Traffic Management Applications Research Articles

A Survey on Emergency Vehicle Route Optimization and Traffic Management Application

Abstract: Ambulance delays in India result in approximately 10,000 lives lost annually, highlighting the critical need for efficient emergency response systems. To address this issue, we present the "Emergency Vehicle Priority with Route Optimization and Traffic Management Application." Leveraging advanced technology, including an Android application, Firebase, and IoT integration with traffic signals, our system dynamically calculates and optimizes ambulance routes using Google API, minimizing delays and improving overall efficiency. The application facilitates seamless coordination between ambulance drivers and traffic police officers, triggering real-time route updates and traffic signal adjustments to expedite ambulance passage. By streamlining traffic flow and enhancing emergency response coordination, our solution aims to significantly reduce ambulance delays and save lives in urban areas.

Enhancing Bangladeshi License Plate Recognition: A YOLOv8 Approach with Roboflow Integration for Accuracy and Speed Optimization

Abstract: Automatic License Plate Recognition (ALPR) systems are indispensable in modern transportation services, offering crucial benefits in traffic management, parking, toll collection, and surveillance. However, implementing ALPR in Bangladesh presents challenges due to the intricacies of Bangla characters and low-resolution CCTV images. Despite ALPR's potential to enhance safety and security, issues such as license plate variations and image quality hinder its effective implementation. This research addresses these challenges by developing a customized YOLOv8 model tailored for recognizing Bengali license plates, with a focus on precise localization, character segmentation, and script deciphering. Leveraging advanced deep learning techniques, the study aims to enhance efficiency and accuracy for applications in law enforcement and traffic management. Through a comprehensive workflow integrating Roboflow and YOLOv8, effective dataset collection, annotation, augmentation, and model training are demonstrated. System evaluation on diverse test datasets confirms the reliable detection and recognition of Bangladeshi license plates, underscoring the model's practical utility and robustness in real-world scenarios. Additionally, the research showcases notable advancements in ALPR technology specific to Bangladesh's unique context. Noteworthy is the model's exceptional performance in detecting license plates from corner angles, even across various vehicle types, as well as its ability to accurately identify plates amid challenging conditions such as broken or obscured plates and low-resolution images. Moreover, the system's proficiency in scenarios with multiple license plates, like those on buses adorned with banners, contributes significantly to improved road safety and traffic management.

Real-Time Traffic Sign Detection and Recognition System using Computer Vision and Machine Learning

The use of computer vision technology for our system has taken a huge leap and has revolutionized the way in which road safety is managed and maintained. This part of the paper represents the key components and functionalities of such a system which helps in enhancing the road safety and also in traffic management. The proposed system is meant to contain computer vision algorithms and some machine learning techniques. These will help in detecting and recognizing the traffic signs in real time video streams. With the help of deep learning models like convolutional neural networks, the proposed system can precisely identify various traffic signs present on roads. The major components of the proposed system include a camera interface which will capture real time video frames from the road, image processing modules for color space conversions, binary thresholding, contour detection and a classification module for the purpose of sign recognition. Deep learning frameworks like TensorFlow and OpenCV libraries are used for image processing and model training In the proposed system. The real time nature of this system allows the spontaneous detection and recognition of traffic signs enabling us to change the road conditions. Hence, the system contributes to prevent accidents and provide timely warnings to drivers. In addition, this system offers potential applications in traffic management and for autonomous driving system. With the help of accurate information, the system can contribute towards more efficient traffic flow and reduce congestion on roads. Overall, the system has very good impacts in road safety and efficiency. It shows advancement in road safety and traffic management.

