市政道路平面交叉口的交通组织设计

市政道路平面交叉口处路况复杂且多变,为了更好地建设市政道路平面交叉口,做好交叉口的交通组织设计,对市政道路的交叉口分类进行介绍,分析各类交叉口的通行能力,对交叉口的交通路径优化方法进行分析。在此基础上,提出市政道路平面交叉口渠化设计策略,为改进城市交通组织方式、改善城市道路平面交叉口交通拥堵状况提供思路和方法。     

平面信号控制交叉口交通优化设计方法研究 ——以盐城市胜利路与海阔路交叉口为例

通过对平面信号控制交叉口交通优化设计,对提高交叉口通行能力有着重要作用.本文以盐城市胜利路与海阔路交叉口为例,针对分析交叉口现状交通量、交叉口渠化现状、混合交通冲突点,信号配时现状,提出改善措施,提高交叉口的通行能力和安全水平.     

基于VISSIM仿真的交通组织优化研究

近年来,随着人民生活水平的提高,道路上的人流车流越来越多,所以各交叉口以及交叉口之间路段的交通流密度也在逐步增加,所以,如何发现所研究区域内的主要交通问题,并拟定出具有针对性的交通道路组织优化方案与措施,具有十分重要的意义。文章详细分析了影响城市平面交叉口车辆通行的因素,对交叉口的通行能力、饱和度、延误、排队长度等基本评价指标作出分析,最后选择焦作市某交叉口作为案例进行验证优化,提出三种优化方案,并运用VISSIM仿真软件对优化方案进行仿真评价。     

城市道路平面交叉口安全评价研究

城市平面交叉口是城市交通冲突和事故频繁发生的地点。文章针对城市道路平面交叉口的交通安全现状,分析了平面交叉口的安全影响因素,提出引入事故率和冲突率为评价指标,建立了灰色理论评价方法,为城市道路平面交叉口的安全治理提供理论支持。     

平面信号交叉口设计优化方法探析

平面信号交叉口是城市交通中非常重要的环节。但现阶段的平面信号交叉口仅仅能够满足现有的交通流量,随着人口和车辆的不断增加,必须对平面信号交叉口设计进行优化。鉴于此,在分析平面交叉口交通流运行特性的基础上,提出了平面交叉口设计优化方法,以期能够在一定程度上促进交通的发展和城市化进程。     

城市主要道路平面交叉口安全管理方法

文章基于现今城市干道平面交叉口交通流量密集的状况,分析讨论影响平面交叉口安全管理的因素,提出了交叉口安全管理方法和一些改进措施,为提高城市道路交叉口的交通安全管理提供依据。     

信控交叉口不同左转车道设置位置对交通安全的影响

城市道路信号干扰控制交叉口右侧处于与左转横向交通道路产生的信号冲突点最多,对车辆直行道和车流双向通行的信号干扰最大,使得城市平面道路交叉口左转交通信号延误程度增大。左转专用车道的交通设置使用方式主要分为两种:内置左转车道与外置左转车道。不同方向车道灯的布置及其形式对车辆左转方向交通的通行影响很大程度也会有所很大不同。本文通过实地调查,选取典型地区和交通重要路口情况进行模拟仿真实验分析,从转弯运行能力和通行能力的角度出发,探讨交叉口处不同左转车道的布置形式在不同交通特性下对整个交叉口的影响,从而明确其适用范围,为我国城市道路交通安全的发展提供参考。     

基于VISSIM的平面交叉口交通仿真与改善研究

平面交叉口是交通系统中极易发生拥堵的地方,运用先进的仿真软件VISSIM对交叉口进行改善设计具有较强的优势。文章在分析VIS-SIM仿真软件原理的基础上,提出了平面交叉口的改善方法,并以兰州市建宁路与宝石花路交叉口为例进行了交叉口改善与仿真分析。研究结论对城市平面交叉口改善设计具有一定的借鉴意义。     

广西公路项目平交式工程安全评价探讨

文章介绍了公路项目平面交叉口工程安全评价的目的,分析了影响公路项目平面交叉口工程安全的因素,并以广西来马高速马山南互通出口与G210公路平交口项目为工程实例,从平纵线形、视距、交叉口间距、交通工程、施工方案等方面对公路项目平交式工程进行安全评价。     

