Modeling travel time of car with varying demand on an urban midblock section

The present study analyzes the stochastic nature of travel time distribution under the uncertainty of traffic volume and the proportion of cars in the traffic stream. Stochastic response surface method (SRSM) is adopted for modeling the travel time variation under the influence of traffic composition and traffic volume. This model is applied to an uninterrupted urban arterial corridor of 1.7 km length in New Delhi. Video graphic data were collected for 2 days during morning hours between 8 AM and 12 noon and evening hours of 3–7 PM. License plate matching technique was used for measuring the travel time in the study area. This study focused on travel time variation of cars with varying traffic volume and proportion of car in the traffic stream. Linear regression analysis was carried out initially to know the functional relation and significance relation between the input and output variables, and then SRSM analysis was performed. Artificial neural network (ANN) is also considered to map the relation among travel time, traffic volume and composition of traffic stream. A comparative evaluation is made among ANN, SRSM and regression analysis. Results indicate that apart from traffic volume, the influence of car population is more on travel time variation than motorized two-wheelers. It is attributed to the smaller size and comparability better operating condition of motorized two-wheelers. Also, the ANN and SRSM models are more efficient for analyzing the stochastic relation between the response and uncertain explanatory variable than the regression model.     

采用序列响应面法的大客车结构振动频率优化

提出基于序列响应面模型的方法结合遗传算法的优化思路,对某全承载式大客车的振动频率进行优化。在有限元模型的基础上,借助灵敏度分析,结合零件分组,选取一部分重要结构件的厚度作为设计变量,以质量不超过原设计质量作为约束,在Matlab环境下对指定的振动频率分别进行优化。优化过程中将遗传算法每轮迭代得到的最优解作为新一轮有限元分析的输入,并用分析结果更新响应面模型。优化结果表明,该客车被优化的振动频率有明显的提高,所采用的方法有效减少了计算的相对误差,改善了优化效果。