Traffic Management System Research Articles

Application of AI and IoT in Traffic Management of Large Metropolitan Cities

Urban traffic congestion is a growing problem worldwide, leading to wasted time, fuel, and increased emissions. To address this challenge, smart traffic management systems powered by the Internet of Things (IoT) and Artificial Intelligence (AI) are emerging as a promising solution. This paper explores how these technologies can be combined to create a more efficient and sustainable traffic management system for large cities.

Serverless Computing in the Edge-Cloud Continuum : Challenges, Opportunities, and a Novel Framework

This article explores the integration of serverless computing across the edge-cloud continuum, addressing the growing demand for low-latency, data-intensive applications in the era of the Internet of Things (IoT). We present EdgeServe, a novel framework that extends the serverless paradigm to encompass edge devices and cloud resources, overcoming the limitations of traditional cloud-centric approaches. Through comprehensive simulations and real-world case studies, including a smart city traffic management system, an industrial IoT predictive maintenance application, and a mobile augmented reality gaming platform, we evaluate the performance, scalability, and cost-effectiveness of our proposed framework. Our results demonstrate significant improvements in application response times, with latency reductions of up to 82% in time-critical functions, and an average cost reduction of 43% compared to cloud-only serverless deployments. We also observe a 28% decrease in overall energy consumption in certain scenarios. The article addresses key challenges in resource management, data consistency, adaptive function placement, and security in distributed environments. Our findings have important implications for application architects and cloud service providers, paving the way for a new generation of edge-native serverless platforms. This research contributes to the growing body of knowledge on distributed systems and offers insights into the future evolution of serverless computing in heterogeneous computing environments.

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.

Performance-based surveillance time delay parameters calculation methods and evaluation practice

Abstract ICAO introduced the Performance-Based Surveillance (PBS) framework in 2020 to define performance requirements for surveillance systems in various operational environments. PBS allows flexibility in considering different surveillance system designs or technologies, facilitating system improvement and deployment. Implementation of PBS technology can significantly enhance aviation safety, increase airspace capacity, optimize resource allocation, improve energy efficiency, and reduce emissions, aligning with global aviation goals. However, the evaluation of time-related parameters within the PBS framework poses challenges, as methods for evaluation are not included in the ICAO PBS manual. This paper explores theoretical methods, practical implementations, and data analysis regarding critical time-related parameters in China’s Air Traffic Management (ATM) system, namely HPMDA, PAMDA, IDMUD, and FSMUD. These methods have been accepted and included in the ICAO PBS manual.

An integrated framework for eye tracking-assisted task capability recognition of air traffic controllers with machine learning

To effectively address the continuously increasing demands of air transport, air traffic management (ATM) systems are evolving towards a human-artificial intelligence (AI) hybrid automation paradigm. In this paradigm, air traffic controllers (ATCOs) play a crucial role in ensuring safe and efficient operations. Recognizing ATCOs’ task capability is essential for evaluating performance, optimizing task assignments, and personalizing training strategies. Physiological signals, such as eye movements, offer objective insights into human behavior and cognitive processes, making them valuable for identifying the task capability of ATCOs. In this study, an integrated framework leveraging machine learning is proposed to achieve this goal. First, a Transformer-attentional long short-term memory (LSTM) network is developed to analyze eye movement patterns, capturing both global and long-term dependencies for precise skill level detection. Next, manual parameters are extracted from the raw eye tracking data and the SHAP (SHapley Additive exPlanation) method is utilized to determine the importance of these parameters, aiding in the selection of relevant performance metrics. Furthermore, a radar chart is implemented to intuitively visualize and compare performance metrics across different skill levels based on the selected parameters. A case study and extensive experiments are conducted to validate the effectiveness of the proposed framework. This research advances task capability recognition for ATCOs in a human-in-the-loop scenario, with a focus on expertise level detection, parameter importance identification, and performance metric comparison.

When segment anything model meets inventorying of roadway assets

Automating the identification, localization, and monitoring of roadway assets distributed widely in the roadway network is critical for the traffic management system. It can efficiently provide up-to-date information in supporting transportation asset management. Collecting videos with vehicle-mounted cameras and processing the data with computer vision-based deep learning methods is garnering increased attention from transportation agencies. While promising, challenges arise due to the lack of high-quality annotations for roadway assets in images, difficulties in identifying these assets, and limited solutions. The Segment Anything Model (SAM), a visual foundation model, demonstrates robust zero-shot capability for general image segmentation under various prompts. This study evaluates SAM’s applicability and efficiency in extracting roadway assets from images. Specifically, it examines the impacts of model size and prompt quality on SAM’s performance in segmenting roadway assets. Five state-of-the-art semantic segmentation models are trained and compared with SAM. Results show that a lightweight SAM with human-rendered prompts outperforms the five semantic segmentation models. Based on the evaluation results, future work will explore incorporating SAM into transportation asset management applications, promoting collaboration between human experts and artificial intelligence.

