Urban Traffic Signal Control System Research Articles

Editorial

Dear Readers,  It gives me great pleasure to announce the tenth regular issue of 2023. In this issue, various topical aspects of computer science are covered by 18 authors from 8 countries in 6 articles. As always, I would like to thank all the authors for their sound research and the editorial board for their highly valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, sustain the quality of our journal.  In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and trends. Please consider yourself and encourage your colleagues to submit high-quality articles or special issue proposals for our journal.  As we want to secure the financial support also for the years to come, we are looking for institutions and libraries to financially support our diamond open access journal as consortium members, who will then benefit from the research community, international visibility, and the opportunity to manage special issues and focused topics within the journal. Please think about the possibility of such financial participation by your institution, we would be very grateful for any kind of support.  In this regular issue, I am very pleased to introduce the following 6 accepted articles: In a collaboration between researchers from Palestine and Jordan, Rasha R. Atallah, Ahmad Sami Al-Shamayleh, and Mohammed A. Awadallah look into the impact of plastic surgery on face recognition models and propose a model based on an artificial neural network with model-agnostic meta-learning (ANN-MAML) for plastic surgery face recognition which results in an accuracy of 90% in all evaluation experiments. In another research collaboration between colleagues from Tunisia and the Kingdom of Saudi Arabia, Samar Bouazizi, Emna Benmohamed, and Hela Ltifi propose in their article an advanced approach to recognize human emotions by using electroencephalogram (EEG) signals and focus their analysis on two specific classes of emotion recognition: H/L Arousal and H/L Valence. Anderson Melo de Morais, Fernando Antonio Aires Lins, and Nelson Souto Rosa from Brazil report on their survey on integration of consensus mechanisms into IoT-based blockchains, analyzing eight dimensions that help understand existing proposals: ease of integration, scalability, latency, throughput, power consumption, configuration issues, integrated algorithms, and adversary tolerance. In the next article Aymane Ezzaim, Aziz Dahbi, Abdelfatteh Haidine, and Abdelhak Aqqal from Morocco carry out a systematic mapping of the literature on AI-based adaptive learning environments and approaches. They examine 93 articles published between 2000 and 2022 and discuss the findings, including the types of AI algorithms used, the objectives targeted by these systems as well as the factors related to adaptation. Sergio-Daniel Sanchez-Solar, Gustavo Rodriguez-Gomez, and Jose Martinez-Carranza from México present their research on the control of a spherical robot rolling over irregular surfaces, which is achieved by controlling two motors for longitudinal and lateral motion in this non-holonomic system, and showed improvements by tuning the controller’s gains using stochastic signals for the longitudinal controller. Last but not least, Layse Santos Souza and Michel S. Soares from Brazil propose in their article the joint use of the SmartCitySysML with TCPN (Timed Coloured Petri Nets) to refine and formally model SysML diagrams that specify internal behavior, and then verify the developed model to prove behavioral properties of an urban traffic signal control system.  Enjoy Reading!  Cordially,  Christian Gütl, Managing Editor  Graz University of Technology, Graz, Austria

Combining SysML and Timed Coloured Petri Nets for Designing Smart City Applications

A smart city is an urban centre that integrates a variety of solutions to improve infrastructure performance and achieve sustainable urban development. Urban roads are a crucial infrastructure highly demanded by citizens and organisations interested in their deployment, performance, and safety. Urban traffic signal control is an important and challenging real-world problem that aims to monitor and improve traffic congestion. The deployment of traffic signals for vehicles or pedestrians at an intersection is a complex activity that changes constantly, so it is necessary to establish rules to control the flow of vehicles and pedestrians. Thus, this article describes the joint use of the SmartCitySysML, a profile proposed by the authors, with TCPN (Timed Coloured Petri Nets) to refine and formally model SysML diagrams specifying the internal behaviour, and then verify the developed model to prove behavioural properties of an urban traffic signal control system.

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.

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.

