Vehicle Tracking System Research Articles

Vehicle tracking system using working sensors of a partially functional android mobile phone

Vehicle tracking has found its applications in a variety of domains. Most of the currently available vehicles tracking systems are based upon using some expensive tracking units that employ only GPS signals to determine position and have very high monthly subscription charges from their service providers. To reduce the cost for such applications, this paper proposes the use of a partially damaged or low-cost android device with GSM capability and some other useful working sensors to periodically report the location and then display and monitor the determined location set on a remote digital map. This device can be integrated with a vehicle and made to boot up and start tracking automatically as soon as the vehicle’s ignition is turned on and stop tracking and shutdown automatically after the vehicle’s ignition is turned off. These devices may find application in fleet management, public transport systems, etc. as discussed inside this paper.

Refined nonlinear fractional derivative model of vehicle-track coupling dynamics

The coupled vehicle-track system (CVTS) dynamics have been extensively investigated for decades. However, the calculation accuracy of prevailing vehicle-track coupling models needs to be improved in the high frequency range due to the inappropriate model simplification and neglect of material nonlinearity. In this study, we propose a refined numerical model of the CVTS that considers the nonlinear properties of the railpads and primary suspension using the fraction derivative Zener model. Furthermore, we more realistically simulate the wheelset, rail and railpad configuration with the elastic axle, solid finite element and surface-support models, respectively, and improve the computation efficiency by employing the mode superposition method. The results demonstrate that the refined CVTS model is more accurate than the classical model in simulating vehicle-track coupling dynamics above 2 kHz. In particular, there are significant differences in the dynamic response of the elastic wheelset model compared to the rigid model over a broad frequency range, with an 11% difference in the bogie acceleration response at the first dominant frequency. When the railpads are modeled using the surface-support model, the rail acceleration differences exceed 41% near 1 kHz and 44% near 2650 Hz, compared to the point-support model. Additionally, the rail response at various locations across the rail cross section can be calculated using the finite element method in this refined model. Overall. the proposed CVTS model provides high accuracy and efficiency for random vibration analysis, especially in the high frequency domain.

Smart Vehicle Transportation System Using Node MCU

In this paper, vehicle accident monitoring and tracking systems are implemented using IOT webpage acting as a medium for data transfer and visualization. The system is developed to monitor various driver help parameters like seatbelt earing, speed indication, and vehicle parameters like GPS Module, the distance between the vehicles and tracking of the live location of the Vehicle. The Ultrasonic sensoris placed in the front part of the vehicle, if any two vehicles draw near to one another then an alert message is sent to the SMS through IOT Webpage application. The seatbelt sensor is placed in the side part. When the panic button is pressed, the panic alert messageis send to SMS(IOTWEBPAGE) The developed system takes care of vehicles and s driver’s safety. Key Word: NODE MCU,IOT Webpage, GPS, Sensors.

A novel vehicle-track coupled dynamics model with disc brake systems: modelling and validation

This study established a novel vehicle–track coupled dynamics model (VTCDM) considering the disc brake system of a high-speed train (HST). The structure parameters of each friction block are applied to compute the non-linear friction forces and torques. This more elaborate model enables detailed investigations on the coupled mechanical system excited by the brake sliding friction, wheel-rail rolling friction and track irregularity. Further, it is validated by comparing with the experimental results from the aspects of time domain, frequency domain, Poincaré map and phase plane. Results indicate that the brake system substantially influences the vehicle–track system (VTS) and causes strong non-linear characteristics. Furthermore, this model can be used for tribological and dynamics-related assessment, like braking squeal and friction-induced vibration.

