Urban Traffic System Research Articles

Investigating traffic characteristics for merging sections in Iraq

Abstract Recurring congestion on highways is the primary cause of traffic congestion in the urban traffic system while jams on merging sections are the worst section along the expressway. Due to the presence of heavy congestion in the site of University of Technology, this study aims to discover the traffic characteristics of this site to determine the appropriate solutions. Therefore, this study selects a merging section on Mohammed Al-Qassim expressway to investigate some characteristics of such a section in Iraq. Field data have been collected from this site for 4 h. The data were collected from video cameras installed along the expressway in Baghdad city which is called Mohammed Al-Qassim Expressway. These data present flow of upstream and ramp. The Advanced Interactive Microscopic Simulator for Urban and Non-Urban Networks (AIMSUN) program was also adopted to investigate other characteristics which are difficult to get such as speed and occupancy for the current case. The results showed that the simulated and real data significantly corresponded. In this study, the AIMSUN program received recognition by utilizing Geoffrey E. Havers a method for comparing two sets of traffic volumes that are used in traffic engineering, traffic forecasting, and traffic modeling as it was discovered to be comparable to reality, and the road management was then enhanced by the addition of ramp metering and cycle (green) time control.

A guidance assistance strategy for intersections based on vehicle speed prediction sequences

To improve the efficiency of urban traffic systems and environments, a speed guidance model based on a predictive vehicle speed sequence is proposed in this study. The model introduces a multi-step model of vehicle speed prediction based on nonlinear autoregressive models with a multi-source exogenous inputs (NARXs) neural network, with fusion of multi-source exogenous inputs into the speed guidance model to assist the vehicles in efficiently passing the intersections. The implementation of traffic flow simulation of urban road networks was established using SUMO-Python software, and experiments were carried out from the two dimensions of different speed guidance models and traffic networks with different numbers of lanes. The results demonstrate that the guidance accuracy of the proposed guidance strategy is higher than that of the previous vehicle speed guidance strategy, which can be improved by 16.5 %, significantly reducing the idle time of vehicles at intersections and improving urban traffic safety and operational efficiency. Moreover, the proposed speed guidance strategy is applicable to different urban road networks, which can reduce fuel consumption and pollutant emissions to a greater extent, efficiently improving the economy, sustainability, and operational environment of urban traffic.

Dynamic Management of Urban Coastal Traffic and Port Access Control

Urban traffic congestion and vehicle/passenger port recurring delays are major obstacles of coastal urban area sustainability. Most research in coastal urban road management has focused on congestion detection without the effective integration of the dynamic interactions with port queueing systems. For securing coastal city environmental, social and economic efficiency, this paper develops and tests a dynamic urban coastal traffic and port management system. The integrated system controls traffic and port gates’ operations based on ITS/C-ITS methodologies. The system integrates dynamic models for congestion detection, using ANN and a parameterized model, on a coastal urban road network that leads to a city port and identifies optimal solutions for road traffic and port queuing gate control. The system communicates with users via connected vehicles and VMS. The system was tested in a coastal urban road leading to Patras Southern Port, Greece, and at port control gates. Field and simulation data were used to assess system performance and social–environmental impacts. The results reveal that the system’s application offers benefits to the individual driver moving towards the Port to board a ship (gaining at least 7 min and consuming 0.306 L less fuel) as well as to society (39.72% increase in traffic safety) and environment (1,445,132 g CO2 emission reduction).

Risk‐averse perimeter control for alleviating the congestion of an urban traffic network system with uncertainties

AbstractThe perimeter control method is an effective way to alleviate traffic congestion that is based on Macroscopic Fundamental Diagrams (MFDs). However, the strategy may lead to congestion when it ignores the uncertainty of MFDs. To address this problem, this paper presents a risk‐averse perimeter control method. First, the urban traffic network system with uncertainty is modeled using a neural network and scenario tree. Then, this research quantifies the congestion risk caused by uncertainty using an average value‐at‐risk. The next step sees the design of a risk‐averse model predictive control (MPC) controller that takes the multi‐stage risk as the optimization objective and improves robustness by interpolating between the conventional stochastic and worst‐case MPC formulations. Finally, this paper analyzes the risk‐sensitive stability of an urban traffic network system and gives a solvable form of risk‐averse optimal control for this system. Finally, two simulations are conducted to verify the presented method's validity and superiority for an urban traffic network system with uncertainties. The simulation results show that the risk‐averse perimeter control method presented by this paper is superior because it reduces the total travel time by 12.98% compared to Stochastic MPC, by 15.96% compared to bang‐bang control, and by 14.54% compared to proportional‐integral.

