Short-Term Traffic Flow Prediction Based on Road Network Topology

Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).

A Novel Network Screening Methodology for Rural Low-Volume Roads

Low-volume roads (LVRs) are an integral part of the rural transportation network providing access to remote rural areas and facilitating the movement of goods from farms to markets. These roads pose unique challenges for highway agencies including those related to safety management on the highway network. Specifically, traditional network screening methods using crash history can be effective in screening rural highways with higher traffic volumes and more frequent crashes. However, these traditional methods are often ineffective in screening LVR networks due to low traffic volumes and the sporadic nature of crash occurrence. Further, many of the LVRs are owned and operated by local agencies that may lack access to detailed crash, traffic and roadway data and the technical expertise within their staff. Therefore, there is a need for more efficient and practical network screening approaches to facilitate safety management programs on these roads. This study proposes one such approach which utilizes a heuristic scoring scheme in assessing the level of risk/safety for the purpose of network screening. The proposed scheme is developed based on the principles of US Highway Safety Manual (HSM) analysis procedures for rural highways and the fundamentals in safety science. The primary application of the proposed scheme is for ranking sites in network screening applications or for comparing multiple improvement alternatives at a specific site. The proposed approach does not require access to detailed databases, technical expertise, or exact information, making it an invaluable tool for small agencies and local governments (e.g. counties, townships, tribal governments, etc.).

Dynamic evaluation of traffic flows on city roads

This paper presents an analysis of the random fluctuations, deferred conduction effect and periodicity of road traffic based on the basic features of road networks. It also discusses the limitations of road network evaluation theories based on road "V/C". In addition, it proposes a set of theoretical and technical methods for the real-time evaluation of traffic flows for entire road networks, and for solving key technical issues, such as real-time data collection and processing in areas with no blind zones, the spatial-temporal dynamic analysis of road network traffic, and the calibration of key performance index thresholds. It also provides new technical tools for the strategic transportation planning and real-time diagnosis of road traffic. The new tools and methodology presented in this paper are validated using a case study in Beijing.

Traffic organization during urban road constructions

This research presented a bi-level programming approach to optimize the schedule of ur- ban road construction activities based on a hypothetical transport network, with an objective of mini- mizing the overall traffic delays. A heuristic algorithm was utilized to identify a set of road construction schedules, while PARAMICS was adopted to estimate the total travel time in the network under each road construction scenario. To test the performance of proposed heuristics-simulation methodology, a numerical test was implemented. The overall results suggested that the proposed methodol- ogy could quickly find the optimum solution with good convergence.

Perspective of Adaptive CN System for Forecasting Congestion of Road Traffic Flow

Basing upon the Weber-Fechner Law with respect to the stimulus (distance-headway) to the vehicle driver and the driver’s sensation (speed), the characteristic speed Vβ is defined, which is the critical vehicles flow speed just before going to congestion in road traffic flow. From the information of real time measurement of traffic flow speed (V) and time-headway (T) at the specific positions along the road, the value of Vβ is calculated and used for forecasting the flow. Discussed is how to use each Vβ to forecast the congestion. The CN system devoted to the management of road traffic flow is proposed. The idea may contribute not only to easing the traffic flow but also to optimizing it to get high efficient traffic flow.

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.

基于SA-Apriori的道路事故风险评估研究

道路事故风险评估是提高道路安全水平的有效方式。为实现在道路网络特征视角下的道路事故风险评估,融合线段分析与频繁集算法,提出SA-Apriori的特征提取算法,进而构建SA-Apriori道路事故风险评估模型,以重庆市大渡口区作为分析对象,建立以道路网络特征变量为指标的道路事故风险等级划分体系,评估其道路事故风险水平,结合商业点位分布情况对该评估方法的有效性进行验证。并针对大渡口区道路事故风险情况,提出道路事故风险的管理对策与建议。结果表明:该道路事故风险评估方法具有较高的可靠性,可为基于路网特征的道路事故风险评估提供参考。

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。

基于双层规划模型的“才”字形交叉口禁流向交通组织方法研究

为解决城市多路交叉口车流复杂导致的交通拥堵和交通安全问题,针对多路交叉口中的“才”字形交叉口,明确其特点,对交叉口部分交通流向禁行的必要性进行分析。基于双层规划构建交叉口禁流向交通组织模型,以局部路网中车均行驶时间最小为目标,判别交叉口各流向车流是否禁行,并确定禁流向后交通组织方案,减少交叉口内车流及冲突点数量。以哈尔滨市学府路—西大直街交叉口为例,比较禁流向交通组织方案和单向交通组织方案,发现此交叉口禁流向交通组织更优,冲突点个数减少52%,路网车均行驶时间下降0.98%。考虑交通量变化分析禁流向交通组织模型的适用性,发现OD量变化超过26%时模型适用性较差,潮汐现象明显的交叉口,应设置单向交通组织。