Out-of-round railway wheels and polygonisation

Out-of-roundness in railway wheels, in particular polygonal wear resulting in regular, multi-lobed, out-of-round wheels, has become a significant problem in recent years. It is of concern to railway operators due to the increased noise and vibration it can cause. This polygonisation is caused by cyclic wear but the exact mechanism leading to this type of wear is not fully understood. It appears to be the result of dynamic linking between a resonance or other periodic excitation in the coupled vehicle–track system and the existing wear at the wheel. This paper reviews the developing body of research being carried out in many countries by research groups, manufacturers and operators. Some examples of polygonisation on different types of railway vehicles are reported including feight trains, urban transit trains and high-speed trains. The main theories for the formation mechanisms are presented and the current measurement methods, computer simulation techniques and the effects and potential mitigation methods are reviewed. In parallel, the mechanisms and consequences of discrete wheel tread irregularities, such as wheel flats and material fall-out due to rolling contact fatigue cracking, are addressed.

Developed Smart Vehicle Tracking System using GPS and GSM Modem

 Fast technological development has streamlined daily living. On the other hand, as a result of a shortage of emergency services, the growth of technology has also increased the incidence of traffic accidents, which cause enormous loss of life and property. Systems for monitoring vehicles and detecting accidents are highly dependable and secure, and they are especially useful for remotely monitoring vehicle activity. In this paper, based on the Global Positioning System (GPS) and the Global System for Mobile (GSM) Communication has been implementing, an effective system for monitoring vehicle movement and identifying any accidents that occur has been established GSM. The suggested method successfully made use of widely available hardware elements that integrate a smartphone and a vehicle unit that is mounted within the car. The proposed system would use General Packet Radio Service (GPRS) to automatically communicate and store vehicle data to the Internet of Things (IoT) every 10 seconds. In addition, the system will create an SMS Short Message Service (SMS) and send it to the user's smartphone in response to an SMS request received with the vehicle's current position information. At the user side, it has been an android smartphone for software application connected to the Thinger.io platform that has been used to view up-to-date car information. Only the authorized user who has access to the Thinger.io platform and the login and password may access this application due to its security. The suggested system's accuracy has been evaluated. The findings show that the suggested method can follow the vehicle high accurately.

Grinding limit and profile optimization in the high-incidence section of rail corrugation in mountainous city metro

As the most common and direct control method for rail corrugation, the rail grinding can effectively improve the service life of rails, but requires scientific theoretical guidance to achieve the efficient grinding. Therefore, it is necessary to formulate corresponding grinding limits and profile optimizations for different characteristics of rail corrugations. According to the on-site investigation of rail corrugation in the overlapping section of horizontal-vertical curves of the mountainous city metro, the corresponding dynamic model of vehicle-track system is constructed. Through the safety evaluation of the dynamic characteristics of the vehicle-track system, the safety limit of the rail corrugation grinding is preliminarily determined. Then, based on the wheel-rail friction-coupled vibration theory, the finite element model of the wheel-rail system is built. By exploring the wheel-rail friction-coupled vibration characteristics in the corrugated section with typical characteristics, the optimal limit of rail corrugation grinding is further confirmed. Finally, the treatment effects before and after rail grinding are compared using the profile optimization. Results show that when the corrugation wavelengths in the overlapping section are 30 mm, 40 mm, 50 mm, 60 mm, and 70 mm, the safety limits for rail corrugation grinding are 0.03 mm, 0.04 mm, 0.05 mm, 0.08 mm, and 0.15 mm. For the typical rail corrugation with the wavelength of 50 mm, the optimal limit of the rail corrugation grinding is 0.02 mm. Moreover, the earlier the rail corrugation is polished, the better the relative optimization effect is, but it is necessary to consider the grinding cost for targeted grinding.

Effect of the wheelset structure on short-pitch corrugation on tight curved track of subway