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets.

Multi-agent simulation for multi-mode travel policy to improve park and ride efficiency

The efficiency of the existing park and ride transfer system is often restricted by the supply level of public transport and parking shortage. We aim to analyze multiple policy scenarios such as public transport supply and parking reservation through simulation to provide operable suggestions for urban traffic managers. So, an agent-based model (ABM) is established by us to simulate a multi-mode network that includes roads, buses, metro, and walking. Through this network, the whole travel process of travellers’ parking, transferring to public transport, and then walking to their destination can be simulated. Taking Suzhou Guanqian Street Commercial District as a case shows that compared with reducing the existing public transport fares, reducing the waiting time can attract car travellers to choose park and ride. Under the coexistence of reserved and non-reserved parking vehicles, with the increase in the proportion of reservations, the system can reduce cruising-for-parking and improve the traffic environment in the downtown area. In addition, the combination strategy simulation is employed to investigate the effects of various strategies on the urban traffic system. This approach offers a basis for policymakers to formulate informed decisions.

Real‐time vehicle detection using segmentation‐based detection network and trajectory prediction

AbstractThe position of vehicles is determined using an algorithm that includes two stages of detection and prediction. The more the number of frames in which the detection network is used, the more accurate the detector is, and the more the prediction network is used, the algorithm is faster. Therefore, the algorithm is very flexible to achieve the required accuracy and speed. YOLO's base detection network is designed to be robust against vehicle scale changes. Also, feature maps are produced in the detector network, which contribute greatly to increasing the accuracy of the detector. In these maps, using differential images and a u‐net‐based module, image segmentation has been done into two classes: vehicle and background. To increase the accuracy of the recursive predictive network, vehicle manoeuvres are classified. For this purpose, the spatial and temporal information of the vehicles are considered simultaneously. This classifier is much more effective than classifiers that consider spatial and temporal information separately. The Highway and UA‐DETRAC datasets demonstrate the performance of the proposed algorithm in urban traffic monitoring systems.

Robustness Analysis of an Urban Public Traffic Network Based on a Multi-Subnet Composite Complex Network Model.

An urban public traffic network is a typical high-order complex network. There are multiple types of transportation in an urban public traffic network, and each type has different impacts on urban transportation. Robustness analyses of urban public traffic networks contribute to the safe maintenance and operation of urban traffic systems. In this paper, a new cascading failure model for urban public traffic networks is constructed based on a multi-subnet composite complex network model. In order to better simulate the actual traffic flow in the composite network, the concept of traffic function is proposed in the model. Considering the different effects of various relationships on nodes in the composite network, the traditional cascading failure model has been improved and a deliberate attack strategy and a random attack strategy have been adopted to study the robustness of the composite network. In the experiment, the urban bus-subway composite network in Qingdao, China, was used as an example for simulation. The experimental results showed that under two attack strategies, the network robustness did not increase with the increase in capacity, and the proportion of multiple relationships had a significant impact on the network robustness.

Multiobjective-Based Decision-Making for the Optimization of an Urban Passenger Traffic System Structure

