基于元胞自动机的双车道变道交通流特性研究

在庞杂的城市交通环境下,驾驶员为了寻求更快的速度,常常采用主动的换道行为。由于汽车使用量逐年增长,换道引起的交通事故经常发生。研究车辆变道行为,寻求有效措施减少交通事故的发生,对提高道路安全性具有积极的意义。本文以多车道系统中车辆变道行为为研究对象,以元胞自动机理论为基础,对比分析单向单车道、单向双车道换道行为,并运用MATLAB仿真软件进行分析,获得变道交通流的相关特性曲线。     

Short-Term Prediction of Travel Time using Neural Networks on an Interurban Highway

The main purpose of this study was to investigate the predictability of travel time with a model based on travel time data measured in the field on an interurban highway. Another purpose was to determine whether the forecasts would be accurate enough to implement the model in an actual online travel time information service. The study was carried out on a 28-kilometre-long rural two-lane road section where traffic congestion was a problem during weekend peak hours. The section was equipped with an automatic travel time monitoring and information system. The prediction models were made as feedforward multilayer perceptron neural networks. The main results showed that the majority of the forecasts were close to the actual measured values. Consequently, use of the prediction model would improve the quality of travel time information based directly on the sum of the latest measured travel times.