RESEARCH ON FRACTAL CHARACTERISTICS OF URBAN TRAFFIC NETWORK STRUCTURE BASED ON GIS

Traffic network is an importance asp ect of researching controllable parameters of an urban spatial morpholo-gy.Based on GIS,traffic network str ucture complexity can be understood by using fractal geometry in which th e length-radius dimension describes change of network density,and ramification-radius dimension describes complexity and accessibility of urban network.It i s propitious to analyze urban traffic network and to understand dynamic c hange process of traffic network using expanding f ractal-dimension quantification.Meanwhile the length-radius dimension and ramifica-tion-radius dimension could be rega rd as reference factor of quantitative describing urban traffic network.

A fast network partition method for large-scale urban traffic networks

In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.

Seismic vulnerability assessment of urban buildings and traffic networks using fuzzy ordered weighted average

Urban buildings and urban traffic network are considered as the vital arteries of cities which have particular effects especially after the crisis in the search and rescue operations. The aim of this study is to determine the vulnerability of urban areas especially, buildings and traffic networks using multicriteria geographic information systems and decisionmaking methods. As there are many effective criteria on the seismic vulnerability that they have uncertain and vague properties, the method of this paper is applying fuzzy ordered weighted average(OWA) to model the seismic vulnerability of urban buildings and traffic networks in the most optimistic and pessimistic states. The study area is district 6 of Tehran that is affected by the four major faults, and thus will be threatened by the earthquakes. The achieved results illustrated the vulnerability with different degrees of risk levels including very high, high, medium, low and very low. The results show that in the most optimistic case 14% and in the pessimistic case 1% of buildings tolerate in very low vulnerability. The vulnerability of urban street network also indicates that in the optimistic case 12% and in the pessimistic case at most 9% of the area are in appropriate condition and the North and NorthEast of the study area are more vulnerable than South of it.

Joint optimization traffic signal control for an urban arterial road

This paper considers the optimal traffic signal setting for an urban arterial road. By introducing the concepts of synchronization rate and non-synchronization degree, a mathematical model is constructed and an optimization problem is posed. Then, a new iterative algorithm is developed to solve this optimal traffic control signal setting problem. Convergence properties for this iterative algorithm are established. Finally, a numerical example is solved to illustrate the effectiveness of the method.

Effect of Residential Quarters Opening on Urban Traffic from the View of Mathematical Modeling

With the great prosperity of national economy, there has been a dramatic rise of vehicles on city road, which makes increasing pressure of road transportation. Currently, many countries are confronting the severe situation of traffic jam in different degrees. Nevertheless, there are many triggers contributing to this congestion, one of which is the blocking of residential quarters towards vital traffic line. Therefore, it is extremely necessary to study whether the opening of residential quarters can improve the road capacity of the entire city and remit the traffic pressure. Our paper is based on graph theory, density theory and random utility theory. First of all, we demonstrate a mathematical model of road traffic. Secondly, we explore the influence of residential quarters opening on urban traffic, taking three factors into account listed as road traffic capacity, road network density and network average running time. On the basis of above contents, the impact analysis of vehicle traffic caused by pedestrians is added afterwards. Finally, our paper takes three different types of residential areas into account as an example to empirically analyze the tangible impact of the opening, and finally come to the benefit of the traffic system after the opening.

Characterizing Peak-Time Traffic Jam Incidents in Kampala Using Exploratory Data Analysis