Research on Urban Traffic Signal Control Systems Based on Cyber Physical Systems

In this paper, we present a traffic cyber physical system for urban road traffic signal control, which is referred to as UTSC-CPS. With this proposed system, managers and researchers can realize the construction and simulation of various types of traffic scenarios, the rapid development, and optimization of new control strategies and can apply effective control strategies to actual traffic management. The advantages of this new system include the following. Firstly, the fusion architecture of private cloud computing and edge computing is proposed for the first time, which effectively improves the performance of software and hardware of the urban road traffic signal control system and realizes information security perception and protection in cloud and equipment, respectively, within the fusion framework; secondly, using the concept of parallel system, the depth of real-time traffic control subsystem and real-time simulation subsystem is realized. Thirdly, the idea of virtual scene basic engine and strategy agent engine is put forward in the system design, which separates data from control strategy by designing a general control strategy API and helps researchers focus on control algorithm itself without paying attention to detection data and basic data. Finally, considering China, the system designs a general control strategy API to separate data from control strategy. Most of the popular communication protocols between signal controllers and detectors are private protocols. The standard protocol conversion middleware is skillfully designed, which decouples the field equipment from the system software and achieves the universality and reliability of the control strategy. To further demonstrate the advantages of the new system, we have carried out a one-year practical test in Weifang City, Shandong Province, China. The system has been proved in terms of stability, security, scalability, practicability and rapid practice, and verification of the new control strategy. At the same time, it proves the superiority of the simulation subsystem in the performance and simulation scale by comparing the different-scale road networks of Shunyi District in Beijing and Weifang City in Shandong Province. Further tests were conducted using real intersections, and the results were equally valid.

Optimizing multi-agent based urban traffic signal control system

Agent-based approach is a popular tool for modelling and developing large-scale distributed systems such as urban traffic control system with dynamic traffic flows. This study proposes a multi-agent-based approach to optimize urban traffic network signal control, which utilizes a mathematical programming method to optimize the signal timing plans at intersections. To improve the overall network efficiency, we develop an online agent-based signal coordination scheme, underpinned by the communication among different intersection control agents. In addition, the initial coordination scheme that pre-adjusts the offsets between the intersections is developed based on the historical demand information. Comparison and sensitivity analysis are conducted to evaluate the performance of the proposed method on a customized traffic simulation platform using MATLAB and VISSIM. Simulation results indicate that the proposed method can effectively avoid network oversaturation and thus reduces average travel delay and improves average vehicle speed, as compared to rule-based multi-agent signal control methods.

Multi Layer Perception Type Artificial Neural Network based Traffic Control

Real time traffic control is a main criteria of the urban traffic signal control system, and giving viable ongoing traffic signal control for a substantial complex traffic system is a testing issue. The objective of the work is to find and adjust the timing of signals based on the traffic density. Such a situation arises in a city where outbound vehicles during morning time and inbound vehicles during evening time is more while the vehicular movement in the opposite direction is less. To predict and adjust the timings of the signals on both sides of the road at the same time, Artificial Neural Network technique is used. A real time traffic survey of Light Motor Vehicle, Heavy Motor Vehicle, two and three wheeler vehicular movement in Thanjavur city is done. The number of vehicles (cars, auto, bikes, trucks and buses) and width of the road was given as a input and the output predicted was in terms of timing for the traffic signal at any particular place and for any particular width of the road. The width of the road is also taken into account which is essential in planning a city based traffic consisting of different road widths. Keywords: Artificial Neural Network, Timings for Traffic, Traffic Control, Width of the Road

Dynamic Simulation and Optimization of Traffic Signal System

With the economic development and urbanization, the number of motor vehicles increased rapidly, because the contradictions between urban traffic supply and demand imbalances are becoming increasingly acute. We used the cell transmission model (CTM) to optimize on the basis of a single intersection by observing the delay time and the traffic capacity, research into the intelligent urban traffic signal control system, improve the operating efficiency of the existing transportation system. To a certain extent, it addresses the issue of urban traffic congestion.

