A graph deep learning method for short‐term traffic forecasting on large road networks

Short‐term traffic flow prediction on a large‐scale road network is challenging due to the complex spatial–temporal dependencies, the directed network topology, and the high computational cost. To address the challenges, this article develops a graph deep learning framework to predict large‐scale network traffic flow with high accuracy and efficiency. Specifically, we model the dynamics of the traffic flow on a road network as an irreducible and aperiodic Markov chain on a directed graph. Based on the representation, a novel spatial–temporal graph inception residual network (STGI‐ResNet) is developed for network‐based traffic prediction. This model integrates multiple spatial–temporal graph convolution (STGC) operators, residual learning, and the inception structure. The proposed STGC operators can adaptively extract spatial–temporal features from multiple traffic periodicities while preserving the topology information of the road network. The proposed STGI‐ResNet inherits the advantages of residual learning and inception structure to improve prediction accuracy, accelerate the model training process, and reduce difficult parameter tuning efforts. The computational complexity is linearly related to the number of road links, which enables citywide short‐term traffic prediction. Experiments using a car‐hailing traffic data set at 10‐, 30‐, and 60‐min intervals for a large road network in a Chinese city shows that the proposed model outperformed various state‐of‐the‐art baselines for short‐term network traffic flow prediction.     

Topological Structure Matching Measure between Two Graphs

There have been a plethora of algorithms and techniques for the one‐to‐one correspondence matching between two small graphs at the element level. However, it is a daunting task for large graphs. It is necessary to design an aggregate statistical measurement to measure the degree of matching at the coarse grained level between two large graphs. This work presents a novel contribution with the proposal of an aggregate statistical measurement of the matching between two large networks at the macro topological structure level. In the viewpoint of strategic planning application, decision makers want to know whether the road infrastructure network meets the traffic flow network at the macro level rather than the micro level. The macro topological structure of the graph is described by a partition of all the vertices by the singular value decomposition based on the weighted vertex‐path incidence matrix. The topological structure matching measurement (TSMM) of the two graphs is defined as the degree of similarity between two partitions. As a case study, the TSMM is considered between the road network and the traffic flow network for Shanghai. The result is 0.2129, which shows that the two networks mismatch to a certain degree. This, agreeing with the current situation of the traffic congestion in Shanghai, suggests the improvement in the urban traffic.     

Partitioning of urban networks with polycentric congestion pattern for traffic management policies: Identifying protected networks

Large-scale urban networks are usually loaded heterogeneously with a polycentric congestion pattern, resulting in a highly scattered network macroscopic fundamental diagram (NMFD or MFD). Thus, researchers have tried to partition city networks into homogeneous subzones. In this study, a six-step partitioning algorithm is proposed. The framework allows the NMFD information to be used. It combines traffic variables and geographic connectivity to obtain partitions. The framework includes graph definition, data preprocessing, feature handling, clusters and partitions identification, and boundaries reshaping. Tests on a simplified grid network and the city of the Melbourne road network demonstrate the suitability of the framework for characterizing the traffic states by the partitions. The framework on the missing data scenarios and monocentric and polycentric traffic concentrations scenarios, as well as applying multiple data types, has been challenged. Thereafter, parameter impact analysis demonstrates that manipulation of the parameters enables users to find the desired partitions. Last but not least, a comparison with an existing method also implies the uniqueness and efficiency of the developed framework.     

Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data

The accurate forecasting of traffic states is an essential application of intelligent transportation system. Due to the periodic signal control at intersections, the traffic flow in an urban road network is often disturbed and expresses intermittent features. This study proposes a forecasting framework named the spatiotemporal gated graph attention network (STGGAT) model to achieve accurate predictions for network-scale traffic flows on urban roads. Based on license plate recognition (LPR) records, the average travel times and volume transition relationships are estimated to construct weighted directed graphs. The proposed STGGAT model integrates a gated recurrent unit layer, a graph attention network layer with edge features, a gated mechanism based on the bidirectional long short-term memory and a residual structure to extract the spatiotemporal dependencies of the approach- and lane-level traffic volumes. Validated on the LPR system in Changsha, China, STGGAT demonstrates superior accuracy and stability to those of the baselines and reveals its inductive learning and fault tolerance capabilities.     

