城市轨道交通各发展阶段的运行特征及在公交系统中的作用

城市轨道交通网络的形成分为三个发展阶段:成网前阶段,轨道骨架网阶段和稳定的轨道网络阶段.分析了不同发展阶段轨道交通运行特征指标的取值范围,采用客运量和客运周转量指标研究了轨道交通不同发展阶段在公共交通系统中所起的作用.     

The influence of personal security fears on women's travel patterns

This paper investigates the influence that women's fears and apprehension about attack and harassment have on use of transport facilities. After initial consideration of the factors that most affect perceived risk, the personal security aspects of different modes are reviewed by reference to a survey conducted in Southampton in 1986. High levels of perceived insecurity were found, particularly for walking at night, in parks and subways and when waiting for public transport services in isolated areas. The conclusions argue that personal security is an important but neglected issue, deserving of greater attention by transport planners.     

Bridge and tunnel toll elasticities in New York

In 1992, the authors carried out a statistical analysis of Triborough Bridge and Tunnel Authority (TBTA) crossings in New York City, to determine the impact of toll increases on traffic volumes and revenue. Using twelve years of monthly time-series data, we developed a set of multiple regression models that estimated traffic volumes on each TBTA bridge and tunnel as a function of the toll level and other explanatory variables. In most cases, the estimated toll elasticities were negative and much less than 1.0 in absolute value; the median toll elasticity for automobiles was found to be –0.10. Our finding that automobile travel demand is highly inelastic with respect to toll rates is consistent with most previous travel demand studies.     

基于IC卡数据挖掘获取公交OD矩阵的方法

以广州市羊城通IC卡以及广州市公交系统信息为例子,探讨了实现基于IC卡信息获取公交客流出行特征的方法。在IC卡数据挖掘和统计基础上,对线路站点OD矩阵、区域OD矩阵进行了推算。相比传统的人工统计法。该方法易于实现,能运用到实际中且运算效率高,适用于海量数据的客流出行特征统计分析。     

交通运输方式选择多属性决策的信息公理方法

基于信息公理的多属性决策方法为旅客的出行方式选择提供了一种有效工具。通过分析旅客出行行为特性的影响因素,确定出旅客出行方式选择的评价指标。利用成功概率计算出每种方案所包含的信息量,信息量最小的方案为最佳方案。由于避免了权重分配所带来的人为影响以及量纲不统一等问题,使得评价结果更趋合理,评价过程计算简单易于实现。     

Are young Americans carless across the United States? A spatial analysis

This study uses the National Household Travel Survey (NHTS) data to investigate the most recent correlates of vehicle ownership among young Americans. This study performs a spatial analysis to examine the potentially non-stationary relationships between sociodemographic factors and vehicle ownership. Consistent with previous studies, modeling results from this study showed that young Americans are more likely to be carless than older adults. The spatial analysis answers the research question – in which regions(s) young Americans are even less likely to have a car. The results highlighted the Northeast states for the young American’s extra-lower vehicle ownership if the influences of all other factors are held constant. The cost of living and availability of transportation alternatives are possible reasons. Further, this study built separate models for young adults (25–34 years old) and three older age groups. The vehicle ownership correlates within the young adults are found to be generally consistent with the correlates among all adults. Among young adults, vehicle ownership is still significantly related to their gender, educational attainment, employment status, household characteristics, and travel demand. However, young adults’ vehicle ownership seems to be less sensitive to household income than mid-age adults’ (35–44 years old), perhaps because young people may not perceive financial stress such as child support and mortgage. This study contributes by using a spatial analysis approach to reveal the non-stationary correlates of vehicle ownership. This approach is useful for future travel behavior research and transportation policy considering the spatial heterogeneity.     

基于出行行为模型的出行感染风险评估 ——以新冠肺炎疫情期间江苏省地区为例

为准确评估新冠肺炎疫情期间的出行感染风险，在对出行行为进行全出行链建模基础上提出出行风险评估模型.基于江苏省确诊病例流行病学调查报告和线上问卷调查数据，分别对病毒携带者和普通出行者出行行为模型进行参数标定，对比分析病毒携带者和普通出行者出行行为的异同.进一步对疫情期间不同出行方式和出行活动进行感染风险评估. 得到结论：交通管制措施可有效降低出行感染风险，就医出行风险明显高于其他出行活动，差旅在疫情传播初期风险较高，非机动车出行风险相对较低.     

共享单车出行空间异质性特征及驱动因素研究

为实现基于轨迹数据挖掘的共享单车出行空间异质性特征及其驱动因素评估，本文应用核密度分析和热点探测，获取采样分析区域并以热力值表征共享单车出行发生量，减少尺度效应的干扰；引入空间统计学的半变异函数模拟共享单车出行发生量的结构性和随机性变化规律，挖掘空间异质性特征，确定邻域效应的尺度范围；利用空间序列的斜率表征变化趋势，同时，结合改进的空间滞后和残差模型，区分土地利用、邻域效应和其他建成环境各自对共享单车出行空间异质性特征的驱动力。以北京市为案例进行分析，结果表明：北京市的共享单车出行存在中等的、 正的空间自相关性，空间异质性特征的最佳拟合模型为指数模型；空间自相关性的衰减半径为 1860 m，大于此距离时邻域效应消失；建成环境对空间异质性特征的相对驱动力最大，邻域效应对其的相对驱动力则处于中间水平，而土地利用对其的相对驱动力最小。     

基于狄利克雷过程混合模型的城市活动聚类方法研究

手机信令数据不仅记录个体出行轨迹，也为分析城市活动空间分布特征提供了基础.本文提出一种基于狄利克雷混合模型的城市活动特征聚类方法，以手机信令提取居民出行OD为基础，将每个基站的到发出行量作为表征该基站所处空间位置的活动特征，研究特征的聚类方法.引入狄利克雷分布作为先验分布，由中餐馆模型推定特征聚类数量.与其他聚类方法相比，该方法最大的优点在于无需事先指定聚类数量，避免了传统聚类方法的缺陷.将本文方法应用到三亚市城市活动特征聚类当中，结果能够有效地反应不同城市功能组团的活动特征.     

Application of Lagrangian relaxation approach to α-reliable path finding in stochastic networks with correlated link travel times

This study investigates the important problem of determining a reliable path in a stochastic network with correlated link travel times. First, the distribution of path travel time is quantified by using trip records from GPS probe vehicles. Second, the spatial correlation of link travel time is explicitly considered by using a correlation coefficient matrix, which is incorporated into the α-reliable path problem by Cholesky decomposition. Third, the Lagrangian relaxation based framework is used to handle the α-reliable path problem, by which the intractable problem with a non-linear and non-additive structure can be decomposed into several easy-to-solve problems. Finally, the path-finding performance of this approach is tested on a real-world network. The results show that 15 iterations of calculation can yield a small relative gap between upper and lower bounds of the optimal solution and the average running time is about 5 s for most OD settings. The applicability of α-reliable path finding is validated by a case study.