一种行程时间检测和预测的实现方法

行程时间预报是交通信息服务的一项重要内容，章提出了一种计算行程时间的方法。在检测行程时间时采用了自动车牌识别技术，提高了结果的准确度。通过分析行程时间时间序列的时变特性，利用指数平滑模型进行预测，最后提出合理的修正方法。     

基于探测车数据的路段行程时间估计

利用探测车数据进行路段行程时间估计面临着两类误差：采样误差和非采样误差,从而导致估计结果精度不高和可靠性差。在回顾已有估计方法的基础上,有针对性地引入了自适应式卡尔曼滤波,建立了相应的状态方程和观测方程,利用相似时间特征的历史数据标定了状态转移系数,并对滤波进行了求解。以实际数据对估计方法进行了验证,平均相对误差为13.13%。研究表明,自适应式卡尔曼滤波能够应用到基于探测车数据的路段行程时间估计中来,并具有估计精度高、收敛速度快、参数少、对初值不敏感等优点。     

基于实验分析的驾驶员路线选择模式研究

驾驶员的信息响应模式对交通信息的效果有很大影响，是先进交通信息系统（ATIS）的重要研究内容之一，本文通过驾驶员调查和计算机仿真测试等实验分析方法研究驾驶员的路线选择模式，对在行程时间信息作用下驾驶员在熟悉程度不同路线之间的选择进行分析，讨论行程时间信息使驾驶员由常用路线改换到其它路线上的倾向性，并使用多项式Logit模型对实验数据进行分析。使用多因素方法分析驾驶员的路线选择行为较为合理，但因较为复杂而不易使用。本文提出了以行程时间为变量，路线熟悉程度为参数的简化的多项式Logit模型。     

基于行程质量的随机用户平衡分配模型

提出行程质量的概念以描述出行者在不确定环境下的路径选择准则。将行程质量定义为行程时间和行程时间可靠性的线性加权和,综合了影响路径选择的两个不同的重要因素:行程时间和行程时间可靠性。假定在路段通行能力随机变化的情况下出行者以估计行程质量费用最小作为路径选择的标准,建立了基于行程质量的随机用户平衡分配模型。证明了模型解的等价性和唯一性,给出了求解模型的MSA算法。在一个小型测试网络上的计算结果表明:模型能够反映出行者在随机路网中的路径选择行为。     

基于实时交通信息的行程时间估算及路径选择分析

路段行程时间的估计和预测是诱导系统的关键技术之一。由于路网参数不断变化,路段行程时间的估计必须满足实时性的要求。以城市交通控制系统的基本设施为基础,根据我国城市交通目前的发展状况,分析了影响路段行程时间的各种因素和路段行程时间的组成。利用设置在路段上的车辆自动检测装置搜集到的实时交通流信息,并结合随机服务系统的相关理论建立了城市道路路段行程时间的动态计算模型,提出了一种具有真实最短路径意义的实时动态最短路径选择的方法。     

交通信息对驾驶员选择行为的影响研究

论述了交通信息对驾驶员选择行为的影响,指出了交通信息系统可能导致的过激反应、信息过剩和集聚反应等。研究了系统中信息质量、信息服务对象、信息发布方式、信息发布位置等同题,提出了分析交通信息对驾驶员选择行为影响的方法和有待进一步研究的几个问题。     

基于浮动车移动检测与感应线圈融合技术的行程时间估计模型

综合考虑到浮动车检测技术与感应线圈检测技术的优缺点,为了提高道路行程时间估计的精度及完备性,提出基于浮动车与感应线圈的融合检测技术的行程时间估计模型。该模型利用神经网络技术对两种检测技术同一路段的检测数据进行融合,从而达到提高道路行程时间估计精度和完备性的目的。最后,以广州市7 000多辆装有GPS装置的出租车所提供的浮动车数据、100多个安装在广州市各主要道路口上的感应线圈检测器提供的感应线圈数据以及广州市交通电子地图为基础,在10条道路上分别随机选取的500个两种检测数据对提出的模型进行了验证,试验结果表明,此模型在道路行程时间估计的精度方面较浮动车移动检测技术与感应线圈技术有较大提高。     

上海小汽车驾驶员交通信息需求及ATIS潜在市场初步调查分析

了解驾驶员的交通信息需求对于先进的交通信息系统ATIS至关重要。本文以上海小汽车驾驶员为对象群体，讨论如何通过问卷调查方法，得到出租车驾驶员，公车驾驶员及私车驾驶员的交通信息需求。以及对现有交通信息系统的评价，并对ATIS潜在市场进行了分析。     

汽车质量索赔信息与可靠性分析

汽车质量索赔信息来源于汽车生产企业的特约维修站,可以从多方面对汽车的可靠性进行评价和分析。分析 了质量索赔信息的内涵及其与可靠性的关系,以一国产轿车为实例阐述了基于质量索赔信息的可靠性评价方法。     

VMS交通可变信息标志与静态标志的协调设置分析

在对VMS交通可变信息标志与静态标志的差异和互补性分析及道路网和交通流信息需求特性分析的基础上,对VMS交通可变信息标志与静态标志的协调设置进行了探讨,为二者的协调设置提供了依据。     