Data Fusion of Fixed Detector and Probe Vehicle Data for Incident Detection

An important feature of many advanced traveler information systems (ATIS) is real-time information about incidents on the street network. This paper describes a system for automatically detecting incidents for such an ATIS developed using artificial neural networks and statistical prediction methods. The system monitors traffic conditions using two types of data: inductive loop detectors (ILDs) and vehicle probes. For both neural network and statistical methods, incident detection is accomplished using two approaches: by processing traffic input data directly and by processing the output of specialized algorithms that detect incidents using information from each data source. Analysis data generated from a simulation of a typical suburban signalized major arterial street are used. Different model configurations are examined and tested to identify the input variables and methods that are the best predictors of incident occurrence. The neural network approaches consistently perform at least as well as the discriminant analysis models, especially when results are adjusted to avoid false alarms.     

Jams and superjams: a systems approach to congested traffic networks

The road traffic engineer attempts to solve the problem of congestion and delay to road traffic in urban road networks by increasing capacity. He does this by computer control of traffic signals, by traffic management (one-way systems, banned turns etc.), by junction design, and to a limited extent by building new or improved roads. Such increases in capacity do not increase traffic speed if traffic demand is sufficiently high. However, they do increase the risk of traffic jams when blockages of the network, and especially of junctions occur, because there is increased traffic on essentially the same network as before. Traffic speed in congested road networks is set by an equilibrium with the best alternative public transport system. It can only be improved if public transport is improved and road space devoted to the more efficient user of space, the bus or tram, or, if demand is sufficiently high, by the construction of segregated rail systems.     

Dynamic Optimal Traffic Assignment and Signal Time Optimization Using Genetic Algorithms

Abstract:   In this article a dynamic system-optimal traffic assignment model is formulated for a congested urban road network with a number of signalized intersections. A simulation-based approach is employed for the case of multiple-origin-multiple-destination traffic flows. The artificial intelligence technique of genetic algorithms (GAs) is used to minimize the overall travel cost in the network with fixed signal timings and optimization of signal timings. The proposed method is applied to the example network and results are discussed. It is concluded that GAs allow the relaxation of many of the assumptions that may be needed to solve the problem analytically by traditional methods.     

A data‐driven approach to determining freeway incident impact areas with fuzzy and graph theory‐based clustering

Determining spatiotemporal impact areas of incidents plays a significant role in incident impact analysis. Although existing empirical methods have proven to be promising, they suffer from the drawbacks that limit their wide applications in automated freeway safety management. This study presents a data‐driven approach to automatically determining the spatiotemporal impact areas of freeway incidents. The spatiotemporal contour plots were first constructed using three representative traffic measures. Next, a nonrecurrent congestion area identification method based on fuzzy clustering was developed. To distinguish possible multiple independent blocks in the nonrecurrent congestion area, a clustering algorithm based on graph theory was adopted. The incident impact areas were then determined by conducting a postprocessing strategy. The incident records and the associated traffic flow data, collected on I‐5 freeway segments in San Diego Region, CA, were used to evaluate the proposed approach. Experimental results show the proposed approach can automatically and properly determine incident impact areas while accounting for the uncertainty resulting from traffic variations.     

Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow

Abstract:   This article addresses the problem of the accuracy of short-term traffic flow forecasting in the complex case of urban signalized arterial networks. A new, artificial intelligence (AI)-based approach is suggested for improving the accuracy of traffic predictions through suitably combining the forecasts derived from a set of individual predictors. This approach employs a fuzzy rule-based system (FRBS), which is augmented with an appropriate metaheuristic (direct search) technique to automate the tuning of the system parameters within an online adaptive rolling horizon framework. The proposed hybrid FRBS is used to nonlinearly combine traffic flow forecasts resulting from an online adaptive Kalman filter (KF) and an artificial neural network (ANN) model. The empirical results obtained from the model implementation into a real-world urban signalized arterial demonstrate the ability of the proposed approach to considerably overperform the given individual traffic predictors .     

Graph‐Based Modeling of Information Flow Evolution and Propagation under V2V Communications‐Based Advanced Traveler Information Systems