Real-Time Vehicle Detection for Traffic Monitoring: A Deep Learning Approach

Vehicle detection is an essential technology for intelligent transportation systems and autonomous vehicles. Reliable real-time detection allows for traffic monitoring, safety enhancements and navigation aids. However, vehicle detection is a challenging computer vision task, especially in complex urban settings. Traditional methods using hand-crafted features like HAAR cascades have limitations. Recent deep learning advances have enabled convolutional neural networks (CNNs) like Faster R-CNN, SSD and YOLO to be applied to vehicle detection with significantly improved accuracy. But each technique has tradeoffs between precision and processing speed. Two-stage detectors like Faster R-CNN are highly accurate but slow at 7 FPS. Single-shot detectors like SSD are faster at 22 FPS but less precise. YOLO is extremely fast at 45 FPS but has lower accuracy. This paper reviews prominent deep learning vehicle detectors. It proposes a new integrated method combining YOLOv3 detection, optical flow tracking and trajectory analysis to enhance both accuracy and speed. Results on highway and urban datasets show improved precision, recall and F1 scores compared to YOLOv3 alone. Optical flow helps filter noise and recover missed detections. Trajectory analysis enables consistent object IDs across frames. Compared to other CNN models, the proposed technique achieves a better balance of real-time performance and accuracy. Occlusion handling and small object detection remain open challenges. In summary, deep learning has enabled major progress but enhancements in model architecture, training data and occlusion handling are needed to realize the full potential for traffic management applications. The integrated method proposed offers improved performance over baseline detectors. We have achieved 99 % accuracy in our project

TrafficGPT: Viewing, processing and interacting with traffic foundation models

With the promotion of ChatGPT to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance. These capabilities hold significant promise for their application in urban traffic management and control. However, large language models (LLMs) struggle with addressing traffic issues, especially processing numerical data and interacting with simulations, limiting their potential in solving traffic-related challenges. In parallel, specialized traffic foundation models exist but are typically designed for specific tasks with limited input-output interactions. Combining these models with LLMs presents an opportunity to enhance their capacity for tackling complex traffic-related problems and providing insightful suggestions. To bridge this gap, we present TrafficGPT—a fusion of multiple LLMs and traffic foundation models. This integration yields the following key enhancements: 1) empowering LLMs with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes. By seamlessly intertwining large language model and traffic expertise, TrafficGPT not only advances traffic management but also offers a novel approach to leveraging AI capabilities in this domain. The TrafficGPT demo can be found in https://github.com/lijlansg/TrafficGPT.git.

Implementation of Traffic System Management to Overcome Congestion at the Intersection

This study aims to determine the performance of two roads in Medan, namely Jamin Ginting Road and Dr. Mansyur Road with the change of traffic direction and to determine the application of traffic management system. After the change in traffic flow, Dr. Mansyur Road obtained the highest volume with traffic flow (Q) = 1663.4 vehicles per hour, side obstacles = 128.8 per hour, travel speed (Vs) = 6.00 m/s, Degree of Saturation (DoS) = 0.49. Then the analysis of the Jalan Jamin Ginting Road section obtained the highest volume with traffic flow (Q) = 2171.4 vehicles per hour, side obstacles = 77.6 per hour, travel speed (Vs) = 5.72 m / s, Degree of Saturation (DoS) = 0.64. As well as the application of appropriate traffic management in the area of both roads, namely applying capacity management with the application of parking space engineering for vehicles, applying priority management from minor roads to major roads, and applying traffic sign management. Keywords: Traffic management, PKJI2014.

