Vehicle-Type Identification Through Automated Virtual Loop Assignment and Block-Based Direction-Biased Motion Estimation

This paper presents a method of automated virtual loop assignment and direction-based motion estimation. The unique features of our approach are that first, a number of loops are automatically assigned to each lane. The merit of doing this is that it accommodates pan-tilt-zoom (PTZ) actions without needing further human interaction. Second, the size of the virtual loops is much smaller for estimation accuracy. This enables the use of standard block-based motion estimation techniques that are well developed for video coding. Third, the number of virtual loops per lane is large. The motion content of each block may be weighted and the collective result offers a more reliable and robust approach in motion estimation. Comparing this with traditional inductive loop detectors (ILDs), there are a number of advantages. First, the size and number of virtual loops may be varied to fine-tune detection accuracy. Second, it may also be varied for an effective utilization of the computing resources. Third, there is no failure rate associated with the virtual loops or physical installation. As the loops are defined on the image sequence, changing the detection configuration or redeploying the loops to other locations on the same image sequence requires only a change of the assignment parameters. Fourth, virtual loops may be reallocated anywhere on the frame, giving flexibility in detecting different parameters. Our simulation results indicate that the proposed method is effective in type classification.published_or_final_versio

SenSys: A Smartphone-Based Framework for ITS applications

Intelligent transportation systems (ITS) use different methods to collect and process traffic data. Conventional techniques suffer from different challenges, like the high installation and maintenance cost, connectivity and communication problems, and the limited set of data. The recent massive spread of smartphones among drivers encouraged the ITS community to use them to solve ITS challenges. Using smartphones in ITS is gaining an increasing interest among researchers and developers. Typically, the set of sensors that comes with smartphones is utilized to develop tools and services in order to enhance safety and driving experience. GPS, cameras, Bluetooth, inertial sensors and other embedded sensors are used to detect and analyze drivers\u27 behavior and vehicles\u27 motion. The use of smartphones made the data collection process easier because of their availability among drivers, the set of different sensors, the computation ability, and the low installation and maintenance cost. On the other hand, different smartphones sensors have diverse characteristics and accuracy and each one of them needs special fusion, processing, and filtration methods to generate more stable and accurate data. Using smartphones in ITS faces different challenges like inaccurate readings, weak GPS reception, noisy sensors and unaligned readings.These challenges waste researchers and developers time and effort, and they prevent them from building accurate ITS applications. This work proposes SenSys a smartphone framework that collects and processes traffic data and then analyzes and extracts vehicle dynamics and vehicle activities which can be used by developers and researchers to create their navigation, communication, and safety ITS applications. SenSys framework fuses and filters smartphone\u27s sensors readings which result in enhancing the accuracy of tracking and analyzing various vehicle dynamics such as vehicle\u27s stops, lane changes, turn detection, and accurate vehicle speed calculation that, in turn, will enable development of new ITS applications and services

Evaluation of Opportunities and Challenges of Using INRIX Data for Real-Time Performance Monitoring and Historical Trend Assessment

In recent years there has been a growing desire for the use of probe vehicle technology for congestion detection and general infrastructure performance assessment. Unlike costly traditional data collection by loop detectors, wide-area detection using probe-sourced traffic data is significantly different in terms of measurement technique, pricing, coverage, etc. This affects how the new technology is applied and used to solve current traffic problems such as traffic incident management and roadway performance assessment. This report summarizes the experiences and lessons learned while using probe data for traffic operations and safety management in the state of Nebraska and makes recommendations for opportunities to maximize the use of probe data in light of its limitations. A detailed analysis of performance monitoring and historical trend analysis, including identification of the top 10 congested segments, congestion per mile across metro areas, congested hour(s) during summer and winter months, and yearly travel time reliability, for Interstate 80 segments in Nebraska were performed. Two main conclusions can be drawn from this study. First, there is almost always a speed bias between data streaming from probes and traditional infrastructure-mounted sensors. It is important to understand the factors that influence these biases and how to cope with them. Second, lack of confidence score 30 (real-time) probe data is a critical issue that should be considered precisely for incident detection, roadway performance assessment, travel time estimation, and other traffic analyses. Ultimately, the authors present several recommendations that will help transportation agencies gain the best value from their probe data

