Traffic speed estimation based on normal location updates and call arrivals from cellular networks

Information and communication technologies have improved the quality of Intelligent Transportation Systems (ITS). The real-time traffic information has traditionally been collected via stationary vehicle detectors or GPS-based probe cars. Compared to the traditional ways, estimating traffic information from Cellular Floating Vehicle Data (CFVD) is more cost-effective, and easier to acquire. In this paper, this study proposes a novel approach to evaluate the relation of call arrival, handover, traffic flow, and traffic density. Moreover, the traffic speed is estimated by the proposed approach according to CFVD. Through the analytical analysis, this study analyzes the effects of traffic information (e.g. traffic flow and vehicle speed) and communication behaviors (e.g. call arrival rate and call holding time) on handovers and call arrivals. In the simulation, this study compares the estimated traffic information with the real traffic information. The experiment results show that the accuracy of traffic speed estimation is 89.75%. Therefore, the proposed approach can be used to estimate traffic speed from CFVD for ITS.

Abstract- With the rapid growth of urban populations and the increasing number of vehicles on the road, traffic congestion has become a significant problem in metropolitan areas. Conventional traffic control systems, which rely on pre-set signal timers, often fail to address real-time traffic conditions effectively, leading to inefficient traffic flow, increased fuel consumption, and environmental degradation. The paper “Smart Traffic Control System Based on Traffic Density Using YOLO” introduces a novel approach to address this issue by integrating artificial intelligence and computer vision into traffic management. The proposed system utilizes the YOLO (You Only Look Once) object detection algorithm to detect and classify vehicles in real-time from live video feeds captured at intersections. YOLO, known for its speed and accuracy in object detection tasks, calculates the vehicle density in each direction. Based on the detected density, the system dynamically adjusts the green light duration for each lane, ensuring that the direction with higher traffic receives longer green light periods. This method aims to optimize signal switching and minimize overall vehicle wait time. The paper outlines the complete architecture of the system, which includes modules for video input processing, vehicle detection using YOLO, traffic density computation, and adaptive signal timing control. By leveraging deep learning, the system enhances decision-making capabilities without relying on costly hardware like inductive loops or infrared sensors. This review evaluates the effectiveness and innovation of the proposed system, comparing it with traditional and sensor-based models. It highlights the advantages of using YOLO, such as real-time processing, high accuracy, and scalability. Additionally, the review discusses the system’s limitations, such as dependency on video clarity and environmental conditions, and proposes areas for future enhancement, including integration with cloud computing and edge AI for broader deployment. Keywords- Smart Traffic Control System, YOLO Object Detection, Traffic Density Estimation, Real-Time Traffic Monitoring, Adaptive Traffic Signal Control, Vehicle Classification ( Cars, Bikes, Buses, Trucks, Rikshaws), Intelligent Transportation System.

Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.

Traffic estimation and traffic simulation are essential parts of the intelligent transportation system. In recent years, massive traffic data has brought many data-based traffic estimation methods, but few are utilized in traffic simulation. The cause is that the data-based traffic simulation has high requirements for data quality, needing the trip information of all vehicles, not met by common traffic data. Fortunately, the electronic registration identification (ERI) data of the vehicle can satisfy. Therefore, we utilize ERI data to study the traffic estimation and simulation for the road network. The core of this research is the travel time estimation model, which is constructed by combining traffic theory models and the deep neural network. The traffic theory models are a group of linear models, which represent the relationship between traffic flow or traffic density and travel time. The deep neural network can extract the temporal and spatial characteristics of the road network traffic by Moving Average Convergence-Divergence (MACD) and Graph Convolutional Network (GCN), respectively. We named the travel time estimation model MGCN. Then, we employed MGCN for traffic simulation. In the experiment section, we employed Chongqing ERI data to verify the research content. Compared with some baseline methods, our method is superior in travel time estimation and traffic simulation.

