信号控制对动态路线选择的影响研究

以动态路线选择模型为基础的先进的出行者信息系统（ＡＴＩＳ）的实施必然对城市交通控制系统产生影响，同时交通控制系统的控制方案对路线诱导信息“运行时间”的估计也发生作用，影响用户对最优路线的造选择。对两系统的相互关系进行了分析，并建立了两系统相互关系模型最后给出了实际案例分析。     

基于期望间距的车辆跟驰模型的建立

以“期望间距”为基准参理，考虑驾驶员的心理和生理因素，建立了车辆跟驰模型，为研究驾驶行为提供了一种新的思路。     

A conceptual and methdological framework for the generation of activity-travel patterns

This paper develops a conceptual framework for the generation of activity and travel patterns in the context of more general structures and presents an integrated model system as a step toward development of an improved travel demand forecasting model system. We propose a two-stage structure to model activity and travel behavior. The first stage, the stop generation and stop/auto allocation models, consists of the choices for the number of household maintenance stops and the allocation of stops and autos to household members. The second stage, the tour formation model, includes the choices for the number of tours and the assignment of stops to tours for each individual, conditional on the choices in the first stage. Empirical results demonstrate that individual and household socio-demographics are important factors affecting the first stage choices, the generation of maintenance stops and the allocation of stops and autos among household members, and the second stage choices, the number of tours and the assignment of stops to tours. This revised version was published online in June 2006 with corrections to the Cover Date.     

Conformity: How VMT-speed distributions can affect mobile emission inventories

Transportation conformity is a US regulatory process that requires that transportation modeling be integrated with air quality modeling. Consequently, every change to either modeling process is undertaken with great scrutiny by the regional governments, who have to use the models for demonstrating conformity. This paper explores the "trip versus link debate," which stems from the fact that the standard travel demand models used by most metropolitan planning organizations are primarily link oriented, while the air quality models have been primarily trip oriented. Using the Sacramento region we examine the effects on mobile source emissions inventories when speed-VMT distributions are constructed using the trip and link-based philosophies. The results of our study indicate that trip-based VMT-speed distributions produce consistently lower emissions estimates than the link-based distributions. We use the results to assert that deciding between a trip-based or link-based conformity modeling process involves more than the technical difficulty of changesto the models or the potential political ramifications, it involves assessing which method will provide the most accurate estimates of regional motor vehicle emissions. We also examine ways to think about constructing mobile source emission inventories.     

砂卵石地层台阶法施工隧道围岩-支护结构应力应变特性三维离散元分析

为了解砂卵石地层隧道围岩和支护结构的应力应变特性，以青海循隆高速公伯峡隧道为依托，借助PFC3D离散元软件对公伯峡隧道穿越砂卵石地层进行三维模拟，重点研究以密排短管棚预支护为根本前提，以三台阶预留核心土为施工方法的砂卵石地层围岩和支护结构的应力应变特性，并与现场实测进行对比分析。研究结果表明： 隧道台阶开挖时，围岩应力集中范围逐渐从拱顶过渡到拱腰，直到拱脚，对应的塑性区范围不断扩大，且密排短管棚对塑性区的发展有一定的“遮拦效应”； 围岩横纵向变形规律一致，主要是向隧道临空面产生收敛变形，且密排短管棚形成的梁拱效应限制了掌子面前方位移发展； 2种方法得到的初期支护变形规律一致，均呈阶段性变化，拱顶下沉累计值大于周边收敛累计值，且两者的最终变形量均满足规范限值要求。     

最小二乘平差在纸海图矢量化过程中的应用

在矢量化纸海图时,由于基准点的采样误差,导致转换参数的误差,最终导致电子海图的空间对象的精度不高。本文应用最小二乘法对基准点的采样误差实施平差,提高转换参数的精度,最终可较大幅度地提高电子海图中空间对象的精度     

城市综合交通调查的规范与创新

城市综合交通调查在中国已有30余年的广泛实践,当前的信息化和大数据背景下正历经发展与变革.在分析城市综合交通调查现状问题的基础上,提出制定《城市综合交通体系规划交通调查导则》(以下简称《导则》)的必要性.从目标与总体框架、调查项目分类、术语定义、调查内容与问题选项分类、调查流程规范几个层面对《导则》的主要思想与关键内容进行解析.提出城市综合交通调查未来发展方向应是将信息化技术与传统调查手段结合,提高调查效率与质量,关注信息化手段未能涵盖的交通特征.     

Comparing travel mode and trip chain choices between holidays and weekdays

Choices of travel mode and trip chain as well as their interplays have long drawn the interests of researchers. However, few studies have examined the differences in the travel behaviors between holidays and weekdays. This paper compares the choice of travel mode and trip chain between holidays and weekdays tours using travel survey data from Beijing, China. Nested Logit (NL) models with alternative nesting structures are estimated to analyze the decision process of travelers. Results show that there are at least three differences between commuting-based tours on weekdays and non-commuting tours on holidays. First, the decision structures in weekday and holiday tours are opposite. In weekday tours people prefer to decide on trip chain pattern prior to choosing travel mode, whereas in holiday tours travel mode is chosen first. Second, holiday tours show stronger dependency on cars than weekday tours. Third, travelers on holidays are more sensitive to changes in tour time than to the changes in tour cost, while commuters on weekdays are more sensitive to tour cost. Findings are helpful for improving travel activity modeling and designing differential transportation system management strategies for weekdays and holidays.     

Using the built environment to oversample walk,transit, and bicycle travel

Characteristics of the built environment (BE) have been associated with walk, transit, and bicycle travel. These BE characteristics can be used by transportation researchers to oversample households from areas where walk, transit, or bicycle travel is more likely, resulting in more observations of these uncommon travel behaviors. Little guidance, however, is available on the effectiveness of such built environment oversampling strategies. This article presents measures that can be used to assess the effectiveness of BE oversampling strategies and inform future efforts to oversample households with uncommon travel behaviors. The measures are sensitivity and specificity, positive likelihood ratio (LR+), and positive predictive value (PPV). To illustrate these measures, they were calculated for 10 BE-defined oversampling strata applied post-hoc to a Seattle area household travel survey. Strata with an average block size of <10 acres within a ¼ mile of household residences held the single greatest potential for oversampling households that walk, use transit, and/or bicycle.     

Multi-step prediction of experienced travel times using agent-based modeling

This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60 min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.