Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review

Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors.Because of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal control demonstrates significant improvements over existing conventional signal control systems.Though various CV-based signal control systems have been investigated in the past decades,these approaches still have many issues and drawbacks to overcome.We summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital concepts.Last,future research directions are discussed with some suggestions.We hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.

Measurement and influencing factors of urban traffic ecological resilience in developing countries: A case study of 31 Chinese cities

An urban traffic ecosystem is a spatial structure composed of air,population,vehicles,roads,green spaces,and regions.Traffic ecological resilience is a critical issue in high-quality urban development.From the perspective of system optimization,it is important to study the level of urban traffic ecological resilience and analyze its influencing factors.In this study,we evaluated traffic ecological resilience,characterized its spatio-temporal differentiation,and explored its influencing factors by constructing a system of urban traffic ecological resilience and by analyzing the environmental protection and urban construction data in 31 Chinese cities during 2011-2018.By conducting Kernel density analysis,standard deviation ellipse,comprehensive weight determination,panel data regression analysis,andχ2test,we found that traffic ecological resilience was low on the whole and exhibited the temporal trend of“decreasing first and then increasing”and the spatial characteristic of“high in the east,second in the middle,and low in the west”.The cities with high traffic ecological resilience density values were located in Southeast China and tended to move from northwest to southeast.Governance capability,market activity,technological innovation capability,opening degree,and financial resources had significant effects on urban traffic ecological resilience.Finally,we gave some suggestions for improving the urban traffic ecological resilience in Chinese cities as well as other developing countries in the world.

An urban traffic environment model

Urban environment pattern depends heavily upon urban traffic pattern, the balance between traffic (implicit production) and environment leads to the urban sustainable development. An integrated urban traffic environment model consists of three components of urban production variables (population density, GDP, salary, etc. in blocks), urban traffic variables and urban environmental variables; and two links between urban traffic planning variables and urban environment variables, and between spatial interaction model (SIM) and traffic planning variables as well. The model is quite useful in urban environment impact assessment; urban traffic management; urban sustainable development planning; and urban development decision\|making.

Long-range correlation analysis of urban traffic data

This paper investigates urban traffic data by analysing the long-range correlation with detrended fluctuation analysis. Through a large number of real data collected by the travel time detection system in Beijing, the variation of flow in different time periods and intersections is studied. According to the long-range correlation in different time scales, it mainly discuss the effect of intersection location in road net, people activity customs and special traffic controls on urban traffic flow. As demonstrated by obtained results, the urban traffic flow represents three-phase characters similar to highway traffic. Moreover, compared by the two groups of data obtained before and after the special traffic restrictions （vehicles with special numbered plates only run in a special workday） enforcement, it indicates that the rules not only reduce the flow but also avoid irregular fluctuation.

Research on an Urban Traffic Control System Based on DGPS

The basic principles of GPS (Global Positioning System) and DGPS (Differential GPS) are described. The principle and structure of vehicle navigation systems, and its application to the urban traffic flow guidance are analyzed. Then, an area coordinated adaptive control system based on DGPS and a traffic flow guidance information system based on DGPS are put forward, and their working principles and functions are researched. This is to provides a new way for the development of urban road traffic control systems.

Improving Vehicular Mobility in Urban Traffic Using Ubiquitous Computing

The paper introduces a novel paradigm to use ubiquitous computing in urban traffic control as a methodology to include the benefits of considering physical elements implicated in the environment. This promising idea arises from previous results in the literature, where ubiquitous computing achieves significant and promising results in diverse scenarios. Some works in the state-of- the-art clearly state that traditional traffic light signals are not capable of offering higher service levels when they should control the vehicular mobility because of different constraints. In this sense, the paper proposes an adaptation of the main ideas of ubiquitous computing as a metaphor to facilitate the interaction between users and traffic infrastructures in order to improve the users’ experience on the road.

Research on Deformation Control Indicators and Standard of Urban Traffic Tunnel Construction Adjacent to High-rise Building

The biggest environmental problem caused by the construction of tunnels adjacent to high-rise buildings is the settlement of buildings.The paper analyzes the influence of tunnel excavation on the deformation of the superstructure and the deformation mode of the superstructure.It introduces the indicators and standards for the construction control of tunnel adjacent to the building at home and abroad.Combined with the Yuzhong tunnel project under construction in Chongqing,the main monitoring indicators and control standards of the Yuzhong Tunnel passing through the main buildings are given after comprehensive analysis and considerations,which provide a reference for the deformation control indicators of similar urban traffic tunnels adjacent to high-rise buildings.