Accurate and timely estimation of freeway traffic speeds by short segments plays an important role in traffic monitoring systems. In the literature, the ability of machine learning techniques to capture the stochastic characteristics of traffic has been proved. Also, the deployment of intelligent transportation systems (ITSs) has provided enriched traffic data, which enables the adoption of a variety of machine learning methods to estimate freeway traffic speeds. However, the limitation of data quality and coverage remain a big challenge in current traffic monitoring systems. To overcome this problem, this study aims to develop a hybrid machine learning approach, by creating a new training variable based on the second-order traffic flow model, to improve the accuracy of traffic speed estimation. Grounded on a novel integrated framework, the estimation is performed using three machine learning techniques, that is, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). All three models are trained with the integrated dataset including the traffic flow model estimates and the iPeMS and PeMS data from the Utah Department of Transportation (DOT). Further using the PeMS data as the ground truth for model evaluation, the comparisons between the hybrid approach and pure machine learning models show that the hybrid approach can effectively capture the time-varying pattern of the traffic and help improve the estimation accuracy.

Vehicle counting is a process to estimate traffic density on roads to assess the traffic conditions for intelligent transportation systems (ITS). Real-time traffic management systems have become popular recently due to the availability of high end cameras and technology. The present traffic management systems focus on speed detection, signal jumping, zebra crossing but not on traffic density estimation. Proposed video-based vehicle counting and tracking method using a video captured on CCTV and handheld mobile cameras. The system can be used in smart cities to create smart traffic light signals, in which duration of each signal depends on real time vehicle density in a particular lane of road. Vehicle counting is performed in two steps: the captured video is sent to You Only Look Once (YOLO) based deep learning framework to detect, count and classify the vehicles. Multi vehicular tracking is adopted using Deep SORTalgorithm to track the vehicles in video frames. Model was trained for six different classes, using Google Colaboratory. Datasets of vehicles specifically pertaining to Indian roads environment is considered for implementation. The performance of the model was analyzed, proposed model has tested and obtained an average counting accuracy of 86.56% while the average precision is 93.85%. The model can be implemented for ascertaining the traffic density on roads and this provides knowledge for infrastructural development to authorities. It can also be an integral part of smart city projects to develop intelligent and smart traffic surveillance system.

Multiple model stochastic filtering for traffic density estimation on urban arterials

Urban traffic situation information is the basis of effective traffic guidance and traffic control. In order to promote communication and practical applications of traffic information, then can provide full, accurate, real time and sufficient traffic information service to public, government and correlative enterprises, in this paper, the authors proposed the dynamic traffic information collection and processing methods of intelligent transportation systems (ITS) common information platform (CIP) based on floating car technology. According to the characteristics of the ITS CIP data collection, they were required large scale, accurate and dynamic data collecting. A complete set of dynamic traffic information processing methods based on floating car technology were studied, including road data storage by two queues, error data elimination by taximeter, map matching by region dividing method and boundary rectangle, and traffic condition calculation by circle queues and A* algorithm. The application of the methods in the ITS CIP of Guangzhou City proved that the methods were quite effective and valuable to be popularized.

With the rapid advancement of big data and multi-sensor fusion technology, it becomes easier to obtain traffic flow information. According to these related data, some methods are designed to forecast the traffic flow, which can provide decision support for scheduling and optimizing the intelligent transportation systems (ITS). Thus, the forecasting methodology of traffic flow becomes one of the most popular fields of ITS. To predict traffic flow more accurately, we propose a novel LSTnet-GRU-A model, which adds an attention module to the progressive LSTnet model, then, by regulating the weight of the hiding state, the LSTnet-GRU-A model is developed to model multi-dimensional traffic flow data. The dataset MITV is used as the sample library and the performance of different models is given to show the superiority of LSTnet-GRU-A. To optimize the model, the strategies of learning rate automatic decline and early stopping are adopted. Furthermore, the five checkpoints of our model are saved for snapshot ensembles to generate the optimal performance. Multiple experimental results prove that the LSTnet-GRU-A proposed in this paper achieves SOTA performance consistently.

