Traffic Regulation Research Articles

Development of a prioritized traffic light control system for emergency vehicles

This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies the ambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.

The Hippo Signaling Pathway, Reactive Oxygen Species Production, and Oxidative Stress: A Two-Way Traffic Regulation

The Hippo signaling pathway is recognized for its significant role in cell differentiation, proliferation, survival, and tissue regeneration. Recently, the Hippo signaling pathway was also found to be associated with oxidative stress and reactive oxygen species (ROS) regulation, which are important in the regulation of cell survival. Studies indicate a correlation between components of the Hippo signaling pathway, including MST1, YAP, and TAZ, and the generation of ROS. On the other hand, ROS and oxidative stress can activate key components of the Hippo signaling pathway. For example, ROS production activates MST1, which subsequently phosphorylates FOXO3, leading to apoptotic cell death. ROS was also found to regulate YAP, in addition to MST1/2. Oxidative stress and ROS formation can impair lipids, proteins, and DNA, leading to many disorders, including aging, neurodegeneration, atherosclerosis, and diabetes. Consequently, understanding the interplay between the Hippo signaling pathway, ROS, and oxidative stress is crucial for developing future disease management strategies. This paper aimed to review the association between the Hippo signaling pathway, regulation of ROS production, and oxidative stress to provide beneficial information in understanding cell function and pathological processes.

The effect of the ultra-low emission zone on PM2.5 concentration in Seoul, South Korea

As studies regarding the positive effect of Low Emission Zones (LEZs) and people's risk perception about air pollution have increased, more powerful and specific traffic regulation policies have been required. London is the first city in the world to implement an Ultra-low Emission Zone (ULEZ) in addition to the existing LEZ. Benchmarking London's ULEZ, a ULEZ policy was implemented in Seoul, South Korea on December 1, 2019. The goal of the policy is to improve air quality by prohibiting entry of vehicles registered nationwide into Seoul's ULEZ that do not meet a specific emission standard including diesel, gasoline, and LPG fuel-based vehicles. This study analyzed the effect of Seoul's ULEZ policy on the five major atmospheric pollutants (PM2.5, PM10, NO2, CO, SO2, O3) concentration in the zone, particularly focusing on PM2.5 concentration. The analysis employs Difference-in-Differences (DD) approach, comparing data from one year before and after the policy's implementation on December 1, 2019. The findings indicate that Seoul's ULEZ policy resulted in a 9.8% increase in PM2.5 concentrations. Conversely, the policy led to reductions in PM10, NO2, CO, and SO2 concentrations by 12.0%, 17.3%, 5.9%, and 10.8%, respectively, while the effect on O3 was statistically insignificant. These empirical results suggest that the ULEZ may need to incorporate more stringent emission standards, expand its coverage, or introduce additional measures to address the unintended increase in PM2.5 concentration.

Enhancing Traffic Management in Cyber Physical Systems – A Gradient Based Fuzzy Controller Approach

Traffic forecast is a critical aspect of effective traffic management and planning in cyber-physical systems (CPS). In this study, we present a novel approach to traffic prediction and regulation within cyber-physical systems (CPS), introducing the Gradient Rule based Fuzzy Controller. This innovative methodology utilizes dynamic fuzzy logic control enhanced with gradient-based rules to adapt signal timings in real-time, effectively addressing the variable nature of traffic. Our results demonstrate significant improvements in reducing total queue length and delay at intersections, with reductions of up to 91.23%. Furthermore, extensive simulations and evaluations underscore the superiority of our approach compared to state-of-the-art models, highlighting its flexibility and adaptability to diverse traffic scenarios. This research emphasizes the novelty of integrating gradient-based rules into fuzzy control techniques, offering a promising avenue for advancing traffic management systems in CPS environments.

A Retrospective Evaluation of Mandibular Fracture in Kabul, Afghanistan.

The mandible, a key bone in the lower face, is prone to trauma. Although mandibular fractures are widely recognized as common and costly, there is a research gap on this topic in Afghanistan. This study explores the causes of lower jaw fractures and their link to demographic factors in the Afghan population. A retrospective analysis of 535 medical records from a Kabul tertiary hospital (March 2021 to March 2022) examined lower jaw fractures, patient demographics (age, sex), and injury causes. Statistical methods, including chi-square tests and Pearson's contingency coefficient, were employed to explore correlations between mandibular fractures and demographic factors. In this study, we analyzed 579 mandibular fractures in 535 patients, with road accidents (33.08%), falls (22.61%), and interpersonal conflicts being the leading causes. Most patients were aged 21-30, with single fractures being the most common (92.3%) and parasymphyseal fractures being the most prevalent (28.5%). Although certain fracture types were significantly correlated with age, no notable association was found between age and injury cause. This study reveals that road accidents, falls, and interpersonal conflicts are the main causes of lower jaw fractures in Kabul, with the highest incidence in individuals aged 21-30. These findings highlight the necessity for targeted public health interventions, such as improved road safety campaigns and stricter traffic regulation enforcement, to lower injury rates. Incorporating these insights into clinical practice can also help healthcare providers more effectively manage lower jaw fractures in affected populations.