A MULTI-INTERSECTION MODEL AND SIGNAL TIMING PLAN ALGORITHM FOR URBAN TRAFFIC SIGNAL CONTROL

The urban traffic signal control system is complex, non-linear and non-equilibrium in real conditions. The existing methods could not satisfy the requirement of real-time and dynamic control. In order to solve these difficulties and challenges, this paper proposes a novel Multi-Intersection Model (MIM) based on Cellular Automata (CA) and a Multi-Intersection Signal Timing Plan Algorithm (MISTPA), which can reduce the delay time at each intersection and effectively alleviate the traffic pressure on each intersection in the urban traffic network. Our work is divided into several parts: (1) a multi-intersection model based on CA is defined to build the dynamic urban traffic network; (2) MISTPA is proposed, which truly reflects the real-time demand degree to green time of the traffic flow at each intersection. The MISTPA is composed Single Intersection Volume Algorithm (SIVA), Single-Lane Volume Algorithm (SLVA) and single intersection signal timing plan algorithm (SISTPA). Extensive experiments show that when the saturation is greater than 0.3, the MIM and the MISTPA achieve good performance, and can significantly reduce the vehicle delay time at each intersection. The average delay time of the traffic flow at each intersection can obviously be reduced. Finally, a practical case study demonstrates that the proposed model and the corresponding algorithm are correct and effective.

MOTION - A Developing Urban Adaptive Traffic Signal Control System

This paper mainly focuses on the recent development of MOTION system. MOTION system, as a developing urban adaptive traffic signal control system, is now more and more worldwide recognized. In this paper, first the original idea of MOTION is descript. Calculation procedures of MOTION algorithm are followed. Then methods of optimization, including optimization of green split, optimization of cycle time, optimization of offset times are introduced. Platoon Model, as a vital concept in optimization of offset times, are explained in details. Multi functional levels in MOTION system including the tactical level and the operational level are introduced in figures. At last, implementation results of MOTION system in Germany are shown to prove MOTION as a very efficient method in improving urban traffic quality.

Research on Fast Dynamic Division Method of Coordinated Control Subarea

摘要: 通过建立基于交叉口关联度的协调控制子区划分模型,设计最佳控制子区划分方 案获取流程,实现了交通信号控制子区的动态划分;针对最佳控制子区划分方案求 解过程中可能面临的维数灾难问题,提出利用降维处理与遗传算法两种方法进行 子区划分方案的快速寻优,并给出了一套协调控制子区快速动态划分流程.通过算 例分析说明,在一定规模路网之下本文提出的控制子区快速动态划分方法将以一个 较大概率搜索到次优子区划分方案,且因其计算时间可以被有效控制,因此完全能够 满足控制子区划分的实时性要求,可推广应用于城市区域交通信号实时控制系统中. 关键词: 交通信号控制 / 控制子区 / 快速划分 / 降维处理 / 遗传算法

Study of Self-Organizing Control of Traffic Signals in an Urban Network Based on Cellular Automata

Aiming at the problems of known traffic signal control methods, such as heavy system and huge computation, this paper presents a macroscopic two-dimensional cellular automata (CA) model of an urban traffic signal control system, in which each intersection is regarded as a cell and the flow pressure is treated as the state. The hierarchical control architecture of traffic signal is built. Off the support of the upper level (traffic control center), the lower level (intersection controllers) can implement self-organizing control of traffic signal. The signal decision at an intersection is based on the thought of "perception-decision." We establish the simple state perception rule of typical intersection. By means of the computer simulation, the application of CA in signal self-organizing control is proved effective in urban traffic.

LRT PRIORITY WITHIN THE SCATS ENVIRONMENT IN DUBLIN: A TRAFFIC FLOW SIMULATION STUDY.

This paper focuses on the feasability of operating a Light Rail Transit (LRT) priority system within the environment of an existing Urban Traffic Signal Control System (UTC) in Dublin. The city uses the Australian UTC-System SCATS (Sydney Co-ordinated Adaptive Traffic System). So far no LRT is running in Dublin yet, but the objective of the study was to establish the degree of LRT priority to be achieved within the existing SCATS UTC. The study was carried out using the microscopic traffic simulation model VISSIM. The simulations clearly show that LRT priority is possible within SCATS on a tactical (local) level at each controller.