Urbanization has aggravated the conflict between continuously increasing urban travel demands and limited supply. Moreover, the inability to expand urban roads due to previous land planning and utilization has resulted in significant traffic congestion, traffic safety issues, and environmental problems. To address these problems, this work attempted to develop a multiobjective model to optimize the passenger traffic system while considering carbon emissions, transport costs, and resource utilization. In addition, the ideal point method and entropy weight method were combined to obtain the optimal solution. Based on the operational data on traffic modes and travel data on passengers in Harbin, the northern capital of China, the proposed method was used to solve the case in Harbin. The results show that the proportion of buses increased by 1.05%, that of subways increased by 36.60%, that of taxis decreased by 11.86%, and that of private cars decreased by 25.78% after optimization. Furthermore, the analyses of the results show that the optimized passenger traffic system structure can promote the sustainable development of urban transport and demonstrate the practicality of the proposed method for solving multiobjective optimization problems. Relative to the ideal point method and genetic algorithm, the proposed method is more applicable for optimizing the passenger traffic structure in Harbin. In addition, this study explored the sensitivity of the optimization goals to the four motorized modes. The results show that subways and private cars are the key areas to prioritize in adjusting the urban passenger traffic system structure. Based on the analysis results, recommendations for the development of transportation in Harbin are given. This study provides a reference for decision-makers to formulate policies for the urban sustainable development of Harbin as well as for transportation development in other cities.

Exploring the Application of Big Data Visualization Platform in Urban Traffic Data Analysis

With the rapid development of information technology and the popularity of the Internet, the era of big data has come. The urban traffic system continuously generates a large amount of traffic data, which are so large and diverse that the traditional data processing methods have been unable to effectively process and analyze them. Therefore, it is necessary to process and analyze urban traffic data with the help of emerging big data technologies and methods. Starting from introducing the characteristics of big data visualization platforms and common big data visualization tools and technologies, the article analyzes the scale and characteristics of urban traffic data and the limitations of traditional data analysis methods, discusses the advantages and application prospects of visualization platforms, and focuses on the four perspectives of data cleaning and pre-processing, traffic flow and congestion analysis, traffic behavior and trend analysis, traffic simulation, and planning support. The application of big data visualization platforms in urban traffic data analysis is discussed.

A Blockchain-Based Shared Bus Service Scheduling and Management System

With the continuous development of urbanization, it has become an important issue to effectively alleviate urban road traffic congestion and improve traffic efficiency. By combining blockchain technology and shared buses, this paper builds an intelligent traffic-service scheduling management system based on blockchain. The system effectively solves the core problems of shared buses, improves data security and privacy protection, realizes intelligent scheduling and route planning, and simplifies cross-organization cooperation and settlement processes. The research shows that the system can reduce the distance and number of buses, and improve the service quality and operation efficiency while ensuring the waiting time of passengers. The results of this paper verify the feasibility and advantages of the system, bring innovation and improvement to the field of traffic management, and promote the sustainable development of urban intelligent traffic management system. Future research could further explore the application of blockchain technology in traffic management to achieve more intelligent and sustainable urban traffic management.

Challenges to the Operational Safety and Security of eVTOL Aircraft in Metropolitan Regions: A Literature Review

The start of urban air mobility operations using helicopters began in 1940s in Los Angeles to transport passengers and mail between various locations. However, the concept of urban air mobility (UAM) only emerged in the 1960s. Its implementation used helicopters to avoid congestion by land, typical of populated metropolitan regions. However, there was a severe limitation in using these aircraft because of accidents, huge operating costs, and high noise generation levels. In 2010, a new UAM concept conceived electric vertical take-off and landing (eVTOL) vehicles. These vehicles, in principle, would be safer, with lower operating costs and less noisy than helicopters. Several aircraft manufacturers have developed dozens of eVTOL models, focusing on the feasibility of using them for UAM in some metropolitan regions. However, we must consider several challenges to materialize this new concept, including those related to operational safety and security. Among these challenges is these vehicle operations integration to the airspace structure and the air traffic control system, the new embedded and ground systems development, the ground infrastructure implementation to support the operations, the reliability of air navigation systems, the meteorological information provision in real-time and with complete coverage of metropolitan airspaces to be used by eVTOL, and others. Despite some similarities, this new UAM concept will not be identical for all urban regions due to differences related to topographies, weather, urban construction patterns and air traffic control systems. Therefore, local studies are essential to support gradual and safe implementations of operations with these vehicles. This paper presents a literature review, identifying and discussing the utmost safety and security-related challenges researchers observe.