Vehicle Tracking and Classification via 3D Geometries for Intelligent Transportation Systems

In this dissertation, we present generalized techniques which allow for the tracking and classification of vehicles by tracking various Point(s) of Interest (PoI) on a vehicle. Tracking the various PoI allows for the composition of those points into 3D geometries which are unique to a given vehicle type. We demonstrate this technique using passive, simulated image based sensor measurements and three separate inertial track formulations. We demonstrate the capability to classify the 3D geometries in multiple transform domains (PCA & LDA) using Minimum Euclidean Distance, Maximum Likelihood and Artificial Neural Networks. Additionally, we demonstrate the ability to fuse separate classifiers from multiple domains via Bayesian Networks to achieve ensemble classification

Real-Time Network Assessment and Updating Using Vehicle-Locating Data

This project explores the ability to use vehicle-locating data to assess the state of the road network, including identifying road blockages along different segments of the transportation system. Compared to prior work using stationary data sources, such as loop detectors, traffic cameras, or traffic monitoring stations, or individual human-collected data collected either directly or through third-party sources, this project utilizes the mobile sources of Georgia Department of Transportation (GDOT) vehicles and their associated vehicle-tracking information to infer the state of the road network and perform transportation network assessment. These data are already currently being collected, demonstrating the utility of these data in performing road network assessment without the need to invest in new technologies, dedicate additional resources, or implement new instrumentation or infrastructure. The raw dataset of vehicle-locating data is large and, in many cases, messy. In this project, we develop and implement multiple data trimming and processing methods using ArcGIS-specific Python algorithms to transform this initially large dataset into a usable format for network assessment. To utilize the vehicle-locating data in particular, we create a workflow to enable comparison of the vehicle routes with optimal routes to detect suboptimal routing decisions that may be indicative of blockages in the road network. This workflow includes the creation of vehicle route segments based on the individual vehicle-locating data points, the linking of segments into routes, the identification of optimal routes between these points, and the comparison of distances between the actual taken routes and the optimal routes to detect the degree of suboptimal routing and its association with the likelihood of the presence of a road blockage. We use the resulting datasets as inputs and create machine learning models with multiple variables to detect the presence of a road blockage. We explore both regression-based and classification-based models and find that the classification model performs particularly well for this task. In this project, through the use of multiple data processing and data analysis methods combined with machine learning approaches, we show how the vehicle-locating data can be used to perform network assessment and accurate detection of blockages in the road network.M.S

Analyse et classification des signatures des véhicules provenant de capteurs magnétiques pour le développement des algorithmes « Intelligents » de gestion du trafic