Short-pitch corrugation is a very common and frequent wear pattern on tight curved track of subway with complicated formation mechanism and various influencing factors. Service failures of structure components of the vehicle-track system and wheel-rail noise have been associated with short-pitch corrugation. Effective control measures against short-pitch corrugation are still limited. This study was driven by developing mitigation measures of the short-pitch corrugation by investigating the effect of the wheelset structure on rail corrugation. A 3D wheel-rail steady-state rolling contact finite element model was established with the consideration of the wheel inertia. The mixed Lagrangian/Eulerian method was used to describe the steady-state rolling of the wheel. Frequency domain characteristics of the friction-induced vibration of the wheel-rail system were studied by using complex eigenvalue analysis, and the relationship between the friction-induced vibration of the wheel-rail system and rail corrugation was revealed. Based on the stability analysis of wheel-rail system, the influence of the wheelset structure on short-pitch corrugation was studied. The numerical simulation results show that the shape of wheel web presents evident influence on the friction-induced vibration of wheel-rail system. The wheel-rail system with S-shape web is more prone to unstable than that with straight web. Rail corrugation is more likely to occur in the wheel-rail system with S-shape web. Further studies show that the axial distance between the rim and hub has a remarkable impact on the friction-induced vibration of the wheel-rail system. A significant reduction in the probability of rail corrugation can be achieved by reducing the axial distance between the rim and hub. When the rim-hub axial distance of the wheel with S-shape web is less than 0 mm, the friction-induced vibration of wheel-rail system will disappear, which means that the short-pitch corrugation caused by the friction-induced vibration of the wheel-rail system will no longer occur. As for the straight-web wheel, the friction-induced vibration will disappear when the axial distance is less than 6 mm. The rim thickness also has an obvious influence on the friction-induced vibration of the wheel-rail system. The probability of the friction-induced vibration increases with the decrease of the rim thickness, which indicates that the speed of rail corrugation will increase with the aggravation of wheel wear.

Long-Range Traffic Monitoring Based on Pulse-Compression Distributed Acoustic Sensing and Advanced Vehicle Tracking and Classification Algorithm

We introduce a novel long-range traffic monitoring system for vehicle detection, tracking, and classification based on fiber-optic distributed acoustic sensing (DAS). High resolution and long range are provided by the use of an optimized setup incorporating pulse compression, which, to our knowledge, is the first time that is applied to a traffic-monitoring DAS system. The raw data acquired with this sensor feeds an automatic vehicle detection and tracking algorithm based on a novel transformed domain that can be regarded as an evolution of the Hough Transform operating with non-binary valued signals. The detection of vehicles is performed by calculating the local maxima in the transformed domain for a given time-distance processing block of the detected signal. Then, an automatic tracking algorithm, which relies on a moving window paradigm, identifies the trajectory of the vehicle. Hence, the output of the tracking stage is a set of trajectories, each of which can be regarded as a vehicle passing event from which a vehicle signature can be extracted. This signature is unique for each vehicle, allowing us to implement a machine-learning algorithm for vehicle classification purposes. The system has been experimentally tested by performing measurements using dark fiber in a telecommunication fiber cable running in a buried conduit along 40 km of a road open to traffic. Excellent results were obtained, with a general classification rate of 97.7% for detecting vehicle passing events and 99.6% and 85.7% for specific car and truck passing events, respectively.

Multiple vehicles tracking and detection using weight high order singular value decomposition dimensionality reduction and double classifiers

Controlling and managing city traffic is one of them. In order to use image processing to prevent accidents on the road, vehicle tracking and detection are essential. By following moving objects, surveillance video monitoring and human activity recording are carried out. By taking this into account, a useful technique for image processing that detects automobiles from the image is suggested. For numerous vehicle tracking and detection systems, the ECNN-SVM (Enhanced Convolution Neural Network with Support Vector Machine) has just been introduced. However, the larger dimensional data space and inaccurate edge recognition make this system’s performance difficult. The WHOSVD (Weight High Order Singular Value Decomposition) approach, which reduces the dimension and breaks up the positive and negative training picture samples, is established to improve training speed and visual vehicle recognition. To effectively identify the edges at corners, improved canny edge detection is used for edge detection. Mean Kernel Fuzzy C Means (MKFCM) clustering algorithm-based three-dimensional bounding box estimation is used to identify the vehicle items. By merging the feature value of samples with their class labels, the Speed Factor Based Cuckoo Search Algorithm (SFCSA) is introduced for feature selection. The WHOSVD algorithm was used as the input for the enhanced convolutional neural network (ECNN), which is introduced for low-dimensional space and is used for vehicle detection and tracking. Occlusion problems are resolved and target features are further identified using a machine learning classifier. For common algorithms like CNN+SVM, Support Vector Machine (SVM), and the proposed technique, experimentation is done in regards to the metrics of accuracy, f-measure, precision, and recall for performance evaluation.