Traffic congestion is associated with increased environmental pollutions, as well as reduced socio-economic productivity due to significant delays in travel times. The consequences are worse in least developed countries where motorized road transport networks are often inefficiently managed in addition to being largely underdeveloped. Recent research on traffic congestion has mostly focused on infrastructural aspects of road networks, with little or no emphasis at all on motorists’ on-the-road behavior (MB). The current study thus aimed to bridge this knowledge gap by characterizing traffic jam incidents (TJI) observed over a period of 80 days in Uganda’s Capital City, Kampala. MB as well as road network infrastructural factors such as road blockage (RB), were captured for each of the observed TJI. A total of 483 peak-time TJI were recorded, and exploratory data analysis (EDA) subsequently performed on the TJI dataset. EDA involved Hierarchical clustering analysis (HCA) and K-means clustering of the TJI dataset, as well as a detailed descriptive statistical analysis of both the entire dataset and the emerging TJI clusters. A highlight finding of this study is that 48.2% of the observed TJIs were as a result of on-the-road motorist behavior. Furthermore, the intervention of traffic police officers in a bid to regulate traffic flow was equally responsible for 25.9% of the TJIs observed in this study. Overall, these results indicate that whereas road infrastructural improvement is warranted in order to improve traffic flow, introducing interventions to address inappropriate on-the-road motorists’ behavior could alone improve traffic flow in Kampala, by over 48%. Additionally, in-order to effectively regulate traffic flow in Kampala and other least developed cities with similar traffic congestion management practices, motorists’ on-the-road behavior ought to be factored into any data-driven mechanisms deployed to regulate traffic flow and thus potentially significantly curbing traffic congestion.

面向城市道路的智能网联汽车多车道轨迹优化方法

为提高城市路网下智能网联汽车的通行效率以及燃油效率,提出面向城市道路的多车道时空轨迹优化方法。首先,结合多车道时空位置关系定义智能网联汽车状态与约束,综合考虑通行效率与燃油经济性构建时空轨迹复合优化模型,并采用庞特里亚金极大值算法进行求解。然后,本文设定协同换道的规则,并通过Q-learning算法获取最优的换道策略。最后,通过SUMO/Python联合仿真验证了该方法可以在不同车辆饱和程度、绿信比状态及最低通行速度条件下有效提高通行效率,且燃油效率得到明显改善。

地铁线网性能分析及最小化韧性损失恢复策略

为更好地分析地铁线网性能,优化其在扰动状态下的恢复策略,采用基于复杂网络的地铁线网性能分析方法,并提出最小化韧性损失恢复策略.鉴于地铁线网复杂网络特征,采用Space L拓扑方法处理地铁线网,并运用复杂网络理论计算线网内各站点的基本参数,包括线网的度、平均路径和线网效率等.其次根据韧性理论以线网效率作为性能指标求解各站点韧性损失,采用遗传算法求解站点恢复序列.最后,以福州市为例,分析其地铁线网的复杂网络特征并进行扰动仿真,比较在多站点失效情况下的最小化韧性损失恢复策略与随机恢复策略的恢复效果.研究结果表明:在多个地铁站点受到扰动失效,采用最小化韧性损失恢复策略能够令地铁线网性能在恢复期间仍处于较优水平,恢复效果明显优于具有不确定性的随机恢复策略.

暴雨内涝下城市路网抗涝韧性评估

极端降雨给城市路网的正常运行带来了极大挑战,为了评估在应对极端降雨时路网的抗涝韧性,本文采用场景模拟方法分析暴雨内涝灾害对路网的影响;以道路水深是否超过水深阈值为标准,判断道路的连通性,构建以节点强度和不同水深下道路速度的衰减函数为计算指标的综合评价框架;基于降雨—积水过程下道路系统的性能变化曲线和4R特征理论,分析4种暴雨重现期下路网的抗涝韧性;提出通过改造易涝交通枢纽提高路网抗涝韧性的提升策略。结果表明:降雨对路网服务性能的影响要大于对路网结构性能的影响,路网的抗涝韧性随着降雨重现期的增大而减小,道路的淹没程度与道路等级和降雨强度两个方面有关;易涝交通枢纽对路网抗涝韧性有较大影响,降雨重现期越小,影响越大。在实施提升策略后,可以显著提升路网的鲁棒性和快速性,增强路网的抗涝韧性。