Advanced Data Science Applications in Vehicles: A Comprehensive Review

Purpose: This research paper aims to explore the transformative impact of data science applications within the automotive industry, with a focus on the evolution of vehicles into intelligent, connected, and autonomous entities. Through an extensive literature review and analysis of recent developments, the paper seeks to provide a comprehensive understanding of the current state and future prospects of data science in vehicles.
 Methodology: Advancements in sensor technologies, such as LiDAR, HD maps, radars, location tracking and cameras, are discussed, highlighting their role in data collection for applications like advanced driver assistance systems (ADAS) and autonomous driving. Relevant citations are provided. This subsection covers the collection and transmission of real-time data through telematics systems, showcasing their importance for predictive maintenance, fleet management, and personalized insurance programs. Citations support the presented information. The deployment of edge computing for real-time data processing in vehicles is discussed, emphasizing its significance for safety-critical applications like collision avoidance. Citations are provided to support the information. This section explores the application of machine learning algorithms to predict vehicle failures, optimize fuel efficiency, and analyze driver behavior. The importance of leveraging historical data to create accurate models is highlighted with supporting citations.
 Findings: The pivotal role of data science in enabling autonomous vehicles to navigate complex environments is discussed, emphasizing the use of machine learning models for real-time decision-making. Citations support the presented information. This subsection explores the integration of data science in traffic management systems, covering dynamic traffic signal control, congestion prediction, and route optimization. Citations support the findings related to traffic management applications. The paper discusses challenges associated with widespread implementation, including data privacy concerns, cybersecurity risks, and the need for standardized communication protocols. Additionally, potential future directions are outlined, such as the integration of blockchain for secure data sharing and the development of advanced human-machine interfaces.
 Unique Contribution to Theory, Practice and Policy: This research paper provides a well-rounded contribution by seamlessly integrating theoretical concepts, practical applications, and policy considerations in the realm of advanced data science applications in vehicles.

Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor

In this study, we developed a real-time connected vehicle (CV) speed advisory application that uses public cloud services and tested it on a simulated signalized corridor for different roadway traffic conditions. First, we developed a scalable serverless cloud computing architecture leveraging public cloud services offered by Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we developed an optimization-based real-time CV speed advisory algorithm by taking a modular design approach, which makes the application automatically scalable and deployable in the cloud using the serverless architecture. Third, we developed a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called Simulation of Urban Mobility (SUMO). Our analyses based on different roadway traffic conditions showed that the serverless CV speed advisory application meets the latency requirement of real-time CV mobility applications. Besides, our serverless CV speed advisory application reduced the average stopped delay (by 77%) and the aggregated risk of collision (by 21%) at the signalized intersections of a corridor. These prove the feasibility as well as the efficacy of utilizing public cloud infrastructure to implement real-time roadway traffic management applications in a CV environment.

Resource-aware fog service placement using deferred acceptance in edge computing

Fog computing aims at reducing network latency, bandwidth and processing delay by bringing computation resources closer to the data sources. Modern IoT applications, such as smart surveillance systems, remote health monitoring applications, ambient-aware applications etc., are time-critical and computationally intensive. For such applications, it is ideal to consider an intermediate layer of processing i.e a fog layer in addition to cloud. However, processing the workload of such IoT applications with an intermediate layer of fog nodes has several challenges. The significant challenges include the mobility of end-user nodes, dynamic patterns of workload generation leading to highly-variant resource requirements and sensitive quality of service (QOS) requirements. Hence a dynamic approach is required to precisely map the service requests from end-user devices to a suitable fog node. The optimality of a fog node for service placement depends on several factors, including location, resource utilization rates such as CPU, Memory and Disk utilization % and energy consumption. Several meta-heuristic techniques and evolutionary algorithms have been proposed in the past for optimal service placement among fog nodes. This paper investigates the applicability of a game theory-based approach called Deferred acceptance (DA) for the same. The DA algorithm, originally designed as a stable matching mechanism for the marriage market is adapted for service placement problems in fog-based IoT systems. The effectiveness of optimized service placement through the DA approach is validated experimentally. The experiment is carried out by creating a test bed that mimics a fog computing-based intelligent traffic management application. The proposed approach's performance is evaluated through parameters such as the startup time for the DA approach to produce outcomes, total execution time and handshake latency. The numerical results of the DA approach are compared with those of a simple scheduling algorithm ( FIFO) and a basic genetic algorithm-based approach. The DA approach appears to perform on par with the genetic algorithm in terms of start-up time, but exceeds the genetic algorithm with respect to stability of the outcomes produced.