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 Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

Advanced information and communication technologies can be used to facilitate traffic incident management. If an incident is detected and blocks a road link, in order to reduce the incident-induced traffic congestion, a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work. Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information. A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled.Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles, operation time interval of the proposed strategy, traffic density in the traffic network, and the scope of the area where traffic incident information is delivered. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

Model and algorithm of optimizing alternate traffic restriction scheme in urban traffic network

An optimization model and its solution algorithm for alternate traffic restriction(ATR) schemes were introduced in terms of both the restriction districts and the proportion of restricted automobiles. A bi-level programming model was proposed to model the ATR scheme optimization problem by aiming at consumer surplus maximization and overload flow minimization at the upper-level model. At the lower-level model, elastic demand, mode choice and multi-class user equilibrium assignment were synthetically optimized. A genetic algorithm involving prolonging codes was constructed, demonstrating high computing efficiency in that it dynamically includes newly-appearing overload links in the codes so as to reduce the subsequent searching range. Moreover,practical processing approaches were suggested, which may improve the operability of the model-based solutions.

Application of GIS for Urban Traffic Accidents: A Critical Review

The analysis of huge data is a complex task that cannot be executed without a proper system. Geographic information systems (GISs) have been used by many transportation agencies and police departments to analyze and manage urban traffic accident (UTA) data and for decision making aimed at decreasing accident rates and increasing safety. The exact location of accidents and environmental characteristics must be analyzed as UTAs occur in specific locations with specific characteristics. ArcGIS software is the best choice for obtaining meaningful information and analysis results from UTAs in an observational time span. GIS technology is a fundamental element for investigating and evaluating the complex spatial relationship among different components and urban traffic accident is one of them. Micro or macro analysis of UTAs through the spatial prospective within the geographical environment and urban structure can make a deep micro understanding of UTAs patterns in addition to assisting in decision making. UTAs can be considered complex events that occur in two aspects which are spatial and temporal or space and time in other word. A GIS can integrate more than two different and unrelated databases. The evaluation among different spatial objects in a geographical environment and associated factors in urban structure which are included but not limited to land use category, road transportation network qualification, population density, etc., is one of the GIS specification. Traffic safety organizations and UTA researchers use GISs as a key technology to support their research and operational needs. In particular, GIS-T is an often-used GIS application used for planning and decision-making in transportation.

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.

Topology Optimization for Urban Traffic Sensor Network

This paper presents an optimized topology for urban traffic sensor networks. Small world theory is used to improve the performance of the wireless communication system with a heterogeneous transmission model and an optimal transmission radius. Furthermore, a series of simulations based on the actual road network around the 2nd Ring Road in Beijing demonstrate the practicability of constructing artificial ＂small worlds＂. Moreover, the particle swarm optimization method is used to calculate the globally best distribution of the nodes with the large radius. The methods proposed in this paper will be helpful to the sensor nodes deployment of the new urban traffic sensor networks.

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.

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.

Urban traffic modeling and pattern detection using online map vendors and self-organizing maps

Typical traffic modeling approaches,such as network-based methods and simulation models,have been shown inadequate for urban-scale studies due to the fidelity issue of models.As a go-around,data-driven models have received increasing attention recently.However,most data-driven methods have been restricted by their data source and cannot be scaled up to manage urban-and regional-scale studies.Regarding this issue,this research proposes a pipeline that collects traffic data from online map vendors to bypass data limitations for large-scale studies.The study consists of two experiments:1)recognizing the dominant traffic patterns of cities and 2)site-specific predictions of typical traffic or the most probable locations of patterns of interests.The experiments were conducted on 32 Swiss cities using traffic data that were collected for a two-month period.The results show that dominant patterns can be extracted from the temporal traffic data,and similar patterns exist not only in various parts of a city but also in different cities.Moreover,the results reveal that a country-level lockdown decreased traffic congestions in regional highways but increased those connections near the city centers and the country borders.

Identification Method of Urban Road Traffic Conditions in the Internet of Vehicles Environment

In accordance with the specific deployment way of infrastructure and data exchanging technology in the Internet of vehicles(IoV),the acquiring and calculating method for three basic traffic flow parameters in IoV scenarios,including traffic flow,speed and density,was researched.Considering the complexity of traffic flow and fuzziness of human thinking,fuzzy c-means clustering algorithm based on the genetic algorithm(GA-FCM) was adopted in soft classification of urban road traffic conditions.Genetic algorithm(GA) introduced into fuzzy clustering could avoid fuzzy c-means(FCM) algorithm converging to the local infinitesimal point,which made the cluster result more precise.By means of computer simulation,data exchanging environment in IoV was imitated,and then test data set was divided into four parts.The simulation indicates that the identification method is feasible and effective for urban road traffic conditions in IoV scenarios.