西宁市道路交通时间特性研究

城市道路交通的时间特性包括交通流的年变化、月变化、周变化以及时变化。研究城市道路交通时间特性,了解和掌握城市道路交通量的变化情况和规律,可为城市道路交通规划、设计、管理运营等决策提供科学依据。本文依据西宁市区域道路交叉口交通量调查数据,对西宁市道路交通在早晚高峰时期的交通量随时间的变化情况和规律进行了深入研究。     

城市中心区大街区与小街区交通效率比较分析

城市道路路网是城市交通的基础,合理的城市路网需要合理的城市路网密度。在控制道路面积率和交通需求不变的基础上,本文提出了一种对城市中心区大街区和小街区下不同路网密度的交通效率进行比较的定量分析法。通过利用Synchro分别对大街区和小街区情形下路网进行仿真,对路网交叉口的延误、交叉口停车、路网容量以及路段速度四个主要效率指标进行定量对比分析。以上海临港新城道路网和流量为基础,进行仿真和对比分析,分析结果表明小街区路网较大街区路网有较高交通效率。     

南宁市城市道路建设的影响因素分析

如何科学合理地进行城市道路网建设是目前城市建设的核心问题。文章以南宁市十二五期间城市道路网建设规划为例,介绍了城市道路建设影响因素的类别及内容,分析了交通基础设施建设、重大项目建设、旧城改造、管网建设、外围城镇发展等与城市道路路网建设的相互关系,并通过指标测算确定规划期内道路建设的合理规模。     

基于交通状态识别的信号交叉口模糊控制方法

1引言城市交通的拥堵问题已成为城市化发展过程中最为突出的问题之一。城市道路的信号控制技术,是调节路网交通需求,改善路网服务水平,最终缓解交通拥堵的重要手段。长期以来,对交通系统的控制一直是一个具有挑战性的难题。传统的控制方法在简化假设的条件下对交通流状况做出预测,并以平均延误、排队长度和停车次数等为优化目标,运用模糊控制算法,遗传算法,迭代学习,神经网络,多智能体算法等实施相应的控制策略。然而,这些模型多     

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。     

基于车辆延误的左转车道远引掉头设置方法

以信号控制路口为研究对象,以车辆行驶的效率和安全为主要指标,研究不同道路条件、交通流组成及交通控制等因素对远引掉头的影响,结合信号控制路口交通流的特点确定远引掉头形式。采用VISSIM仿真软件,以交叉口内车辆平均延误作为评价指标,从行车安全性及行车效率两方面综合分析远引掉头方式的使用对路网交通流的影响。结果表明:在低流量下,让行控制在延误水平上相比信号控制具有明显优势,但交通安全性较差。当流量达到一定数量时,采用信号控制方式可以将交叉口的交通流在绿灯周期内以连续流的形式通过交叉口,使得交叉口的延误水平降低、安全水平提高。     

城市占道施工区交通流分配研究

文章阐述了占道施工区交通流分配的概念及相关措施,采用改进的LOGIT多路径概率模型作为占道施工区路网交通流分配模型,确定路网的有效出行路径及合理的交通分配量,并以广州市天河区黄埔大道占道施工为例,运用该模型对其周围路网交通流分配量进行分析,所得结果较实际地反映了交通状况,证明该模型算法是科学合理的。     

城市道路交通咽喉防范对策

交通咽喉是城市路网中的关键地段，在研究提高城市道路服务水平、满足交通需求的进程中，关注交通咽喉问题，不仅能够促进咽喉区交通流有序的组织，又可兼顾于城市道路整体功能的发挥，所以对交通咽喉的形成机理进行深入研究，建立种种有效的咽喉疏导策略、寻找解决咽喉区交通问题的途径与方法，为制定相关的交通管理体系与政策奠定基础，具有较高的理论价值和指导意义。以往对此问题的研究，往往忽略了交通咽喉在其成因，特征等方面的不同，缺乏针对性，本文将交通咽喉分为静态和动态两大类，在此基础上对以上问题做一些探讨。     

城市主干路建设方案分析

城市道路的建设方案确定涉及多方面的因素,从道路路网中的功能定位及建设标准确定道路建设的技术标准和重要指标,并通过对道路沿线现状构筑物的现状进行综合评价,结合重要节点对道路选线的局部方案进行对比分析,从而确定城市道路的建设方案,为今后城市道路的建设提供参考。     

广州市道路交通规划建设探讨

文章针对广州市道路系统规划的基本状况及存在的问题,结合国外城市道路交通规划建设及管理经验,提出了基于小尺度道路网、合理道路等级级配和快速路系统的城市道路交通规划建设优化措施及建议,为缓解城市交通拥堵提供理论依据。     