分形视角下城市非规则道路网近百年演化过程研究

城市道路网在不同时期具有独特的网络结构,然而在近百年历程中如何演变,其过程并不清晰。研究以城市非规则道路网为例,通过历史资料整理提取了南京市鼓楼片区1900年来4个时期道路网,运用分形理论量化表达道路网覆盖均匀度和发育复杂度。结果表明:鼓楼片区非规则路网是由贯通性主干路牵引形成的;其路网相对稀疏,依然有更新和改进的空间;路网覆盖度和连通度近年来趋于平缓,城市应重视干道的功能拓展和“大院制”支路的打通。通过文献史料分析,解释了不同阶段规划思想、社会变革等因素对道路网演变起到的作用,认为历史古城道路网的更新保护应重视起牵引作用的骨架干道,同时重视非机动车和行人的需求,提升其可达性。

考虑抵达时间成本的道路交通事故风险评估方法

道路交通事故频发,给生命财产造成重大损失,给社会生活带来重大影响。现有针对道路交通事故风险的研究未建立有效的道路网络模型,难以准确描述交通事故风险在道路上的传播特点,评估准确度不高。基于此,提出了一种基于抵达时间成本的网络地理加权回归方法,并利用某县级市2018—2020年的道路、交通违法、交通事故、城市POI等数据开展实验,结果表明,基于抵达时间成本的网络地理加权回归方法融合了交通事故风险在道路上的传播性质,显著降低了评估误差,能够有效评估道路交通事故风险及其影响因素;市中心区域道路交通事故高风险区域主要集中在车流量较大的道路交会处与部分交通设施尚不完备的道路;各类交通违法数量、城市POI对道路交通事故风险的影响程度不同,且具有很强的空间异质性。

不确定环境下收费与信息对路径选择行为影响的实验研究

在不确定环境下考虑信息发布与拥挤收费两种措施,基于交通网络均衡,推导了两路径简单路网的最优收费模型,并推广至一般路网。基于两路径路网设计逐日路径选择行为实验,分析了两种措施对路径选择行为的影响。结果表明:完美信息与收费的实施均能减少流量波动,单独实施收费的效果最佳,但只有两种措施相结合时,路网流量趋于用户均衡的趋势最稳定;完美信息会增加路径切换行为,而收费可有效抑制大幅的路径切换;无收费时路径切换往往会造成出行成本增加,而收费时则相反;收费与信息结合时的出行时间比两种措施单独实施时小。

基于集装箱卡车GPS数据的港内物流通道区域交通状态识别

为解析港内物流通道区域路网交通的时空分布特征,探索高效的港内物流通道交通管理和优化方法,基于某港内货运集卡的真实运营和轨迹数据,对港内货运物流通道的交通状态识别与划分进行了研究。首先,应用H3网格对DBSCAN算法进行改进,构建了基于网格领域的区域交通状态时空矩阵,对案例港内货运路网区域进行识别划分。然后,结合港内船舶装卸作业生产特征,对造成港内物流区域瓶颈点进行分析总结。研究结果表明,案例港口码头前沿的装卸区域、堆场道路及与主干道交叉区域的交通状况尤为复杂,是影响港内运输效率的关键区域;在多船舶不同装卸作业场景下,同一装卸泊位对不同货运通道的制约力存在显著差异。研究可为识别和解析多船舶装卸作业场景下港口物流通道潜在“热点”区域及特征、探索缓解港内物流通道货运交通压力的措施及提升港口总体生产作业效率,提供新的思路和方法。

基于知识图谱的公路气象灾害研究

为更好掌握现有公路气象灾害研究的知识结构及发展进程,收集中国知网(CNKI)核心集1992—2022年和Web of Science核心集2000—2022年收录的1840篇论文,基于CiteSpace软件,从文献分布、共现网络、聚类分析、关键词突现等方面进行分析。结果表明:1)随着学科不断发展,公路气象灾害领域论文年发文量总体呈增长趋势;2)公路气象灾害研究具有多学科交叉性质,研究学者来自交通、气象及地质学等相关研究机构及院校;3)国内外研究热点主要有气象灾害对交通基础设施的破坏、气象灾害对交通运行及安全的影响、气象灾害模拟及风险评估、路网监测及交通管控措施等;4)公路边坡灾害及恶劣天气对公路正常运行的影响在多时期引起国内外学者的广泛关注;5)随着研究的不断深入,公路抗灾韧性、智慧交通管控及全寿命公路气象灾害评估等方向近几年引起研究学者关注。