信号控制交叉口左转掉头设置方式分析

左转掉头交通是平面交叉口转向交通的重要构成部分,左转掉头位置是否合理直接影响着交叉口的通行能力和运行效率。文章分析了信号控制交叉口左转掉头的交通影响因素,并通过归纳分析我国现有的3种左转掉头设置方式的优劣,提出了合理的左转掉头设置方式,同时阐述了左转掉头的空间设计要求。     

Evaluating the impacts of urban corridor traffic signal optimization on vehicle emissions and fuel consumption

This study investigates the impacts of traffic signal timing optimization on vehicular fuel consumption and emissions at an urban corridor. The traffic signal optimization approach proposed integrates a TRANSIMS microscopic traffic simulator, the VT-Micro model (a microscopic emission and fuel consumption estimation model), and a genetic algorithm (GA)-based optimizer. An urban corridor consisting of four signalized intersections in Charlottesville, VA, USA, is used for a case study. The result of the case study is then compared with the best traffic signal timing plan generated by Synchro using the TRANSIMS microscopic traffic simulator. The proposed approach achieves much better performance than that of the best Synchro solution in terms of air quality, energy and mobility measures: 20% less network-wide fuel consumption, 8–20% less vehicle emissions, and nearly 27% less vehicle-hours-traveled (VHT).     

Evaluating effects of traffic and vehicle characteristics on vehicular emissions near traffic intersections

Urban air quality is generally poor at traffic intersections due to variations in vehicles’ speeds as they approach and leave. This paper examines the effect of traffic, vehicle and road characteristics on vehicular emissions with a view to understand a link between emissions and the most likely influencing and measurable characteristics. It demonstrates the relationships of traffic, vehicle and intersection characteristics with vehicular exhaust emissions and reviews the traffic flow and emission models. Most studies have found that vehicular exhaust emissions near traffic intersections are largely dependent on fleet speed, deceleration speed, queuing time in idle mode with a red signal time, acceleration speed, queue length, traffic-flow rate and ambient conditions. The vehicular composition also affects emissions. These parameters can be quantified and incorporated into the emission models. There is no validated methodology to quantify some non-measurable parameters such as driving behaviour, pedestrian activity, and road conditions     

A traffic noise model for road intersections in the city of Cartagena de Indias,Colombia

Road traffic noise models are fundamental tools for designing and implementing appropriate prevention plans to minimize and control noise levels in urban areas. The objective of this study is to develop a traffic noise model to simulate the average equivalent sound pressure level at road intersections based on traffic flow and site characteristics, in the city of Cartagena de Indias (Cartagena), Colombia. Motorcycles are included as an additional vehicle category since they represent more than 30% of the total traffic flow and a distinctive source of noise that needs to be characterized. Noise measurements are collected using a sound level meter Type II. The data analysis leads to the development of noise maps and a general mathematical model for the city of Cartagena, Colombia, which correlates the sound levels as a function of vehicle flow within road intersections. The highest noise levels were 79.7 dB(A) for the road intersection María Auxiliadora during the week (business days) and 77.7 dB(A) for the road intersection India Catalina during weekends (non-business days). Although traffic and noise are naturally related, the intersections with higher vehicle flow did not have the highest noise levels. The roadway noise for these intersections in the city of Cartagena exceeds current limit standards. The roadway noise model is able to satisfactorily predict noise emissions for road intersections in the city of Cartagena, Colombia.     

Mobility and environment improvement of signalized networks through Vehicle-to-Infrastructure (V2I) communications

Traffic signals, even though crucial for safe operations of busy intersections, are one of the leading causes of travel delays in urban settings, as well as the reason why billions of gallons of fuel are burned, and tons of toxic pollutants released to the atmosphere each year by idling engines. Recent advances in cellular networks and dedicated short-range communications make Vehicle-to-Infrastructure (V2I) communications a reality, as individual cars and traffic signals can now be equipped with communication and computing devices. In this paper, we first presented an integrated simulator with V2I, a car-following model and an emission model to simulate the behavior of vehicles at signalized intersections and calculate travel delays in queues, vehicle emissions, and fuel consumption. We then present a hierarchical green driving strategy based on feedback control to smooth stop-and-go traffic in signalized networks, where signals can disseminate traffic signal information and loop detector data to connected vehicles through V2I communications. In this strategy, the control variable is an individual advisory speed limit for each equipped vehicle, which is calculated from its location, signal settings, and traffic conditions. Finally, we quantify the mobility and environment improvements of the green driving strategy with respect to market penetration rates of equipped vehicles, traffic conditions, communication characteristics, location accuracy, and the car-following model itself, both in isolated and non-isolated intersections. In particular, we demonstrate savings of around 15% in travel delays and around 8% in fuel consumption and greenhouse gas emissions. Different from many existing ecodriving strategies in signalized road networks, where vehicles’ speed profiles are totally controlled, our strategy is hierarchical, since only the speed limit is provided, and vehicles still have to follow their leaders. Such a strategy is crucial for maintaining safety with mixed vehicles.     