Research and Application of Traffic Flow Prediction in Chengdu City Based on Neural Network Algorithms

As urban traffic systems increase in complexity, accurately predicting traffic flow has become a critical task in traffic management and planning. This paper employs neural network technologies, specifically Graph Convolutional Networks (GCN) and Long Short-Term Memory networks (LSTM), to develop a traffic flow prediction model for Chengdu that incorporates spatio-temporal characteristics. Through empirical analysis, the model demonstrates high accuracy and good generalization capability, effectively predicting and analyzing traffic flow at different times and regions. Additionally, this study explores the application of the prediction model in traffic law regulation and policy formulation, providing data support and scientific basis for advancing smarter traffic systems.

Deep Learning based Road Traffic Assessment for Vehicle Rerouting: An Extensive Experimental Study of RetinaNet and YOLO Models

Currently, there is a notable prevalence of substantial traffic congestion and frequent vehicular accidents on roadways in contemporary times. Amalgamation of latest front-line technologies involving Internet of Things (IoT) and image classification has immense potential to advance the progress of a proficient traffic regulation system. To mitigate the occurrence of vehicular accidents, our research endeavors revolve around the comprehensive analysis of the prevailing road conditions. This meticulous examination allows us to effectively automate traffic routes orchestration, ensuring smooth vehicular movement across all lanes of the road network. The analysis of traffic patterns is conducted through the utilization of visual data images. The real time captured traffic images undergo processing using various object detection models named RetinaNet and the YOLO (You Only Look Once) models. A series of comparative evaluations suggests an improved traffic object identification capacity for the RetinaNet model as compared to all YOLO models.

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

Impacts of brightness contrast, road environment complexity, travel direction and judgement type on speed perception errors among older adult pedestrians' road-crossing decision-making.

This study aimed to explore how various factors affect older people's vehicle speed perception to enhance their road safety as pedestrians, focusing on the impact of their cognitive and perceptual abilities on road-crossing decisions. The study evaluated the effects of brightness contrast (high, medium and low), road complexity (high and low) and vehicle travel direction (same and opposite) on speed perception errors in simulated traffic settings. It involved 38 older participants who estimated the speed of a comparison vehicle under two judgement conditions. Findings showed a consistent underestimation of speed in all conditions. A repeated-measure ANOVA revealed that speed perception errors were significantly higher with low brightness contrast, in simpler road environments, with vehicles travelling in the same direction, and when using absolute judgements. These results have practical importance for public safety initiatives, traffic regulation and road design catering to older adults' perceptual needs. They also provide valuable insights for driver training programs for older adults, aimed at enhancing their understanding and management of perceptual biases.

Socio-Economic Impacts Resulting From The Integration Of Artificial Intelligence Into Electronic Surveillance Systems In Traffic

In the last decade, electronic surveillance systems have been actively employed for monitoring traffic rule violations with the aim of enhancing traffic regulation. The utilization of these systems has resulted in increased compliance with traffic regulations, consequently leading to a reduction in losses attributed to traffic accidents. The impact created by these systems is expected to be further amplified through the incorporation of artificial intelligence (AI) support. Within the scope of this study, a detailed analysis of the socio-economic impact of AI-assisted Electronic Traffic Monitoring Systems has been conducted, focusing on economic, mobility, health, environmental, and quality of life aspects.

Intelligent Traffic Signal Management with PLC

This study introduces a Smart Traffic Light Control System (STLCS) that uses advanced technologies to improve traffic regulation. The key components include a camera-based traffic density detection module and a specialized vehicle identification system using Radio Frequency (RF) technology. The system’s core is an Arduino Uno board along with several converters and a Programmable Logic Controller (PLC). Field tests show that STLCS effectively analyses traffic density and prioritizes emergency vehicles, proving its potential for global traffic management improvements

Design and Development of A Traffic Light Simulation Model Based on Arduino Microcontroller

Congestion is one of the transportation problems with the highest percentage to occur at every intersection in all corners of the region. Efforts can be made to solve these problems by making the traffic light regulation system (APILL) in general, APILL still uses static cycle time with cable networks as interconnects between lamp poles, making it difficult to adjust the phase and cycle time referring to traffic conditions that tend to be dynamic, even requiring high procurement and maintenance costs for underground cables. Based on the above, the research team wants to create a simulation mockup of APILL whose cycle time and phase can be set through calculations on the Android platform with wireless interconnection, as well as can be used as a learning facility for cadets/I to provide an understanding of the applicable phase at the intersection, to be able to analyze the performance of an intersection and how APILL works on roads.

