Urban Traffic System Research Articles

Design of On-line Simulation System for Signal Control of Urban Intersections Based on Visual Sensing Technology

Intersection area is the area with the most serious conflicts and accidents in the whole urban traffic system. Therefore, it is of great significance to combine modern computer technology, sensing technology and communication technology to intelligently control intersections, improve the efficiency of traffic signal control and reduce the delay time in conflict areas. Starting with video detection and using deep learning in the detection module, a video detection mechanism based on restricted Boltzmann machine network is proposed. Based on C # programming language, the intersection traffic signal simulation module is designed through COM interface provided by traffic simulation software VISSIM, which realizes road network import, parameter setting, signal timing change, simulation operation and simulation result output. Numerical experiments show that fuzzy control method with self-correcting factor is superior to timing control method and classical fuzzy control method in average delay time of vehicles.

Simulation of Traffic Microcirculation Optimization in Residential Area

Nowadays, there are many congestion problems in urban main roads, so the traffic flow on the main roads is drained to small branches, the density of road network is increased, a more perfect traffic network is formed, and many vehicle diversion paths are formed. Become one of the effective ways to alleviate traffic congestion. Through the optimization of traffic microcirculation network in residential area and the optimization of intersection signal timing scheme by Webster method, and the data analysis and simulation by using Aidaroe software and Vissim software, It can be concluded that the service level and network density of intersections around residential area are improved and the traffic condition is improved obviously. The results show that this optimization study has an important effect on improving the operation of traffic microcirculation in residential area and the quality of life of residents, realizing the efficiency of traffic microcirculation in residential area and affecting the whole urban traffic system.

Strategies to promote pedestrian safety from the viewpoints of traffic and transport stakeholders in a developing country: A mixed-method study

IntroductionPedestrians are among the most vulnerable groups in traffic accidents. The purpose of this study was to explore the strategies associated with pedestrian safety from the perspective of traffic and transport stakeholders. MethodsIn 2018, this mixed-method study with an explanatory design was conducted in two phases: a quantitative cross-sectional study (the first phase) was conducted among 508 residents in Tabriz, Iran. The valid and reliable Pedestrian Safety Behaviors (PSB) scale was used to collect data through self-report. In the second phase, the quantitative results were presented to twenty-four urban traffic and transport stakeholders during individual interviews (as the qualitative phase) to explore the strategies to promote the safety behaviors of pedestrians. The qualitative data were analyzed applying the conventional content analysis approach. FindingsIn the quantitative phase, the residents acquired more than 62% of the maximum possible score in all constructs. Among all the domains, aggressive behaviors (85.6%), distractive behaviors (77.2%), violation (21.56%), and PSBs (70.5%) were found with the highest scores. Age, gender, and educational level were significant predictors of safety pedestrians' behaviors (p ≤ 0.001). In the qualitative phase, the strategies to promote pedestrian safety were explored and grouped into four main categories: building public education policies on traffic safety, developing traffic safety infrastructure, improving the traffic management system, and strengthening urban traffic safety legislation. ConclusionOur findings provided strategies to promote pedestrian safety for urban traffic and transport systems of developing countries with specific complexities, particularly in the management system. The mixed-method approach used in the present study was helpful in identifying the most relevant strategies for promoting safe behaviors of pedestrians in Iran, as a developing country. These strategies may be considered while traffic safety policy-making and/or designing pedestrian safety promotion programs within urban environments. The strategies reported here may pertain not only to the Iranian community but also to other potential sectors in developing countries that have the responsibility of providing pedestrians with a safe roadway environment.

A genetic timing scheduling model for urban traffic signal control

As urban traffic condition is diverse and complicated, maximizing the use of urban traffic signal control system to solve traffic issues becomes one of the hot and promising topics. Especially, how to control traffic signals at multiple intersections is a key challenge. To speed up urban traffic flow, this research presents a genetic timing scheduling model (GTSM) for urban traffic signal control. GTSM constructs cellular automata to update the timing cycles of traffic signals at multiple intersections, where state update functions are formulated to coordinate traffic signals. In addition, a proposed genetic optimization algorithm (GOA) in GTSM optimizes the timing cycles of traffic signals at multiple intersections in the dynamic timing optimization. The experimental results on urban road networks show that the performance of our model is excellent in various scenarios to control traffic signals to speed up urban traffic flow.

