An Interpretation of the Recent Evolution of the City of Barcelona through the Traffic Maps

Annual Average Daily Traffic (AADT) maps show, in a comprehensive way for expert as well as non-experts, the evolution of the relatively recent past (1965) until the most current image (2005). They help to analyse Barcelona’s city street network and retrace traffic congestion. The changes in the traffic patterns are due to random actions, resulting from individual liberties and voluntary planning, serving the general interest.

Traffic Maps and Smartphone Trajectories to Model Air Pollution, Exposure and Health Impact

In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emission, traffic density versus traffic velocity, traffic pollution concentration, exposure along individual trajectories, and health risk. A generic street with 100 km/h speed limit was used as an example to test the model. A single fixed-time trajectory had maximum exposure at velocity of 45 km/h at maximum pollution concentration. The street population had maximum exposure shifted to a velocity of 15 km/h due to the congestion density of vehicles. The shift is a universal effect of exposure. In this approach, nearly every modeling step of traffic pollution depended on traffic velocity. A traffic map is a super-efficient pre-processor for calculating real-time traffic pollution exposure at global scale using big data analytics.

A Data-Driven Intersection Geometry Mapping Technique to Enhance the Scalability of Trajectory-Based Traffic Signal Performance Measures

Connected vehicle (CV) trajectory data provides practitioners with opportunities to assess traffic signal performance with no investment in detection or communication infrastructure. With over 500 billion trajectory records generated each month in the United States, operations can be evaluated virtually at any of the over 400,000 traffic signals in the nation. The manual intersection mapping required to generate accurate movement-level trajectory-based performance estimations is the most time-consuming aspect of using CV data to evaluate traffic signal operations. Various studies have utilized vehicle location data to update and create maps;however, most proposed mapping techniques focus on the identification of roadway characteristics that facilitate vehicle navigation and not on the scaling of traffic signal performance measures. This paper presents a technique that uses commercial CV trajectory and open-source OpenStreetMap (OSM) data to automatically map intersection centers and approach areas of interest to estimate signal performance. OSM traffic signal tags are processed to obtain intersection centers. CV data is then used to extract intersection geometry characteristics surrounding the intersection. To demonstrate the proposed technique, intersection geometry is mapped at 500 locations from which trajectory-based traffic signal performance measures are estimated. The results are compared to those obtained from manual geometry definitions. Statistical tests found that at a 99% confidence level, upstream-focused performance estimations are strongly correlated between both methodologies. The presented technique will aid agencies in scaling traffic signal assessment as it significantly reduces the amount of manual labor required.

Feedback control scheme of traffic jams based on the coupled map car-following model

Based on the pioneering work of Konishi et al. [Phys. Rev. E （1999） 60 4000], a new feedback control scheme is presented to suppress traffic jams based on the coupled map car-following model under the open boundary condition. The effect of the safe headway on the traffic system is considered. According to the control theory, the condition under which traffic jams can be suppressed is analyzed. The results are compared with the previous results concerning congestion control. The simulations show that the suppression performance of our scheme on traffic jams is better than those of the previous schemes, although all the schemes can suppress traffic jams. The simulation results are consistent with theoretical analyses.

A control method for congested traffic in the coupled map car-following model

Based on the pioneer work of Konishi et al, a new control method is presented to suppress the traffic congestion in the coupled map （CM） car-following model under an open boundary. A control signal concluding the velocity differences of the two vehicles in front is put forward. The condition under which the traffic jam can be contained is analyzed. The results axe compared with that presented by Konishi et al [Phys. Rev. 1999 E 60 4000-4007]. The simulation results show that the temporal behavior obtained by our method is better than that by the Konishi＇s et al. method, although both the methods could suppress the traffic jam. The simulation results are consistent with the theoretical analysis.

A control method applied to mixed traffic flow for the coupled-map car-following model

In light of previous work [Phys. Rev. E 60 4000 （1999）], a modified coupled-map car-following model is proposed by considering the headways of two successive vehicles in front of a considered vehicle described by the optimal velocity function. The non-jam conditions are given on the basis of control theory. Through simulation, we find that our model can exhibit a better effect as p = 0.65, which is a parameter in the optimal velocity function. The control scheme, which was proposed by Zhao and Gao, is introduced into the modified model and the feedback gain range is determined. In addition, a modified control method is applied to a mixed traffic system that consists of two types of vehicle. The range of gains is also obtained by theoretical analysis. Comparisons between our method and that of Zhao and Gao are carried out, and the corresponding numerical simulation results demonstrate that the temporal behavior of traffic flow obtained using our method is better than that proposed by Zhao and Gao in mixed traffic systems.

