Integrated reliability of travel time and capacity of urban road network under ice and snowfall conditions

In order to evaluate and integrate travel time reliability and capacity reliability of a road network subjected to ice and snowfall conditions,the conceptions of travel time reliability and capacity reliability were defined under special conditions.The link travel time model(ice and snowfall based-bureau public road,ISB-BPR) and the path choice decision model(elastic demand user equilibrium,EDUE) were proposed.The integrated reliability was defined and the model was set up.Monte Carlo simulation was used to calculate the model and a numerical example was provided to demonstrate the application of the model and efficiency of the solution algorithm.The results show that the intensity of ice and snowfall,the traffic demand and supply,and the requirements for level of service(LOS) have great influence on the reliability of a road network.For example,the reliability drops from 65% to 5% when the traffic demand increases by 30%.The comprehensive performance index may be used for network planning,design and maintenance.

Optimization Strategy of Road Traffic System in Urban Renewal Area

At present,problems such as insufficient road infrastructure and supporting facilities,and limited parking spaces are increasingly prevalent,and there are large conflicts of interest in the process of urban renewal.Therefore,it is crucial to improve the quality of the road network.This paper presents an analysis on the current situation of the road traffic system in a completed area outside the Third Ring Road in Xindu District,Chengdu,and provides corresponding road traffic optimization strategies,with aims of solving the existing road traffic problems,improve road service levels,and promote the overall development of the area and improve the quality of urban space.

Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network

An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.

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.

Bi-objective optimization models for mitigating traffic congestion in urban road networks

Traffic congestion in road transportation networks is a persistent problem in major metropolitan cities around the world.In this context,this paper deals with exploiting underutilized road capacities in a network to lower the congestion on overutilized links while simultaneously satisfying the system optimal flow assignment for sustainable transportation.Four congestion mitigation strategies are identified based on deviation and relative deviation of link volume from the corresponding capacity.Consequently,four biobjective mathematical programming optimal flow distribution(OFD)models are proposed.The case study results demonstrate that all the proposed models improve system performance and reduce congestion on high volume links by shifting flows to low volumeto-capacity links compared to UE and SO models.Among the models,the system optimality with minimal sum and maximum absolute relative-deviation models(SO-SAR and SO-MAR)showed superior results for different performance measures.The SO-SAR model yielded 50%and 30%fewer links at higher link utilization factors than UE and SO models,respectively.Also,it showed more than 25%improvement in path travel times compared to UE travel time for about 100 paths and resulted in the least network congestion index of1.04 compared to the other OFD and UE models.Conversely,the SO-MAR model yielded the least total distance and total system travel time,resulting in lower fuel consumption and emissions,thus contributing to sustainability.The proposed models contribute towards efficient transportation infrastructure management and will be of interest to transportation planners and traffic managers.

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.

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。

快速路与所接普通道路资源利用动态评价方法

中国的城市道路网络大多是由快速路和普通道路组成的混合路网,快速路与其衔接普通道路间的交通运行高度关联.为了能够从整个混合路网系统的角度准确评价交通运行状况,从而提出针对性的拥堵改善措施,本研究构建快速路与所接普通道路资源利用动态评价方法.综合考虑两个路网系统的差异性,以及交通运行的随机性和动态性,提出基于运行通行能力的动态道路利用率指标,在此基础上建立道路利用率不均衡度用于描述该区域的均衡特征.建立基于生存分析法的动态道路利用率计算方法,进行混合路网交通流失效判别,并分别计算交通流失效前后道路运行通行能力,最终得到动态道路利用率的取值.以上海市南北高架路为例,进行道路资源利用动态评价分析.结果表明,快速路与所接普通道路在不同路段组合和不同时段的道路资源利用程度差异较大,快速路与所接普通道路的道路利用率存在显著不均衡特征.研究结果可为交通管理部门实施动态交通诱导与协同控制策略提供依据.

