On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level

This paper proposes a new spatial multivariate count model to jointly analyze the traffic crash-related counts of pedestrians and bicyclists by injury severity. The modeling framework is applied to predict injury counts at a Census tract level, based on crash data from Manhattan, New York. The results highlight the need to use a multivariate modeling system for the analysis of injury counts by road-user type and injury severity level, while also accommodating spatial dependence effects in injury counts.     

Door-to-door travel times in RP departure time choice models: An approximation method using GPS data

A common way to determine values of travel time and schedule delay is to estimate departure time choice models, using stated preference (SP) or revealed preference (RP) data. The latter are used less frequently, mainly because of the difficulties to collect the data required for the model estimation. One main requirement is knowledge of the (expected) travel times for both chosen and unchosen departure time alternatives. As the availability of such data is limited, most RP-based scheduling models only take into account travel times on trip segments rather than door-to-door travel times, or use very rough measures of door-to-door travel times. We show that ignoring the temporal and spatial variation of travel times, and, in particular, the correlation of travel times across links may lead to biased estimates of the value of time (VOT). To approximate door-to-door travel times for which no complete measurement is possible, we develop a method that relates travel times on links with continuous speed measurements to travel times on links where relatively infrequent GPS-based speed measurements are available. We use geographically weighted regression to estimate the location-specific relation between the speeds on these two types of links, which is then used for travel time prediction at different locations, days, and times of the day. This method is not only useful for the approximation of door-to-door travel times in departure time choice models, but is generally relevant for predicting travel times in situations where continuous speed measurements can be enriched with GPS data.     

高速公路交通动态数据检测技术应用研究

在分析高速公路各种交通动态数据检测技术特性和精度的基础上,通过现场调研与试验,分析交通检测技术的应用特性,重点研究超声波与微波非接触式检测技术的应用功能;通过建立交通数据采集站,系统地研究基于微波——线圈的组合应用及其数据融合。试验结果表明,非接触式交通检测技术在高速公路动态数据检测中可以使用,但不宜单独使用;应采用多种检测技术,通过多检测技术数据融合的检测精度能够提高10％～20％。     

Gas anxiety and the charging choices of plug-in hybrid electric vehicle drivers

Plug-in hybrid electric vehicles (PHEVs) can provide many of the benefits of battery electric vehicles (BEVs), such as reduced petroleum consumption and greenhouse gas emissions, without the “range anxiety” that can accompany driving a vehicle with limited range when there are few charging opportunities. However, evidence indicates that PHEVs are often plugged in more frequently than BEVs in practice. This is somewhat paradoxical: drivers for whom plugging in is optional tend to do so more frequently than those for whom it is necessary. This has led to the coining of a new term – “gas anxiety” – to describe the apparent desire of PHEV drivers to avoid using gasoline. In this paper, we analyze the variables influencing the charging choices of PHEV owners, testing whether drivers express preferences consistent with the concept of gas anxiety. We analyze data collected in a web-based stated preference survey using a latent class logit model. The results reveal two classes of decision-making patterns among the survey respondents: (1) those who weight the cost of gasoline and the cost of recharging approximately equally (the cost-minimizing class), and (2) those who weight the cost gasoline more heavily than the cost of recharging (the gas anxiety class). Respondents in the gas anxiety class expressed a willingness to recharge at a charging station even when doing so would cost approximately four times as much as the cost of the gasoline avoided. While the gas anxiety class represents the majority of our sample, more recent PHEV adopters are more likely to be in the cost-minimizing class. Looking forward, this suggests that public charging station operators may need to price charging competitively with gasoline on a per-mile basis to attract PHEV owners.     

多雷达与ADS数据融合的可变周期更新算法

为提高空中交通管制系统的整体监视精度, 在研究自动相关监视(automatic dependent surveillance简称ADS)信息与雷达监视信息构成和特征差异的基础上, 通过建立ADS目标与雷达目标之间的数据关联映射关系, 构造了一个将ADS数据与多雷达数据融合处理的过程模型, 提出了基于ADS报告周期调整ADS航迹与雷达航迹融合权重因子的可变周期更新算法。应用结果表明更新算法实现了两类信息的深度融合和对有效信息的综合利用, 既保证了当ADS报告更新率较高时ATC监视的整体精度, 又提高了ADS报告更新率较低时目标航迹的连续性, 降低雷达测量误差对系统监视精度的影响, 是一种用于空中交通管制系统监视数据处理的有效方法。     

基于数据仓库的决策支持系统体系结构

在分析传统DSS的不足之后，提出了基于数据仓库决策支持系统的体系结构，主要包括模型库、方法库、知识库和数据仓库及其相应的管理系统，联机分析处理、数据挖掘单元和问题综合处理单元等几部分组成．研究了系统中各部分的功能、相互关系及基于数据仓库的决策支持系统的关键技术．     

基于Bezier函数的列车特性曲线数据处理方法研究

在列车运行计算领域, 对列车特性曲线数据处理方法的研究一直得不到应有的重视,因此利用插值法求解列车特性数据的方法沿用至今. 本文研究了插值法计算列车特性数据的误差及其影响. 对比分析了Bezier曲线与列车特性曲线特点,得出可用Bezier曲线拟合列车特性曲线,并提出一种基于Bezier函数的列车特性曲线数据求解方法. 该方法是通过人机交互方式,运用Bezier曲线拟合列车特性曲线,该Bezier曲线对应的Bezier函数为列车特性曲线的拟合函数. 用该拟合函数求解列车特性数据,计算速度与精度均明显提高. 最后对CRH3型动车组的牵引特性曲线作了实际拟合,得出的结果令人满意.     

大秦线Locotrol系统无线数传工作频段的选择

介绍大秦线Locotrol系统的无线数据传输方式,分析800MHz和450MHz频段电波在平原开阔地带、长直隧道、曲线隧道、短隧道群弯曲线路中的测试数据,得出不同频段电波在不同地形条件下的传播特性及误码特性.认为采用800MHz频段无线数据传输系统,能够满足在大秦线开行4×5000吨的2万吨组合列车Locotrol系统应用的需要.     

长株潭城市群连续运行参考站系统的研究与实现

采用虚拟参考站(VRS)技术,结合应用长株潭城市群一体化的实际情况;从VRS的关键技术、总体方案、控制中心和基准站方案等方面对连续运行参考站系统进行分析和设计.     

激光线扫描测量数据点的处理方法研究

在分析激光数字化测量数据点的基础上,提出利用圆心距偏差判别法对测量数据进行平滑处理,修正个别测量异常点位置,再利用基于弦高的自适应数据采样法,对数据点列进行精简,消除冗余数据,使数据点的分布自适应于曲线曲率,达到优化数据点的分布及减少描述曲线数据量的目的.实验结果表明该方法能够对测量数据进行较好的精简优化.