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.

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.

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.

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.

The comprehensive measure model for urban traffic congestion based on value function

According to the distribution characteristics of traffic congestion in time and space, a measure index system of urban traffic congestion is set up based on the spatial and temporal distribution. Based on the analysis of the main characteristics of traffic congestion and the generation process of traffic congestion, the measure model for urban traffic congestion is constructed by the value function. Moreover, based on the measure values of traffic congestion in urban road networks with defined different levels, a method to prevent and control traffic congestion is designed. The application results confirm that the proposed method is feasible in comprehensive measures for urban traffic congestion and they are consistent with the results of other methods. The measuring results can therefore reflect the actual situation. The comprehensive measure model is scientific and the process is simple, and it has wide application prospects and practical value.

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 Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

Advanced information and communication technolo-gies 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.

Effects of real-time traffic information systems on traffic performance under different network structures

The effects of real-time traffic information system(RTTIS)on traffic performance under parallel,grid and ring networks were investigated.The simulation results show that the effects of the proportion of RTTIS usage depend on the road network structures.For traffic on a parallel network,the performance of groups with and without RTTIS level is improved when the proportion of vehicles using RTTIS is greater than 0 and less than 30%,and a proportion of RTTIS usage higher than 90%would actually deteriorate the performance.For both grid and ring networks,a higher proportion of RTTIS usage always improves the performance of groups with and without RTTIS.For all three network structures,vehicles without RTTIS benefit from some proportion of RTTIS usage in a system.

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.

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.

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.

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.

Calibration of a Microscopic Model of an Urban Traffic Network

Traffic simulation models have the potential to provide an objective, cost-effective and flexible approach to assessing system design, traffic operations and management strategies. In that regard, the calibration and validation of simulation model is crucial for appropriate decision making process. This paper presents an application of microscopic simulation model calibration and validation procedure for a multimodal urban traffic network. Model is developed by VISSIM and VISSIG software tools.

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.

Medieval Towns, Traffic and Urban Planning Half a Century since the First Pedestrian Zone in Siena, Italy

The Italian town is known as a collection of wails, gates, towers, palaces and cathedrals. The lanes and the squares were created in the Middle Ages, with an urban fabric suitable for horses and carriages, not for motor cars. At the beginning of the 1960s, it was no longer possible to delay a solution to the problem of traffic and the first ＂pedestrian isle＂ was realized in the centre of Siena in 1965. Other towns in a few years followed this virtuous example. Acts against traffic avoided the building of urban motorways and the demolition of ancient buildings that would have given the ＂coup de grace＂ to several important historic centres.

Fuzzy Rules to Improve Traffic Light Decisions in Urban Roads

Many researchers around the world are looking for developing techniques or technologies that cover traditional and recent constraints in urban traffic con-trol. Normally, such traffic devices are facing with a large scale of input data when they must to response in a reliable, suitable and fast way. Because of such statement, the paper is devoted to introduce a proposal for enhancing the traffic light decisions. The principal goal is that a semaphore can provide a correct and fluent vehicular mobility. However, the traditional semaphore operative ways are outdated. We present in a previous contribution the development of a methodology capable of improving the vehicular mobility by proposing a new green light interval based on road conditions with a CBR approach. However, this proposal should include whether it is needed to modify such light duration. To do this, the paper proposes the adaptation of a fuzzy inference system helping to decide when the semaphore should try to fix the green light interval according to specific road requirements. Some experiments are conducted in a simulated environment to evaluate the pertinence of implementing a decision-making before the CBR methodology. For example, using a fuzzy inference approach the decisions of the system improve almost 18% in a set of 10,000 experiments. Finally, some conclusions are drawn to emphasize the benefits of including this technique in a methodology to implement intelligent semaphores.

城市交通承载力系统动力学评估及多情景预测

为了城市交通的可持续发展,对城市交通承载力的可靠性进行评估和情景预测。重新定义了城市交通承载力,构建基于系统动力学的计算模型。以兰州市为例,对2023-2030年的未来交通在5种情景发展模式下的交通承载力进行可靠性评估和预测。结果表明:系统动力学模型具有较高的精度水平,绝对误差在3%以内。兰州市在5种设定情景下,2030年资源承载力均处于临界可靠状态;环境承载力在环境改善型时达到最高30.88,为基本可靠;社会经济承载力在综合发展时达到最大24.8,为临界可靠。分别提升交通资源和社会经济都会提升相应的子系统承载力,然而交通环境承载力却具有一定的下降趋势,这符合城市交通实际情况,验证了系统之间存在着复杂的正负反馈关系。城市交通承载力呈现出逐步提升的趋势,将在2026年以后均达到20以上,处于临界可靠区间。且综合发展型提升效果最好,2030年相比2022年增长47.2%,已接近基本可靠区间。

基于YOLOv5车辆识别和北斗定位的城市拥堵解决方案

为有效缓解城市交通拥堵,基于YOLOv5车辆识别和北斗定位技术构建了城市拥堵解决方案,实现了车辆运行信息实时采集、道路流量预测和智能交通控制等功能。YOLOv5车辆识别技术采集流量、车速等交通运行特征数据结合北斗定位技术采集的车辆运行特征数据,为智能交通控制决策与用户路线规划的数据支撑;智能控制终端分析采集到的数据,预测道路流量;数据传输模块完成采集输入并上传至智能控制终端和预测结果输出至智能交通控制设施,调节潮汐车道,实现智能信控;同时向用户反馈道路拥堵状况、提供路径选择建议。仿真运行结果表明,该解决方案面对两种不同的道路网可以使平均停车延误分别降低了9.0%、6.0%,总行驶时间分别降低了4.5%、6.3%,能够缓解交通拥堵。