台州市信号灯交叉口碳排放测算及优化策略

近年来随着小汽车保有量的不断增多,机动车尾气成为碳排放的主要组成部分。文中以信号灯控制交叉口运行的小汽车为研究对象,通过研究小汽车在交叉口减速、怠速、加速三种工况下车辆的排放特性,将信号灯交叉口小汽车温室气体排放量量化。本文以台州市市府大道-中心大道交叉口为例,通过实地调查该交叉口当前平峰期和高峰期信号灯配时特性、交通运行特性等实际参数,对二氧化碳排放量进行测算,最终得出该交叉口在平峰期和高峰期的二氧化碳小时排放量,为后续该交叉口采取优化策略后在碳排放指标方面的量化效果提供参考依据,同时为台州市信号灯交叉口碳排放量测算提供参考方法。     

A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials

Estimation of time-dependent arterial travel time is a challenging task because of the interrupted nature of urban traffic flows. Many research efforts have been devoted to this topic, but their successes are limited and most of them can only be used for offline purposes due to the limited availability of traffic data from signalized intersections. In this paper, we describe a real-time arterial data collection and archival system developed at the University of Minnesota, followed by an innovative algorithm for time-dependent arterial travel time estimation using the archived traffic data. The data collection system simultaneously collects high-resolution “event-based” traffic data including every vehicle actuations over loop detector and every signal phase changes from multiple intersections. Using the “event-based” data, we estimate time-dependent travel time along an arterial by tracing a virtual probe vehicle. At each time step, the virtual probe has three possible maneuvers: acceleration, deceleration and no-speed-change. The maneuver decision is determined by its own status and surrounding traffic conditions, which can be estimated based on the availability of traffic data at intersections. An interesting property of the proposed model is that travel time estimation errors can be self-corrected, because the trajectory differences between a virtual probe vehicle and a real one can be reduced when both vehicles meet a red signal phase and/or a vehicle queue. Field studies at a 11-intersection arterial corridor along France Avenue in Minneapolis, MN, demonstrate that the proposed model can generate accurate time-dependent travel times under various traffic conditions.     

Assessing the importance of transportation activity data for urban emission inventories

The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a medium-sized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NO_x, CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from −15 to 49% for CO, −14 to 31% for VOC, −19 to 46% for NO_x and −22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories.     

Spatiotemporal intersection control in a connected and automated vehicle environment

Current research on traffic control has focused on the optimization of either traffic signals or vehicle trajectories. With the rapid development of connected and automated vehicle (CAV) technologies, vehicles equipped with dedicated short-range communications (DSRC) can communicate not only with other CAVs but also with infrastructure. Joint control of vehicle trajectories and traffic signals becomes feasible and may achieve greater benefits regarding system efficiency and environmental sustainability. Traffic control framework is expected to be extended from one dimension (either spatial or temporal) to two dimensions (spatiotemporal). This paper investigates a joint control framework for isolated intersections. The control framework is modeled as a two-stage optimization problem with signal optimization at the first stage and vehicle trajectory control at the second stage. The signal optimization is modeled as a dynamic programming (DP) problem with the objective to minimize vehicle delay. Optimal control theory is applied to the vehicle trajectory control problem with the objective to minimize fuel consumption and emissions. A simplified objective function is adopted to get analytical solutions to the optimal control problem so that the two-stage model is solved efficiently. Simulation results show that the proposed joint control framework is able to reduce both vehicle delay and emissions under a variety of demand levels compared to fixed-time and adaptive signal control when vehicle trajectories are not optimized. The reduced vehicle delay and CO₂ emissions can be as much as 24.0% and 13.8%, respectively for a simple two-phase intersection. Sensitivity analysis suggests that maximum acceleration and deceleration rates have a significant impact on the performance regarding both vehicle delay and emission reduction. Further extension to a full eight-phase intersection shows a similar pattern of delay and emission reduction by the joint control framework.     

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.