哈尔滨市城市交通拥堵原因及解决措施

城市交通拥堵问题表象在城市道路交通,其根源受城市的自然环境、气候、历史、规划以及市民岀行习惯等方面影响。全面、客观地分析总结了影响哈尔滨城市交通拥堵的原因,对解决城市交通拥堵问题进行了思考,并提出了建议和具体措施。     

Fuzzy-logic-based incident detection for signalized diamond interchanges

This paper documents a fuzzy-logic-based incident detection algorithm for signalized urban diamond interchanges. The model is capable of detecting lane-blocking incidents whose effects are manifested by patterns of deterioration in traffic conditions that require adjustments in signal control strategies. As a component of a real-time traffic adaptive control system for signalized diamond interchanges, the algorithm feeds an incident report (i.e., the time, location, and severity of the incident) to the system's optimization manager, which uses that information to determine the appropriate signal control strategy.The performance of the model was studied using a simulation of an actual diamond interchange. The simulation study evaluated the model's performance in terms of detection rate, false alarm rate, and mean time to detect. The model's performance was encouraging, and the fuzzy-logic-based approach is considered promising.     

GSM-R中FAS话务量计算

介绍了GSM-R系统的结构,FAS系统及子系统在整个网络中的位置以及系统话务量的计算方法.分别按不同站型计算各站话务量,以此来确定网络的组网方式.     

基于混沌理论的高速公路网短时交通流量预测研究

随着高速公路网的建成及其交通流量的不断增大,对高速路网交通流实时控制和诱导服务的需求日益突出,而高速公路网短时交通流量的预测,不仅是交通流实时控制和诱导服务的基础和依据,而且预测结果的准确性对改善高速公路网的通行能力和服务水平有重要影响。基于混沌时间序列分析和预测的理论,建立了高速公路网短时交通流预测模型,计算给定区域高速公路网多断面短时交通流量预测值,结果表明利用多维混沌时间序列法预测高速公路网短时交通流量可行且具有较高的精度。     

基于微观评价指标的城市道路交通拥堵评价研究

为了客观地分析城市道路交通拥堵现状,基于交通流基本原理及国内外已有研究成果,构建了城市道路路段及交叉口交通拥堵的微观评价指标体系,并量化分析了各单项评价指标的表征方法及数据采集机制。以此为基础,结合实地交通调查数据,对金华市城市道路交通拥堵现状进行了评价分析,揭示了该市交通拥堵的部分特征。研究成果为后期缓解交通拥堵方案及交通发展政策的制订提供了有力的技术支持和保障。     

Reliable sensor deployment for network traffic surveillance

New sensor technologies enable synthesis of disaggregated vehicle information from multiple locations. This paper proposes a reliable facility location model to optimize traffic surveillance benefit from synthesized sensor pairs (e.g., for travel time estimation) in addition to individual sensor flow coverage (e.g., for traffic volume statistics), while considering probabilistic sensor failures. Customized greedy and Lagrangian relaxation algorithms are proposed to solve this problem, and their performance is discussed. Numerical results show that the proposed algorithms solve the problem efficiently. We also discuss managerial insights on how optimal sensor deployment and surveillance benefits vary with surveillance objective and system parameters (such as sensor failure probabilities).     

A shockwave profile model for traffic flow on congested urban arterials

In this paper a new traffic flow model for congested arterial networks, named shockwave profile model (SPM), is presented. Taking advantage of the fact that traffic states within a congested link can be simplified as free-flow, saturated, and jammed conditions, SPM simulates traffic dynamics by analytically deriving the trajectories of four major shockwaves: queuing, discharge, departure, and compression waves. Unlike conventional macroscopic models, in which space is often discretized into small cells for numerical solutions, SPM treats each homogeneous road segment with constant capacity as a section; and the queuing dynamics within each section are described by tracing the shockwave fronts. SPM is particularly suitable for simulating traffic flow on congested signalized arterials especially with queue spillover problems, where the steady-state periodic pattern of queue build-up and dissipation process may break down. Depending on when and where spillover occurs along a signalized arterial, a large number of queuing patterns may be possible. Therefore it becomes difficult to apply the conventional approach directly to track shockwave fronts. To overcome this difficulty, a novel approach is proposed as part of the SPM, in which queue spillover is treated as either extending a red phase or creating new smaller cycles, so that the analytical solutions for tracing the shockwave fronts can be easily applied. Since only the essential features of arterial traffic flow, i.e., queue build-up and dissipation, are considered, SPM significantly reduces the computational load and improves the numerical efficiency. We further validated SPM using real-world traffic signal data collected from a major arterial in the Twin Cities. The results clearly demonstrate the effectiveness and accuracy of the model. We expect that in the future this model can be applied in a number of real-time applications such as arterial performance prediction and signal optimization.     

Increasing the capacity of signalized intersections with separate left turn phases

A separate turn phase is often used on the approach leg to an intersections with heavy left turns. This wastes capacity on the approach because some of its lanes cannot discharge during its green phases. The paper shows that the problem can be eliminated by reorganizing traffic on all the lanes upstream of an intersection using a mid-block pre-signal. If drivers behave deterministically, the capacity that can be achieved is the same as if there were no left turns. However, if the reorganization is too drastic, it may be counterintuitive to drivers. This can be remedied by reorganizing traffic on just some of the available lanes. It is shown that such partial reorganization still increases capacity significantly, even if drivers behave randomly and only one lane is reorganized. The paper shows how to optimize the design of a pre-signal system for a generic intersection. It also identifies both, the potential benefits of the proposed system for a broad class of intersections, and the domain of application where the benefits are most significant.     

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.     

Hysteresis phenomena of a Macroscopic Fundamental Diagram in freeway networks

Observations of traffic pairs of flow vs. density or occupancy for individual locations in freeways or arterials are usually scattered about an underlying curve. Recent observations from empirical data in arterial networks showed that in some cases by aggregating the highly scattered plots of flow vs. density from individual loop detectors, the scatter almost disappears and well-defined macroscopic relations exist between space-mean network flow and network density. Despite these findings for the existence of well-defined relations with low scatter, these curves should not be universal. In this paper we investigate if well-defined macroscopic relations exist for freeway network systems, by analyzing real data from Minnesota’s freeways. We show that freeway network systems not only have curves with high scatter, but they also exhibit hysteresis phenomena, where higher network flows are observed for the same average network density in the onset and lower in the offset of congestion. The mechanisms of traffic hysteresis phenomena at the network level are analyzed in this paper and they have dissimilarities to the causes of the hysteresis phenomena at the micro/meso level. The explanation of the phenomenon is dual. The first reason is that there are different spatial and temporal distributions of congestion for the same level of average density. Another reason is the synchronized occurrence of transitions from individual detectors during the offset of the peak period, with points remain beneath the equilibrium curve. Both the hysteresis phenomenon and its causes are consistently observed for different spatial aggregations of the network.