Urban Traffic Research Articles

Research on Queue Equilibrium Control Algorithm of Urban Traffic Based on Game Theory

The intersection traffic signal control is an essential means of urban traffic. To solve the problem of urban congestion, it is necessary to consider the optimal signal control strategy for intersections. Using the store-and-forward method of traffic control modeling, the in-queue vehicle number of the key signal phase as the payoff index, this paper designs an optimal intersection signal-timing strategy based on the game theory method. In this strategy, each key phase is regarded as a game player, and each player competes for the release time to maximize their own payoff and minimize the queue. To optimize the intersection efficiency, a game strategy is designed to achieve the Nash equilibrium state, which is the queueing equilibrium of each key phase. Finally, by VISSIM simulation, the total number of stops can be decreased by 5% to 10% compared with the MA-DD-DACC method.

Human Risk Factors for Severity of Injuries in Urban and Suburban Traffic Accidents in Southern Iran: An Insight from Police Data

Background: Globally, transport injuries persist as the leading preventable cause of adolescent harm. Objectives: This study aimed to determine the role of human factors in causing traffic accidents in urban and suburban areas of Jahrom, Fars province, Iran. Materials and Methods: This descriptive study used the recording data of 598 accidents and incidents on urban and suburban roads recorded in the accident registration forms (KAM) of the Police Information and Communication Technology (ICT- FAVA) system in Jahrom in 2020. The obtained data were analyzed by SPSS 20 software using descriptive statistics, chi-square test, and multivariable logistic regression. Simultaneously, population-attributable risks for violations of drivers involved in accidents on urban and suburban roads were determined. Results: The multivariable logistic regression analysis identified sudden diversion (OR = 11.02, 95% CI: 3.79 - 32.00), inattention to the front (OR = 6.68, 95% CI: 3.27 - 13.61), non-observance of the right of priority (OR = 6.25, 95% CI: 2.80 - 13.98), and inability to control the vehicle (OR = 4.05, 95% CI: 1.81 - 8.90) as risk factors for death or injury in urban roads. Meanwhile, death or injury on suburban roads was associated with failure to yield to the right of way (OR = 2.25, 95% CI: 1.08 - 4.67), inattention to the front (OR = 1.95, 95% CI: 1.08 - 3.51), and inability to control the vehicle (OR = 41.86, 95% CI: 1.001 - 4.63). Among humans factors of accidents on urban and suburban roads, inattention to the front (78.84% vs. 37.73%) and failure to yield to the right of way (62.75% vs. 32.31%) had the greatest population-attributable fraction risk factors of death or injury. Conclusions: Inattention to the front and non-observance of the right of way by drivers were the first and second ranks in accidents leading to injury and death. It is suggested that the relevant laws and legislations be intensified and enforced more seriously.

Reducing Urban Traffic Congestion via Charging Price

Traffic jams are one of the major transportation problems. The United States spends USD billions to mitigate the problem, and not always with good outcomes. This problem increases and has effects on sustainable transport, such as life quality, pollution, perishables, and costs. Large cities reduce traffic jams through congestion charges. This paper aims to reduce urban traffic congestion by estimating the charge through a multivariable model. It studies the main jammed areas in Santiago, Chile. The data came from published surveys. The model evaluation included Fisher multiple regression (F) and the determination coefficient (R2). These validations showed that the model is statistically significant. They also showed that the parameter estimation was good. Finally, this model contributes to improving the Sustainable Development Goals, such as SDG 3, SDG 11, and SDG 13, which may be successfully applied to Santiago City, as well as to any city worldwide.