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。

融合知识图谱的时空多图卷积交通流量预测

现有的交通流量预测方法关注交通信息的时空相关性,未充分考虑外部因素对交通的影响,为此提出融合静态和动态知识图谱的时空多图卷积交通流量预测模型.基于道路交通信息和外部因素,构建城市交通知识图谱和4个不同语义的路网拓扑图,将城市交通知识图谱输入关系演化图卷积神经网络,实现知识嵌入;使用知识融合模块将车流量矩阵与知识嵌入融合;将4个路网拓扑图和融合知识的车流量矩阵输入时空多图卷积模块,提取时空特征,通过全连接层输出交通流量预测值.在杭州交通数据集上评估模型性能,与先进的基线模型对比,所提模型的性能提高了5.76%~10.71%.鲁棒性实验结果表明,所提模型具有较强的抗干扰能力.

Assessing the impacts of traffic calming at network level:A multimodal agent-based simulation

The reduction of speed limits in urban roads through traffic calming schemes intends to ensure safer traffic conditions among road users by reducing the probability related to the occurrence of severe accident.Looking it from a different perspective,traffic calming measures can potentially resolve congestion problems at the same time by lowering the overall accessibility and attractiveness of private cars in urban areas.This study proposes a new methodological approach to explore and assess the direct impacts of traffic calming in the transport system efficiency of a metropolitan area.The multi-agent transport simulation(MATSim)and Open-Berlin scenario are utilized to perform this simulation experiment.By developing a new external tool,the free flow speed and road capacity of each network link is updated based on new speed limits and different compliance rates,which are defined per road hierarchy level.The test scenarios that are formulated present radical conditions,where the speed limit in most urban roads of Berlin drops to 30 km/h or even 15 km/h.The findings of this study show a considerably high increase in trips,passenger hours,and passenger kilometers using public transport modes,when traffic calming links are introduced,the reserve change is observed in private cars trips.Although the speed limits are decreased in inner urban roads in most of the scenarios,the decrease of average travel speed of private cars is not so high as it was expected.Surprisingly,private cars are used for longer distances in all test scenarios.Car drivers seem to use already existed motorways and private road to commute.In simulations,driver compliance to the new speed limits seems to be a determinant factor that is strongly influenced by the design interventions applied in a traffic calming area.

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ArcGIS的路网交通可达性分析

城市交通是城市经济发展和人口活动的基础,已成为不可或缺的重要环节。而城市轨道交通路网由于其运量大、速度快以及准点率高的特点,成为了改善城市公共交通的主要手段。在此背景下,提出基于ArcGIS对城市轨道交通进行交通可达性分析,更加利于提升目前交通网络使用效率,并优化轨道交通网络规划。

兰州市环路和城市路网互联互通方案研究

兰州城市交通拥堵在时间和空间范围上不断扩张,主要道路饱和度较高,提出按照“内联外拓、快捷优化、系统整合、整体提升”的总体思路以及“纵横并重、先纵后横、保通兼畅、交通成网”总体措施,以构建区域交通大循环和互联互通为指导,疏通市内城区道路网和联通对外瓶颈公路网,实现城市内外路网合理对接、高效衔接。

西藏高原城市群道路交通系统韧性评估

评估西藏高原城市群道路交通系统韧性,有助于保障交通系统的稳定运行,帮助更快建成高原城市综合交通运输体系。基于西藏交通的特殊情况,文章从服务性、鲁棒性、安全性、可修复性、经济可持续性、自适应性入手选取15个指标,构建了贝叶斯网络模型,对6个高原城市2015到2021年的道路交通系统韧性进行评估。结果表明:西藏城市道路交通系统韧性整体呈现增强趋势,但面临着发展不平衡的问题:拉萨市韧性水平远高于其他市,日喀则、昌都市韧性增长较快,年平均值超过2.5%,林芝、山南和那曲市韧性一直处于较低的水平,且增长缓慢。敏感性分析结果指出,增强高原城市群道路交通系统韧性的首要措施是道路基础设施的建设。文章以韧性评估结果为依据,力求为促进西藏城市道路交通系统健康可持续发展提供有效参考。