基于生成对抗网络的追尾事故数据填补方法研究

深入分析交通事故数据可以为规避事故发生、降低事故严重程度提供重要理论依据,然而,在事故数据采集、传输、存储过程中往往会产生数据缺失,导致统计分析结果的准确性下降、模型的误判风险上升。本文以芝加哥2016—2021年的101452条追尾事故数据为研究对象,将原始数据按照7∶3随机分为训练集和测试集。在训练集数据上,利用生成式插补网络(Generative Adversarial Imputation Network,GAIN)实现对缺失数据的填补。为对比不同数据填补方法的效果,同时选择多重插补(Multiple Imputation by Chained Equations,MICE)算法、期望最大化(Expectation Maximization,EM)填充算法、缺失森林(MissForest)算法和K最近邻(K-Nearest Neighbor,KNN)算法对同一数据集进行数据填补,并基于填补前后变量方差变化比较不同填补算法对数据变异性的影响。在完成数据填补的基础上,构建LightGBM三分类事故严重程度影响因素分析模型。使用原始训练集数据,以及填补后的训练集数据分别训练模型,并使用未经填补的测试集数据检验模型预测效果。结果表明,经缺失值填补后,模型性能得到一定改善,使用GAIN填补数据集训练的模型,相较于原始数据训练的模型,准确率提高了6.84%,F1提高了4.61%,AUC(Area Under the Curve)提高了10.09%,且改善效果优于其他4种填补方法。

考虑出行阻抗的城市群综合客运网络可达性

为阐明城市群综合客运交通网络的多层空间结构并丰富网络的功能属性,以耦合这一全新视角提出考虑城市交通换乘的城市群综合客运网络模型,并从出行费用与出行时间出发分析节点与网络的可达性。首先,基于复杂网络理论Space-P建模方法,将城市群中的客运站点抽象为网络中的节点并进行编号,以票价与出行时间标定边权,分别构建公路客运子网、铁路客运子网。其次,划设城市群中的各城市中心城区为独立的交通小区,将有城市公共交通、步行等换乘方式连接的节点间添加耦合边,构建耦合子网。再次,将两类客运子网通过耦合子网组合叠加为综合网络,改进传统网络效率指标,构建加权的阻抗效率指标评估节点与网络的阻抗可达性。最后,以成渝城市群为实例研究,构建成渝城市群综合客运网络并分析公路、铁路、综合网络的阻抗可达性。结果表明:多个节点的耦合使得这些节点的阻抗可达性较好,因此在研究城市群综合客运交通网络时,城市交通换乘是不可忽略的因素;综合网络的阻抗可达性相较于公路子网、铁路子网分别提升了83.4%,28.5%,公路与铁路客运子网的耦合提高了城市群客运交通网络的阻抗可达性;推进站点间、多种运输方式间的耦合协调,继而构建更加便捷顺畅的城市群综合客运交通网络,是降低旅客的出行成本的重要途径。

基于级联卷积神经网络的城市交通汽车目标图像检测方法

由于现有的城市交通汽车目标检测方法主要使用神经网络来定位回归检测区域,这种方法忽略了特征在损失函数中的关联性,易受到场景立体匹配距离变化的影响,进而导致平均检测精度较低。基于级联卷积神经网络,设计一种全新的城市交通汽车目标图像检测方法。提取城市交通汽车目标检测特征,采用逐级匹配法提升检测样本质量。不同类型的检测目标的IOU分配阈值不同,利用Iterative Bbox at Inference级联卷积神经网络进行分类回归处理,得到基于级联神经网络的汽车目标检测损失函数,对于每个栅格,需要预设先验框根据损失函数,计算预测参数,设计城市交通汽车多目标检测算法,从而实现城市交通汽车目标检测。实验结果表明:该设计方法的平均检测精度较高,说明所设计方法的检测效果较好,具有较高的准确性,有一定的应用价值,能够为城市交通安全性的提升作出一定的贡献。

基于ICAE算法的城市群高速公路网布局优化研究

为提升城市群高速公路网通达程度和可达性水平,建立了体现城市群特征的优化模型,并改进了帝国竞争算法,以哈长城市群为案例,探究布局优化效果。采用高速公路出行时间确定城市群可达性等级,通过考虑可达性、距离等参数建立优化模型;引入进化算子改进算法,构建进化帝国竞争算法;根据哈长城市群特征确定约束条件并求解,利用求解节点和周边道路网确定优化方案。研究表明:改进的算法收敛精度提高16.4%,时间缩短42.4%,共求解出6个节点,规划新建高速公路6条,改建高速公路14条,优化后的城市群高速公路2 h交通圈覆盖范围达到65.3%,14个县域交通圈等级上升,优化方案扩大了城市群2 h交通圈范围。