Traffic Signal Optimization Based on Fuzzy Control and Differential Evolution Algorithm

Urban traffic congestion is often concentrated at urban intersections. An urban road traffic signal control system is needed to prevent problems such as driving delays caused by frequent traffic congestions on trunk lines, exhaust emissions owing to frequent start and stop of vehicles, and fuel wastage due to long idling times. Maximizing the traffic capacity of an intersection and reducing the delay rate of vehicles has always been a problem for traffic control research. The coordinated control of urban traffic signals is regarded as a multi-objective optimization problem. A mathematical model for urban trunk traffic is studied herein. An average delay model, average queue length model, total delay calculation model for vehicles at intersections, and vehicle exhaust emission model are established to obtain an optimization model for a new traffic trunk coordinated control system. In addition, our study combines the fuzzy control theory with the adaptive sequencing mutation multi-objective differential evolution algorithm (FASM-MDEA). This new optimization method for traffic signal control at urban intersections is proposed as a solution for the traffic flow optimization model to solve the problem of traffic signal coordination and control of urban trunk lines. The simulation results demonstrate the effectiveness of the model optimization algorithm proposed in this study.

Vehicular Traffic Flow Analysis and Minimize the Vehicle Queue Waiting Time Using Signal Distribution Control Algorithm.

The real-time vehicular traffic system is an integral part of the urban vehicular traffic system, which provides effective traffic signal control for a large multifaceted traffic network and is a highly challenging distributed control problem. Coordinating vehicular traffic enables the network model to deliver an efficient service flow. Consider that there are four lanes of vehicular traffic in this situation, allowing parallel vehicle movements to occur without causing an accident. In this instance, the vehicular system's control parameters are time and vehicle volume. In this work, vehicular traffic flow is examined, and an algorithm to estimate vehicle waiting time in each direction is estimated. The effectiveness of the proposed vehicle traffic signal distribution control system by comparing the experimental results with a real-time vehicular traffic system is verified. This is also illustrated numerically.

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

AbstractThe millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system.

ETMS: Efficient Traffic Management System for Congestion Detection and Alert using HAAR Cascade

Rapid social development has resulted in the emergence of a new major societal issue: urban traffic congestion, which many cities must address. In addition to making it more difficult for people to get around town, traffic jams are a major source of the city's pollution crisis. In order to address the problems of automobile exhaust pollution and congestion, this paper uses the system dynamics approach to develop a model to study the urban traffic congestion system from the perspectives of trucks,private cars, bikes and public transportation. This project proposes a system for detecting vehicles and sending alerts when traffic levels rise to dangerous levels using Haar Cascade and Fuzzy Cognitive Maps (FCP). The proposed system uses Haar Cascade to detect moving vehicles, which are then classified using FCP. The system can make decisions based on partial or ambiguous information by utilising FCP, a soft computing technique, which allows it to learn from past actions. An algorithm for estimating traffic density is also used by the system to pinpoint active areas. In congested areas, the system will alert the driver if it anticipates a collision with another vehicle and also Experiments show that the proposed system is able to accurately detect vehicles and provide timely alerts to the driver, drastically lowering the probability of accidents occurring in heavily travelled areas.
 The importance of introducing such a system cannot be overstated in today's transportation system. It's a big deal for the future of intelligent urban planning and traffic control. Congestion relief, cleaner air, and increased security are just some of the long-term benefits that justify the high initial investment. To add, this system is adaptable to suburban and rural areas, which can also experience traffic congestion issues.

Modelling the Coupling Relationship between Urban Road Spatial Structure and Traffic Flow

In order to promote the sustainable development of urban traffic systems, improve the accuracy of traffic system analysis in the urban planning stage and reduce the possibility of traffic congestion in the operation stage of road networks, the coupling relationship and evolution mechanism between urban road spatial structure and traffic flow were studied, and a model of the relationship between the metrics was established in this study based on real road network and traffic flow data. First, the road spatial structure model of the study area was established from the perspective of road space, and the spatial syntax method was applied to verify the rationality of the spatial structure of the road network. Secondly, the initial OD matrix was determined by OD backpropagation based on the measured traffic flow data. Thirdly, the coupling rule between the spatial structure and the traffic flow of the road network was explored by loading the increment in the OD matrix to the initial OD matrix step by step based on a simulation experiment. Finally, the relationship between the degree of integration of the spatial syntactic feature parameter and the saturation of the traffic flow feature parameter was modelled on the basis of experimental results and verified by an example. This research shows that the spatial structure of urban roads has a significant impact on the characterisation of the traffic flow distribution of road networks, and a strong correlation can be found between the integration degree and saturation degree. An optimal fit, which can be used as a reference for the design of road spatial structure, was explored in this research.

