大数据在城市交通拥堵监测与管理决策中的应用

本文探索了基于大数据技术的城市交通拥堵监测建模分析方法,建立了一套城市交通拥堵评价指标体系,提出了基于路网拓扑结构的城市拥堵自动检测方法,在此基础上利用百度地图交通云开发城市交通实时监测与研判分析平台,并以江苏为例,对基于大数据的城市交通管理决策进行应用实践。     

城市交通拥堵成因与多元化解决方案分析

为解决城市交通拥堵问题,阐述城市交通拥堵的危害,探讨城市交通拥堵问题的形成原因,从创新交通出行管理和规划、加大城市公共交通建设力度、科学应用智能化交通系统、倡导可持续交通出行等方面提出多元化解决方法,以期有效提升交通效率,推动城市可持续发展。     

基于城市空间结构的公共交通模式选择评价方法初探

我国城市各具特色,不同城市自身的规模、结构、发展方向不同,形成的交通需求是不同的,因而解决交通问题的方式和办法也不尽相同。选择合理的城市交通模式是解决城市交通拥堵的有效途径之一。本文在对不同的城市空间结构进行分析的基础上,结合公共交通模式的概念,分析不同城市空间结构与交通模式的关系,并依据对典型城市公共交通模式案例的总结、归纳,提出了基于城市空间结构的城市公共交通模式选择方法,尝试构建城市公共交通模式选择评价体系,形成一整套基于城市空间结构的公共交通模式选择评价方法。     

城市规划要重视公共交通的引导

全国政协委员、原交通部副部长、中国公路学会理事长胡希捷谈到解决城市交通拥堵的思路时表示：“国内外城市交通发展实践表明，解决城市交通拥堵应坚持疏堵结合、综合治理，要打组合拳，而不能单打直拳。其中，注重城市的科学规划和优先发展公共交通是缓解交通拥堵的根本性措施和最有效的手段。”     

城市公共交通特征的新思考

本文对城市公共交通特征的深入分析,有助于从深层次去寻找解决城市交通拥堵等问题的方法。     

车站外迁乃"害"民之举

城市中心区的公路客运站由于站场面积小、客流量大,使车站周边成为城市交通秩序最为混乱、拥堵最为严重的地区之一.而城市中心区的公路客运站的"边缘化"外迁一度成为解决城市交通拥堵的首选策略.     

石家庄市公交专用道现状及发展前景

石家庄市中山路公交专用道试点工程，是石家庄市委、市政府根据优先发展城市公共交通和城市交通管理规划的总体目标，以解决城市交通拥堵、解决市民出行乘车问题为重点，通过对国内外公交专用道典范城市进行实地考察，将其列为石家庄市世行贷款交通项目重点工程。     

大型城市交通拥堵治理互联网模式的路径及对策——以北京市为例

交通拥堵一直以来困扰着包括北京在内的国内外大型城市。传统上,交通拥堵的治理模式主要有三种,分别是增加供给模式、需求管理模式和制度完善模式。本文结合需求管理模式,对近年提出的交通拥堵治理的互联网模式进行了初步的探讨,并针对北京市城市交通的特定情境,提出了借助互联网治理城市交通拥堵的可选路径及相应的对策。     

城市交通拥堵治理模式理论的新进展

<正>城市交通拥堵治理问题是当前我国乃至世界范围的一个热门话题和难题,不停地修路,却不停地拥堵。有人认为,交通拥堵是一种现代城市病,极其复杂且难以解决。因此,有必要将目前城市交通拥堵的主要治理模式加以归类综合,并探讨其理论研究的新进展,从中更好地甄别治理交通拥堵的关键因素。     

城市交通拥堵机理分析与对策体系

本文在分析交通拥堵成因与主要影响因素的基础上,阐述了交通拥堵对策体系,并对我国城市交通拥堵的发展态势和阶段特点进行了分析,最后提出了不同发展阶段和不同供求关系下的城市交通拥堵战略对策。     

基于贝叶斯网络模型的交通状态预测

城市的交通状态是可以预测的。有效的交通状态预测能优化交通状态,减少交通阻塞。贝叶斯网络(Bayesian Networks,BN)是目前不确定知识和推理领域最有效的理论模型之一。文章在综合考虑交通阻塞成因的基础上构建网络模型,在已有的交通状态数据的基础上提出基于贝叶斯法则的学习算法,并通过计算变量间的条件概率来计算交通阻塞发生的可能性,达到预测的目的。     

基于模糊推理的交通拥堵判别方法

文章从交通量、平均速度、天气条件、前一时段的拥堵状况等与交通拥堵有直接关系的模糊条件入手,根据确定的模糊集合和模糊规则,提出了一种用于交通拥堵判别的模糊推理算法,并利用GPS采集数据对算法进行了验证分析。结果表明,该模糊推理算法能准确判别道路的交通拥堵情况。     

Macroscopic relations of urban traffic variables: Bifurcations, multivaluedness and instability