The impact of strategic transport policies on future urban traffic management systems

Urban traffic management and traffic signal control systems, denoted as Urban Traffic Control (UTC) systems, are used extensively worldwide by Local Government Authorities (LGAs) when implementing strategic transport policies. However, it is not clear how well the requirements imposed by LGA policy implementation will be met by UTC systems being developed for the future. Therefore, research was undertaken to analyse how delivery of urban transport policies over the next 5–10 years would shape LGAs' requirements for the next generation of UTC systems, and thereby to identify Key Performance Indicators (KPIs) to provide user-led guidance for future system development. A two-stage survey of LGA policy makers and implementers from around the world was conducted. The results produced consensus among the group of participants (n = 16) on 17 KPIs, representing a synthesis of expert opinions on the desired features of future UTC systems from a policy implementation perspective. The research makes an important contribution in eliciting the wide-ranging breadth of issues associated with delivering strategic transport policies and understanding how these issues affect the requirements LGAs have for future UTC systems. Aligning future UTC system capabilities with LGAs’ requirements will enable more effective implementation of strategic urban transport policies worldwide and allow the benefits to society associated with those policies to be realised.

A node optimization model based on the spatiotemporal characteristics of the road network for urban traffic mobile crowd sensing

Urban traffic mobile crowd sensing (Urban Traffic MCS) has emerged as a new effective paradigm of sensing and collecting data by means of vehicles equipped with various sensors in urban areas. In an Urban Traffic MCS system, the utility directly reflects the effectiveness of the sensing results, and it is essential to maximize the utility of the collected data. Some studies have shown that utility can be effectively improved by optimizing the selection of sensing nodes. However, most previous research has considered only the coverage and critical links of the road network while neglecting the spatiotemporal characteristics of the traffic flow, although the latter are essential for node selection optimization and significantly impact the utility of Urban Traffic MCS. Therefore, most existing methods are not suitable for Urban Traffic MCS systems. In this paper, a novel node optimization model based on the spatiotemporal characteristics of the road network is proposed. First, we introduce the Urban Traffic MCS system, and dynamic accessibility is introduced to describe the spatiotemporal characteristics of the whole road network. Then, the utility function for Urban Traffic MCS is redefined based on the effective coverage and dynamic accessibility to consider both the topological structure of the road network and the dynamic changes in traffic flow. On this basis, a node selection method with the aim of maximizing the utility of Urban Traffic MCS is proposed. Finally, the results of simulation experiments show that the node selection method in this paper can effectively achieve increased utility for an Urban Traffic MCS system.

3D path planning and real-time collision resolution of multirotor drone operations in complex urban low-altitude airspace

Drones have been greatly developed to facilitate the progress of various industries. The safe operation of drones in the urban airspace is critical to ensure a reliable and high-efficient urban air traffic system. This work presents a fusion scheme to achieve autonomous drone collision-free path planning considering static obstacles and dynamic threats detected. Firstly, a 3D voxel jump point search (JPS) based path planning model is developed to generate the static collision-free reference path. With the optimization, the reference path is then de-diagonalized, reconstructed, and smoothed to obtain the desired path. Subsequently, a local collision resolution method is proposed to avoid near mid-air collision of the dynamic threats. The method is based on the Markov decision process (MDP) to implement real-time dynamic collision avoidance. Simulations are conducted to verify the performance of the proposed model. The simulation results demonstrate that the proposed model is effective to achieve the autonomous path planning and real-time collision resolution of multirotor drones.

A multiagent systems with Petri Net approach for simulation of urban traffic networks

This paper presents a novel model framework for complex urban traffic systems based on the interconnection of a dynamical multi-agent system in a macroscopic level. The agents describe all the types of street segments, intersections, sources and sinks of cars, modelling the behavior of the flow of vehicles through them as simple differential equations. These agents include the phenomena of changes in the flow rate due to congestions, traffic signals and the density of the vehicles. Traffic signal changes are obtained by the evolution of Petri Nets, in order to represent a more real behavior. Therefore, a complex network can be constructed by the interconnection of the agents, in continuous time, and the Petri Nets, in a discrete-event behavior, becoming a hybrid and scalable system. In order to analyze the performance of the approach, a real set of streets and intersections in Montevideo City is studied. Also, the approach is compared with a simulation realized in the software TSIS-CORSIM, which contains real data of density of vehicles. The multi-agent system achieves comparable results, taking into account the differences in the level of details respect to TSIS-CORSIM. Thus, the results can represent the most important issues of vehicular traffic with less computational resources.

Carbon Emission Evaluation in Jinan Western New District Based on Multi-Source Data Fusion

