Residential density and transportation emissions: Examining the connection by addressing spatial autocorrelation and self-selection

This paper examines the effect of residential density on CO₂ equivalent from automobile using more specific emission factors based on vehicle and trip characteristics, and by addressing problems of spatial autocorrelation and self-selection. Drawing on the 2006 Puget Sound Regional Council Household Activity Survey data, the 2005 parcel and building database, the 2000 US Census data, and emission factors estimated using the Motor Vehicle Emission Simulator, we analyze the influence of residential density on road-based transportation emissions. In addition, a Bayesian multilevel model with spatial random effects and instrumental variables is employed to control for spatial autocorrelation and self-selection. The results indicate that the effect of residential density on transportation emissions is influenced by spatial correlation and self-selection. Our results still show, however, that increasing residential density leads to a significant reduction in transportation emissions.     

A structural equation model of activity participation and travel behavior using longitudinal data

Abstract

This paper presents a dynamic structural equation model (SEM) that explicitly addresses complicated causal relationships among socio-demographics, activity participation, and travel behavior. The model assumes that activity participation and travel patterns in the current year are affected by those in previous years. Using the longitudinal dataset collected from Puget sound transportation panel ‘wave 3’ and ‘wave 4,’ these assumptions are tested with suggested SEMs. Within each wave, the model is structured to have a three-level causal relationship that describes interactions among endogenous variables under time-budget constraints. The resulting coefficients representing the activity durations indicate that people tend to allocate their time according to the importance and the obligation of the activity level. Results from the dynamic SEM confirm the fact that people's current activity and travel behavior do have effects on those in the future. The resulting model also shows that activity participation and travel behavior in ‘wave 3’ are closely related to those in ‘wave 4.’ These explicit explanations of relationships among variables could provide important perspectives in the activity-based approach which becomes recognized as a better analytical tool for the transportation planning and policy making process.     

城市客运交通枢纽预警分级阈值简析

交通枢纽作为服务于公共交通之间以及公共交通与其它交通方式之间客流转换的场所,是改善整个交通系统,提高居民出行效率、解决出行换乘等问题的重要影响因素。本文结合东直门综合运输枢纽信息服务平台工程,采集枢纽客流进出量,总结客流统计规律,对东直门交通枢纽预警分级阈值进行简析,相关研究成果可为交通枢纽预警分级阈值分析提供参考。     

交通运输信息融合功能模型

综述一般信息融合功能模型和交通运输信息融合应用的进展,指出这两个方面的联系和差异.针对交通运输应用的信息本质和功能特性,提出交通运输信息融合的一般定义,并建立了交通运输信息融合功能模型.模型对信源引入机器信息与认知信息的划分,提出了分属机器信息和认知信息两个层次的采集校验融合、状态特征融合、预测评估融合、规划设计融合和综合决策融合五级信息融合功能,提供了五级融合功能的三种应用模式,实现了不同交通运输应用功能与信息操作功能的衔接,与一般多传感器数据融合功能模型具有统一性.论文还以交通拥挤管理问题为例,给出了相应的信息融合功能模型.     

基于数据分类的高速铁路运营事故预测方法

高速铁路运营事故预测方法是度量铁路安全管理水平的重要指标.为提高高速铁路的安全运营水平,引入工业数据分类方法,分析反向传播(BP)神经网络和灰色模型在高速铁路安全运营事故预测过程中的适应性.首先,运用事故次数、事故联动系数、月均事故率3个参数对高速铁路安全运营水平进行度量;然后,根据工业数据分类方法判别高速铁路运营事故数据属于块状型,据此建立反向传播(BP)神经网络运营事故预测模型;针对运营事故数据具有波动大的特点,利用均值聚类方法建立K-GM(1,3)预测模型.以近年来高速铁路运营事故数据为样本对模型进行训练和分析,结果表明:BP神经网络、K-GM(1,3)、GM(1,3)预测模型的预测误差分别为8.92%,13.68%,345.25%,BP神经网络在高速铁路安全运营事故预测过程中的适应性要优于灰度模型.      

Case Study of Transportation Planning Based on Land Development Patterns in the Great Chengdu Area

Traffic is a function of land use.Based on the understanding of mutual influences between transportation investment and land development,transportation corridors of various levels in Great Chengdu Area(GCA)were investigated.Trip generation was predicted based on population expansion,and trip attraction was estimated based on land use intensity coefficient.Lane assignment of transportation corridors with case studies is expected with two-way-six-lane routes connecting county-cities,two-way-four-lane routes connecting new towns in plane area,and two-way-two-lane connecting new towns in hilly area and prime villages.     

2010世博会与集约车辆检测技术

集约交通出行具有高效、便捷的优点。2010年上海世博会交通保障方案中将集约交通做为一种解决道路拥堵的交通控制手段向市民公示。什么是集约交通,用怎样的技术来保障集约交通方案的顺利实施,文中将对上海世博会以及相关交通理论和技术进行阐释来回答上述问题。     

基于神经网络的公交车辆行程时间预测模型研究

城市公交丰辆行程时间预测是公共交通信息服务和运营调度的重要内容,要求较高的实时性和准确性。本文以智能交通运输系统为背景,通过分析公交车辆的行驶特性,基于改进的神经网络模型,建立了公交车辆动态行程时间预测模型,并对比了三种不同输入变量方案的神经网络预测模型,表明该模型具有良好的适用性。此外,将该方法与卡尔曼滤波法的行程时间预测模型进行比较,结果表明,基于神经网络的动态行程时间预测模型精确度较高。     

北京海淀区地面公共交通线路网现状评价

本文从线路长度、线路网密度、重复系数、非直线系数、站点密度、平均站距和站点覆盖率等七方面对海淀区地面公共交通线路网现状进行了评价,找出了海淀区地面公交线网布设中存在的问题,并分析了原因。     

《集装箱运输业务》课程情境教学探析

本文指出了《集装箱运输业务》课程具有很强实操性的特点及教材存在的缺陷.根据情境认知理论,论述了《集装箱运输业务》课程采用情境教学以提高该课程的教学质量的观点.并对实施《集装箱运输业务》情境教学相关问题提出一些建议.