Spatial linear transformer and temporal convolution network for traffic flow prediction

Accurately obtaining accurate information about the future traffic flow of all roads in the transportation network is essential for traffic management and control applications. In order to address the challenges of acquiring dynamic global spatial correlations between transportation links and modeling time dependencies in multi-step prediction, we propose a spatial linear transformer and temporal convolution network (SLTTCN). The model is using spatial linear transformers to aggregate the spatial information of the traffic flow, and bidirectional temporal convolution network to capture the temporal dependency of the traffic flow. The spatial linear transformer effectively reduces the complexity of data calculation and storage while capturing spatial dependence, and the time convolutional network with bidirectional and gate fusion mechanisms avoids the problems of gradient vanishing and high computational cost caused by long time intervals during model training. We conducted extensive experiments using two publicly available large-scale traffic data sets and compared SLTTCN with other baselines. Numerical results show that SLTTCN achieves the best predictive performance in various error measurements. We also performed attention visualization analysis on the spatial linear transformer, verifying its effectiveness in capturing dynamic global spatial dependency.

One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial-Temporal Dependency.

Traffic prediction is a keystone for building smart cities in the new era and has found wide applications in traffic scheduling and management, environment policy making, public safety, and so on. Instead of creating a traffic predictor for each city, this article focuses on designing a unified network model that could be directly applied for traffic prediction in any city, by learning the essential spatial-temporal dependencies, i.e., the mutual relationship between traffic and the corresponding fine-grained road network. To achieve this goal, this article proposes a joint knowledge-and data-driven mechanism that novelly divides dependencies into three kinds of correlations, i.e., road segment, intra-intersection, and inter-intersection correlation, which capture the microcosmic, middle, and macroscopic dependencies between traffic and the road network, respectively. Specifically, we first construct traffic datasets that could cover all road segments from real-world trajectory datasets, which makes it possible to model the whole road network as a graph, with the help of fine-grained road topology. Then, we propose meta road segment learner, connection-aware spatial-temporal graph convolutional network (GCN), and multiscale residual networks for capturing the microcosmic, middle, and macroscopic dependencies, respectively. Our experiments on three real-world datasets demonstrate that our proposed method could: 1) achieve better prediction accuracy compared with several approaches and 2) capture the mutual relationship between traffic and the fine-grained road network since our model trained only using data from the source city achieves good performance when it is directly applied for traffic prediction in the target city, without any fine-tuning. The codes will be made publicly available.

A novel approach to traffic modelling based on road parameters, weather conditions and GPS data using feedforward neural networks

This article presents the development of a estimation model using an artificial neural network to estimate the traffic factor parameter, which reflects changes in travel time for a single transport connection. The modeling of traffic is highly challenging due to the complex and non-linear nature of road systems, the interactions between diverse road users, and the ripple effects of congestion. The research problem addressed is the need to consider a wide range of factors that contribute to congestion, as identified in the literature. To address this challenge, a universal Feedforward Neural Network (FNN) was designed, specifically the Multilayer Perceptron (MLP) 38–10-1 model. The input layer neurons of the MLP receive a vector of input values representing various factors such as road types, technical properties, time of day, days of the week, incidents, weather conditions, and population density. The model was trained using a dataset consisting of 41,945 records of fully completed input variables, extracted from a larger dataset of 300,000 travel time measurements collected from a real transport network mapped on a macro scale (Mazovia, Poland). During the research, the model achieved a satisfactory level of Mean Absolute Percentage Error (MAPE) for the test set (9.12%) and a Normalized Root Mean Squared Error (NRMSE) below 0.02, indicating good estimation performance. Furthermore, this work proposes specialized models with the structure of MLP 34–10-1 for individual types of roads, which also demonstrated satisfactory estimation abilities. These models have practical applications in traffic management and planning. Overall, this research addresses the research problem of modeling factors for predicting travel time changes using an artificial neural network approach. The developed models provide accurate estimations and offer potential applications in transportation systems.

Integrating Surrounding Vehicle Information for Vehicle Trajectory Representation and Abnormal Lane-Change Behavior Detection.