A Neural Network and Generalized Predictive Control Framework for Urban Traffic System Optimal Perimeter Control

Optimal perimeter control is one of the effective control technologies to alleviate urban congestion, which is based on macroscopic fundamental diagrams (MFDs). However, most previous optimal perimeter control methods used a linearization model at the desired point to approximate a nonlinear MFDs system, which is vulnerable to causing a potential mismatch between the linearization model and the environmental dynamics. However, this mismatch often leads to performances degradation. To solve this problem, this paper uses a neural network trained from data to approximate the complex nonlinear urban traffic system globally. To facilitate the design and control, this framework linearizes the nonlinear neural network of the traffic system to an instantaneous linearization model. The optimal perimeter control problem is finally solved by generalized predictive control (GPC) with the instantaneous linearization model. A key advantage of the proposed framework is that the global instantaneous linearization model is more accurate than the linearization model around the desired point. Simulation results show that the proposed framework significantly alleviates congestion and reduces the total travel time spent (TTS) in the traffic system compared with no control, “greedy” feedback control and PID control.

GIS-Based Urban Traffic Assessment in a Historical European City under the Influence of Infrastructure Works and COVID-19

Urban areas have developed organically over time, driven by the economic success of cities. However, this development has usually been accompanied by the side effects of urbanization, such as increased traffic and its associated problems: traffic congestion, increased accident rates and pollution. As urban populations grow and expand, the importance of GIS lies in its ability to collect a large amount of geospatial data, including human-generated data. This data is necessary to understand the complexity of the city, set priorities, solve complicated planning problems and perform a variety of spatial analysis, which shows not only the feasibility but also the consistency of the proposed infrastructure with the requirements of a sustainable city. In this paper, we demonstrate the benefits of integrating real-time traffic data with GIS technology and remote sensing data for analyzing the impact of infrastructure works and COVID-19 on traffic in Oradea, Romania. The case study was focused on the historical center of Oradea and was based on remote sensing data collected before, during, and after traffic restrictions. The study also shows the need for using GIS and crowdsourcing-based applications in traffic analysis and planning.

Cooperative Truck–Drone Delivery Path Optimization under Urban Traffic Restriction

In the traditional express delivery sector, trucks are the most available and efficient transportation mode in urban areas. However, due to the pressures of traffic congestion and air pollution problems, many cities have implemented strict measures to restrict trucks’ access to many zones during specified time periods, which has caused significant effects on the business of the industry. Due to their advantages, which include high speed, flexibility, and environmental friendliness, drones have great potential for being combined with trucks for efficient delivery in restricted traffic zones. In this paper, we propose a cooperative truck and drone delivery path optimization problem, in which a truck carrying cargo travels along the outer boundary of the restricted traffic zone to send and receive a drone, and the drone is responsible for delivering the cargo to customers. The objective of the problem is to minimize the completion time of all delivery tasks. To efficiently solve this problem, we propose a hybrid metaheuristic optimization algorithm to cooperatively optimize the outer path of the truck and the inner path of the drone. We conduct experiments on a set of test instances; the results demonstrate that the proposed algorithm exhibits a competitive performance compared to other selected popular optimization algorithms.

Research on Traffic Informatization and Intelligent Construction Based on Urban Traffic Big Data

Urban traffic is a new traffic demand network defined by residents' travel behavior and community relationship. From the management level, we should actively study and explore the construction of urban traffic informatization and intelligence, strengthen the operation efficiency of infrastructure and the dynamic supervision of traffic in various fields and subsystems of the city, and promote the integrated management of urban network operation, management, emergency response, treatment and maintenance. Under the background of big data, build an information and intelligent system of urban traffic to solve the problems of poor standardization, low level of digitalization, poor sharing and wide sources of existing cost control information. From the perspective of system architecture and function, the framework of urban traffic informatization and intelligent system should include four levels, namely, perception layer, network layer, data layer and application layer. The information system under this construction idea can comprehensively collect the operation and management data of urban transportation enterprises, and then analyze all kinds of operation indicators.