基于CactiEZ Weathermap的校园网络气象系统设计

阐述CactiEZ Weathermap平台设计方法,通过相应的功能配置、参数调整、代码修改等优化操作,构建网络气象图,它清晰地展现网络节点运行状态、链路流量的信息,为网络运维、资源扩容提供量化的依据,这是校园网络管理与运维中必不可少的得力工具。

Mapping Road Traffic Crashes in Sfax Governorate (Tunisia)

Sfax is one of the Tunisian governorates with a large number of road accidents, injuries and fatalities every year. This study aimed to analyze and map traffic accidents in this governorate. We analyzed the spatial distribution of accidents, their distribution by cause, by type of road, by size of traffic, by months of the year and days of the week. Accidents were correlated with several variables such as population numbers and densities, motorization rate, length and structure of the road network, and the amount of traffic. On the cartographic level, we have built a database, through which we have produced a series of thematic maps to argue this analysis. Through cartographic production, we also aimed to help road users, decision-makers and researchers in this area and in the field of transport. This work showed that Sfax occupies, among the other Tunisian governorates, an advanced position in gravity. Various human, climatic and technical factors explained this situation, of which human factors were the most important, and contributed to almost 90% of accidents. The current situation of accidents in Sfax requires a series of measures and actions to alleviate and mitigate the gravity of this phenomenon.

Control Chaos of a Unidirectionally Traffic Coupled Map Lattice Model

Spatiotemporal chaos is studied by using the unidirectional traffic coupled lattice model with hyperbolic tangent local map. The coupled map lattice (CML) model can simulate the complex traffic flow phenomenon which is similar to the traditional traffic flow model. The nonlinear feedback method is used to study the control of the chaotic system of the unidirectionally traffic coupled map lattice model. The stability of spatiotemporal chaos in the coupled map lattice is realized. The results of numerical simulation show that there is a relationship between control results and control parameters when controlling spatiotemporal chaos to a uniform stable state in a certain phase space compression parameter region.

考虑运营成本和用户体验的电动汽车充电站布局优化模型

针对电动汽车充电站布局优化问题,综合考虑充电站建设运营成本和交通流量变化情况下的用户体验,据此建立充电站布局优化模型。先分析充电站城市土地转让成本、基建及充电设备成本、运营成本各项构成要素,并建立相应的数学模型,再结合交通拥堵指数建立用户体验数学模型,最终以满足充电站建设运营成本最低和用户体验最优为预期目标,建立充电站布局优化模型。引入Tent混沌映射对初始解空间进行均布化,采用AVOA优化算法对该模型进行问题求解。仿真结果表明:与对比文献模型相比,新的充电站选址方案与随机产生的需求点距离更近、更平均;在典型测试场景下,新选址方案可有效降低配电网络的网损率、提升充电站效能,并能为用户提供更优的用户体验,充分说明该模型能够较好解决充电站优化布局问题。

基于高精度地图的智能网联技术师资培训探究——以《交通管理与控制》为例

在智能化、网联化的大环境下,随着相关政策落地和人才市场的需求,导致高校教师理论和实践技能遇到新的挑战,因此交通智能技术领域的教育教学改革也将成为必然的趋势。据此,文章首先简要介绍了基于高精度地图的智能网联技术的发展现状,随后围绕该新型技术对人才的需求探讨了该专业教学领域师资现状以及存在的问题,最后结合交通工程专业核心课程《交通管理与控制》对教师队伍的建设和培训给予了建议和意见,为更好地构建以高精度地图为基础的智能网联技术专业师资队伍提供借鉴。

智能交通感知新范式:面向元宇宙的交通标志检测架构

交通标志检测对智能交通系统和智能驾驶的安全稳定运行具有重要作用。数据分布不平衡、场景单一会对模型性能造成较大影响,而建立一个完备的真实交通场景数据集需要昂贵的时间成本和人工成本。基于此,该文提出一个面向元宇宙的交通标志检测新范式以缓解现有方法对真实数据的依赖。首先,通过建立元宇宙和物理世界之间的场景映射和模型映射,实现检测算法在虚实世界之间的高效运行。元宇宙作为一个虚拟化的数字世界,能够基于物理世界完成自定义场景构建,为模型提供海量多样的虚拟场景数据。同时,该文结合知识蒸馏和均值教师模型建立模型映射,应对元宇宙和物理世界之间存在的数据差异问题。其次,为进一步提高元宇宙下的训练模型对真实驾驶环境的适应性,该文提出启发式注意力机制,通过对特征的定位和学习来提高检测模型的泛化能力。所提架构在CURE-TSD,KITTI,VKITTI数据集上进行实验验证。实验结果表明,所提面向元宇宙的交通标志检测器在物理世界具有优异的检测效果而不依赖大量真实场景,检测准确率达到89.7%,高于近年来其他检测方法。