Recent experimental work has shown that the average flow and average density within certain urban networks are related by a unique, reproducible curve known as the Macroscopic Fundamental Diagram (MFD). For networks consisting of a single route this MFD can be predicted analytically; but when the networks consist of multiple overlapping routes experience shows that the flows observed in congestion for a given density are less than those one would predict if the routes were homogeneously congested and did not overlap. These types of networks also tend to jam at densities that are only a fraction of their routes’ average jam density.This paper provides an explanation for these phenomena. It shows that, even for perfectly homogeneous networks with spatially uniform travel patterns, symmetric equilibrium patterns with equal flows and densities across all links are unstable if the average network density is sufficiently high. Instead, the stable equilibrium patterns are asymmetric. For this reason the networks jam at lower densities and exhibit lower flows than one would predict if traffic was evenly distributed.Analysis of small idealized networks that can be treated as simple dynamical systems shows that these networks undergo a bifurcation at a network-specific critical density such that for lower densities the MFDs have predictably high flows and are univalued, and for higher densities the order breaks down. Microsimulations show that this bifurcation also manifests itself in large symmetric networks. In this case though, the bifurcation is more pernicious: once the network density exceeds the critical value, the stable state is one of complete gridlock with zero flow. It is therefore important to ensure in real-world applications that a network’s density never be allowed to approach this critical value.Fortunately, analysis shows that the bifurcation’s critical density increases considerably if some of the drivers choose their routes adaptively in response to traffic conditions. So far, for networks with adaptive drivers, bifurcations have only been observed in simulations, but not (yet) in real life. This could be because real drivers are more adaptive than simulated drivers and/or because the observed real networks were not sufficiently congested.     

南宁市交通拥堵成因及对策分析

文章基于对南宁市交通状况的调查,分析了南宁市交通拥堵问题产生的原因,提出了相应的对策与建议。     

山区城市交通拥挤成因分析及对策研究

文章以重庆市道路交通为例,从特殊地理环境因素、城市道路管理及城市规划等方面分析了山区城市道路交通拥挤的原因,并基于道路因素,从城市道路规划和整合建筑与交通空间方面提出了缓解交通拥堵的措施和建议。     

“互联网+”交通信号辅助优化机制探索

为优化城市道路交通信号控制方法,本文结合交通信号控制系统建设发展现状,分析当前各大城市交通信号控制系统普遍存在的问题,立足于互联网环境下的浮动车数据,提出基于互联网平台大数据的交通信号控制辅助优化机制。研究发现可利用互联网路口拥堵报警数据及时有效发现问题路口,利用路段拥堵指数及路口交通流参数变化趋势辅助评估配时方案的优化效果,并通过成都市应用实例证明该机制适用于当前交通控制场景需求,可有效辅助交通信号优化工作,是传统交通模式向真正智能交通模式过渡的阶梯。     

On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models

The speed-density or flow-density relationship has been considered as the foundation of traffic flow theory. Existing single-regime models calibrated by the least square method (LSM) could not fit the empirical data consistently well both in light-traffic/free-flow conditions and congested/jam conditions. In this paper, first, we point out that the inaccuracy of single-regime models is not caused solely by their functional forms, but also by the sample selection bias. Second, we apply a weighted least square method (WLSM) that addresses the sample selection bias problem. The calibration results for six well-known single-regime models using the WLSM fit the empirical data reasonably well both in light-traffic/free-flow conditions and congested/jam conditions. Third, we conduct a theoretical investigation that reveals the deficiency associated with the LSM is because the expected value of speed (or a function of it) is nonlinear with regard to the density (or a function of it).     

小金口枢纽互通立交拥堵改造分析

文章以广惠高速公路小金口枢纽互通立交工程为依托,在预测交通量的基础上从拥堵匝道的通行能力、服务水平和交通事故等方面分析匝道拥堵的原因,并提出基于光流率和边缘率的主线改善措施和匝道改善方案,为小金口枢纽互通立交及其他立交的拥堵改造提供参考。     

桂林市老城区分流交通保护圈规划设计

文章基于桂林市老城区交通现状,分析了老城区交通存在的主要问题,提出了桂林市老城区分流交通保护圈规划的总体思路,并根据这一思路设计出具体的老城区分流交通保护圈规划方案,为桂林市老城区交通系统规划提供决策依据。     

Enhancing the performance of existing urban traffic light control through extremum-seeking

Urban traffic light controllers are responsible for maintaining good performance within the transport network. Most existing and proposed controllers have design parameters that require some degree of tuning, with the sensitivity of the performance measure to the parameter often high. To date, tuning has been largely treated as a manual calibration exercise but ignores the effects of changes in traffic condition, such as demand profile evolution due to urban population growth. To address this potential shortcoming, we seek to use a newly developed extremum-seeker to calibrate the parameters of existing urban traffic light controllers in real-time such that a certain performance measure is optimised. The results are demonstrated for three categories of traffic controllers on a microscopic urban traffic simulation. It is demonstrated that the extremum-seeking scheme is able to seek the optimal parameters, with respect to a certain performance measure, for each of these traffic light controllers in an urban, uni-modal traffic environment.