广州市天河区交通拥堵的微循环解决路径研究

快速城市化致使城市交通量急剧增长,交通拥堵问题日益严重。受社会经济等条件限制,已建的成城区难以进行大规模的改扩工程,交通系统的深化急需跟上城市更新的步伐^[1]。本文以广州市天河区天园街道片区为研究对象,利用互联网电子地图,对该片区周边四条主要交通性道路高峰时段拥堵情况进行实时监测。通过为时一个月的监测,了解到在拥堵高峰时段四条交通性道路规模所能承载的交通量远远低于实际承载的交通通行量。结合实地调研,运用交通微循环理论,提出构建一个合理的交通微循环道路网络方案,即开放片区内部路网增强城市交通的毛细血管,增强四条主干道之间的联系实现交通分流。     

Welfare evaluation with a road capacity constraint

Severe traffic congestion in and around many cities across the world has resulted in programmes of extensive road building and other capacity increasing projects. But traffic congestion has often not fallen in the long run and neither has journey speed increased. Demand for peak period road travel, particularly by car, has grown so strongly that increases in road capacity have been quickly matched by increased road use. This paper develops a model of a road network characterised by insatiable road passenger (car and bus) demand. The model parameters are calibrated on a typical urban road network, and a number of simulations conducted to determine social welfare after the introduction of a road capacity constraint into the optimisation process. The empirical results have an important policy implication for the evaluation of projects that increase road capacity, namely that standard methods of cost-benefit analysis may tend to overestimate the net benefits of such projects by a significant amount. Although the model is developed in the context of roads and road traffic congestion, it could also be applied to air travel.     

Including congestion effects in urban road traffic CO2 emissions modelling: Do Local Government Authorities have the right options?

Tailpipe emissions from vehicles on urban road networks have damaging impacts, with the problem exacerbated by the common occurrence of congestion. This article focuses on carbon dioxide because it is the largest constituent of road traffic greenhouse gas emissions. Local Government Authorities (LGAs) are typically responsible for facilitating mitigation of these emissions, and critical to this task is the ability to assess the impact of transport interventions on road traffic emissions for a whole network.This article presents a contemporary review of literature concerning road traffic data and its use by LGAs in emissions models (EMs). Emphasis on the practicalities of using data readily available to LGAs to estimate network level emissions and inform effective policy is a relatively new research area, and this article summarises achievements so far. Results of the literature review indicate that readily available data are aggregated at traffic level rather than disaggregated at individual vehicle level. Hence, a hypothesis is put forward that optimal EM complexity is one using traffic variables as inputs, allowing LGAs to capture the influence of congestion whilst avoiding the complexity of detailed EMs that estimate emissions at vehicle level.Existing methodologies for estimating network emissions based on traffic variables typically have limitations. Conclusions are that LGAs do not necessarily have the right options, and that more research in this domain is required, both to quantify accuracy and to further develop EMs that explicitly include congestion, whilst remaining within LGA resource constraints.     

A dynamic cordon pricing scheme combining the Macroscopic Fundamental Diagram and an agent-based traffic model

Pricing is considered an effective management policy to reduce traffic congestion in transportation networks. In this paper we combine a macroscopic model of traffic congestion in urban networks with an agent-based simulator to study congestion pricing schemes. The macroscopic model, which has been tested with real data in previous studies, represents an accurate and robust approach to model the dynamics of congestion. The agent-based simulator can reproduce the complexity of travel behavior in terms of travelers’ choices and heterogeneity. This integrated approach is superior to traditional pricing schemes. On one hand, traffic simulators (including car-following, lane-changing and route choice models) consider travel behavior, i.e. departure time choice, inelastic to the level of congestion. On the other hand, most congestion pricing models utilize supply models insensitive to demand fluctuations and non-stationary conditions. This is not consistent with the physics of traffic and the dynamics of congestion. Furthermore, works that integrate the above features in pricing models are assuming deterministic and homogeneous population characteristics. In this paper, we first demonstrate by case studies in Zurich urban road network, that the output of a agent-based simulator is consistent with the physics of traffic flow dynamics, as defined by a Macroscopic Fundamental Diagram (MFD). We then develop and apply a dynamic cordon-based congestion pricing scheme, in which tolls are controlled by an MFD. And we investigate the effectiveness of the proposed pricing scheme. Results show that by applying such a congestion pricing, (i) the savings of travel time at both aggregated and disaggregated level outweigh the costs of tolling, (ii) the congestion inside the cordon area is eased while no extra congestion is generated in the neighbor area outside the cordon, (iii) tolling has stronger impact on leisure-related activities than on work-related activities, as fewer agents who perform work-related activities changed their time plans. Future work can apply the same methodology to other network-based pricing schemes, such as area-based or distance-traveled-based pricing. Equity issues can be investigated more carefully, if provided with data such as income of agents. Value-of-time-dependent pricing schemes then can also be determined.     

Controlling road congestion via a low-complexity route reservation approach

This work introduces a novel route reservation architecture to manage road traffic within an urban area. The developed routing architecture decomposes the road infrastructure into slots in the spatial and temporal domains and for every vehicle, it makes the appropriate route reservations to avoid traffic congestion while minimizing the traveling time. Under this architecture, any road segment is admissible to be traversed only during time-slots when the accumulated reservations do not exceed its critical density. A road-side unit keeps track of all reservations which are subsequently used to solve the routing problem for each vehicle. Through this routing mechanism, vehicles can either be delayed at their origin or are routed through longer but non-congested routes such that their traveling time is minimized. In this work, the proposed architecture is presented and the resulting route reservation problem is mathematically formulated. Through a complexity analysis of the routing problem, it is shown that for certain cases, the problem reduces to an NP-complete problem. A heuristic solution to the problem is also proposed and is used to conduct realistic simulations across a particular region of the San Francisco area, demonstrating the promising gains of the proposed solution to alleviate traffic congestion.     