基于空间句法的城市立体道路网研究——以南京市鼓楼地区为例

南京鼓楼地区经历了近百年的演变,形成了独特的网络特征。文章旨在通过整理2021年的鼓楼地区道路网数据,并运用空间句法分析方法,选取全局整合度、局部整合度和可理解度作为主要指标,来量化分析鼓楼地区道路网的拓扑结构。同时,对2021年的平面道路网和立体道路网进行空间句法对比分析,以更准确地揭示城市道路网的特征。此外,还分析了鼓楼地区公共事业单位与道路网之间的空间关系和作用机制,采用集成核方法进行耦合分析。文章构建了鼓楼地区道路网分析模型,为历史古城片区道路网的发展提供了参考依据。

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。

道路网络多阶段抗灾能力优化模型构建与应用

为了降低路网抗灾成本并保障路网快速连通,研究道路交通网络多阶段灾害应对能力优化的问题.建立综合灾前应急工作站点选址和灾后路网恢复决策的3层规划模型,其中特别考虑了应急救援设施和后勤保障资源在耗尽性、运输方式和恢复作用上的差异,定量建模了两者间的相互协作关系.通过综合双层遗传算法和FrankWolfe算法,获得模型的近似最优解.研究结果表明,与未考虑灾后恢复过程的灾前布设决策和未考虑后勤保障资源的灾前布设决策相比,最优决策可以分别降低10.96%的后勤保障资源运输成本和11.51%的加权恢复成本.后勤保障资源和应急救援设施布设数量共同影响路网的恢复效果,忽略两者间的协作关系,将会高估应急救援设施布设数量增加对恢复效果的影响.

基于24Model的重特大道路交通事故致因网络分析

为探究导致重特大道路交通事故的致因因素及其演化关系,运用24Model分析收集的62起重特大道路交通事故调查报告,从不安全动作因素、不安全物态因素、个体安全能力、管理体系因素、安全文化因素以及外部环境因素6类因素展开分析,确定了53个致因因素,进一步构建重特大道路交通事故致因网络,并分析该网络模型的相关拓扑特征指标,结果表明:安全知识不足,安全意识淡薄,安全习惯不佳,心理状态不佳等10项致因因素中心性较高,处于社会网络核心位置,易于诱发其他致因因素,网络的平均路径长度以及网络直径较小,这表明该致因网络具有小世界性,进一步分析社会网络边权占比,得到了1条致因因素关键影响路径,针对关键致因因素及路径进行协同控制对预防重特大道路交通事故发生具有重要意义。

针对大规模复杂车流的PG强化交通灯控制算法

优化城市道路中的交通信号灯控制是低成本地提升城市交通路网性能的方法之一。该研究提出了一种利用策略梯度(Policy Gradient, PG)强化调优的交通灯控制算法。该算法引入了道路压力项、旅程时间项和黑名单机制项,利用统计方式预测汽车行程轨迹,并采用策略梯度估计的优化算法调整算法中的参数。在数据挖掘国际会议Knowledge Discovery and Data Mining (KDD)组织的算法竞赛KDD Cup 2021城市大脑挑战赛中,获得了冠军的成绩。在该挑战赛提供的城市路网规模复杂车流仿真平台上的实验结果表明,算法具有应用于实际场景的价值。

考虑路径特性的道路交通网络冗余性分析

为探究路段特性对路径冗余的影响以及突发事件下道路交通网络的冗余能力,在传统道路网络路径冗余数量方法的基础上,考虑有效路径之间的效率差异和重叠程度,构建基于路径多样性以及突发事件下冗余能力的冗余性评估模型;分析不同成本对路径多样性的影响和突发事件对冗余能力的影响,并对冗余能力进行敏感性分析。结果表明:道路交通网络中路径多样性取决于出行路径成本的类别以及路段出行成本之间的差异,路段出行成本越接近,有效路径的数量越多,路径多样性越高;突发事件场景下道路网络冗余性取决于失效路段的重要程度,失效路段越重要,网络冗余性越低;出行成本容忍度在一定范围内的增加有助于提高突发事件下道路网络的冗余能力,过高或过低的出行成本容忍度都不会对网络的冗余能力有较为明显的提高。