The detection of abnormal lane-changing behavior in road vehicles has applications in traffic management and law enforcement. The primary approach to achieving this detection involves utilizing sensor data to characterize vehicle trajectories, extract distinctive parameters, and establish a detection model. Abnormal lane-changing behaviors can lead to unsafe interactions with surrounding vehicles, thereby increasing traffic risks. Therefore, solely focusing on individual vehicle perspectives and neglecting the influence of surrounding vehicles in abnormal lane-changing behavior detection has limitations. To address this, this study proposes a framework for abnormal lane-changing behavior detection. Initially, the study introduces a novel approach for representing vehicle trajectories that integrates information from surrounding vehicles. This facilitates the extraction of feature parameters considering the interactions between vehicles and distinguishing between different phases of lane-changing. The Light Gradient Boosting Machine (LGBM) algorithm is then employed to construct an abnormal lane-changing behavior detection model. The results indicate that this framework exhibits high detection accuracy, with the integration of surrounding vehicle information making a significant contribution to the detection outcomes.

Distance in plithogenic product fuzzy graphs

The notions of distance and central concepts are crucial in any fuzzy network. In this study, we newly introduce Plithogenic μ-distance and Plithogenic geodesic distance in Plithogenic product fuzzy graphs (PPFGs), which are multi-attribute graphical representations. Using these metrics, central concepts such as eccentricity, radius, diameter, Plithogenic μ-centre and Plithogenic geodesic centre in PPFGs are newly discussed. An investigation is also made to study the relation between these concepts with an application in traffic management.

Traffic safety performance evaluation in a connected vehicle environment with queue warning and speed harmonization applications

With the increased adoption of connected vehicle (CV) technologies, safety information is becoming increasingly available to drivers. This study investigates three main questions (1) Do CV-based traffic management applications improve safety on roadways with existing infrastructure-based traffic management systems? (2) Can combining two CV technologies have a greater impact on safety than a single CV technology? and (3) Do geometric and traffic composition factors impact the efficiency of CV technologies? We applied a rarely-used CV dataset and conducted a comprehensive simulation analysis of varying conditions and CV penetration rates that studies have not considered. Two CV applications (queue warning and speed harmonization) implemented in the Intelligent Network Flow Optimization experiment in Seattle, WA were evaluated. Results showed that driver safety performance, based on speed metrics (standard deviation and percentage of extreme values) improved under the CV driving conditions. Combining conventional variable speed limit systems with queue warnings also improved safety for CV drivers. Furthermore, the implementation of a single CV application (queue warning) showed positive changes in the aforementioned speed metrics, congestion mitigation, and reduced conflicts. With the two CV applications combined, no significant differences were observed. Additional tests investigated the impacts of lane changes and roadway attributes on safety in the CV environment.

Remote sensing traffic scene retrieval based on learning control algorithm for robot multimodal sensing information fusion and human-machine interaction and collaboration.

In light of advancing socio-economic development and urban infrastructure, urban traffic congestion and accidents have become pressing issues. High-resolution remote sensing images are crucial for supporting urban geographic information systems (GIS), road planning, and vehicle navigation. Additionally, the emergence of robotics presents new possibilities for traffic management and road safety. This study introduces an innovative approach that combines attention mechanisms and robotic multimodal information fusion for retrieving traffic scenes from remote sensing images. Attention mechanisms focus on specific road and traffic features, reducing computation and enhancing detail capture. Graph neural algorithms improve scene retrieval accuracy. To achieve efficient traffic scene retrieval, a robot equipped with advanced sensing technology autonomously navigates urban environments, capturing high-accuracy, wide-coverage images. This facilitates comprehensive traffic databases and real-time traffic information retrieval for precise traffic management. Extensive experiments on large-scale remote sensing datasets demonstrate the feasibility and effectiveness of this approach. The integration of attention mechanisms, graph neural algorithms, and robotic multimodal information fusion enhances traffic scene retrieval, promising improved information extraction accuracy for more effective traffic management, road safety, and intelligent transportation systems. In conclusion, this interdisciplinary approach, combining attention mechanisms, graph neural algorithms, and robotic technology, represents significant progress in traffic scene retrieval from remote sensing images, with potential applications in traffic management, road safety, and urban planning.