考虑快慢车的城轨跨线运营开行方案与时刻表优化

跨线运营是城市轨道交通网络化运营的重要方向,跨线与快慢车组合运营可降低乘客出行时间。本文考虑跨线与快慢车组合模式,构建事件活动图刻画列车运行和乘客出行过程;考虑列车安全运行间隔和列车载客能力等约束,建立以乘客出行时间和列车走行成本最小化为目标的开行方案与时刻表综合优化模型,决策列车运行交路、停站方案、开行频率及各车次在各站的到发时刻;并针对综合优化模型,设计定制的变邻域搜索算法。案例结果表明,在组合模式下,列车走行成本增加了3%,换乘人次降低了82.5%,总乘客和跨线乘客的平均出行时间分别减少了1.3 min和5.3 min,乘客出行效益有明显提升;跨线与快慢车组合模式小幅增加了无需换乘乘客的出行时间,但大幅降低了跨线乘客的出行时间;换乘站换乘走行时间越长,宜开行的跨线列车频率越大,跨线运营降低乘客出行时间的效果越显著。

基于Kolmogorov熵权的城市轨道交通网络节点重要度识别算法

近年来,我国城市轨道交通系统中延误、破坏事件及运营故障等事故频发,事故站点大多客流量大且交通繁忙,因此识别城市轨道交通网络中的重要节点对于保证网络的正常运营具有重要意义。为更真实地反映现实世界城市轨道交通网络的实际情况,本文首先基于复杂网络理论构建城市轨道交通网络模型;其次在网络拓扑指标的基础上,考虑车站自身属性及其周边区域环境对节点重要度的影响,构建城市轨道交通网络指标评价体系;然后基于混沌时间序列与混沌系统指标序列的共性,采用Kolmogorov熵权法计算指标权重,结合城市轨道交通网络指标数据得到城市轨道交通网络节点重要度排序;最后,通过成都地铁网络的实例研究结果表明:排序前10的关键车站均位于城市中心城区且皆为换乘站点。与信息熵权法结果进行对比发现,本文方法有8个共同的关键站点,且排序结果分布更集中、极端值更少、得分差距更小。研究成果有助于更好地理解城市轨道交通网络的结构特征,为预防事故、优化运营提供了重要理论依据。

考虑韧性和调度费用的城市路网修复策略

城市道路交通网络作为城市运行的重要基础设施,在交通系统中发挥着不可替代的作用。然而,随着全球极端天气频繁发生,城市道路交通网络往往最先受到影响,如暴雨引发的严重内涝、洪水、地震等自然灾害能够广泛地对城市内部道路交通系统施加严重的破坏,从而严重地影响城市内部正常运转,造成大量经济损失、负面社会影响等。本文以非常规事件下的城市道路交通网络为对象,针对事件后恢复阶段的路网构建韧性评估框架,综合考虑维修调度成本、路段重要度,以及路网修复过程中用户的出行行为,建立了非常规事件下路网最优修复策略双层规划模型,并设计了基于修复重要度的大邻域搜索启发式算法求解模型。最后运用SiouxFalls道路网络验证本文所提方法的可行性与有效性,通过分析各维修方案在不同资源环境下的路网性能和路段流量变化,得出在确定资源环境下的最优维修方案。结果表明,在一定数量的维修队约束下,该模型得到的路网修复策略能最大限度地提升路网韧性,在修复过程中路网性能呈阶梯式上升。调度费用权重系数影响修复策略的制定,维修队数量为3,调度费用权重系数取值为0.50时,路网的恢复效果最好,韧性最高。本文所提出的方法不仅为实际修复方案制定提供理论支撑,还为管理者在非常规事件下的决策提供建议。