The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework

This article investigates the impact of alternative data smoothing and traffic prediction methods on the accuracy of the performance of a two-stage short-term urban travel time prediction framework. Using this framework, we test the influence of the combination of two different data smoothing and four different prediction methods using travel time data from two substantially different urban traffic environments and under both normal and abnormal conditions. This constitutes the most comprehensive empirical evaluation of the joint influence of smoothing and predictor choice to date. The results indicate that the use of data smoothing improves prediction accuracy regardless of the prediction method used and that this is true in different traffic environments and during both normal and abnormal (incident) conditions. Moreover, the use of data smoothing in general has a much greater influence on prediction performance than the choice of specific prediction method, and this is independent of the specific smoothing method used. In normal traffic conditions, the different prediction methods produce broadly similar results but under abnormal conditions, lazy learning methods emerge as superior.     

Freeway path travel time prediction based on heterogeneous traffic data through nonparametric model

Providing reliable travel time prediction is very much needed for commuters for their upcoming trips to reduce travel time and relieve traffic congestion. This article proposes an integrated model for path and multi-step-ahead travel time prediction on freeways using both historical and real-time heterogeneous traffic and weather data. The model's performance is investigated in a case study under various traffic scenarios. Results indicate that the proposed model provides satisfactory prediction results in various performance tests. For practical purposes, general guidelines for selecting the model's parameter sets as well as the efficient size of historical data are also presented.     

交通信息服务条件下的出行选择分析

首先 ,探讨了出行者的出行心理、行为以及信息之间的相互关系 ,在此基础上 ,简要分析了出行者的出行选择。其次 ,考虑在交通信息服务条件下 ,由于出行者本身以及信息方面的原因 ,出行选择可能出现过激反应、集聚反应等问题。最后 ,针对上述问题 ,提出应该加以重视的或有待进一步研究的几个问题。     

Subjective-utility travel time budget modeling in the stochastic traffic network assignment

ABSTRACT

In this article, we propose a new model called subjective-utility travel time budget (SU-TTB) model to capture travelers' risk-averse route choices. In the travel time budget (TTB) and mean-excess travel time (METT) model, a predefined confidence level is needed to capture the risk-aversion in route choice. Due to the day-to-day route travel time variations, the exact confidence level is hard to be predicted. With the SU-TTB model, we assume travelers' confidence level belongs to an interval that they may comply with in the route choice. The two main components of SU-TTB are the utility function and the TTB model. We can show that the SU-TTB can be reduced to the TTB and METT model with proper utility function for the confidence levels. We can also prove its equivalence with our recently proposed nonlinear-expectation route travel time (NERTT) model in some cases and give some new interpretation on the NERTT with this equivalence. Finally, we formulate the SU-TTB model as a variational inequality (VI) problem to model the risk-averse user equilibrium (RAUE), termed as generalized RAUE (GRAUE). The GRAUE is solved via a heuristic gradient projection algorithm, and the model and solution algorithm are demonstrated with the Braess's traffic network and the Nguyen and Dupuis's traffic network.     

公众出行信息服务系统的建设方式

以内蒙古公众出行信息服务系统建设的实际经验,重点从数据、功能、建设模式等几个方面介绍了公众出行信息服务系统的建设与应用。     

VNS中动态行程时间与多端动态最短路算法

对车辆导航系统中路径引导信息的供给与需求进行了综合分析，提出了最短路划分方法与多端动态最矩路问题，建立了路段动态行程时间计算模型与多端动态最短路改进短阵算法。     

对隧道施工超前地质预报有关问题的探讨

就隧道超前地质预报的目的与任务、预报方法及预报工作中存在的问题进行了探讨,以期对隧道工程的快速顺利施工有所帮助。     

A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

Arterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial.     

Effectiveness of travel time reliability indicators in the light of the assessment of dynamic managed lane strategy

ABSTRACT

The aim of traffic management is to ensure a high quality of service for a maximum number of users by decreasing congestion and increasing safety. Uncertainty of travel times decreases the quality of service and leads end users to modify their plans regardless of the average travel time. Indicators describing travel time reliability are being developed and should be used in the future both for the optimization and for the assessment of active traffic management operations. This article discusses the efficiency of certain reliability indicators in an ex-post assessment of a traffic management strategy. Ex-post assessment is based on an observational before–after study. As some factors other than the studied management strategy may intervene between the two periods, and as most reliability indicators require knowledge of the full travel time distribution and not only its average, a methodology is developed for the identification of the impact of these exogenous factors on the whole distribution. Many reliability indicators are split into different parts allowing the identification of the part due to the management strategy impact. The methodology is tested numerically on a managed lane operation consisting of Hard Shoulder Running (HSR) at rush hour on a section of a French motorway. The variation of some reliability indicators appears misleading, whereas the splitting of the indicators increases our understanding of the strategy and highlights its impact. The paper gives the reliability assessment of the HSR field test and discusses different reliability indicators to identify their potential performances and shortcomings.