Characterizing Performance Tradeoffs from Air Traffic Management Services for Advanced Air Mobility

The emergence of Advanced Air Mobility (AAM) has called for novel Air Traffic Management (ATM) solutions to enable the projected high number of operations, particularly in already dense and complex metroplex airspace. Understanding the multidimensional performance impacts of developing air traffic rules, procedures, and services is key to guiding this new traffic management system design. This paper presents a simulation framework to provide a quantitative assessment of the operational performance of ATM services in support of AAM. The framework embeds several modules for high-fidelity modeling of urban air traffic flows based on actual data, ATM service provision, stochastic simulation, and performance quantification. Two key services are analyzed: airspace geofencing and strategic deconfliction. We evaluate the use of these services in a scenario of air mobility operations associated with daily urban commuting in the Sao Paulo metropolitan region. Monte Carlo experiments are performed to simulate the execution of aircraft trajectories managed under different ATM service configurations, allowing for the characterization of operational performance tradeoffs, particularly in terms of safety and efficiency. The work contributes to quantifying the value of novel ATM services for integrating AAM operations in dense metroplex airspace, supporting their design and implementation.

Self-Powered Traffic Lights Through Wind Energy Harvesting Based on High-Performance Fur-Brush Dish Triboelectric Nanogenerators.

Traffic lights play vital roles in urban traffic management systems, providing clear directional guidance for vehicles and pedestrians while ensuring traffic safety. However, the vast quantity of traffic lights widely distributed in the transportation system aggravates energy consumption. Here, a self-powered traffic light system is proposed through wind energy harvesting based on a high-performance fur-brush dish triboelectric nanogenerator (FD-TENG). The FD-TENG harvests wind energy to power the traffic light system continuously without needing an external power supply. Natural rabbit furs are applied to dish structures, due to their outstanding characteristics of shallow wear, high performance, and resistance to humidity. Also, the grid pattern of the dish structure significantly impacts the TENG outputs. Additionally, the internal electric field and the influences of mechanical and structural parameters on the outputs are analyzed by finite element simulations. After optimization, the FD-TENG can achieve a peak power density of 3.275Wm-3. The portable and miniature features of FD-TENG make it suitable for other natural environment systems such as forests, oceans, and mountains, besides the traffic light systems. This study presents a viable strategy for self-powered traffic lights, establishing a basis for efficient environmental energy harvesting toward big data and Internet of Things applications.

Vehicle Class Prediction at Toll Gate Using Deep Learning

In the era of digitalization and automation, efficiency in the traffic management system at toll gates is very important. One of the efforts to improve this efficiency is to develop an automatic vehicle class detection system using deep learning technology, especially Convolutional Neural Network (CNN). This research aims to design and implement a CNN model that can identify and classify the types of vehicles passing through toll gates. The model development process includes collecting and annotating vehicle image data, data pre-processing, and CNN model training and testing. The evaluation results show that the developed model can achieve an accuracy of about 96% in detecting vehicle classes, so it can be integrated with the toll gate system to increase the speed and accuracy in the vehicle classification process. Thus, this solution is expected to reduce the waiting time of toll users and improve operational efficiency.

ROAD value-based detection for GAE enhanced interference mitigation in LDACS.

