunnel, a traffic control method of highway tunnel emergency combining case-based reasoning (CBR) and rule-based reasoning (RBR) was provided by utilizing knowledge expression methods and rule-based expert system. A response plan generation and management system for highway tunnel vehicle guidance under emergency was set up based on the method. A parallel separate expressway tunnel was taken as example, cases were expressed as a triple-tuple form by using object-oriented expression method, including case attribution information, control scheme information and result information, and case similarity computation method was discussed. IF-THEN expression method was used, and the class attribute of highway tunnel emergency and the attribute of blocked lane numbers were combined, the reasoning rules of vehicle diffluence control strategy for single point traffic accident were produced. Simulation result shows that when the case number is insufficient, the traffic control method can reduce the generation time of response plan by 22% compared with CBR approach, and the method is efficacious.
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Key words:
- traffic control /
- highway tunnel /
- CBR /
- RBR /
- traffic control response plan
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表 1 范例属性信息定义
Table 1. Definitions of case attribute informations
范例属性 属性取值 取值类型 事件时段 白天、夜晚 字符型 事件地点 距隧洞入口距离/m 数值型 事件类型 火灾事故、危险品事故、交通事故、自然灾害、其他 字符型 伤亡人数 非负整数 数值型 阻塞车道数 单车道、双车道 字符型 实时交通流量 流量数据/ (veh·h-1) 数值型 
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表 2 典型隧道单点交通事故交通控制策略
Table 2. Control strategy of single point traffic accident for typical tunnel
规则序号 判断条件 交通控制方式 L D l1 l2 l3 l4 l5 l6 1 l1 d1 u2 u1 u1 u1 u1 u1 2 l1 d2 u3 u1 u1 u1 u1 u1 3 l1 d3 u4 u1 u1 u5 u5 u5 4 l2 d1 u1 u2 u1 u1 u1 u1 5 l2 d2 u1 u3 u1 u1 u1 u1 6 l2 d3 u4 u4 u1 u5 u5 u5 7 l3 d1 u1 u1 u2 u1 u1 u1 8 l3 d2 u1 u1 u3 u1 u1 u1 9 l3 d3 u4 u4 u4 u5 u5 u5
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表 3 范例实例
Table 3. Examples of case
范例编号 事件发生时段 事件发生地点 事件类型 伤亡人数 阻塞车道数 实时交通流量 交通控制方案 C1 白天 540 交通事故 0 单车道 710 方案1 C2 白天 300 交通事故 5 双车道 460 方案2 C3 白天 10 自然灾害 0 双车道 500 方案3 C4 白天 760 交通事故 3 单车道 620 方案4 C5 白天 1 320 火灾事故 4 双车道 430 方案5 C6 夜晚 860 交通事故 1 单车道 650 方案6
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表 4 范例相似度计算结果
Table 4. Computation results of case similarities
范例编号 属性相似度 与范例C0相似度 事件发生时段 事件发生地点 事件类型 伤亡人数 阻塞车道数 实时交通流量 C1 1.000 0.944 1.000 0.867 0.000 0.959 0.761 C2 1.000 0.891 1.000 0.800 1.000 0.977 0.944 C3 1.000 0.828 0.000 0.867 1.000 0.988 0.746 C4 1.000 0.992 1.000 0.933 0.000 0.982 0.784 C5 1.000 0.886 0.000 0.866 1.000 0.970 0.753 C6 0.000 0.987 1.000 0.934 0.000 0.974 0.702
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