Road traffic is at the heart of concerns for society due to issues of spatial development, mobility, the fight for better road safety or, more recently, environmentally friendly considerations. Observation and knowledge of travel patterns can partly help to answer these concerns. The development of a way to measure individual journeys can be achieved using vehicle tracking. To be able to anonymously track vehicles, magnetic sensors are chosen rather than the main traffic sensors. After a preliminary study of the physical properties of both the inductive loop and magnetometer, three steps in the monitoring process (detection, pre-processing and re-identification) are developed. Firstly, a state machine is provided to improve vehicle detection using a magnetometer. Then, two new pre-processing steps are available. The first concerns the use of a novel method of blind deconvolution for the "inductive loop" sensor. The second concerns the selection of characterizing variables by principal component analysis.Subsequently, the SVM method is adapted for the re-identification of vehicles. A unanimous voting process on either fuzzy logic, a Bayesian approach or similarity measurement is offered and compared in relation to the use of a decision threshold. A new independent predictor of traffic modelling is available to evaluate this reidentification. Finally, all the suggestions are evaluated during different experiments with the goal of obtaining individual travel time measurements or estimates of the origin – destination matrix.La circulation routière est au coeur des préoccupations de la société au travers des problématiques d’aménagement du territoire, de mobilité, de lutte contre l’insécurité routière, ou plus récemment de lutte contre la pollution. La connaissance des déplacements des véhicules permet de répondre en partie à ces préoccupations. Le développement de la mesure des déplacements individuels des véhicules peut être réalisé par le suivi des véhicules. Pour réaliser le suivi anonyme des véhicules, le choix des capteurs magnétiques est appréhendé au regard des principaux capteurs de trafic. Après une étude sur les propriétés physiques de la boucle inductive et du magnétomètre, les trois étapes (détection, rétraitement et réidentification) du processus de suivi sont développées. Tout d’abord, un automate d’état est proposé pour améliorer la détection de véhicules par magnétomètre. Ensuite, des prétraitements sont proposés. Le premier concerne la proposition d’une méthode de déconvolution aveugle pour le capteur « boucle inductive ». Le deuxième se situe sur la sélection des variables saillantes par analyse en composantes principales.Par la suite, la méthode SVM est adaptée à la réidentification de véhicules. Un processus de vote à l’unanimité des méthodes logique floue, approche bayésienne et mesures de similarités est proposé et comparé par rapport à l’utilisation d’un seuil de décision. Un nouvel indicateur indépendant de la modélisation du trafic est proposé pour évaluer la réidentification. Enfin, l’ensemble des propositions est évalué lors de différentes expérimentations avec pour objectif de mesurer les temps de parcours individuels ou d’estimer les matrices origine – destination

Simulación de tráfico en ciudades inteligentes con SUMO

Las Smart Cities son el resultado de la necesidad cada vez más imperiosa de orientar nuestra vida hacia la sostenibilidad. Así, estas ciudades se sirven de infraestructuras, innovación y tecnología para disminuir el consumo energético y reducir las emisiones de CO2. En este Trabajo Fin de Grado se demostrará el uso de la herramienta SUMO para simular el tráfico en ciudades, y se propondrán métodos para implementar simulaciones de políticas de control de semáforos mediante la información obtenida de aros inductivos.Grado en Ingeniería de Tecnologías de Telecomunicació

Using audio-based signal processing to passively monitor road traffic

The adaptive management of vehicular tra ffic on roads is a key transportation application. Sensors are required to provide information describing the behaviour of traffic in the region to be monitored. There is scope for a low-budget, efficient and robust traffic monitoring system. The hypothesis is th a t an audio-based approach provides a highly economical and efficient solution to monitor road traffic. The main contributions of this thesis may be summarised as follows. In order to determine their behaviour over time, in d iv idual vehicles are successfully tracked with an efficient source localization technique based on acoustic information. The vehicle source location is determined by the inter-signal time delay of two cross-correlated microphones, known as the time delay of arrival (TDOA) localization method. A moving source model is derived from firs t principles to simulate the time-delay pattern due to changes in source location as a vehicle approaches and passes the array. Using the moving source model, two novel pattern extraction methods are developed to extract vehicle events and parameter values from the cross-correlation array. The first method minimizes the amount of cross-correlation data stored by extracting and tracking the path of predominant peaks, then comparing the path behaviour to the derived model to determine vehicle parameters. The second method draws on image processing techniques to search for regions or shapes of high correlation in the array that match the time-delay shape model of a passing vehicle. Each method was tested w ith real traffic data of 2,267 vehicles recorded at 5 locations under a range of conditions. The shape-matching approach yielded the highest accuracy of 93% for vehicle detection with a velocity tolerance of ± 19 km /h . The positive experimental results indicate th a t the preferred method is a viable, economical audio-based traffic monitoring sensor system

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications