文章编号： 1672-5328（2018）05-0087-04

骆晨1，刘澜1, 2
(1.西南交通大学交通运输与物流学院，四川成都610031；2.西南交通大学综合交通运输智能化国家地方联合工 程实验室，四川成都610031)

摘要： 现有城市道路交叉口交通信息诱导效率相关研究内容笼统，尚未量化驾驶人对交通诱导信息 的认知效率和决策误差。针对这一问题，提出基于云模型的交通信息诱导效率评价方法，设计四种 不同组合方式的交通诱导信息实验情景。采用全功能汽车模拟驾驶器进行实验，结果显示：数字、 语言、图像组合而成的交通诱导信息对驾驶人路径变更影响显著，语言和数字类信息组合发布有利 于提高驾驶人对道路交通状况的认知效率，数字和图像类信息组合发布有利于降低驾驶人路径决策 误差。

关键词： 城市道路交叉口；交通信息诱导；云模型；诱导效率

中图分类号： U491

文献标识码：A

Efficiency of Traffic Guidance at Urban Roadway Intersections

Luo Chen1, Liu Lan1, 2
(1.School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan 610031; 2.National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan 610031)

Abstract： Existing studies on the efficiency of traffic guidance at urban roadway intersections are too general in content and lack of quantitative analysis on drivers' cognitive capability and decision-making error. To address the problem, this paper proposes a cloud model-based method to assess the efficiency of traffic guidance devices, and the traffic guidance experiment information under four different schemes. A fullscaled and fully functioned vehicle simulator was used for the experiments. The results demonstrate that the traffic guidance expressed by the combination of numerical, language and imagery materials significantly affect driver's travel path change. Communicating to driver by language and numerical materials improves drivers' cognitive capability in recognizing the traffic conditions, while numerical and imagery materials help to reduce the decision-making error in drivers' path selection.

Keywords： urban roadway intersections; traffic guidance; cloud model; induction efficiency