An acceptability-based multi-objective traffic flow adjustment method for environmental sustainability and equity

As a product of digital development, connected autonomous vehicles (CAVs) offer a unique prospective solution to alleviate the possible performance deterioration of road networks under the mixed environment of human-driven vehicles (HVs) and CAVs. In this paper, we propose a traffic flow adjustment method (TFAM) that treats CAVs as mobile regulators with the purpose to reshape traffic flow distribution on road networks by guiding rather than controlling CAVs. More specifically, we deploy subsidy nodes to briefly outline travel routes and achieve higher acceptability than traditional route-based control schemes. The TFAM is a multi-objective bi-level programming problem where the upper-level problem optimizes the network performance through regulating location and subsidy on the subsidy nodes. The lower-level problem is a dual dynamic traffic assignment (DDTA) model. Apart from the total travel time cost (TTTC), total emission cost (TEC) and network equity (NE) are also introduced as optimization objectives to highlight environmental sustainability and acceptability. To obtain the Pareto solution frontier, a meta-heuristic algorithm with an improved encoding process is proposed. Results of two numerical case studies demonstrate the effects of TFAM on traffic flow distribution and network performance, which yields valuable insights on the optimization of urban traffic systems.

FOXS-GSC—Fast Offset Xpath Service with HexagonS Communication

Congestion in large cities is widely recognized as a problem that impacts various aspects of society, including the economy and public health. To support the urban traffic system and to mitigate traffic congestion and the damage it causes, in this article we propose an assistant Intelligent Transport Systems (ITS) service for traffic management in Vehicular Networks (VANET), which we name FOXS-GSC, for Fast Offset Xpath Service with hexaGonS Communication. FOXS-GSC uses a VANET communication and fog computing paradigm to detect and recommend an alternative vehicle route to avoid traffic jams. Unlike the previous solutions in the literature, the proposed service offers a versatile approach in which traffic road classification and route suggestions can be made by infrastructure or by the vehicle itself without compromising the quality of the route service. To achieve this, the service operates in a decentralized way, and the components of the service (vehicles/infrastructure) exchange messages containing vehicle information and regional traffic information. For communication, the proposed approach uses a new dedicated multi-hop protocol that has been specifically designed based on the characteristics and requirements of a vehicle routing service. Therefore, by adapting to the inherent characteristics of a vehicle routing service, such as the density of regions, the proposed communication protocol both enhances reliability and improves the overall efficiency of the vehicle routing service. Simulation results comparing FOXS-GSC with baseline solutions and other proposals from the literature demonstrate its significant impact, reducing network congestion by up to 95% while maintaining a coverage of 97% across various scenery characteristics. Concerning road traffic efficiency, the traffic quality is increasing by 29%, for a reduction in carbon emissions of 10%.

Comprehensive Benefit Evaluation of Intersections Controlled by Signals Based on Stochastic Frontiers

As an important part of urban road traffic system, signal-controlled intersection has become one of the hot spots in urban road traffic research due to its geographical location and complex operation state. Due to the current goals and means of the optimal design of intersections are not clear enough, the intersection has not played a full role, and eventually the traffic efficiency is not high, congestion not only does not decrease but increases, but also leads to a high proportion of traffic accidents, hindering social economic development. In order to establish an evaluation system for signal-controlled intersections, this paper proposes a comprehensive evaluation index of signal-controlled intersections with traffic efficiency, safety and environment, and establishes an evaluation method for signal-controlled intersections based on stochastic frontier analysis. Finally, an example is used to verify that the method is feasible.