| 引用本文: | 沈强儒,杨少伟,曹慧,顾镇媛,葛婷.立交区域交叉口交通信息识别概率预测[J].哈尔滨工业大学学报,2020,52(9):152.DOI:10.11918/201908085 |
| SHEN Qiangru,YANG Shaowei,CAO Hui,GU Zhenyuan,GE Ting.Prediction for recognition probability of traffic information at intersection of interchanges[J].Journal of Harbin Institute of Technology,2020,52(9):152.DOI:10.11918/201908085 |
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| 摘要: |
| 为预测立交区域交叉口交通信息识别概率,运用汽车动力学理论、驾驶员特性原理及动态交通特性获取动态识别视距值,在此基础上采用几何学及概率统计学原理建立典型车型在识别视距范围内交通信息识别框架,运用长期和短期时间序列对立交区域交叉口的交通量进行预测,形成其长期和短期时间序列的交通信息识别概率预测模型,运用实测值对识别概率预测模型仿真标定并检验其可靠性. 结果表明:长期时间序列预测下,交通量大小与交通信息识别概率具有明显的相关性,相关系数达0.849；一周时间的短期时间序列预测交通信息识别概率,其95%的置信区间的实测值与预测值相似度达87.65%,预测模型具有较好的可靠性. 预测交通信息识别概率较大的立交区域交叉口,应考虑加强交通信息灵活性设置并加强交通管控措施. |
| 关键词: 立交区域 交叉口 交通信息 识别视距 概率预测 |
| DOI：10.11918/201908085 |
| 分类号:U491 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(51808370)； 南通市市级基础科学研究项目(JC2018096)； 江苏省高等学校自然科学研究面上项目(17KJB580009) |
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| Prediction for recognition probability of traffic information at intersection of interchanges |
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SHEN Qiangru1,YANG Shaowei2,CAO Hui1,GU Zhenyuan1,GE Ting3
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(1. School of Transportation and Civil Engineering, Nantong University, Nantong 226019, Jiangsu, China; 2. School of Highway, Chang’an University, Xi’an 710064, China; 3. School of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, Jiangsu, China)
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| Abstract: |
| To predict the recognition probability of traffic information at the intersection of interchanges, an approach using vehicle dynamics theory, driver characteristic principle, and dynamic traffic characteristics was adopted to obtain the recognition distance. On the basis of this approach, geometric and statistical principles were applied to establish a framework of traffic information within the recognition distance of typical automobiles. Furthermore, the traffic volume at the intersection of interchanges was predicted using short-and long-term time series, and the prediction results were subsequently implemented to form a prediction model for the recognition probability of traffic signs, which was validated with actual measurement. Results show that under long-term time series prediction, the traffic volume had a significant correlation with the traffic information recognition probability, and the correlation coefficient was 0.849. As for the short-term time series within a week, the overlapped area of 95% prediction interval band between the predicted value and the measured value reached 87.65%, which signifies a high reliability of the prediction model. Based on the high probability of traffic information recognition problems, for the intersection of interchanges with large traffic volume, consideration should be given to strengthen flexible traffic information settings and traffic control measures. |
| Key words: interchanges intersection traffic information recognition distance probalility prediction |