A Multi-objective Cash-in-transit Pollution-location-routing Problem Based on Urban Traffic Conditions

Cash transfer from the central treasury to the bank branches and automated teller machines (ATMs) all over the city is one of the vital processes in a banking system. There are multiple factors (e.g., location of the treasury, transportation fleet, geographic distribution of the branches and ATMs, the demand for cash, customer satisfaction, and traffic that influence the efficiency of the cash transfer). Moreover, environmental issues, and in particular the issue of greenhouse gas (GHG) emissions are given weight. In this paper, a new mathematical model for a location-routing problem with transport vehicles in the banking system is developed based on urban traffic in such a way that three objectives of decreasing greenhouse emissions, reducing location and routing costs, and increasing customer satisfaction are taken into consideration simultaneously. Furthermore, a new multi-objective genetic algorithm hybridized with a PROMETHEE method, namely the multi-objective genetic-PROMETHEE algorithm (MOGPA), is developed to tackle the proposed model. The efficiency of the proposed algorithm is examined by comparing it with the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective imperialist competitive algorithm (MOICA) for the real-case issue of Saman Bank. Because management assumptions are considered in the preference functions of the proposed algorithm, the results show that the solutions of the proposed algorithm are more efficient and closer to reality.

Impact of Construction Work Zone on Urban Traffic Environment

Abstract Objectives Traffic management is challenging during construction because of the effects of traffic congestion, travel time, delay, and queue length. Long-term work zones on urban roads lead to many problems such as speed, inconvenience, and economic losses to drivers, which are focused on in previous studies. Methods Moreover, due to the construction work zone (CWZ), the impact on environmental factors such as air quality and noise levels was not focused on. Because of the building work zones, this research focused on comprehending how traffic congestion measurements and environmental factors affect urban traffic management. Findings The present research uses TransCAD to estimate air pollution due to increased traffic in the urban areas. Furthermore, three nonlinear AI-based models (ANFIS, FFNN, and SVR) and one linear black box model were developed to predict the noise level in the city, in which each contained the total traffic and speed as well as the ratio of heavy vehicles in the traffic. Novelty For traffic control, a variety of techniques are available, including video data analysis, infrared sensors, inductive loop detection, wireless sensor networks, etc. These are all practical techniques for efficient traffic management. It is necessary to conduct studies on the amount of traffic, the topography, accidents, time delays, and the level of safety offered in the work area. Construction operations are facilitated by the implementation of traffic flow, and during this process, long-term CWZs are inevitable. Therefore, the proposed model accomplishes the goal, namely that only analytical research and a few traffic diverter signs point drivers to alternate routes to their destinations.

Smart urban traffic zones – demonstration of three smart zones in Sweden based on geofencing and sensor data

Starting September 2020 and ending September 2022, the project “Smart Urban Traffic Zones” has demonstrated three different set-ups of smart urban traffic zones in the Cities of Stockholm and Gothenburg, Sweden. These zones are based on geofencing technology, smart sensors and V2X communication. The pilots have mainly been aimed at improving road safety for vulnerable road users, increasing transport efficiency in the urban environment, and creating a more flexible use of public space in the city. The results show that it is possible to set-up smart zones with this purpose and that the main challenges for scale-up are related to legal requirements, processes for data sharing and information management.

RSG-GCN: Predicting Semantic Relationships in Urban Traffic Scene With Map Geometric Prior

Automated identification of the relationships between traffic actors and surrounding objects, in order to describe their behavior and predict their intentions, has become the focus of increasing attention in the field of autonomous driving. Therefore, in this work, we propose a Road Scene Graphs-Graph Convolutional Network (RSG-GCN) as a novel, graph-based model for predicting the topological graph structure of a given traffic scene. The status of the actors and HD map information are integrated as prior knowledge, allowing the edges linking the actor nodes to capture potential semantic relationships, such as “vehicle approaching pedestrian” and “pedestrian waiting at intersection”. To train this model, we created our own RSG dataset, as well as a relational dataset and benchmark derived from nuScenes. Our extensive range of experiments demonstrate that our model can more accurately predict semantic relationships and behavior in a given traffic scene than other popular traffic scene prediction models. In particular, regarding the use of HD map prior knowledge, we found that the resulting increase in accuracy significantly outweighs performance loss caused by the increase in graph size. The downstream applications of RSG include traffic scene retrieval and synthetic traffic scene generation, which are briefly described.