车网互动场景下电动网约车运营与充放电动态调度策略

利用电动网约车的集中性和灵活性,结合车网互动(Vehicle-to-grid,V2G)技术,使得车辆可以在电网负荷高峰期间为电网提供应急和需求响应服务。本文旨在评估电动网约车车队参与V2G系统的潜力和效益,并对网约车的订单分配、空车调度、充放电调度方案进行动态决策。首先建立时间—空间—能量三维网络刻画车辆调度问题,并通过滚动时域优化模型最大化网约车车队的期望收益,进一步设计可行弧筛选算法降低模型规模,求解车队动态调度决策。以上海市嘉定区为例进行案例分析,结果表明,所提出的电动网约车动态调度策略可以较好地满足出行订单需求,通过空车调度平衡未来的出行需求和供给。在电网需求应急响应时段,可调度10.3%的闲置车辆参与放电,参与车辆平均收益可达104.8元·h^(-1),有助于减少车辆的闲置率,提升车辆净收益,应对运输服务市场逐渐饱和的问题。

基于改进网络核密度和负二项回归的事故黑点鉴别

已有的交通事故黑点鉴别研究大多基于事故频数或事故率,并未考虑交通事故对不同发生地的影响特征。为了综合考虑交通事故在不同交通环境和路网特征下的差异影响,并解决交通事故数据中零值远超经典离散分布的零膨胀问题,本文提出一种考虑节点综合重要度的改进网络核密度估计法,并基于零膨胀负二项回归模型对城市交通事故黑点进行鉴别。首先,在拓扑路网中综合考虑事故发生地的交通环境和道路条件构建事故综合影响度指标,连同事故严重程度指数嵌入到传统网络核密度估计中,通过在道路网络上生成平滑的密度表面定性体现点事件的空间聚集性。在此基础上,构建基于零膨胀负二项回归鉴别模型,明晰事故高发区域边界范围,定量刻画不同严重等级的事故黑点路段空间分布特征。最后,以深圳市华强北街道为例进行实例分析。结果表明,在90%、80%和70%的阈值水平下本文提出的事故黑点鉴别法的有效搜索率均高于平面核密度估计法,且考虑节点综合影响度后,部分无道路区域不再被误识,模型准确率比传统网络核密度法分别提升了3.60%、5.31%和7.20%。

基于多图时空图卷积模型的城市交通流长时预测

交通流预测是智能交通系统中的重要组成部分,准确、及时和有效的预测信息对于城市道路的交通控制、诱导具有重要意义。然而,由于城市路网交通流时刻受到用地性质、天气变化等多种外部因素的影响,其预测面临着巨大挑战。为了有效预测城市路网交通流而提出了一种融合多源数据的多图时空图卷积模型。将影响城市路网交通流的外部因素分为静态和动态两类,并提供了一种明确和有结构的分类依据来理解影响交通流的各种外部因素。再将静态因素编码为多图,具体为距离矩阵、功能相似性矩阵和连通性矩阵,使用多图组成三通道输入时空相关性建模模块。该模块使用图卷积网络对交通流的空间相关性进行建模,学习节点特征和邻接信息;使用门控循环单元对交通流的时间相关性进行建模,捕捉交通流数据的动态变化和周期性规律。最后,使用融合层将多通道输出与动态因素进行融合作为最后预测输出。为了验证模型的有效性,使用SZ-TAXI数据集,与7种基准预测模型进行对比试验,结果表明,融合了多源外部因素的多图时空图卷积模型在评价指标MAE和RMSE上都比基准模型预测值更具准确性。并设计消融试验分析,处理静态因素的多图方法以及融合动态因素的方法均有效提高了城市路网交通流长时预测性能。

基于开放数据的云南迪庆藏族自治州综合交通优势度

国土空间规划中科学评价规划区域的交通基础设施发展水平,对提高规划科学性具有现实意义。利用低成本开放数据,从交通本质出发,构建由4个因子组成的评价模型,在云南迪庆藏族自治州国土空间规划中进行综合交通优势度研究。结果表明:利用开放数据可降低模型应用难度,提高评价精度和可靠性;评价显示迪庆藏族自治州综合交通优势度与其自然地理空间分布特征吻合,呈明显的“带”状分布,因此未来应优先提升澜沧江带的优势度;基于数据分析利于布局时“以流定形”,分析表明缺乏快速道路体系是迪庆藏族自治州交通供给侧的最大短板,在城市网络中其对外交通在西藏、四川方向优势度较弱,通道建设亟待加强。研究结果可为数慧赋能实现交通系统与“三生空间”融合发展和在地市级国土空间规划中应用综合交通优势理论提供参考价值。