A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel

In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e., self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components. The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.

溢油指纹数字化鉴别方法

数字化鉴别法借助于计算机技术具有处理效率高、易于存储等特点,近年来,运用该方法对溢油指纹进行鉴别分析受到越来越多的关注。介绍了聚类分析、主成分分析、判别分析、t检验法、相关分析、重复性限法等数字化鉴别方法的基本概念以及这些方法应用于溢油指纹鉴别的原理、步骤等,对这些数字化鉴别分析方法的适用条件、优缺点等进行分析比较,以期为更好地将数字化鉴别方法应用于油指纹鉴别分析奠定基础。     

Passenger travel characteristics and bus operational states: a study based on IC card and GPS data in Yinchuan,China

ABSTRACT

In recent years, public transport has been developing rapidly and producing large amounts of traffic data. Emerging big data-mining techniques enable the application of these data in a variety of ways. This study uses bus intelligent card (IC card) data and global positioning system (GPS) data to estimate passenger boarding and alighting stations. First, an estimation model for boarding stations is introduced to determine passenger boarding stations. Then, the authors propose an innovative uplink and downlink information identification model (UDI) to generate information for estimating alighting stations. Subsequently, the estimation model for the alighting stations is introduced. In addition, a transfer station identification model is also developed to determine transfer stations. These models are applied to Yinchuan, China to analyze passenger flow characteristics and bus operations. The authors obtain passenger flows based on stations (stops), bus lines, and traffic analysis zones (TAZ) during weekdays and weekends. Moreover, average bus operational speeds are obtained. These findings can be used in bus network planning and optimization as well as bus operation scheduling.     

Analysis of child pick-up during daily routines and for daytime no-notice evacuations

In a no-notice disaster (e.g., nuclear explosion, terrorist attack, or hazardous materials release), an evacuation may start immediately after the disaster strikes. When a no-notice evacuation occurs during the daytime, household members are scattered throughout the regional network, and some family members (e.g., children) may need to be picked up. This household pick-up and gathering behavior was seldom investigated in previous work due to insufficient data; this gap in our understanding about who within families handles child-gathering is addressed here. Three hundred fifteen interviews were conducted in the Chicago metropolitan area to ascertain how respondents planned their response to hypothetical no-notice emergency evacuation orders. This paper presents the influencing factors that affect household pick-up and gathering behavior/expectations and the logistic regression models developed to predict the probability that parents pick up a child in three situations: a normal weekday and two hypothetical emergency scenarios. The results showed that both mothers and fathers were more likely to pick up a child under emergency conditions than they were on a normal weekday. For a normal weekday, increasing the distance between parents and children decreased the probability of parents picking up children; in other words, the farther parents are from their children, the less likely they will pick them up. In an emergency, effects of distance on pick-up behavior were significant for women, but not significant for men; that is, increasing the distance between parents and children decreased the probability that mothers pick up a child, but had a less significant effect on the fathers’ probability. Another significant factor affecting child pick-up behavior/expectations was household income when controlling for distance. The results of this study confirm that parents expect to gather children under emergency conditions, which needs to be accounted for in evacuation planning; failure to do so could cause difficulties in executing the pick-ups, lead to considerable queuing and rerouting, and extend the time citizens are exposed to high levels of risk.     

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.     

城市机动车辆停放选择模型

为了增强出行前停车信息查询功能和提高停车诱导效果,提出一种服务于驾驶员出行前对目的地首选以及备选停车场进行车辆停放选择的优化模型,建立了以驾驶员使用最便利,可达性最强,出行停放成本最低为目标的车辆停放选择模型,利用启发式算法计算了模型的备选解集。经实例验证,该模型可以得到多个满足约束条件的合理性停车方案,从而为驾驶员在出行前选择停车场提供了决策依据。     

模糊模式识别在交通量预测模型选择中的应用

本文将模糊模式识别应用于交通规划预测模型的评价．以出行受约束的重力模型与双约束重力模型为例,说明常用的一些检测方法存在的不严密性,并用模糊模式识别方法定量确定预测结果与实际出行量的拟合程度．本文的分析结果能为交通规划预测模型的选择提供可靠的依据,并为预测结果的评价提供新的方法．     

基于GMM-CHMM 的城市道路换道行为识别

高级驾驶辅助系统(ADAS)是提高车内乘员安全性的主动安全系统之一,将车载参数和车辆位置参数相结合,提出一种能够应用到ADAS的城市道路换道行为识别模型. 在西安城市道路环境中进行实验,采集18 位驾驶员的9 个车载实时参数数据,以及前后车辆间的相对速度、相对距离、相对角度,提取412 个换道行为单元和824 个车道保持行为单元,共 88 992 条数据. 运用数理统计方法分析表明,方向盘转角、转向角速度、相对安全距离比在换道行为和车道保持行为之间有显著性差异,在这3 个特征参数的基础上,建立混合了高斯混合模型(GMM)和连续型隐马尔可夫模型(CHMM)的识别模型,用部分样本对模型效能评价. 结果表明,混合模型对换道行为的识别精度为93.6%,具有良好的识别效果,可以很好地应用到 ADAS.     

基于集成学习算法的航路网络航段交通拥挤识别方法研究

基于航路网络ADS-B航迹数据定义航路网络航段交通流量、航段交通密度、航段交通饱和度、航段交通接近率4 项交通拥挤状态评价指标;采用模糊C均值聚类算法和航段历史交通拥挤状态评价指标参数划分航段交通拥挤状态等级;结合集成学习算法构建航路网络航段交通拥挤状态识别模型,实现航段交通拥挤状态的识别. 实证分析表明：航路网络交通拥挤状态集成学习识别模型对实验航路网络航段交通拥挤状态识别准确率达到98.34%,采用决策树基学习器优于k 近邻基学习器,且增加的集成学习基学习器数量可提升模型的识别精度;集成学习识别模型的识别性能优于BP神经网络模型,识别方法符合实际且具有应用价值.     

基于持续跟踪调查的防御性驾驶行为分析模型

为解决防御性驾驶行为心理作用过程缺少定量描述的问题,在界定防御性驾驶行为内涵的基础上,设计防御性驾驶行为量表. 基于计划行为理论,构建以过去防御性驾驶行为、行为态度、主观规范和知觉行为控制为预测变量,行为意图为中介变量,后续防御性驾驶行为为结果变量的分析模型. 对非职业驾驶员在间隔3 个月的两个时间点跟踪调查,第1 次调查内容包括过去防御性驾驶行为、TPB变量、个人基本信息,第2 次调查包括后续防御性驾驶行为和个人基本信息,获得两次调查数据完全匹配的有效问卷213 份. 对有效数据分析显示：主观规范对防御性驾驶行为意向不显著,知觉行为控制、行为态度和过去防御性驾驶行为对防御性驾驶行为意向影响值分别为0.40、0.29 和0.26;行为态度、知觉行为控制和过去驾驶行为通过行为意向(0.36),可以在一定程度上对后续行为进行解释.