НЕЙРОСЕТЕВОЕ ПРОГНОЗИРОВАНИЕ ПАРАМЕТРОВ ТРАНСПОРТНЫХ ПОТОКОВ В ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМАХ СВЕТОФОРНОГО РЕГУЛИРОВАНИЯ

A method is proposed for neural network prediction of congestion on road sections outside the control zones of transport detectors in the systems of “flexible” transport flow control at intersections with the traffic light regulation. The paper describes the operating principle of the original control system based on fuzzy logic. To develop a universal neural network solution that can be used to predict traffic on most road sections with common characteristics without the need for training for each case separately, it is proposed to identify 9 main types of sections and, accordingly, 9 neural networks. Each typical neural network differs in the amount of the input data and parameters. All neural networks are trained on samples obtained from analyzing traffic flow on road sections of each type. Typical characteristics of road sections, architecture, and parameters of neural networks are determined. Neural networks are trained and tested on the model data. Using computer simulation methods, the author assesses the control quality of the original intelligent traffic light control system, which has complete information about the transport flow (however, it is difficult to implement in practice), as well as a control system that uses a set of data obtained using transport detectors and supplemented with a neural network forecast. According to the experiment results, a system with a neural network forecast of congestion on road sections is inferior in quality of control by no more than 1.92% compared to an idealized system, which gives grounds to consider this method applicable in intelligent transport flow control systems at intersections with traffic light regulation.

Egyes közlekedési szabályszegésekre vonatkozó közigazgatási bírságolási kérdések

Aim: The law of misdemeanours, which emerged in the mid-1950s, embodied the legal policy will that administrative substantive offences should be dealt with by means of misdemeanour proceedings. The legal policy objective was not fulfilled, and the institution of administrative fines was 'spawned' by the impetus of the social laws in question as the law of administrative offences developed. Since the beginning of the 21st century, this has been spreading - according to the legislator's unknown intentions - and has typically developed in the direction of objective sanctions. Perhaps most strikingly in traffic regulation. The aim of this paper is to illustrate some of the specific features of administrative fines, limited to traffic rules. Among the administrative sanctions, the author examines those which are applied by the fining authorities for infringements of road traffic rules. The author tries to justify the need to modify the grounds for exemption in the substantive law as it stands, on the one hand, and the regulation of on-the-spot fines, on the other. Methodology: According to the author, the objective can be achieved if the will to codify embraces the objectives set out above. To this end, the methodology of the study is based on an analysis of the relevant legal norm, from which valid deductive assertions are made. Findings: The infringements transferred from the law of misdemeanours to the law on administrative fines require further codification. Value: The study could help to improve the legal framework for dealing with administrative substantive offences.

Supeltas dalam Tinjauan Asas Kepastian Hukum

Volunteer traffic controllers (Supeltas) continue to increment along with the increase in traffic flow. Their existence has a two-sided assessment, some welcoming their presence and others rejecting it. Departing from the opinion or information that supeltas have an explication legal basis in the traffic and transportation regulation, the researcher tries to analyze deeper. How is the legal framework of supeltas and how is the position of supeltas in terms of the principle of legal certainty. The purpose of this research is first, to see and describe the position of supeltas in terms of the principle of legal certainty. Second, as material for further research that focuses on the issue of traffic order in legal science perspective. This research uses descriptive analytical with normative juridical research, with a research approach used statue approach and library approach. The kind of data used is secondary consisting of primary legal materials such as regulations and undang-undang, secondary legal materials law like books, scientific journals of law, other scientific journals and tertiary materials such as websites, electronic mass media news, legal and general encyclopedias, language dictionaries. The results showed that the rules of supeltas has not fulfilled the principles of legal certainty and defined authority and has not fulfilled the elements of legal protection for all parties including the traffic control volunteers themselves.

Analysis of the Relationship between Speed Consistency and Traffic Accident Probability (on the Mojokerto-Surabaya Toll Road)

Vehicle speed consistency plays a crucial role in maintaining traffic safety, with speed limits set to reduce accident fatalities. While the direct impact of speed limits on vehicle speed is minimal, their implementation significantly influences driver behavior, with about 85% of drivers complying with speed limits. However, variables such as traffic volume, weather conditions, and travel urgency often cause drivers to be inconsistent in adhering to these limits, increasing accident risk. On the Mojokerto-Surabaya toll road, data from IRSMS Korlantas Polri and Jasa Marga recorded 193 accidents with 101 fatalities from 2018 to August 2023, mostly caused by driver behavior such as excessive speed. This study analyzes the relationship between speed consistency and accident probability on this road, highlighting accident issues due to speed violations and inconsistency at ramp off/on locations. Results show the importance of toll road infrastructure considering vehicle volume, identifying accident blackspots, and implementing urgent speed management as key factors in improving safety on the Surabaya-Mojokerto toll road. The findings emphasize the need for better traffic management and speed regulation to reduce fatal accident risks.