LICENSE PLATE DETECTION USING MACHINE LEARNING

A crucial technology with many applications in traffic management, security, and law enforcement is license plate detection utilizing machine learning. In this abstract, a machine learning-based method for precisely locating and identifying license plates in pictures and video streams is presented. Data gathering, preprocessing, object detection using deep learning methods like YOLO, license plate recognition using optical character recognition (OCR), and post-processing to improve findings are all part of the mPlate Detection, OpenCV module, OCR Technology.ethodology. Metrics including precision, recall, F1-score, are used to assess performance. The system offers enormous promise for improving efficiency and safety in numerous sectors when it is implemented in real-world circumstances, whether on edge devices or in the cloud. This abstract emphasizes how important machine learning is for automating difficult processes, solving real-world problems, and improving CV. The trained model can also be used in a variety of applications, including security systems, toll booth monitoring, parking enforcement, traffic management systems, and parking enforcement. The system can be implemented on edge devices for real-time processing or in the cloud for scalability depending on the individual requirements of the application.

A constrained spectral clustering method for lane identification using trajectory data

The rapid development of information and communications technologies acts as an enabler for the successful implementation of vehicle automation and advanced traffic management applications. In particular, the appearance of new sources of high-resolution trajectory data, such as videos obtained from drones, provides an opportunity to build accurate maps and enrich applications in traffic research at an unprecedented resolution. However, existing methods cannot handle certain features, such as, among others, accurate of road lane identification.This paper proposes a constrained spectral clustering method to identify lane information from high-resolution trajectory data. Contrary to state-of-the-art methods, such as the Gaussian mixture model, the proposed method is directly applicable to two-dimensional trajectory data, without assuming a constant number of lanes characterised by the same lane width. The trajectory data is clustered by taking into account the neighbourhood distances and prior knowledge via defining so-called must-link and cannot-link constraints, which significantly improve the clustering results, especially in cases where the number of lanes or the lane width changes. The proposed method has been evaluated through numerical experiments using data obtained from drone videos, and the results indicate that the method performs well on complex road segments, even in the presence of a varying number of lanes or lane-changing manoeuvres.

Overview of Research of Machine Learning in Air Traffic Management

As the demand for air traffic has grown at a fast pace in recent years, the efficiency and safety of air traffic management is facing greater challenges with limited airspace resources. As an important part of the civil aviation air traffic system, the existing air traffic management capability is no longer able to meet the demand of air traffic growth. Machine learning, as an advanced method of current computer modelling, has shown good application value and promise for application in air traffic management. This paper starts by introducing the application methods and modelling process for machine learning in air traffic management, followed by the current research status of machine learning in three areas, namely air traffic flow management, air traffic services and airspace management, respectively, and finally points out the challenges and further development outlook of applying machine learning to air traffic management. Overall, the introduction of machine learning into air traffic management represents a major trend with significant implications for its development.

Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information

Traffic management and control applications require comprehensive knowledge of traffic flow data. Typically, such information is gathered using traffic sensors, which have two basic challenges: First, it is impractical or impossible to install sensors on every arc in a network. Second, sensors do not provide direct information on origin-to-destination (O–D) demand flows. Consequently, it is essential to identify the optimal locations for deploying traffic sensors and then enhance the knowledge gained from this link flow sample to forecast the network’s traffic flow. This article presents residual neural networks—a very deep set of neural networks—to the problem for the first time. The suggested architecture reliably predicts the whole network’s O–D flows utilizing link flows, hence inverting the standard traffic assignment problem. It deduces a relevant correlation between traffic flow statistics and network topology from traffic flow characteristics. To train the proposed deep learning architecture, random synthetic flow data was generated from the historical demand data of the network. A large-scale network was used to test and confirm the model’s performance. Then, the Sioux Falls network was used to compare the results with the literature. The robustness of applying the proposed framework to this particular combined traffic flow problem was determined by maintaining superior prediction accuracy over the literature with a moderate number of traffic sensors.