基于交通大数据的成都市中心城区驾驶等时圈划分及特征分析

为提升交通出行分析中心城区交通可达性的表达精度与可视化水平,实践交通出行类大数据在国土空间规划编制及城市体检评估过程中的应用,文章以成都市作为研究对象,通过Python获取腾讯地图开放平台工作日及休息日14:00和18:00时段的实时驾驶通行信息,分别生成4个时段的中心城区等时圈范围。通过本次研究可以看出:以成都市人民政府为出发点的60min等时圈几乎可以覆盖中心城区全部范围,60min~90min等时圈则可覆盖成都市市域近半区域;成都市中心城区向南方向的交通条件最好,中心城区以南大部分区域都位于60min等时圈内,北向交通则稍弱;从4个不同时段的等时圈划分结果来看,成都市中心城区在工作日的14:00(平峰)时段交通可达性最好,其次为休息日的18:00时段,工作日的18:00(晚高峰)时段交通可达性最低。

MapGIS在专题地图编制中的应用

Map GIS专业软件具有强大的图形处理和数据编辑功能,制作专题图方便、高效。本文以苏州市吴江区为例,细述了Map GIS编制吴江区交通图和航道图的流程和方法,介绍了Map GIS制图的经验和技巧。

基于模糊认知图的智能汽车驾驶权交互决策建模方法

针对人机共驾中的由自动驾驶接管到驾驶人接管切换的动态问题,提出一种基于模糊认知图(fuzzy cognitive map,FCM)的驾驶权交互决策模型。首先分别分析影响驾驶风险的车辆和人的动态风险因素,并选取车距、加速度、方向盘转向熵、驾驶员眼动信息、心脑电波信息等相关影响指标构建基于FCM的车辆动态风险模型与人的动态风险模型,再结合环境因素,车辆状况以及驾驶员水平构建基于FCM的驾驶员交互决策模型。为降低对专家知识的依赖,运用基于实数编码的遗传算法(real coded genetic algorithm,RCGA)对模型进行优化,进一步提高模型决策的准确性。仿真结果表明,经RCGA优化后模型适应度值达到0.979,可见该模型能够较好地应对动态、复杂的驾驶环境。

基于LOD1城市模型的噪声数据可视化

近年来,噪声污染日益严重,已成为生态环境类投诉的主要内容。为更直观地查看和分析噪声污染,提升噪声治理和管控能力,本文基于城市信息模型(CIM)和噪声监测数据,结合数据叠加和三维渲染技术,研究大尺度三维噪声地图技术,实现了融合倾斜摄影和白膜噪声数据的三维可视化,在噪声污染监督和治理中,通过该技术的噪声可视化分析进行优化决策,可为智慧城市建设增添动力。

基于Super Map Is.net的交通信息实时发布

在阐述Super Map Is.net平台的结构及其发布交通信息的原理的基础上,重点讨论了实时发布动态交通信息的方法,并通过广州交通信息服务平台实时地发布道路拥挤状况图,解决了传统交通服务内容匮乏、信息滞后的问题,很好地满足了公众对高质量交通出行服务的需求,取得了良好的社会效果和经济效益.

基于多元复合映射的道路交通数字孪生体系探究

在建设数字孪生城市与道路交通的背景下,本文通过分析数字孪生概念及其难点,探讨其背后的双向映射本质,推导出基于多元复合映射的数字孪生理论和体系。结合道路交通领域的专业特点和多元化的数据及引擎需求,提炼出“端边物云座擎”道路交通数字孪生体系,强调通过支持数据及引擎多元化适配的独立数字孪生底座中间层,构建“人车路环境”物理实体与全要素数字模型的双向映射和交互。案例验证表明,该体系可行且有效。

利用Google Maps API提取交通地理数据的可行性研究

Google Maps的开放策略为WebGIS的应用开发提供方便的平台。针对具有特殊需求的路径规划、行车诱导等应用,在深入分析路网组成及寻径特点的基础上,探讨如何根据Google Maps API提取局部交通地理数据的策略,并提出以探索法进行提取的算法思路。

基于改进北方苍鹰优化随机配置网络的网络流量预测模型

网络流量预测作为一种关键技术,能帮助实现网络资源的合理分配、优化网络性能以及提供高效的网络服务。随着网络环境的演变和发展,网络流量的多样性和复杂性增加,为了提高网络流量的预测精度,提出了一种基于改进北方苍鹰优化随机配置网络(CNGO-SCN)的网络流量预测模型。随机配置网络作为一种具有监督机制的增量式模型,在解决大规模数据回归和预测问题方面具有良好的优势。但是,一些超参数的选择影响了随机配置网络的准确性。针对这一问题,利用北方苍鹰算法对影响随机配置网络性能的正则化参数和比例因子进行优化,得到最佳数值。而北方苍鹰算法由于初始种群的随机分布导致种群个体质量不佳,因此引入混沌逻辑映射提升初始解的质量。将优化后的模型应用于英国学术网、欧洲某城市核心网网络流量数据集和合作企业搭建的网络协同制造云平台交换机接口的真实流量数据集,并与多种神经网络模型进行对比,以验证所提模型的网络流量预测能力。实验结果表明,该模型对比其他神经网络模型具有更高的预测精度,在实际应用场景中处理复杂数据时具备更加优秀的预测能力,该模型的预测误差下降了0.9%~99.7%。