A new method to derive rail roughness from axle-box vibration accounting for track stiffness variations and wheel-to-wheel coupling

Railways require frequent inspection to maintain acceptable levels of rail roughness. Accelerometers mounted on in-service railway vehicles potentially offer a cost-effective method of near-continuous roughness monitoring by using a transfer function in the frequency domain to derive rail roughness spectra from measurements of axle-box vibration. This paper addresses two phenomena that currently limit the effectiveness of such a method: the variation in track support stiffness along the track, which is the least known property of the vehicle-track system, resulting in a variable transfer function; and the vibration coupling between wheels, which limits the effectiveness of a transfer function based on a single wheel. A new method is presented that estimates the track stiffness by curve-fitting a transfer function to the peak in the axle-box acceleration spectrum associated with the so-called ‘P2’ resonance of the wheel-rail system. Wheel-to-wheel coupling is addressed by refining the transfer function to account for multiple wheels running on the same rail. Axle-box acceleration is also affected by wheel roughness, and the use of a comb filter is briefly demonstrated to mitigate this effect. The developments are evaluated by both numerical simulation and measurement trials conducted on London Underground’s Victoria line.

From Model to Algorithms: Distributed Magnetic Sensor System for Vehicle Tracking

A novel vehicle localization and tracking methods are presented based on magnetic anomaly detection by distributed magnetic sensors. First, taking advantage of total magnetic field, in this article, we propose a total field matching (TFM) method that is immune of rotational vibrations to perform target localization. Instead of directly inverting the nonlinear magnetic dipole equations, we use the TFM approach to find the suboptimal target position, and then apply the linear Kalman filter to tail after the target. Because the relationship is linear between the target dynamics and the localization equations. A case study is performed by simulation to result in an estimated trajectory of ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϕ</i> ) = (70.8 m, 44.9°) that agrees well with the real one of ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϕ</i> ) = (70.5 m, 45°). For a vehicle tracking, the outdoors experiment results show good estimation accuracy based on four different sensor networking configurations.

젯슨 나노 기반의 차량 추적 캔위성 시스템 개발

본 논문은 인공지능 알고리즘을 운용할 수 있는 고성능 소형 컴퓨터인 젯슨 나노를 기반으로 하여 차량 추적 기능을 수행하는 캔위성 시스템을 제안하는 내용이다. 캔위성 시스템은 캔위성과 지상국으로 구성하는데, 캔위성이 대기권 내에서 낙하하면서 센서를 통해 수집한 데이터를 무선통신으로 지상국에 전송하는 임무 구조를 가진다. 기존에 연구된 캔위성들은 수집한 데이터를 그대로 지상국에 전송하는 임무만 수행하는, 단어 그대로 단순 데이터 수집기라는 한계점을 갖고 있었다. 본 논문에서 제안하는 젯슨 나노 기반의 캔위성은 학습된 신경망 모델을 이용하여 상공에서 촬영한 영상에서 차량의 위치를 실시간으로 탐지 후, 2축 모터를 이용하여 카메라를 움직여 차량을 추적하는 기능을 수행한다. 제안한 캔위성 시스템의 성능은 자체 제작한 영상 데이터셋을 이용해 차량 추적의 정확도와 속도를 측정하여 검증하였다.

