某新型森林灭火弹运输车

针对新型森林灭火弹运输车所需实现的任务功能及技术要求进行了总体设计,并对按此要求设计的某森林灭火弹运输车进行了详细介绍。     

Real‐time identification of traffic conditions prone to injury and non‐injury crashes on freeways using genetic programming

This study applied the genetic programming (GP) model to identify traffic conditions prone to injury and property‐damage‐only (PDO) crashes in different traffic states on freeways. It was found that the traffic conditions prone to injury and PDO crashes can be classified into a high‐speed and a low‐speed traffic state. The random forest (RF) analyses were conducted to identify the contributing factors to injury and PDO crashes in these two traffic states. Four separate GP models were then developed to link the risks of injury and PDO crashes in two traffic states to the variables selected by the RF. An overall GP model was also developed for the combined dataset. It was found that the separate GP models that considered different traffic states and crash severity provided better predictive performance than the overall model, and the traffic flow variables that affected injury and PDO crashes were quite different across different traffic states. The proposed GP models were also compared with the traditional logistic regression models. The results suggested that the GP models outperformed the logistic regression models in terms of the prediction accuracy. More specifically, the GP models increased the prediction accuracy of injury crashes by 10.7% and 8.0% in the low‐speed and high‐speed traffic states. For PDO crashes, the GP models increased the prediction accuracy by 7.4% and 6.0% in the low‐speed and high‐speed traffic states. Copyright © 2016 John Wiley & Sons, Ltd.     

基于AFC数据和RF模型的城轨车站服务功能分类

城轨线网上服务不同类型客流车站的分布能反映城市结构特征,本文基于多年的城轨AFC数据,应用随机森林模型(RF),分析了全网车站服务客流类型的时空演变.对于传统RF在选择训练集时过于依靠主观经验的问题,本文首先利用传统随机森林度量车站之间的相似性,再采用Partitioning Around Medoid(PAM)方法对度量结果进行聚类,结果表明无监督随机森林方法拥有更好的准确性;最后采用无监督随机森林对北京市2014-2017年的车站服务客流属性的时空变化情况进行分析,结果显示,北京市近4年来城市职住结构在大的空间尺度上基本保持不变,但城市内部各功能区正经历缓慢变化,而服务于居住与工作混合类客流的车站所处区域将是今后城市结构演变中热点区域.本文的研究结果可以为城市轨道交通与城市结构互动关系的认识提供新视角.