The increasing volume of air traffic has placed significant stress on the current Air Traffic Management (ATM) systems, especially concerning the use of Very High Frequency (VHF) communication bands. As air traffic continues to grow, the limitations of the existing spectrum and infrastructure necessitate significant upgrades to ensure safety, efficiency, and capacity. The modernization of air traffic management systems has led to the development and introduction of the L-band Digital Aeronautical Communication System (LDACS), a new communication protocol. LDACS is designed to operate alongside existing L-band systems, ensuring compatibility with legacy users. The coexistence of LDACS with legacy systems poses significant interference challenges, as any disruption in data retrieval can critically impact flight safety. This paper proposes four potential interference mitigation techniques that LDACS can employ to detect and reduce the primary source of interference: Distance Measuring Equipment (DME). The authors introduce a prototype LDACS receiver that uses Rank-Ordered Absolute Differences (ROAD) statistics for effective interference sensing and employs GAE-enhanced pulse peak processors to mitigate Distance Measuring Equipment (DME) interference. Unlike the current GAE-enhanced pulse peak processors, the proposed methods use ROAD value-based detection for identifying DME interference. The performance of the proposed methods - ROAD GAE enhanced Pulse Peak Attenuator (RGPPA), ROAD GAE enhanced Pulse Peak Limiter (RGPPL), ROAD joint GAE enhanced Pulse Peak Attenuator (RJGPPA), and ROAD joint GAE enhanced Pulse Peak Limiter (RJGPPL) is analyzed across different threshold ROAD values to determine their efficacy in various signal conditions. Moreover, the performance of the proposed methods is compared to existing methods such as conventional pulse blanking and GAE-enhanced nonlinear devices, which use the amplitude of the received signal for the detection of interference. Furthermore, the proposed method's performance is compared to another method, ROAD PB, which uses ROAD statistics to detect DME interference and pulse blanking for DME mitigation. The comparative results show that the proposed methods outperformed conventional pulse blanking and ROADPB. Besides, these methods outperformed existing GAE-enhanced methods for their optimum threshold ROAD value.

Reinforcement Learning-Based Adaptive Optimal Control Model for Signal Light in Intelligent Transportation Systems

Day-to-day mobility among the population has increased with economic growth. Smart cities are renovated with advanced technologies to admire modern life in which intelligent transportation becomes highly focused. Because the traffic signal control systems are fixed at a constant time. They split the traffic signal into predetermined intervals and function inefficiently; they result in long wait times, waste fuel and increased carbon emissions. This research study introduces a novel technique for traffic light management to reduce the uncertainties in the system. A dynamic and intelligent traffic light adaptive optimal management system (DITLAOCS) is implemented in this research. It does this by modifying the traffic signal duration in run time and using real-time traffic data as input. Furthermore, the proposed DITLAOCS executes based on three modes: fairness mode (FM), priority mode (PM) and emergent mode (EM). In fairness mode (FM), all vehicles are prioritized equally, while vehicles in different categories receive varying priority levels. Emergency vehicles, on the other hand, receive the highest priority. Furthermore, a fuzzy inference method based on traffic data is shown to choose one mode out of three (FM, PM and EM). This model uses deep reinforcement learning to switch traffic lights in three different phases (red, green and yellow). We evaluated and accurately simulated DITLAOCS on the Shaanxi city map in China using Simulation of Urban MObility (SUMO), an open-source simulator. The simulation results illustrate the efficiency of DITLAOCS when compared to other cutting-edge algorithms on several performance measures.

Short-term Traffic Flow Prediction Based on KELM Optimized By Improved Slime Mould Algorithm

Traffic flow prediction plays a crucial role in improving transportation efficiency and enhancing Intelligent Transportation Systems (ITS). However, the temporal, spatial, and nonlinear nature of traffic flow data presents challenges for accurate short-term prediction. We propose a short-term traffic flow prediction model based on Kernel Extreme Learning Machine (KELM) optimized by the improved Slime Mould Algorithm (SMA). KELM is an improved version of Extreme Learning Machine (ELM) that incorporates kernel functions for improved generalization and stability. SMA is a meta-heuristic algorithm inspired by the behavior of slime mould in foraging, known for its strong global searching ability. For better performance, three strategies are introduced: the Good Point Set method for optimizing the initial population, the combination of Opposition Based Learning (OBL) and Differential Evolution (DE) to improve the slime mould generation mechanism, and the use of adaptive t distribution mutation to enhance convergence speed. After comparing the performance of these improved SMAs on twelve test functions, the ISMA improved by integrating three strategies as mentioned above is best. Then the ISMA is applied to search for the optimal parameters of KELM model. Finally, the optimized KELM with optimal parameters is applied to predict the short-term traffic flow on given traffic data set. Experimental results demonstrate that the proposed model, KELM optimized by ISMA namely ISMA-KELM, outperforms existing models such as Random Forest (RF), Least Squares Support Vector Machine (LSSVM), KELM optimized by Tuna Swarm Optimization Algorithm (TSO-KELM), and KELM optimized by SMA (SMA-KELM) in terms of traffic flow prediction accuracy. The proposed model ISMA-KELM provides a promising approach for addressing the challenges of traffic flow prediction, offering improved accuracy and efficiency in real-time traffic management systems.