A vehicle equipped with a vehicle‐to‐vehicle (V2V) communications capability can continuously update its knowledge on traffic conditions using its own experience and anonymously obtained travel experience data from other such equipped vehicles without any central coordination. In such a V2V communications‐based advanced traveler information system (ATIS), the dynamics of traffic flow and intervehicle communication lead to the time‐dependent vehicle knowledge on the traffic network conditions. In this context, this study proposes a graph‐based multilayer network framework to model the V2V‐based ATIS as a complex system which is composed of three coupled network layers: a physical traffic flow network, and virtual intervehicle communication and information flow networks. To determine the occurrence of V2V communication, the intervehicle communication layer is first constructed using the time‐dependent locations of vehicles in the traffic flow layer and intervehicle communication‐related constraints. Then an information flow network is constructed based on events in the traffic and intervehicle communication networks. The graph structure of this information flow network enables the efficient tracking of the time‐dependent vehicle knowledge of the traffic network conditions using a simple graph‐based reverse search algorithm and the storage of the information flow network as a single graph database. Further, the proposed framework provides a retrospective modeling capability to articulate explicitly how information flow evolves and propagates. These capabilities are critical to develop strategies for the rapid flow of useful information and traffic routing to enhance network performance. It also serves as a basic building block for the design of V2V‐based route guidance strategies to manage traffic conditions in congested networks. Synthetic experiments are used to compare the graph‐based approach to a simulation‐based approach, and illustrate both memory usage and computational time efficiencies.     

Comparing Delay Minimization and Emissions Minimization in the Network Design Problem

Abstract: Traditionally, transportation road networks are designed for minimal congestion. Unfortunately, such approaches do not guarantee minimal vehicle emissions. To fill this apparent gap in network design research, an emissions network design problem and solution method is proposed in this article for the purposes of comparing to the traditional network design results. Three air pollutants are considered on two road networks. The model is formulated as a bi‐level optimization problem and a solution is approximated using a genetic algorithm. The influence of demand uncertainty is also incorporated into the model. Designing for minimal congestion tends to increase emissions of criteria air pollutants. However, not adding capacity to a road network also increases emissions of pollutants. Therefore, an optimization problem and solution method, such as the emissions network design problem and solution method presented here, is useful for identifying capacity additions that reduce vehicle emissions. It is also useful for understanding the tradeoffs between designing a network for minimal congestion versus minimal vehicle emissions.     

沪南公路现状拥堵分析及改善对策

对上海沪南公路区域路网,交通流量,交叉口和出入口影响以及现状交通构成进行拥堵分析。在拥堵原因分析基础上,提出了完善区域路网;取消支路交叉口信控,采用右进右出交通组织;增加车道规模,局部路段采用"主路+辅路"的交通组织形式等改善对策。最终指出解决沪南公路交通拥堵问题的最佳策略是增加车道规模,局部路段采用"主路+辅路"的交通组织形式,可满足周围居民出行需求。     

Genetic Algorithm-Simulation Methodology for Pavement Maintenance Scheduling

Abstract:   Pavement maintenance activities often involve lane closures, leading to traffic congestion and causing increases in road users' travel times. Scheduling of such activities should minimize the increases in travel times to all the travelers at network level. This article presents a hybrid methodology for scheduling of pavement maintenance activities involving lane closure in a network consisting of freeways and arterials, using genetic algorithm (GA) as an optimization technique, coupled with a traffic-simulation model to estimate the total travel time of road users in the road network. The application of this scheduling method is demonstrated through a hypothetical problem consisting of assigning three maintenance teams to handle 10 job requests in a network in 1 day. After 10 generations of genetic evolution with a population size of four, the hybrid GA-simulation model recommended a schedule that reduced the network total travel time by 5.1%, compared to the initial solution.     

Author Index to Volume 18

Abstract:   Route guidance system (RGS) is considered as a low-cost alternative for reducing congestion by providing real-time information to drivers to redistribute traffic in space and time so as to use roadway networks more efficiently. This article focuses on the behavioral component, one of the three components (the other two being dynamic traffic component and information supply strategy component) of a practical RGS developed through a 4-year project at the University of Delaware. Development of the behavioral model is based on the premise that different drivers perceive and behave differently in response to the information provided. Understanding the behavior of RGS-equipped drivers' acceptance or nonacceptance of provided information is essential for understanding the reliability of the system. Backpropagation neural network with its ability to map complex input–output relationships has been used to structure the model. This model was tested on two networks under both recurring and nonrecurring congestion. A comparative analysis of the measures of effectiveness revealed that the performance of the developed RGS is significantly better than the performance under existing non-RGS conditions.     