Traffic planning in modern large cities Paris and Istanbul

The enhancement of flexibility, energy efficiency, and environmental friendliness constitutes a widely acknowledged trend in the development of urban infrastructure. The proliferation of various types of transportation vehicles exacerbates the complexity of traffic regulation. Intelligent transportation systems, leveraging real-time traffic status prediction technologies, such as velocity estimation, emerge as viable solutions for the efficacious management and control of urban road networks. The objective of this project is to address the complex task of increasing accuracy in predicting traffic conditions on a big scale using deep learning techniques. To accomplish the objective of the study, the historical traffic data of Paris and Istanbul within a certain timeframe were used, considering the impact of variables such as speed, traffic volume, and direction. Specifically, traffic movie clips based on 2 years of real-world data for the two cities were utilized. The movies were generated with HERE data derived from over 100 billion GPS (Global Positioning System) probe points collected from a substantial fleet of automobiles. The model presented by us, unlike the majority of previous ones, takes into account the cumulative impact of speed, flow, and direction. The developed model showed better results compared to the well-known models, in particular, in comparison with the SR-ResNet model. The pixel-wise MAE (mean absolute error) values for Paris and Istanbul were 4.299 and 3.884 respectively, compared to 4.551 and 3.993 for SR-ResNET. Thus, the created model demonstrated the possibilities for further enhancing the accuracy and efficacy of intelligent transportation systems, particularly in large urban centres, thereby facilitating heightened safety, energy efficiency, and convenience for road users. The obtained results will be useful for local policymakers responsible for infrastructure development planning, as well as for specialists and researchers in the field. Future research should investigate how to incorporate more sources of information, in particular previous information from physical traffic flow models, information about weather conditions, etc. into the deep learning framework, as well as further increasing of the throughput capacity and reducing processing time.

Combination of H∞ perimeter control and route guidance for heterogeneous urban road networks

Due to the effects of capacity differences and other factors, congestion may occur at local nodes of a network. This situation reduces the reliability of the network and leads to regional deadlock. To improve system stability and reduce the influence of local road network heterogeneity on traffic efficiency, a macroscopic fundamental diagram (MFD) model of a nonuniform density road network is proposed. Second, based on the MFD model, a two-level strategy for H∞ perimeter control and traffic flow regulation is proposed. The upper layer uses an H∞ controller to adjust the inflow at the boundary of congested region. The lower layer uses a traffic density regulator (TDR) inside the region. Finally,a simulation is conducted to compare with traditional H∞ control, bang-bang-TDR, PI-TDR and MPC-TDR strategy. The results show that the H∞-TDR strategy proposed in this paper can improve operation efficiency and reduce delay in network.

Advanced Helmet and License Plate Recognition: A Breakthrough in Safety and Surveillance Systems

Abstract: Helmet detection and license plate recognition have emerged as crucial components in ensuring safety and security in various domains, particularly in transportation and law enforcement. This review paper presents a comprehensive survey and analysis of methodologies, techniques, and advancements in the field of helmet detection and license plate recognition using computer vision and deep learning approaches. The paper investigates diverse datasets, algorithms, and architectures utilized for accurate detection and recognition of helmets worn by individuals in critical settings and the identification of license plates on vehicles. Furthermore, it explores the challenges, limitations, and future directions within these domains. The paper also evaluates the performance metrics, technological advancements, and potential applications in real-world scenarios, emphasizing the importance of these technologies in enhancing safety measures, traffic regulation, and security enforcement systems. Through this review, we aim to provide a consolidated overview and critical analysis of existing methodologies while identifying opportunities for future research and development in helmet detection and license plate recognition.

Features of the Application of Traffic Flow Management Methods and Tools

This article examines the causes and consequences of traffic jams, describes typical traffic flow behavior and analyzes traffic control methods and means. The paper demonstrates the proposed classification of traffic lights by type of regulation. In summary, the article represents a detailed overview of existing cyber-physical traffic control systems, such as SEA TCS, InSync and MASSTR. The article analyzes the existing methods of traffic regulation, examines the causes and consequences of congestion, the division of intersections into regulated and unregulated, and the classification of traffic lights by type of traffic control. Among the main parameters of traffic flow used by cyberphysical traffic control systems, the primary and most used are speed, density, and volume of vehicles. The article also reviews the existing cyber-physical traffic control systems and the primary technologies.