Modeling Correlation of Family Holiday Activities Based on Structural Equation Model

Previous researches in activities-decision have more in-depth study in context of individuals, while less work has been done within household activity decisions. The structural equation model is applied to analyze the relationship of three household activity categories on holiday using data from 2009 Jinan travel survey. Six attributes were first chosen as exogenous variables for the structural equation model. Meanwhile, six activity characteristic indexes were selected as endogenous variables including activity duration time and activity times in three kinds of activity, hereby Subsistence activity, maintenance activity and discretionary activity. respectively. Then, the paper attempted to discuss the correlation of all types of activities behavior through analyzing not only the influence of household economics and family members factors on activity but the correlation of holiday activities. The research shows that complex relationships among composition of the family, economic status, and activities-decision can be captured by the structural equation model structure. Specifically, the results show that flexibility of three categories activities will affect the activities of decision-making.     

交通拥堵反思

通过对交通拥堵进行反思,认为交通拥堵是充满活力的成功城市的伴生品,应从不同角度考虑拥堵问题.提出了十项有别于传统观点的提议,主要包括:机动性至关重要,交通拥堵是社会、经济活力的象征;道路扩容效果短暂,并会诱发潜在需求,但仍可带来一定的社会、经济效益;土地使用对出行行为的改变需要长期的努力;紧凑型发展会加剧交通拥堵;降低...     

城市土地利用特征对居民出行方式的影响——以深圳市为例

为缓解日益严重的城市交通问题、优化居民出行方式,有必要研究城市土地利用与交通系统的协调问题.以深圳市为例,应用多元Logit模型(MNL模型),纳入城市土地利用变量与出行者的家庭、社会、经济等变量,分析其对居民工作与非工作出行方式的影响.研究结果表明,出行起点的城市区位是影响居民出行方式的重要因素之一.相对于组团中心和...     

基于WebGIS的铁路旅客辅助决策系统的研究与开发

以旅客出行和换乘中转站的选择及优化作为实现目标,研究了针对旅客不同出行需求的查询算法,开发出基于WebGIS的铁路旅客出行辅助决策系统.系统为旅客出行决策提供综合查询信息,将最优线路以直观的方式显示出来,实现乘车线路的交互式查询和地理信息系统的实时衔接,后台决策软件综合考虑线路状况,实时更新铁路客运时刻表数据库,给出最优乘车路径选择.     

交通事件持续时间预测贝叶斯网方法研究

交通事件持续时间的预测是事件管理系统的重要组成部分,根据I-880实测数据集,利用逐步回归分析的方法确定事件持续时间的主要影响因素,分别建立了应用于事件持续时间预测的朴素贝叶斯(NB)模型、加树朴素贝叶斯(TAN)模型以及一般贝叶斯网(BN)模型,在分析数据特点的基础上确定了贝叶斯网的推理算法、参数学习以及结构学习方法.在不同数据缺失的程度和不同训练样本规模下,分别对三种模型的预测准确率进行了评价,结果表明贝叶斯网预测模型在数据缺失30%的情况下30min准确率高于80%.     

区域运输通道内影响旅客出行方式选择的因素

首先给出运输通道的概念,阐述我国现有的旅客出行方式。从我国现有的这些交通方式入手,探讨影响旅客出行方式选择的因素,即影响人们选择出行方式的因素可分为宏观因素和微观因素。宏观因素如社会经济发展水平、城市化水平、交通政策等,决定着出行方式的总结构;微观因素如出行目的和出行时间,则决定交通方式的选择。     

基于公交智能调度系统的公交换乘算法实现

通过对城市居民公交出行选择影响因素和选择逻辑的分析,利用地理信息系统（GIS）技术构建公交换乘数据模型和公交智能调度系统数据计算出行时间,以此为基础实现以最少换乘次数为第一目标,出行时间最短为第二目标的公交换乘算法。该算法考虑了步行换乘、行驶时间、换乘时间及公交线路上、下行因素对换乘查询的影响,能够较快地提供公交换乘方案。     

考虑行驶时间不确定性的合乘路径鲁棒优化方法

为应对实际合乘过程中时间不确定性带来的负面影响,本文研究不确定行驶时间下的合乘问题。采用预算不确定集合描述时间变量,引入不确定性水平可调节的预算系数,构建以车辆总里程最短和车辆数最少为目标的合乘路径鲁棒优化模型。并设计两阶段算法求解,第1阶段以两乘客间的可行合乘路径为基础,从车辆总里程节省率和乘客时间窗匹配灵活性两方面设计公式量化合乘匹配机会,以匹配机会为权重构建乘客图网络并聚类乘客需求;第2阶段设计以顺序插入启发式方法构造初始解的禁忌搜索算法求解。案例数据实验结果表明：本文聚类方法能保证优化质量并提高85%以上的计算效率,同时能缩减乘客等车时间和绕行距离;增大预算系数时解的鲁棒性逐渐提高,但会增加10%~40%的车辆数并降低1%~10%的里程节省率;大规模乘客案例和窄时间窗案例的合乘路径对不确定时间的敏感性更高,宽时间窗案例无需增加过多额外车辆和总里程就能达到较高水平的路径鲁棒性。     

Optimal locations and travel time display for variable message signs

This paper first develops a network equilibrium model with the travel time information displayed via variable message signs (VMS). Specifically, the equilibrium considers the impact of the displayed travel time information on travelers’ route choices under the recurrent congestion, with the endogenous utilization rates of displayed information by travelers. The existence of the equilibrium is proved and an iterative solution procedure is provided. Then, we conduct the sensitivity analyses of the network equilibrium and further propose a paradox, i.e., providing travel time information via VMS to travelers may degrade the network performance under some poor designs. Therefore, we investigate the problem of designing the VMS locations and travel time display within a given budget, and formulate it as a mixed integer nonlinear program, solved by an active-set algorithm. Lastly, numerical examples are presented to offer insights on the equilibrium results and optimal designs of VMS.     

Quantifying travel time variability at a single bottleneck based on stochastic capacity and demand distributions

Travel time reliability, an essential factor in traveler route and departure time decisions, serves as an important quality of service measure for dynamic transportation systems. This article investigates a fundamental problem of quantifying travel time variability from its root sources: stochastic capacity and demand variations that follow commonly used log-normal distributions. A volume-to-capacity ratio-based travel time function and a point queue model are used to demonstrate how day-to-day travel time variability can be explained from the underlying demand and capacity variations. One important finding is that closed-form solutions can be derived to formulate travel time variations as a function of random demand/capacity distributions, but there are certain cases in which a closed-form expression does not exist and numerical approximation methods are required. This article also uses probabilistic capacity reduction information to estimate time-dependent travel time variability distributions under conditions of non-recurring traffic congestion. The proposed models provide theoretically rigorous and practically useful tools for understanding the causes of travel time unreliability and evaluating the system-wide benefit of reducing demand and capacity variability.