Approximate network loading and dual-time-scale dynamic user equilibrium

In this paper we present a dual-time-scale formulation of dynamic user equilibrium (DUE) with demand evolution. Our formulation belongs to the problem class that Pang and Stewart (2008) refer to as differential variational inequalities. It combines the within-day time scale for which route and departure time choices fluctuate in continuous time with the day-to-day time scale for which demand evolves in discrete time steps. Our formulation is consistent with the often told story that drivers adjust their travel demands at the end of every day based on their congestion experience during one or more previous days. We show that analysis of the within-day assignment model is tremendously simplified by expressing dynamic user equilibrium as a differential variational inequality. We also show there is a class of day-to-day demand growth models that allow the dual-time-scale formulation to be decomposed by time-stepping to yield a sequence of continuous time, single-day, dynamic user equilibrium problems. To solve the single-day DUE problems arising during time-stepping, it is necessary to repeatedly solve a dynamic network loading problem. We observe that the network loading phase of DUE computation generally constitutes a differential algebraic equation (DAE) system, and we show that the DAE system for network loading based on the link delay model (LDM) of Friesz et al. (1993) may be approximated by a system of ordinary differential equations (ODEs). That system of ODEs, as we demonstrate, may be efficiently solved using traditional numerical methods for such problems. To compute an actual dynamic user equilibrium, we introduce a continuous time fixed-point algorithm and prove its convergence for effective path delay operators that allow a limited type of nonmonotone path delay. We show that our DUE algorithm is compatible with network loading based on the LDM and the cell transmission model (CTM) due to Daganzo (1995). We provide a numerical example based on the much studied Sioux Falls network.     

Modeling capacity flexibility of transportation networks

Flexibility of the transportation system is one of the important performance measures needed to deal with demand changes. In this paper, we provide a quantitative assessment of capacity flexibility for the passenger transportation network using bi-level network capacity models. Two approaches for assessing the value of capacity flexibility are proposed. One approach is based on the concept of reserve capacity, which reflects the flexibility with respect to changes in terms of demand volume only. The second approach allows for variations in the demand pattern in addition to changes in demand volume in order to more fully capture demand changes. Two models are developed in the second approach to consider two types of capacity flexibility. The total capacity flexibility allows all users to have both route choice and destination choice when estimating capacity flexibility. The limited capacity flexibility estimates how much more demand volume could be added to a fixed demand pattern by allowing the additional demand to deviate from the fixed demand pattern. Numerical examples are provided to demonstrate the different concepts of capacity flexibility for a passenger transportation system under demand changes.     

谈高职院校图书馆的读者咨询服务

本文从高职院校图书馆工作性质出发,提出读者咨询活动在图书馆工作中的重要作用。再通过分析高职院校图书馆咨询服务存在的问题,从而提出怎样才能更好地完成高职院校图书馆的读者咨询活动。     

上海世博集约交通的实践与启示

为应对大规模、长时间、高强度的世博客流,世博会交通运行方案以集约化模式为主导,在没有实施强制性交通管控的前提下,世博交通组织及全市日常交通平稳有序、运行效果良好.通过分析世博游客出行特征及世博会期间对外交通、道路交通、轨道交通的运行特征,从设施建设、交通管理、配套政策、交通服务等方面总结世博集约交通的实践经验.借鉴世博...     

随机条件下固定公交线路服务频率优化模型

在考虑客流需求波动及车辆运行时间随机变动的情况下,建立了以乘客候车期望和公交车辆利用率总体最优为目标的公交线路服务频率优化模型.利用蒙特卡罗方法随机模拟一条高频线路的公交服务,计算得到了给定线路参数下的最优服务频率.算例表明该模型可用来确定高频服务公交线路的发车时间间隔.     

交通信息对驾驶员逐日路径选择行为影响研究

在无信息、发布历史信息和发布预测信息3种信息条件下,分别建立了驾驶员的路径理解行程时间期望值的更新模型.通过建立一个含有2条平行路径的简单路网,对3种交通信息条件下驾驶员逐日路径选择进行了仿真,结果表明:交通信息对驾驶员的作用与驾驶员路径选择的随机程度和对信息依赖程度有关;在3种交通信息条件下,路网均不能达到用户均衡平衡状态;交通信息的预测方法不同,预测信息对驾驶员路径选择的影响有差异.     

基于行程时间可靠性的多类用户交通分配模型

分析了路网在随机因素作用下造成的出行者行程时间的不确定性.假设出行者基于期望行程时间和行程时间可靠性的均衡选择路径,根据出行者对待行程时间可靠性的不同态度,将其路径选择行为分类,建立了基于行程时间可靠性的多类用户交通分配的变分不等式模型.给出了模型的对角化算法.对一个小型测试网络的计算结果表明,该模型能够反映出行者在不确定环境下的路径选择行为.     

A stochastic analysis of highway capacity: Empirical evidence and implications

This paper presents a stochastic characterization of highway capacity and explores its implications on ramp metering control at the corridor level. The stochastic variation of highway capacity is captured through a Space–Time Autoregressive Integrated Moving Average (STARIMA) model. It is identified following a Seasonal STARIMA model (0, 0, 2₃) × (0, 1, 0)₂, which indicates that the capacities of adjacent locations are spatially–temporally correlated. Hourly capacity patterns further verify the stochastic nature of highway capacity. The goal of this paper is to study (1) how to take advantage of the extra information, such as capacity variation, and (2) what benefits can be gained from stochastic capacity modeling. The implication of stochastic capacity is investigated through a ramp metering case study. A mean–standard deviation formulation of capacity is proposed to achieve the trade-off between traffic operation efficiency and robustness. Following that, a modified stochastic capacity-constraint ZONE ramp metering scheme embedded cell transmission model algorithm is introduced. The numerical experiment suggests that considering capacity variation information would alleviate the spillback effect and improve throughput. Monte Carlo simulation further supports this argument. This study helps verify and characterize the stochastic nature of capacity, validates the benefits of using capacity variation information, and thus enhances the necessity of implementing stochastic capacity in traffic operation.     

高速路网不停车收费车道优化布设方法

合理设置高速公路收费站ETC (Electronic Toll Collection)车道数量,对高速公路通行效率至关重要。针对目前路网中ETC与MTC (Manual Toll Collection)车辆混行的情况,考虑ETC的普及率,结合多用户路网均衡模型和排队论方法,建立基于双层规划模型的高速路网ETC车道优化布设方法。上层模型以车辆总通行时间最小为目标,优化设置进出收费站的ETC车道数量;下层模型为多用户路网均衡模型,反映ETC和MTC车辆的路径和收费车道选择行为。下层模型通过设计收费站的等价拓扑结构,表征收费站的车道使用规则及车辆的收费车道选择行为,并采用排队论方法估计ETC和MTC车道的收费排队时间。根据模型的特点设计了基于主动集的启发式算法,利用参数二进制与拉格朗日函数法确定迭代下降方向,解决了下降方向与步长难以计算的问题;通过内嵌优化函数的方式,保证在主动集转化过程中上层约束均不会失效,且避免了迭代过程中的模型解退化问题。基于上海市绕城高速进行实证分析,结果表明：随着ETC普及率的提升,收费排队时长按照负指数趋势下降;与按比例布设ETC车道的方法相比,所提方法最高可降低57.4%的收费排队时间,且该方法可以避免ETC车道布设过多对于MTC车道通行能力挤压造成的负面效果。研究成果可以有效指导高速路网ETC车道的布设,提高路网通行效率。