AI-powered decision making for road safety optimization under probabilistic linguistic Sugeno-Weber aggregation information

Traffic accidents, a global concern, pose threats to lives and carry substantial economic and societal burdens. This paper explores the innovative integration of artificial intelligence (AI) into the ambiance of Multi Criteria Decision Making (MCDM) to address the challenge. We investigate AI's potential to enhance road safety globally, utilizing data analysis, predictive modeling, and intelligent traffic management systems. Our goal is to revolutionize accident prevention by optimizing decision-making processes and promoting safer and more efficient road environments. In the realm of information aggregation and fusion, growing interest among researchers has centered on probabilistic linguistic expression sets, which adeptly aggregate uncertain data. This article's primary aim is to explore methodologies for aggregating information within a probabilistic linguistic environment. To achieve this goal, we have introduced procedural laws based on the Sugeno-Weber (SW) framework for handling probabilistic linguistic term elements (PLTEs), rooted in both the product and sum of SW operations. Consequently, we have crafted a range of probabilistic linguistic aggregation techniques, encompassing the probabilistic linguistic SW Average (PLSWA) and Geometric (PLSWG), followed by weighted as well as ordered aggregation operators like PLSWWA and PLSWWG, PLSWOWA and PLSWOWG. By harnessing SW τ-norm and τ-conorm, we have developed versatile aggregation tools that facilitate information reinforcement. Through the utilization of proposed operators, we have presented strategies for effectively integrating probabilistic linguistic term sets (PLTs) in the context of MCDM. We have compared our suggested procedures with the TOPSIS approach and elucidated the diverse features inherent to these operators.

Estrategias de Gestión para la Optimización del Tránsito Urbano en el Cantón La Concordia: Desafíos y Oportunidades

This article analyzes the impact of traffic congestion in La Concordia, Ecuador, from an administrative perspective, and proposes solutions based on a review of existing literature. The study is based on the growing problem of urbanization and insufficient transportation planning, which has caused significant problems in urban traffic, affecting both the quality of life of citizens and the economic productivity of the region. The objective of the study is to propose viable administrative strategies that can reduce vehicular congestion, based on previous experiences in Latin American cities with similar characteristics. In methodological terms, a qualitative literature review was carried out to analyze successful cases in the implementation of sustainable mobility policies, prioritizing those studies that present solutions for medium-sized developing cities. The results of the analysis highlight the importance of implementing intelligent traffic management systems, improving public transport and encouraging the use of non-motorized means of transport, such as bicycles and pedestrian routes. The discussion highlights that, despite technological and administrative advances, there are significant challenges such as lack of funding and social resistance to the use of public transport. The conclusions suggest that the implementation of these solutions must be accompanied by a cultural change and a greater commitment from local authorities to ensure their long-term effectiveness.

IoT Based Smart Traffic Management System using Image Processing and Automated Street Lighting System

Through the integration of image processing, IoT, and automatic street lighting, this study presents a novel solution to urban traffic management. Real-time traffic data, such as vehicle counts and congestion levels, are continuously collected by smart cameras positioned at strategic intersections. A central control unit receives this data and uses sophisticated algorithms to dynamically modify traffic signal timings. Additionally, by including sensors to identify surrounding cars and pedestrians, an automatic street lighting system improves sustainability. This system helps save energy and money by optimizing energy consumption through the ability to change lighting intensity levels as needed. In general, this Internet of Things (IoT)-based smart traffic management system improves efficiency and safety for both commuters and locals by addressing congestion and fostering sustainable urban development

Development of an Intelligent Drone Management System for Integration into Smart City Transportation Networks

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditionally manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large number of drones expected to operate at low altitudes in complex urban environments. Therefore, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include the following: (i) developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; (ii) designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; (iii) improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and (iv) investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

Analysis and Justification of Data Structure Selection for Traffic Flow Management Tasks

Abstract. Problem. Cities around the world are increasingly facing challenges related to congestion, delays and the environmental impact of those problems. The paper examines the critical need for innovative solutions to improve the efficiency of traffic flow management, offering a comprehensive study of the role of data arrays in solving the problem of urban mobility. The main goal is to explore and find out how the use of data sets can improve traffic management, contributing to the development of new traffic management systems and informed decision-making. By summarizing information from various sources, the article aims to provide a holistic understanding of the theoretical foundations, practical applications and new trends in the use of datasets for traffic optimization. Drawing on a range of sources, ranging from dynamic algorithms to real-world case studies in Los Angeles, the paper examines the theoretical foundations, applications of dynamic arrays, and the development of traffic data management. The results demonstrate the transformative effect of dynamic arrays on traffic flow management. Real-world implementations demonstrate tangible benefits, including improved signal synchronization, increased security, and data-driven decision-making. New trends such as 5G, edge computing and advanced sensor networks are considered to be the main elements shaping the future of traffic data management. The practical value of the work lies in its potential to inform urban planners and technology developers about the transformative possibilities of data sets. By understanding practical applications, challenges and emerging trends, stakeholders can make informed decisions to improve urban mobility, reduce congestion and shape the future of intelligent traffic solutions. The work provides a structured study of data sets in the management of traffic flows, offering ideas that contribute to the possible integration of intelligent solutions for traffic and a more comfortable urban life.