Road traffic sensitive routing for efficient waste collection by leveraging varying traffic patterns

Urban Traffic flow is one of the major problems at peak hours, including large stationary vehicles. Occupying the roads during such hours can be unsafe troublesome for motorists and workers. We taken the case of everyday waste collection process and propose a system which analyses the traffic patterns along with the locations. Based on the traffic, distance to the stop and bin capacity for analyses the dynamic traffic patterns at various places, we optimally assign a time by making it easy for both motorists and workers for safe and hassle-free commute with the help of a routing Algorithm. By using this algorithm, the whole process can be tracked and inspected via app and also sends an intimation message to public map applications. Large trucks can cause traffic, so commuters can simply figure out which routes to avoid. Hence it saves time for the people in enhancing the road safety.

A Distributed Platoon Control Framework for Connected Automated Vehicles in an Urban Traffic Network

In this article, we consider a specific scenario, where multiple connected automated vehicles (CAVs) entering one road link from different upstream links are required to form a platoon first, then cross the junction at the end of the link safely, and fuel economically. Each CAV may experience a reaction-time delay and follow a specific nonlinear car-following model. To address this problem, we design a two-stage distributed platoon control strategy. In the first stage, we present a sufficient condition about the length of a platoon that ensures a subsequent safe junction crossing, upon which a distributed cooperative control protocol is designed and an iteration algorithm is proposed to ensure the fast formation of the platoon in a finite time. Once the platoon is formed, in the second stage, we design a method to estimate the leader vehicle’s acceleration adjustment by developing distributed observers that ensure a safe junction crossing of the entire CAV platoon, taking into account follower-vehicle dynamics and an upcoming traffic signal schedule while minimizing the overall platoon fuel consumption. If a platoon has already been formed at the entrance of the link with the appropriate platoon size, then only the second-stage design is needed. Thus, our proposed two-stage CAV link control strategy may potentially become part of a broader fuel-economic solution for the effective management of CAV platoons in a large urban traffic network.

Distributed Control for an Urban Traffic Network

In this paper, we develop a distributed control method for an urban traffic network in order to improve traffic conditions and guarantee a smooth operation for all roads and intersections. An optimal coordination among traffic flows is determined by a model predictive control idea. The control objective is formulated as a constrained optimization problem in which the cost function to be minimized is a combination of some traffic network-related performance indexes and the constraints are derived from capacities of roads and intersections. The green time assigned to each road link is computed from its optimal downstream traffic flow. In the proposed algorithms, every road link uses only its local information to determine its control decision which corresponds to a global optimal solution. The algorithms are developed by using a gradient projection-based method and properties of the minimal polynomial of a matrix pair. The effectiveness of the proposed algorithms is validated via numerical simulations with MATLAB and VISSIM.

Research on Intelligent Urban Traffic Construction for Driverless

Purpose - The purpose of this paper is to explore the existing problems of the barrage video website in view of the current situation that the barrage video website greatly impacts people’s video viewing mod.
 Design/Methodology/Approach - Based on the subversive revolutionary impact of driverless technology on the future of urban intelligent transportation, this paper mainly adopts the method of theoretical analysis. Combined with the current urban intellectual transportation construction status, it conducts theoretical research on future urban thoughtful transportation construction planning.
 Findings - This study puts forward constructive suggestions on the smart construction of future cities from six aspects: top-level design, technical support, intelligent infrastructure construction, intelligent transportation decisionmaking system, industry and talent construction, and social cooperation.
 Research Implications - In the management of this paper, This paper is of great significance to the planning and construction of smart transportation in future cities and the coordinated development of driverless and smart transportation.