Carbon emissions caused by human activities are closely related to the process of urbanization, and urban land utilization, function vitality and traffic systems are three important factors that may influence the emission levels. For clarifying the space structure of a low-carbon eco-city, and combining the concept of “Combining Assessment with Construction” to track and contrast the construction of the low-carbon eco-city, this research selects quantifiable low-carbon eco-city spatial characteristics as indicators, and evaluates and analyzes the potential carbon emissions. Taking the Jinan Western New District as an example, diversity of construction land, travel carbon emission potential, and density and accessibility of adjacent road networks in the overall urban planning were measured. After the completion of the new urban area, the evaluation mainly reflected certain factors, such as the mixed degree of urban functions, the density of urban functions, the walking distance to bus stops and the density and number of bus stops. Dividing the levels and adding equal weights after index normalization, the carbon emission potential is evaluated at the two levels of the overall and fragmented areas. The results show that: (1) The low-carbon emission potential areas in the planning scheme basically reached the planned goals. (2) There is inconsistency between districts and indicators in the planning scheme. The diversity of construction land and the accessibility of the adjacent road network are relatively small; however, there is a large difference between the travel carbon emission potential and the road network accessibility. (3) Carbon emission potential after completion did not reach the planned expectation, and the low-carbon emission potential plots were concentrated in the Changqing Old City Area and Central Area of Dangjia Town Area. (4) The carbon emission indicators varied greatly in different areas, and there were serious imbalances in the density of public transportation lines and the mixed degree of urban functions.

Research on Vehicle-Road Co-Location Method Oriented to Network Slicing Service and Traffic Video

The development of 5G network slicing technology, combined with the application scenarios of vehicle–road collaborative positioning, provides end-to-end, large-bandwidth, low-latency, and highly reliable flexible customized services for Internet of Vehicle (IoV) services in different business scenarios. Starting from the needs of the network in the business scenario oriented to co-location, we researched the application of 5G network slicing technology in the vehicle–road cooperative localization system. We considered scheduling 5G slice resources. Creating slices to ensure the safety of the system, provided an optimized solution for the application of the vehicle–road coordinated positioning system. On this basis, this paper proposes a vehicle–road coordinated combined positioning method based on Beidou. On the basis of Beidou positioning and track estimation, using the advantages of the volumetric Kalman model, a combined positioning algorithm based on CKF was established. In order to further improve the positioning accuracy, vehicle characteristics could be extracted based on the traffic monitoring video stream to optimize the service-oriented positioning system. Considering that the vehicles in the urban traffic system can theoretically only travel on the road, the plan can be further optimized based on the road network information. It was preliminarily verified by simulation that this research idea has improved the relative single positioning method.

A Survey on Operation Concept, Advancements, and Challenging Issues of Urban Air Traffic Management

The recent growth and adoption of unmanned aerial vehicles (UAVs) is due to their low development cost, high aerial mobility, advanced battery technology, rotors, gyroscopes, GPS, cameras, sensors, and wide range of applications. The UAV offers new possibilities for business in civil and noncivil applications such as parcel delivery, aerial mapping, agriculture, wildlife conservation, surveillance, and search and rescue. However, considering the exponential growth of various types of aerial vehicles, it is clear that advanced air traffic management services are urgently needed to control and handle the ever-increasing air traffics. In this paper, we first present the two major low-altitude UAV traffic management systems in the United States and Europe and compare them thoroughly. We present the global vision and advancement of UAV traffic management system. To understand the complexity of future air transportation system, we discuss the necessary elements required for an urban air traffic management system. We provide key strategic elements for an advanced urban traffic management through connectivity, open source, safety enhancement, and automation to maximize its impacts on the innovative future air traffic and mobility system. In the end, we discuss the challenging security issues in urban traffic management system.

Using cloud computing to improve urban traffic management ans optimization system

Using cloud computing to improve urban traffic management ans optimization system

The urban traffic noise monitoring system based on LoRaWAN technology

Noise and air pollution are among the most important problems and challenges in urban environments. Noise can cause several problems in functioning within urban settlements and may result in a variety of health problems. Continuous monitoring, prevention, and active work on solving these problems are in the focus of research communities, local and national agencies and organizations, and many international organizations. Traffic noise on city roads has been identified as one of the major problems in urban settlements. Fortunately, it can be monitored and controlled in a way that can reduce its level and negative effects. One of the most effective and cheapest ways to monitor and control traffic noise is to use wireless sensor networks, IoT technology, and cloud-based architectures. This article presents an approach in designing urban traffic noise monitoring system based on LoRaWAN and microservices oriented architecture supported with open-source hardware and software solutions. Design of the system includes planning LoRaWAN network and development of the IoT system layered architecture. Methods for traffic routing and rerouting in urban areas, based on experiments that uses Dijkstra and Floyd–Warshall algorithms are presented. The system implementation and evaluation of the proposed routing modifications are presented in the case of the city of Zrenjanin in Serbia. Discussion of extensions and improvements of the presented system, as well as future research directions, are also presented.