The GPS and GSM Based Office Cab Monitoring, Alerting and Logging System

To track a vehicle's movement from any location at any time, a tracking system for automobiles is quite helpful. To observe the movement of any equipped vehicle at any time from any location, an effective mechanism for vehicle navigation is created and put into use. The suggested solution effectively utilised the widely used combining technology that connects an Arduino UNO-equipped smartphone. It is inexpensive and simple to make in comparison to other options. One of the most popular methods for vehicle tracking, the built-in gadget uses the Global Positioning System (GPS) and Global System for Mobile Communication (GSM) technologies. The device is integrated within the vehicle, allowing for real-time location determination and tracking. The employee must switch the RFID card when the car arrives to pick them up. The microcontroller compares the RFID card number with the information in its database and then communicates the employee's ID, the cab's ID, and the cab's position coordinates to the company unit via the GSM module. The company unit's GSM Modem will receive the message through GSM. GPS offers geographic coordinates for the vehicle tracking system. The vehicle's location has been transmitted and updated to a database using the GSM module. By delivering SMS using a GSM modem, this article provides a minute-by-minute update on the location of the car.

WatchDog: Real-time Vehicle Tracking on Geo-distributed Edge Nodes

Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent advances in computer vision and machine-learning. Due to the constraints of bandwidth, latency, and privacy concerns, tracking tasks are more preferable to run on edge devices sitting close to the cameras. However, edge devices are provisioned with a fixed amount of computing budget, making them incompetent to adapt to time-varying and imbalanced tracking workloads caused by traffic dynamics. In coping with this challenge, we propose WatchDog, a real-time vehicle tracking system that fully utilizes edge nodes across the road network. WatchDog leverages computer vision tasks with different resource-accuracy tradeoffs, and decomposes and schedules tracking tasks judiciously across edge devices based on the current workload to maximize the number of tasks while ensuring a provable response time-bound at each edge device. Extensive evaluations have been conducted using real-world city-wide vehicle trajectory datasets, achieving exceptional tracking performance with a real-time guarantee.

Opportunities and Challenges in Vehicle Tracking: A Computer Vision-Based Vehicle Tracking System

This study proposes a tracking system for the transportation field utilizing state-of-the-art detection heads and tracking algorithm. We utilize detection heads (YOLO5, FRCNN, DETR) with tracking algorithms (ByteTrack) under different camera settings and three weather scenarios. The tracking results are summarized regarding Recall and precision, IDF1, IDP, IDR, MOTA, MOTP, tracking consistency, and processing time. In this study, we utilize novel Transformer architecture, a deep learning network based on the attention mechanism. Due to its strong capabilities, attention architecture has recently attracted a huge amount of attention in computer vision. This analysis showed that DETR was outperforming other algorithms in terms of tracking consistency, and it was performing similarly to FRCNN in Recall with better performance at the camera installed on intersections. Both FRCNN and DETR were outperforming YOLO5 in terms of Recall. YOLO5 is almost outperforming other algorithms in terms of Precision, MOTA, IDF1, and inference time. Considering intersections and highways, this paper has practical significance in intelligent traffic management and control.

A Simple Application: Cloud-Based Vehicle Tracking System

The world has now fully transitioned into the computing era, especially with the emergence of Cloud Computing. Technologies like wireless sensor networks and RFID (Radio Frequency Identification) are prepared to take on new tasks. A significant amount of data is generated, acquired, and stored to take control of our lives and make them more comfortable and secure. The use of various machine learning techniques and large data processing is made possible by cloud computing technologies. The Internet of Things (IoT) system connects various things, objects, and sensors using a cloud-based universal global neural network. Cloud computing and the Internet of Things are quickly developing services with unique characteristics. With the recent growth of cloud computing and IoT-based smart applications, data production, data integrity, confidentiality, authentication, and verification have become more crucial. Nowadays, every business demands that its data be updated in the cloud to be accessed from anywhere. In this regard, people can easily access the internet on their computers and smartphones in most countries, making online information transmission much simpler and more affordable. The author’s goal in this project is to develop a smart vehicle tracking system that will help universities, colleges, malls, etc. to monitor incoming and outgoing vehicles, within and out of time, and vehicle owners’ details on one website. This project introduces a novel algorithm that delivers real-time information about the vehicles (vehicle in time, out time, current date &amp; owner details) and all the information stored on the cloud so anyone can show all the real-time information. Key Words: JavaScript, HTML, CSS, RFID, IoT, Cloud Computing, ARDUINO.