UAV Low-Altitude Aerial Image Stitching Based on Semantic Segmentation and ORB Algorithm for Urban Traffic

UAVs are flexible in action, changeable in shooting angles, and complex and changeable in the shooting environment. Most of the existing stitching algorithms are suitable for images collected by UAVs in static environments, but the images are in fact being captured dynamically, especially in low-altitude flights. Considering that the great changes of the object position may cause the low-altitude aerial images to be affected by the moving foreground during stitching, so as to result in quality problems, such as splicing misalignment and tearing, a UAV aerial image stitching algorithm is proposed based on semantic segmentation and ORB. In the image registration, the algorithm introduces a semantic segmentation network to separate the foreground and background of the image and obtains the foreground semantic information. At the same time, it uses the quadtree decomposition idea and the classical ORB algorithm to extract feature points. By comparing the feature point information with the foreground semantic information, the foreground feature points can be deleted to realize feature point matching. Based on the accurate image registration, the image stitching and fusion will be achieved by the homography matrix and the weighted fusion algorithm. The proposed algorithm not only preserves the details of the original image, but also improves the four objective data points of information entropy, average gradient, peak signal-to-noise ratio and root mean square error. It can solve the problem of splicing misalignment tearing during background stitching caused by dynamic foreground and improves the stitching quality of UAV low-altitude aerial images.

Smart Traffic Scheduling for Crowded Cities Road Networks

With the fast-expanding number of vehicles in smart cities, the management of road intersections and traffic congestion has grown to be major problems. Drivers often express their opinion that putting traffic lights on while taking traffic flows into account will have a significant impact on how traffic moves. This paper presents a smart Road traffic Control management system termed Urban Traffic Control (UTC) keeping real-time dynamic traffic flow in mind which helps in upgrading the level of road traffic network management. To provide an organized traffic arrangement, UTC presents methodologies such as vehicle counting, controlling process, and evaluation of lanes keeping status in mind, this whole procedure is implemented by taking the complete traffic network into an account instead of just considering intersections. The primary goal of our system is to lessen traffic jams by cutting down on the trip and waiting times vehicles spend at crossings and intersections. We need to assign a plan for traffic flow that has the least amount of traffic congestion and vehicle waiting time, for this purpose some indicators and models are introduced in this study. Lane weight, traffic jam indicator, and vehicle priority are among these models. As this work is an improvement on the current Road Network without much changing, we integrate our system on normal traffic lights which allow each lane a chance to move and we also considered the no-interference lane movement. To simulate our idea, we introduced a smart road traffic control system consisting of multi-agents, by using a NetLogo stimulator. To compare the fixed cycle traffic light, several vehicles (150 in total) with random behaviour were generated and scattered over 25 different intersections for the time duration of 9 h. This setting was used to test our smart traffic control solution on both lane flow and no interference movement flow. According to the obtained results, there was a 25.98% reduction in total average waiting time over simulation period for all vehicles and a reduction of 34.16% for no interference movement flow. These observations clearly state that suggested method is better suited for today's complex traffic conditions where change in infrastructure is minimal.

Interaction of Urban Traffic Network Structure and Carbon Emission Intensity: A Case Study in Shenzhen

Interaction of Urban Traffic Network Structure and Carbon Emission Intensity: A Case Study in Shenzhen

An Efficient Algorithm of Star Subgraph Queries on Urban Traffic Knowledge Graph

Knowledge graph has wide applications in the field of computer science. In the knowledge service environment, the information is large and explosive, and it is difficult to find knowledge of common phenomena. The urban traffic knowledge graph is a knowledge system that formally describes urban traffic concepts, entities and their interrelationships. It has great application potential in application scenarios such as user travel, route planning, and urban planning. This paper first defines the urban traffic knowledge graph and the star subgraph query of the urban traffic knowledge graph. Then, the road network data and trajectory data are collected to extract the urban traffic knowledge, and the urban traffic knowledge graph is constructed with this knowledge. Finally, a star subgraph query algorithm on the urban traffic knowledge graph is proposed. The discussion of the star subgraph query mode gives the corresponding application scenarios of our method in the urban traffic knowledge graph. Experimental results verify the performance advantages of this method.

Rapid and Ultra-lightweight Semantic Segmentation in Urban Traffic Scene

Rapid and Ultra-lightweight Semantic Segmentation in Urban Traffic Scene