Coordinated optimizations of urban traffic and power distribution systems

Coordinated optimizations of urban traffic and power distribution systems

Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

The integration of multi-source transportation data is complex and insufficient in most of the big cities, which made it difficult for researchers to conduct in-depth data mining to improve the policy or the management. In order to solve this problem, a top-down approach is used to construct a knowledge graph of urban traffic system in this paper. First, the model layer of the knowledge graph was used to realize the reuse and sharing of knowledge. Furthermore, the model layer then was stored in the graph database Neo4j. Second, the representation learning based knowledge reasoning model was adopted to implement knowledge completion and improve the knowledge graph. Finally, the proposed method was validated with an urban traffic data set and the results showed that the model could be used to mine the implicit relationship between traffic entities and discover traffic knowledge effectively.

Construction and Evaluation of Urban Sponge Road Traffic System Structure

The imbalance of traffic resources is one of the important reasons that restrict the healthy development of the urban traffic system. Based on the concept of “sponge city”, this paper puts forward a kind of road traffic system, which can explore the flexible space of transportation infrastructure. The Fuzzy Analytic Hierarchy Process is established to comprehensively evaluate the system performance, deeply explore the flexible space of urban transportation facilities, and improve the performance of the urban transportation system. The sponge-type road traffic system evaluation model proposed in this paper can provide a reference for other regions to tap the flexible space of regional traffic infrastructure resources.

Research on the Mechanism of Multi-resource Mapping Matching and Transmission of Road System Based on Meme Theory

Because of the inefficiency and unscientific use of existing urban road traffic facilities, this paper sorts out the problems and optimization methods of domestic and foreign road traffic system, establishes the “set of problems” and “set of countermeasures”. Put forward the replication factor and mutation factor of optimization methods based on Meme theory, carry out the matching and transmission of multiple resource mapping, and feedback the implementation effect of the plan through simulation application and evaluation, so as to improve the mechanism of multi-resource mapping matching and transmission model. Cases study proved that Meme theory can be better applied to the automatic matching and selection of urban road traffic system optimization schemes and guided the optimization of urban road traffic systems more efficiently.

Chaos Analysis of Urban Low-Carbon Traffic Based on Game Theory.

Developing urban low-carbon traffic is an effective measure to reduce traffic carbon emissions, which are important parts of greenhouse gas. In order to understand the development characteristics and regular patterns of urban low-carbon traffic, we present a game model that enables us to predict the possible range of travel mode choice and the impact of low-carbon awareness. Through chaos analysis and simulation of the model, the authors come to realize that the proportions of travel mode choice can reach an equilibrium under a certain urban traffic system. This equilibrium is related to low-carbon awareness and the situation of the urban traffic system. The research we have done suggests that in small cities with undeveloped traffic systems, the most effective measure to achieve urban low-carbon traffic is to increase the comprehensive costs of high-carbon travel. However, in big cities with developed traffic systems, raising low-carbon awareness of residents can greatly increase the proportion of low-carbon travelers and improve the stability of travel mode choice. The results could provide development strategies and policy suggestions for urban low-carbon traffic and reduce the adverse impact of urban traffic emissions on public health.

A control sub-areas division method based on improved FN algorithm

.In order to implement signal control more effectively and improve the operating efficiency of urban traffic systems, this paper proposes a control sub-area division method based on an improved FN division algorithm. Comprehensively consider the intersection spacing, link flow, traffic dispersion, signal cycle, link traffic state and other factors, respectively calculate the correlation degrees of link flow, the intersection signal cycle,the link traffic state of adjacent intersections.Then its introduce the intersection correlation degree to improve the FN algorithm to divide the regional road network traffic district. Finally, the paper select the regional traffic data of Beijing’s road network to verify the model. The result shows that the sub-area division based on improved FN algorithm is more reasonable and effective.

Driver Behavior Classification at Stop-Controlled Intersections Using Video-Based Trajectory Data

Understanding how drivers behave at stop-controlled intersection is of critical importance for the control and management of an urban traffic system. It is also a critical element of consideration in the burgeoning field of smart infrastructure and connected and autonomous vehicles (CAV). A number of past efforts have been devoted to investigating the driver behavioral patterns when they pass through stop-controlled intersections. However, the majority of these studies have been limited to qualitative descriptions and analyses of driver behavior due to the unavailability of high-resolution vehicle data and sound methodology for classifying various driver behaviors. In this paper, we introduce a methodology that uses computer-vision vehicle trajectory data and unsupervised clustering techniques to classify different types of driver behaviors, infer the underlying mechanism and compare their impacts on safety. Two major types of behaviors are investigated, including vehicle stopping behavior and vehicle approaching patterns, using two clustering algorithms: a bisecting K-means algorithm for classifying stopping behavior, and the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm for classifying vehicle approaching patterns. The methodology is demonstrated using a case study involving five stop-controlled intersections in Montreal, Canada. The results from the analysis show that there exist five distinctive classes of driver behaviors representing different levels of risk in both vehicle stopping and approaching processes. This finding suggests that the proposed methodology could be applied to develop new safety surrogate measures and risk analysis methods for network screening and countermeasure analyses of stop-controlled intersections.