基于SSA-CNN-BiLSTM组合模型的短时交通流量预测

为改善城市道路交通拥堵状况,并为智能交通系统决策提供辅助手段,针对短时交通流的非线性和时序性特点,构建了一种基于麻雀搜索算法(SSA)优化的卷积神经网络(CNN)联合双向长短时记忆神经网络(BiLSTM)的组合模型以预测短时交通流量。首先,对原始交通流数据进行异常值清洗、小波阈值去噪和归一化处理。然后,利用SSA算法对CNN与BiLSTM组合网络中的隐藏层单元数、初始学习率和L2正则化系数三个超参数迭代寻优。最后,将搜索得到的最优超参数组合输入搭建好的组合网络中进行训练和预测。实验结果显示:与粒子群优化(PSO)和灰狼优化(GWO)算法相比,SSA算法在网络超参数寻优过程中的收敛速度更快,全局寻优能力更强;与3种对比模型(CNNBiLSTM、BiLSTM和LSTM)相比,在5 min时间尺度划分下,SSA-CNN-BiLSTM组合模型的均方根误差(RMSE)分别降低了5.46、12.78、20.38,平均绝对百分比误差(MAPE)分别降低了0.49%、2.24%、3.11%;在15 min时间尺度划分下,SSA-CNN-BiLSTM组合模型的RMSE分别降低了9.70、28.42、41.18,MAPE分别降低了0.50%、1.98%、2.59%。研究表明,相比既有算法,该短时交通流量预测组合模型在精度和稳定性上都有所提升,可通过提供更精准的短时交通出行信息来改善道路交通状况。

智慧理念在城市道路上的应用浅析

智慧理念在城市管理上的推广应用,符合时代大潮,符合国家导向,是当前城市高质量发展的重要方向。在对城市道路系统特征和组成要素分析的基础上,探讨智慧理念与城市道路各要素的结合需求和应用方向,并对智慧应用的基本功能进行了分析。对城市道路系统进行智慧管理,可以有效提高城市道路系统的运行安全和经济效益,为城市道路的科学管理和决策提供了实时、准确、直观的数据和信息。

基于深度学习的城市主干道路交通拥堵水平预测

为了解决城市主干道路交通拥堵问题,提出一种基于深度学习的城市主干道路交通拥堵水平预测方法。建立城市主干道路交通网络的Katz相似度矩阵,保存路网的结构特征,获得城市主干道路交通流数据。采用局部敏感判别分析模型,将交通流数据映射到低维流形,获得最优投影矩阵,提取城市主干道路交通特征。结合循环神经网络模型(RNN)和长短时记忆网络模型(LSTM),设计长短时记忆循环网络(RNN-LSTM)模型,解决梯度消失问题,输入城市主干道路交通特征,经过训练输出城市主干道路交通拥堵水平预测结果。实验结果表明,所提方法预测准确度在0.8~0.98,预测所需时间平均为24.74 ms,具有一定的应用价值。

Exploratory Methodology for Identification of Urban Bottlenecks Using GPS Data

Bottlenecks have been widely studied for uninterrupted flow. The study of bottlenecks in urban traffic networks implies a challenge, since delays and queuing are implicit in interrupted flows. The objective of this paper is to discuss bottlenecks in urban traffic networks and present a methodology to identify them, based on GPS （Global Position System） data from taxis in Bogota Colombia. In networks, where stops are frequent, the principle we adopted deals with finding recurrent low-speed sections, beyond expected delays. Urban bottlenecks occur in those road network segments that perform poorly in terms of speed, compared with upstream and downstream conditions, producing recurrent and larger than normal delays. The GPS devices reported, via GPRS （General Packet Radio Service）, information every 10 s. Results of this exploratory project are promising. The method allowed the identification of six urban bottlenecks out of seven randomly selected low-speed sections. The most valuable application of this methodology is the prioritization of resources investment in traffic infrastructure improvements. As a low cost option, this exploratory method might be especially attractive in identifying critical points in traffic networks in developing cities, without expensive traffic-monitoring systems.