A major success factor for the implementation of intelligent transportation systems (ITS) is knowledge about the user's reaction and response to such technologies. The objective of each development process is to achieve a broad acceptance among those using the system. User acceptance is a key success factor, so it should be taken into account in early development stages. This applies especially for technical solutions requiring frequent interactions, for example, to influence the driver's behavior toward a more efficient driving style. The complexity of developments in the field of ITS raises the question: At what point do users start feeling overloaded and no longer able to handle all functionalities, ultimately rejecting using them? A Europe-wide study was carried out (N = 5,807) focusing on drivers’ perceptions of cooperative systems that offer assistance on fuel efficiency. The advanced driver assistance systems (ADAS) used in the study have the potential to change driving behavior in the long term and thus have a sustainable impact to reduce fuel consumption and traffic emissions. ADAS functionalities provide up-to-date traffic information, which is disseminated by traffic management to help traffic flow in a more energy efficient way. Six ADAS functions were assessed in pre-, on-, and post-trip driving situations. The chosen research framework was used to compare the users’ perception using a set of relevant acceptance factors that were developed in this study. The results showed significant effects between acceptance factors and the influencing variables. The interrelations between acceptance factors were analyzed using a research model developed in this study. The concept proves to be suitable for studying acceptance of ITS solutions aiming to save fuel.

With the rapid development of intelligent transportation systems, vehicle detection and traffic flow statistics have become increasingly critical in traffic management and the construction of smart cities. However, complex weather conditions lead to challenges in vehicle detection algorithms, increasing false negatives and false positives. Additionally, the rapid growth of vehicle ownership makes traditional algorithms inadequate for meeting practical application needs. Therefore, this paper proposes a collaborative optimization-based method combining YOLOv11 for vehicle detection and traffic flow analysis under complex weather conditions. Vehicle information is accurately extracted through object detection and tracking methods, and further analyzed using Logistic Regression to assess traffic congestion. The main contributions of this study are: (1) Integration of the CBAM attention mechanism into YOLOv11, enhancing the vehicle feature extraction ability through collaborative channel and spatial attention, thereby improving interference resistance under complex weather conditions. (2) Adoption of the Shape-IoU loss function, with dynamic weight allocation to optimize the accuracy of detecting smaller vehicles and the precision of bounding box regression in images where vehicles are located at a greater distance. (3) Construction of a traffic congestion prediction model based on the detection results, integrated with Logistic Regression. The proposed method's effectiveness is validated on the public dataset UA-DETRAC. Through the synergistic effect of the CBAM attention mechanism and Shape-IoU loss function, the improved model's mAP50-95 shows a 0.3 percentage point increase over the YOLOv11 baseline, providing efficient and reliable technical support for intelligent transportation systems. This study enhances the prediction performance of traffic states under complex weather conditions by effectively calculating traffic flow parameters such as vehicle count and density, thus offering foundational data support for traffic management decision-making. The research conclusions are of practical significance for urban traffic improvement.

Intelligent Transportation Systems (ITS) combine emerging communication, computer, and system technologies to deliver intelligent road traffic services and optimize decision making within the transportation infrastructure. The advancement of connected vehicles, which generate dynamic data through wireless communications, enables ITS to improve their efficiency, especially in Traffic Signal Control (TSC), which is the backbone of traffic flow scheduling. However, wireless communications channels are vulnerable to various types of cyberattacks and can pose serious threats to dynamic TSC systems. Attackers may attempt to manipulate normal traffic flows and cause severe traffic congestion. Deep Reinforcement Learning (DRL) is a powerful technique that has been used to improve TSC systems performance in real-time environments. However, it can be used by attackers to exploit the dynamics of the ITS and learn the optimal attack policy under the lack of deterministic information about system behavior. In this work, to highlight and exploit existing vulnerabilities in TSC systems, we leverage DRL to create an intelligent Sybil attack on a traffic intersection, wherein connected vehicles with fake identities are optimally placed to alter traffic signal timings by corrupting traffic data. The results show that this attack leads to substantial increase in the vehicles’ travel time and yields disastrous traffic congestion, especially if carried out for a prolonged period of time, which will give rise to serious problems such as higher fuel consumption and air pollution in heavily dense cities. In the presence of such intelligent attacks, the design assumptions of existing TSC systems become highly questionable.

