Using Electronic Toll Collection Data to Understand Traffic Demand

In this study, we explored the potential of using electronic toll collection (ETC)-derived data that are a part of intelligent transport systems (ITS). Dynamic origin–destination (OD) traffic volumes were estimated using ETC data on the Hanshin Expressway. A dynamic OD estimation model that was suggested in a previous study was used, and abundant ETC data were input to improve the estimation accuracy. The results of OD estimation were analyzed to understand traffic demand and its variation. External factors were clarified that have an influence on variances in the OD flows, and statistical analysis methods for the variations were proposed depending on the factors. Moreover, the improvements in traffic simulation accuracy and performance as a result of using ETC data as input variables in the simulation models were discussed. According to the results of this study, ETC data have potential to assist in understaningd traffic demand and its variation, and the results can be applied to network management.     

丹东市公共交通线网评价与优化

以<丹东市公共交通线网评价与优化研究>项目为背景,结合现代化城市公交系统规划方法与管理技术,利用先进的计算机技术和软件工程的思想和方法,开发设计了公交数据处理软件系统,并利用该系统在实际中处理了大量调查数据,确保了数据处理的真实性和准确性.并在此基础上,分析评价了丹东市的公交出行特征、公交线网、建成区公交日流量及OD分布等指标,预测了未来客运需求.     

城市道路网络动态OD估计模型

针对以动态交通管理与控制为目标的动态交通需求计算问题,分析了OD量与交叉口转向交通量的动态关系,将其作为新的系统测量量引入,以此建立了同时考虑路段断面交通量和交叉口转向交通量的状态空间模型,得到了基于城市道路网络的动态OD估计模型,给出了考虑不等式约束的卡尔曼滤波递推方程及相应算法过程。利用微观仿真软件Paramics所建立的实验平台对该模型进行了仿真验证。结果表明：应用该模型进行交通量计算,与传统的仅考虑路段断面交通量模型相比,绝对误差平均减少9％,相对误差平均减少20％,而且其能更好地反映交通量真实值随时间变化的情况,计算结果明显优于传统模型。     

有序样本聚类方法在城市轨道交通运营时段划分中的应用

为合理划分轨道交通运营时段并指导其开行方案,提出一种基于有序样本聚类技术的运营时段划分方法。根据统计时段内客流数据,引入单向OD(origin-destination)概率矩阵,并给出单向OD概率矩阵的时序模型和提取方法;利用有序样本聚类方法,以最优分割法量化站间客流转移规律,求解聚类方案。最后以某一轨道交通线路为例,提取时间间隔为20 min的上行OD概率矩阵时间序列,以最优分割法进行聚类,将站间客流转移规律相近的统计时段归为一类,提出目标线路运营时段划分方案。     

北亚得里亚海集装箱港口群市场前景分析

欧洲发达市场是全球集装箱海运重要板块,对其市场发展潜力分析是中资企业参与欧洲集装箱港口投资的必要前提。集装箱港口市场发展潜力受多种因素影响,以腹地生产法进行市场预测是当前的主流方法,但该方法主要以外贸需求作为变量,存在片面性。针对当前海外港口集装箱市场分析中存在的问题,提出最优路径模型下港口集装箱市场规模预测,并以亚得里亚海北部港口群作为典型案例予以说明。分析表明,最优路径模型在大范围集装箱港口市场规模分析中具有较好的适应性,能够为投资决策提供参考。     

A Bayesian approach for modeling origin-destination matrices

The majority of origin destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel census studies, in contrast, covers the entire population of a specific study area of interest. In such cases where reliable historical data exist, statistical methodology may serve as a flexible alternative to traditional travel demand models by incorporating estimation of trip-generation, trip-attraction and trip-distribution in one model. In this research, a statistical Bayesian approach on OD matrix estimation is presented, where modeling of OD flows derived from census data, is related only to a set of general explanatory variables. A Poisson and a negative binomial model are formulated in detail, while emphasis is placed on the hierarchical Poisson-gamma structure of the latter. Problems related to the absence of closed-form expressions are bypassed with the use of a Markov Chain Monte Carlo method known as the Metropolis-Hastings algorithm. The methodology is tested on a realistic application area concerning the Belgian region of Flanders on the level of municipalities. Model comparison indicates that negative binomial likelihood is a more suitable distributional assumption than Poisson likelihood, due to the great degree of overdispersion present in OD flows. Finally, several predictive goodness-of-fit tests on the negative binomial model suggest a good overall fit to the data. In general, Bayesian methodology reduces the overall uncertainty of the estimates by delivering posterior distributions for the parameters of scientific interest as well as predictive distributions for future OD flows.     

A dynamic linear model for the estimation of time‐varying origin–destination matrices from link counts

We propose a dynamic linear model (DLM) for the estimation of day‐to‐day time‐varying origin–destination (OD) matrices from link counts. Mean OD flows are assumed to vary over time as a locally constant model. We take into account variability in OD flows, route flows, and link volumes. Given a time series of observed link volumes, sequential Bayesian inference is applied in order to estimate mean OD flows. The conditions under which mean OD flows may be estimated are established, and computational studies on two benchmark transportation networks from the literature are carried out. In both cases, the DLM converged to the unobserved mean OD flows when given sufficient observations of traffic link volumes despite assuming uninformative prior OD matrices. We discuss limitations and extensions of the proposed DLM. Copyright © 2017 John Wiley & Sons, Ltd.     

动态OD估计方法研究综述

根据动态OD估计所利用的数据特性,将动态OD估计模型划分为基于固定源数据的OD估计模型和考虑移动源数据的OD估计模型,开展国内外研究现状分析,在此基础上,提出今后动态OD估计研究的发展方向。     

Real-time traffic estimation using data expansion

This paper presents a method for estimating missing real-time traffic volumes on a road network using both historical and real-time traffic data. The method was developed to address urban transportation networks where a non-negligible subset of the network links do not have real-time link volumes, and where that data is needed to populate other real-time traffic analytics. Computation is split between an offline calibration and a real-time estimation phase. The offline phase determines link-to-link splitting probabilities for traffic flow propagation that are subsequently used in real-time estimation. The real-time procedure uses current traffic data and is efficient enough to scale to full city-wide deployments. Simulation results on a medium-sized test network demonstrate the accuracy of the method and its robustness to missing data and variability in the data that is available. For traffic demands with a coefficient of variation as high as 40%, and a real-time feed in which as much as 60% of links lack data, we find the percentage root mean square error of link volume estimates ranges from 3.9% to 18.6%. We observe that the use of real-time data can reduce this error by as much as 20%.     

基于OD分布的运输通道识别方法

为了弥补运输通道分析方法的缺陷,本文结合运输通道＂带状分布＂、＂客货流密集＂等特征,提出了基于OD分布的运输通道定量识别方法。首先利用图论,在OD分布图上识别出所有抽象的带状路径,并结合OD矩阵筛选出其中OD量最大的一条或多条路径,然后通过区域产业轴线、城镇轴线以及交通轴线的定性验证,确定运输通道。最后,对驻马店市运输通道进行实例分析,结果表明其精确度较高,具有科学合理的实用性。