基于数据流集成回归的短时交通流预测

传统的交通流预测技术使用静态和离线算法,无法对模型的参数值和内部结构进行在线调整.然而,交通流变化具有明显的动态性,其内在模式会随时间发生变化,导致构建好的模型准确度下降.针对上述问题,提出了基于数据流集成回归的短时交通流预测模型.将不断产生的交通流数据划分成数据块,每个数据块训练1个基础回归模型,然后加权组合为集成模型.通过不断训练新的基础模型,并置换出集成模型中准确度最差的基础模型,实现在线更新.在实测数据上的对比实验结果表明,与静态离线的BN模型相比,模型的均方根误差降低了19.5%,运算时间降低了48.7%,并能够快速适应交通状况发生明显变化的情况,适用于城市主干道路的短时交通流预测问题.      

基于改进社会力的单向流电动自行车行为建模研究

电动自行车作为一种便捷高效的出行工具，已在许多国家被广泛使用，但对电动自行车微观行为的研究仍十分有限。在上海海事大学对电动自行车驾驶行为进行观测实验，提取电动自行车轨迹数据，以纵向间距、速度差、侧向净距、水平间距作为特征变量，利用CART(Classification and Regression Tree)决策树分类结果建立电动自行车骑行决策行为规则。然后，基于社会力模型，分别引入超越力、跟随力等改进行为力模拟电动自行车的超越、跟随等骑行决策行为。考虑公式超越力、固定值超越力和临时目标点超越力这3种超越力形式，通过实际观测数据和仿真结果比较进行参数标定和模型验证，选取单向流电动自行车行驶场景开展数值仿真分析。结果表明：临时目标点超越力的改进社会力模型在超越过程中的轨迹误差最小，电动自行车超越时所需要的横向间距与超车时横向速度、超车完成时间成正比，最佳超车横向间距为2 m。     

Comparing decision tree algorithms to estimate intercity trip distribution

Traditional trip distribution models usually ignore the fact that destination choices are made individually in addition to aggregated factors, such as employment and average travel costs. This paper proposes a disaggregated analysis of destination choices for intercity trips, taking into account aggregated characteristics of the origin city, an impedance measurement and disaggregated variables related to the individual, by applying nonparametric Decision Tree (DT) algorithms. Furthermore, each algorithm’s performance is compared with traditional gravity models estimated from a stepwise procedure (1) and a doubly constrained procedure (2). The analysis was based on a dataset from the 2012 Origin-Destination Survey carried out in Bahia, Brazil. The final selected variables to describe the destination choices were population of the origin city, GDP of the origin city and travel distances at an aggregated level, as well as the variables: age, occupation, level of education, income (monthly), number of cars per household and gender at a disaggregated one. The comparison of the DT models with gravity models demonstrated that the former models provided better accuracy when predicting the destination choices (trip length distribution, goodness-of-fit measures and qualitative perspective). The main conclusion is that Decision Tree algorithms can be applied to distribution modeling to improve traditional trip distribution approaches by assimilating the effect of disaggregated variables.     

Investigation of helmet use behavior of motorcyclists and effectiveness of enforcement campaign using CART approach

Use of the helmet is beneficial in reducing the severity of injuries and avoiding fatalities for motorcyclists, therefore, legislation exists in almost all countries. In practice, the situation is different regarding the helmet use, especially in the developing countries. This paper investigates the helmet use behavior of motorcyclists in Karachi, Pakistan. It determines the significant factors affecting the helmet use in Karachi (Pakistan) and recommending effective campaign measures to promote helmet use. It is vital in relation to the exponential growth of motorcycle users and poor enforcement of traffic rules. Repeated cross-sectional data collected before (n = 226) and after (n = 277) the helmet use enforcement campaign is analyzed using univariate and non-parametric classification and regression tree (CART) techniques. A significant number of motorcyclists do not hold a driving license and CART analysis highlighted the significance of this variable along with exposure to the road environment (measured as daily usage of motorcycle) to explain helmet use. Campaign effects are found temporary, therefore, serious efforts are required to design coherent and structured awareness and enforcement programs.     

北京市主干路基本路段交通流特性及状态分析

基于实测数据和交通流模型,对北京市的主干路基本路段的交通流进行了分析,发现主干路交通流呈现出不同的相位特征,应用“三相交通流理论”,在城市主干路基本路段的流量一密度平面中将交通流划分自由流、同步流和堵塞流3个相位,并具体讨论各个相位的交通流特性及交通流的状态转变,确定了各个相位的关键阈值,标定每个相位的区域范围,同时定义了城市主干路基本路段交通拥挤的范围,并用分类与回归树（CART）方法对结果进行了验证。