基于实时路况的交通拥堵时空特征及其影响因素分析——以苏州古城区为例

城市化水平提高的同时交通问题也日趋严重,交通拥堵已然成为制约大中城市发展的"瓶颈"。新数据环境下我们可以获得更加丰富的城市交通特征信息来分析交通拥堵特征。研究采集连续一周的实时路况数据,通过ArcGIS操作平台对苏州古城区范围内的常发性交通拥堵时空特征进行分析,并剖析交通拥堵发生的主要原因。研究显示:时间上,苏州古城区工作日和休息日均有两个出行高峰,但高峰时间段和峰值有所差异;工作日路况拥堵变化程度较大,潮汐现象更为明显;空间上,交通拥堵呈现"点、线"并存的特征,并有向周围区域扩散形成"面"拥堵的趋势。研究表明,常发性拥堵时空分布规律与交通供需矛盾、职住分离、路网结构、用地布局等因素存在一定相关性。     

龙岩中心城区交通拥堵成因及治理措施分析

为解决龙岩中心城区交通拥堵及改善交通环境,基于龙岩中心城区交通现状及各方面实际情况,深入分析龙岩中心城区交通拥堵成因,为找到相应的治理及改善措施提供依据。分析表明:交通基础设施及道路路网建设不完善;道路不通畅、通行能力低;宣传不到位、交通意识差等是交通拥堵的主要成因。鉴于此,提出了四个方面的治理措施:加强交通组织及管理、培养交通意识;完善路网建设;优化公交线路;加大人行过街设施及停车场的建设。     

混合交通网络设计的双层模型及遗传算法求解

根据混合交通网络设计问题的特点,利用双层规划模型和遗传算法对该问题进行求解。对交通网络中的路段进行分类,通过限定决策变量的取值范围,将混合交通网络离散化。建立混合交通网络设计的双层模型。其中,上层模型以方案总投资额最小为目标函数,以路段负荷度和可行域为约束条件;下层模型为交通流分配的用户均衡模型。根据所建模型的离散特性,研究其遗传算法解法,并给出算法的具体实现步骤。以一个抽象的交通网络为例,给定网络中的路段属性、OD交通量等参数,利用MATLAB软件对模型编程求解,能够获得满意的交通网络设计方案,表明双层模型和遗传算法是一种研究混合交通网络设计问题的有效方法。最后,对该模型存在的不足及改进方向进行了探讨。     

地下道路功能定位及其在上海市的适用性分析

简述了城市地下道路在地下交通系统中的地位,结合国内外已有和规划的地下道路实际案例,总结了多功能快速地下道路、改善区域性路网功能和解决交通阻塞节点的地下道路的不同功能定位、基本特征及其适用性.并通过对上海市城市规划和交通问题的分析,介绍了浦东新通道快速地下道路、外滩地区地下道路和徐家汇地区地下道路等规划方案,可为国内其他城市地下道路规划提供参考.     

成都市综合交通枢纽邻接区交通网络优化研究

城市综合交通枢纽是现代城市中最重要的客流集散中心,其邻接区极易出现人群及车辆混杂拥堵等情况,从而影响枢纽乃至整个城市交通系统的正常运行,所以优化枢纽邻接区交通网络以保证枢纽的服务效率与水平极为重要。文章以成都四大枢纽站为例,建立空间句法导向下的枢纽邻接区城市地铁交通网络、城市道路交通网络的拓扑关系模型,通过整体集成度、平均深度、连接度等参量进行宏观及微观层面的分析,对交通网络的可达性、中心性、渗透性等进行评价,并在此基础上提出针对四大枢纽邻接区交通网络的优化建议,指导枢纽邻接区的空间发展。     

An Implementation of the Mode-Split Traffic-Assignment Method

A software called Optimal Traffic Signal Control System (OTSCS) was developed by us for testing the feasibility of dynamically controlling a traffic signal by finding optimal signal timing to minimize delay at signalized intersections. It also was designed as a research tool to study the learning behavior of artificial neural networks and the properties of heuristic search methods. It consists of a level-of-service evaluation model that is based on an artificial neural network and a heuristic optimization model that interacts with the level-of-service evaluation model. This article discusses the latter model, named the Optimal Traffic Signal Timing Model (OTSTM). The OTSTM was applied to determine optimal signal timing of two-phase traffic signals to evaluate the model's performance. Two search methods were employed: a depth-first search method (an enumeration method) and a direction-search method that the authors developed. It was found that the OTSTM with the direction search resulted in "optimal" signal timings similar to the depth-first search, which would always produce a global optimal timing. Yet the cost of the direction search, as measured by the CPU time of the computer used for analysis, was found to be much less than the cost of obtaining an optimal solution by the depth-first search cases—more than 10 times less. The study showed that once the artificial neural network is properly trained, heuristic optimal signal timing combined with artificial networks can be used as a decision-support tool for dynamic signal control. This article demonstrates how OTSTM can quickly find an optimal signal-timing solution for two-phase traffic signals.