Anti-seismic reinforcement strategy for an urban road network

This paper provides a novel practical method for analyzing an anti-seismic reinforcement (ASR) problem involving hundreds of transportation facilities on an urban road network subject to multiple earthquake risks. The relevant properties of the present method are: (i) it evaluates the performance of an ASR strategy, taking into account traffic congestion and travelers’ trip-making or route-choice behavior; (ii) it estimates the realistic damage patterns on the road network and their occurrence probabilities on the basis of recent advances in structural and earthquake engineering; (iii) it has clear, sensible logic and includes neither a black-box nor a “lottery” in the necessary procedures. We examine the computational efficiency and whether the present method is reasonable by applying it to a test scenario of the Kobe urban and suburban area.     

Surrogate‐based optimization of cordon toll levels in congested traffic networks

The benefit, in terms of social surplus, from introducing congestion charging schemes in urban networks is depending on the design of the charging scheme. The literature on optimal design of congestion pricing schemes is to a large extent based on static traffic assignment, which is known for its deficiency in correctly predict travel times in networks with severe congestion. Dynamic traffic assignment can better predict travel times in a road network, but are more computational expensive. Thus, previously developed methods for the static case cannot be applied straightforward. Surrogate‐based optimization is commonly used for optimization problems with expensive‐to‐evaluate objective functions. In this paper, we evaluate the performance of a surrogate‐based optimization method, when the number of pricing schemes, which we can afford to evaluate (because of the computational time), are limited to between 20 and 40. A static traffic assignment model of Stockholm is used for evaluating a large number of different configurations of the surrogate‐based optimization method. Final evaluation is performed with the dynamic traffic assignment tool VisumDUE, coupled with the demand model Regent, for a Stockholm network including 1240 demand zones and 17 000 links. Our results show that the surrogate‐based optimization method can indeed be used for designing a congestion charging scheme, which return a high social surplus. Copyright © 2016 John Wiley & Sons, Ltd.     

Capturing value of reliability through road pricing in congested traffic under uncertainty

Empirical studies showed that travel time reliability, usually measured by travel time variance, is strongly correlated with travel time itself. Travel time is highly volatile when the demand approaches or exceeds the capacity. Travel time variability is associated with the level of congestion, and could represent additional costs for travelers who prefer punctual arrivals. Although many studies propose to use road pricing as a tool to capture the value of travel time (VOT) savings and to induce better road usage patterns, the role of the value of reliability (VOR) in designing road pricing schemes has rarely been studied. By using road pricing as a tool to spread out the peak demand, traffic management agencies could improve the utility of travelers who prefer punctual arrivals under traffic congestion and stochastic network conditions. Therefore, we could capture the value of travel time reliability using road pricing, which is rarely discussed in the literature. To quantify the value of travel time reliability (or reliability improvement), we need to integrate trip scheduling, endogenous traffic congestion, travel time uncertainty, and pricing strategies in one modeling framework. This paper developed such a model to capture the impact of pricing on various costs components that affect travel choices, and the role of travel time reliability in shaping departure patterns, queuing process, and the choice of optimal pricing. The model also shows the benefits of improving travel time reliability in various ways. Findings from this paper could help to expand the scope of road pricing, and to develop more comprehensive travel demand management schemes.     

Improving fuel consumption and CO2 emissions calculations in urban areas by coupling a dynamic micro traffic model with an instantaneous emissions model

Τhis study demonstrates the combination of a microscopic traffic simulator (AIMSUN) with an instantaneous emissions model (AVL CRUISE) to investigate the impact of traffic congestion on fuel consumption on an urban arterial road. The micro traffic model was enhanced by an improved car-following law according to Morello et al. (2014) and was calibrated to replicate measured driving patterns over an urban corridor in Turin, Italy, operating under adaptive urban traffic control (UTC). The method was implemented to study the impact of congestion on fuel consumption for the category of Euro 5 diesel <1.4 l passenger cars. Free flow and congested conditions led to respective consumption differences of −25.8% and 20.9% over normal traffic. COPERT 5 rather well predicted the impact of congestion but resulted to a much lower relative reduction in free flow conditions. Start and stop system was estimated to reduce consumption by 6% and 11.9% under normal and congested conditions, respectively. Using the same modelling approach, UTC was found to have a positive impact on CO₂ emissions of 8.1% and 4.5% for normal and congested conditions, respectively, considering the Turin vehicle fleet mix for the year 2013. Overall, the study demonstrates that the combination of detailed and validated micro traffic and emissions models offers a powerful combination to study traffic and powertrain impacts on greenhouse gas and fuel consumption of on road vehicles over a city network.