Yük Vagonu-Hat Dinamik Analizi

In this study, a six-degrees-of-freedom half-car model for a freight wagon is used to study the ride motions of the vehicle under random rail inputs. As a rail input, the track profiles taken from Federal Railroad-Administration (FRA) international standard are used. A coupled vertical vehicle-track system is formed as a polytopic function of the track roughness parameter and vehicle speed and their influence on the vehicle dynamics is discussed. Then an optimization problem with a single objective function is formulated for a range of track roughness parameters and vehicle speeds. H_∞ control focuses on obtaining a robustness concerning the uncertainty of the system. So, we used H_∞ optimization to obtain the solutions while maximizing the trade-off between the respective performance indices. Later, a controller with a fixed speed value is synthesized and the results are compared by using the frequency response plots and the root-mean-square values of the car body accelerations and secondary and primary suspension deflections. The simulation results demonstrate that the active system is effective in improving ride comfort while keeping the rail holding within allowable limits.

Cooperative multi-camera vehicle tracking and traffic surveillance with edge artificial intelligence and representation learning

Traffic surveillance cameras are the eyes of the Intelligent Transportation Systems (ITS). However, they are currently isolated and can only extract information from each of their fixed views. To track vehicles across multiple cameras and help public agencies collect link travel time and speed information, an Edge-empowered Cooperative Multi-camera Sensing (ECoMS) System is proposed. ECoMS system presents a novel algorithmic and edge-server cooperative system construct to push edge computing and multi-camera re-identification workflow serving for traffic sensing based on Internet of Things (IoT) architecture. On the algorithm side, ECoMS system proposes a featherlight edge-based computer vision framework for vehicle detection, tracking, and features selection process in a real-time manner. Then, by only sending the objects’ representations to the server, the high-bandwidth data transmission and the heavy post-processing system can be abandoned. Furthermore, a hierarchical clip-based deep vehicle re-identification framework is proposed and integrated into the ECoMS system, and significantly outperforms other state-of-the-art methods by 4%–8% on Rank-1 accuracy. Finally, to balance the accuracy level of different camera pairs, a collaborative cross-camera traffic information estimation framework based on kernel density estimation with kernel smoother is implemented, which can get the precise link and region traffic information together with distributions (less than 1.01 KL distance). By maximizing the cooperation of the computational resources, orchestrating the data transmission and integrating road network graph features in the system, the ECoMS can precisely model the network-scale traffic information in a flexible, cost-effective, and scalable workflow. To the author’s best knowledge, ECoMS is the first multi-camera vehicle tracking and traffic monitoring system based on cooperative IoT architecture.

Design and Implementation of Automated Law Enforcement using the Internet of Things (IoT)

Due to a lack of legal enforcement, numerous vehicle accidents occur every year throughout the world. An effective vehicle tracking system is developed, tested, and classified in order to identify the speed of any vehicle in any street at any time and from any location (highways, crowded or secondary roads). The suggested system makes use of IoT technology, which is a relatively new development that has already made significant contributions to scientific research as well as offers many wonderful conveniences in daily life. The in-vehicle device utilizes GPS and GSM/GPRS, two of the most commonly used tracking technologies. The device is integrated into a moving object (vehicle) that is automatically tracked and observed in real-time. Through the OBD interface, the Raspberry Pi microcontroller periodically reads the vehicle speed. The Raspberry Pi microcontroller is equipped with GPS and GSM/GPRS modules to track the location of the vehicle. and, when the speed limit is exceeded, sends the coordinates and speed to the central station over the internet. The data pertaining to each vehicle is kept in a database at the central station. Based on the data gathered from the in-vehicle gadget, the driver who exceeded the speed limit will immediately receive a fine through the central station. The proposed system will help in reducing the likelihood of driver fraudulence and has demonstrated its efficiency and accuracy to reduce excessive speed and traffic accidents, leading to the daily saving of many lives. The system has been successfully built and effectively tested at a lower cost than traditional systems that rely on the use of cameras on each road.