Intelligent transportation systems (ITS) depend on precise vehicle identification and speed estimation to provide real-time traffic monitoring, accident avoidance, and effective traffic flow management. This paper, a novel deep neural network (DNN)-based method for real-time vehicle detection and speed estimate is presented. For accurate vehicle detection, we use cutting-edge object detection models like YOLOv5, and we use computer vision algorithms to determine a vehicle's speed from video frames. The technology operates reliably in a range of environmental circumstances and processes real-time traffic video streams. High detection accuracy (above 95%) and accurate speed estimation with an average error margin of less than 5 km/h are demonstrated by the experimental findings. A scalable and economical architecture for traffic surveillance applications is provided by the suggested approach.

Since last decades world, predominantly urban areas, is experiencing huge voluminous road traffic growth, resulting in heavy congestion, air pollution, accidents, and poor efficiency. Many people every day are the victims of this poor management of tremendous traffic. Since many years, there had been some automation in managing the traffic namely Electronic Toll Collection (ETC), Electronic parking payment, normal traffic information etc. However, there are little efforts for making the system more advanced. Recently, several kinds of research are being launched by many countries to develop Intelligent Transport System (ITS), with the objectives to minimize congestion, ensure better safety, reduce air pollution etc. ITS are planned to establish robust communication between vehicle to vehicle (V2V), vehicle to pedestrian (V2P), vehicle to infrastructure (V2I), and vehicle to network (V2N). Initially, for communication links ITS, deploys Wi-Fi network, but because of limited capacity and huge requirement, some links use 5.8 GHz radio frequency for such purposes. IEEE, International Telecommunications Union (ITU) and other advanced research organisations are studying 700 MHz band and mm frequency bands for advanced ITS. ITS is poised to use Information & Communication Technology (ICT) networks for such purposes. ITU has established Study Groups/study questions for addressing ITS issues. The World Radio Conference (WRC-2019) has made a Recommendation 208 regarding harmonization of frequency bands for ITS applications. This paper presents a comprehensive overview of ITS, its applications and analysis etc. The radio frequency spectrum aspects and role of 5 G in ITS are also described in detail.

Local data are essential for the analysis of abrupt conditions in traffic flow. In this research, we address the shortcomings of real-time traffic flow detection data, which inadequately represent the density characteristics of vehicular traffic. To mitigate this issue, we establish mathematical relationships among traffic flow density, time headway, and vehicle speed, based on the fundamental characteristics of traffic flow dynamics. This framework allows for the estimation of density across various road segments. Recognizing the nonlinear dynamics that characterize traffic flow, we introduce a microscopic traffic flow cusp catastrophe potential function that incorporates headway, utilizing cusp catastrophe theory alongside microscopic traffic flow parameters to analyze real-time variations in traffic flow potential characteristics. In the experimental section, we developed fitting regression functions for traffic flow parameters using road detection data from the cities of Nanjing and Yueyang in China. We also compared outcomes derived from the theoretical model with the parameter estimation results obtained from the data, which facilitated the calculation of traffic flow characteristics, such as cusp density and volume over a one-month period. The results derived from the proposed potential function exhibit the functions’ greater capacity to capture the complex fluctuations in traffic flow compared to traditional traffic potential functions, thereby providing a valuable instrument with which to evaluate traffic flow characteristics in conjunction with real-time monitoring data.