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Developing the Model and Environment for Validation of a Class-8 Truck
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作者 Mitchel J. Keil Upul Attanayake +1 位作者 Pavel Ikonomov Richard B. Hathaway 《Journal of Civil Engineering and Architecture》 2012年第7期777-786,共10页
Many studies have been conducted by analyzing crash data that included road profile, site conditions, vehicle configurations and weights, driver behavior, etc.. However, limited studies have been conducted evaluating ... Many studies have been conducted by analyzing crash data that included road profile, site conditions, vehicle configurations and weights, driver behavior, etc.. However, limited studies have been conducted evaluating the impact of these factors on crashes and/or rollover through simulations. This is mainly due to lack of availability of verified full vehicle flexible-body models. The verification process is costly as it requires instrumentation of a heavy vehicle, scanning of road surfaces, and collection of data by running the vehicle over different road conditions, performing various maneuvering, etc. This paper presents the reverse engineering process of a class-8 truck and validation of a full flexible-body simulation model of a Wabash 53-foot trailer against the strain data recoded from proving ground testing of an instrumented truck. Simulation results show that, with the exception of the noise from the strain gage data from instrumented test run at 30 mph, there is a good agreement in periodicity and relative amplitude with the ADAMS model. A comparison of strain data from the flex-body model and the instrumented truck shows that the modeling and verification approach presented in this paper can be confidently used to validate the full flexible-body models developed for specific analyses. 展开更多
关键词 ADAMS simulation class-8 truck flexible-body INSTRUMENTATION road profile.
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基于K-均值聚类算法的城市道路拥堵分级研究 被引量:4
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作者 方德春 许虔虔 +2 位作者 万华森 徐大伟 高雁波 《公路交通科技(应用技术版)》 CSCD 北大核心 2017年第2期261-264,共4页
针对不同城市高峰期路网的运行特征,依托高德地图144个城市道路运行数据,利用高峰延时指数和高峰平均运行速度2个指标及与道路拥堵指数相关性,引入了K-均值聚类算法,对各城市道路拥堵程度进行定量分析和等级划分,弥补了以往定性分析的... 针对不同城市高峰期路网的运行特征,依托高德地图144个城市道路运行数据,利用高峰延时指数和高峰平均运行速度2个指标及与道路拥堵指数相关性,引入了K-均值聚类算法,对各城市道路拥堵程度进行定量分析和等级划分,弥补了以往定性分析的不足。结果表明,在K-均值聚类分析算法分析的基础上,通过这2个指标对交通拥堵进行等级划分。无论是用Pearson相关系数还是用Spearman相关系数,都能得出高峰期道路平均速度与高峰期拥堵延时指数及案例类别号是负相关的,高峰期拥堵延时指数与案例类别号是正相关的。且高峰期道路平均速度与案例号的相关性比高峰拥堵延时与案例类别号的的相关性高,因此按高峰平均车速为指标的分类结果比按高峰拥堵延时指数分类结果要合理。 展开更多
关键词 道路运行数据 K-均值聚类 评价指标 高峰拥堵延时指数 高峰平均速度
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