Revolutionizing Urban Mobility: Smart Transportation & Vehicle-to-Infrastructure Communication – A Review

The burgeoning popularity of IoT technology has significantly altered transportation, with an extensive variety of repercussions. Intelligent Transportation Systems (ITS) are at the vanguard of this change, which has the potential to fundamentally change urban transportation for both individuals and businesses. ITS has expanded considerably in recent years, owing to global urbanization and industrialization advancements. It is regarded as a game-changing technology that improves road safety, improves traffic flow, and improves the entire driving experience. Despite many research efforts on ITS applications and simulation platforms, formal semantic descriptions for intelligent transportation systems have been noticeably lacking. Rising rates of accidents suggest the necessity for a robust Smart Transport System (STS) that prioritizes improving mobility, and reliability, as well as reducing carbon dioxide and methane emissions from automobiles beyond dispersing gains throughout everywhere. Multiple traffic signal management approaches have been designed to enhance real-time traffic movement at intersections, but none have achieved seamless, congestion-free traffic movements. The creation of an interconnected unified framework that integrates and links vehicles with VANET, IoT, and AI technologies will significantly affect the establishment of an innovative reliable mobility network. Internet of Vehicles (IoV) systems will connect several automobiles to share vital information through an IoT-enabled network. Vehicle-to-infrastructure (V2I) communication is essential in developing intelligent transportation and rail traffic management systems, significantly improving road traffic safety and efficiency. A V2I system integrates transmission and analysis capabilities into vehicles and corresponding infrastructure. Wireless sensors are strategically positioned along highways or driving zones in this system. The significance of safe and effective V2X communication between automobiles and facilities expands substantially as intelligent modes of transport progress.

Runway assignment optimisation model for Istanbul Airport considering multiple parallel runway operations

Abstract The aviation industry has rapidly developed in recent years. Due to the increased number of flight operations, managing air traffic has become essential. The air traffic management system aims to reduce the air traffic control workload and use existing resources more efficiently. This study proposed a new mixed integer linear programming model to minimise the total fuel consumption during taxi operations for the runway assignment problem, comparing the actual Istanbul Airport runway assignment data. The average taxi times are calculated using the 30,000-flight operations data for each arrival and departure taxi route. Also, 47 different aircraft types are obtained using the data for the fuel consumption calculation. The International Civil. Aviation Organisation (IACO) aircraft engine emissions databank provides the fuel consumption values for each aircraft according to engine type. This approach allows our model to calculate more realistic fuel consumption for taxi operations, as each aircraft engine type has a different fuel consumption value. The proposed model is implemented at Istanbul Airport, the busiest airport in Turkey, where multiple parallel runway operations are applied. The results showed that the proposed model reduced total fuel consumption for taxi operations between 6.6% and 14.4% compared to the actual Istanbul Airport runway assignment data.

Design of urban low-carbon green transportation demonstration area in the context of "dual carbon" --Taking Wuhua District of Kunming City (Zhengyi Road and Sanshi Street area) as an example

In the context of "dual-carbon", by reconstructing the relationship between people and vehicles, optimizing spatial quality, and taking street space renovation as a starting point, we can continuously improve the travel experience of human-centered neighborhoods, build a higher-quality living environment, and guide green travel. Under this context, this study takes Wuhua District, Kunming, Zhengyi Road and Sanshi Street as a case to explore the design of urban low-carbon green transportation demonstration area under the background of "dual-carbon". By analyzing the "dual-carbon" strategy and related policies, and taking into account the current transportation situation in Wuhua District, Kunming, it is found that there are problems such as traffic congestion, chaotic parking, and poor bus routes in this area. To address these challenges, this study proposes a series of optimization measures based on successful cases at home and abroad, including improving the transportation network, strengthening non-motorized parking management, introducing intelligent traffic management systems, and optimizing the connection between bus routes and rail transit. These measures will help improve transportation efficiency, reduce congestion, promote green travel, and provide useful lessons and references for the construction of low-carbon transportation demonstration zones in other cities.