Multimodal Traffic Speed Monitoring: A Real-Time System Based on Passive Wi-Fi and Bluetooth Sensing Technology

SED-000080This manuscript was originally printed in the IEEE Transactions on Intelligent Transportation Systems, Volume 9, Issue 14.Traffic speed is one of the critical indicators reflecting traffic status of roadway networks. The abnormality and sudden changes of traffic speed indicate the occurrence of traffic congestions, accidents, and events. Traffic control and management systems usually take the spatiotemporal variations of traffic speed as the critical evidence to dynamically adjust the traffic signal timing plan, broadcast traffic accidents, and form a management strategy. Meanwhile, transport is multimodal in most cities, including vehicles, pedestrians, and bicyclists. Traffic states of different traffic modes are usually used simultaneously as the significant input of advanced traffic control systems, e.g., multiobjective traffic signal control system, connected vehicles, and autonomous driving. In previous studies, Wi-Fi and Bluetooth passive sensing technology was demonstrated as an effective method for obtaining traffic speed data. However, there are some challenges that greatly affect the accuracy the estimated traffic speed, e.g., traffic mode uncertainty and the errors caused by sensors\u2019 detection range. Thus, this study develops a real-time method for estimating the multimodal traffic speed of road networks covered by Wi-Fi and Bluetooth passive sensors. To address the two identified challenges, an algorithm is developed to correct the biased estimated traffic speed based on the received signal strength indicator of Wi-Fi and Bluetooth signals, and a novel semisupervised Possibilistic Fuzzy C-Means clustering algorithm is proposed for identifying traffic modes of Wi-Fi and Bluetooth device owners. The performance of the proposed algorithms is evaluated by comparing with the selected baseline algorithms. The experimental results indicate the superiority of the proposed algorithm. The proposed method of this study can provide accurate and real-time multimodal traffic speed information for supporting traffic control and management, and, thus, improving the operational performance of the whole road network

Predicting real-time roadside CO and NO2 concentrations using neural networks

The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data. The location of the study site is at a road intersection in Melton Mowbray, which is a town in Leicestershire, U.K. Several NNs, which can be classified into three types, namely, the multilayer perceptron, the radial basis function, and the modular network, were developed to model the nonlinear relationships that exist in the pollutant concentrations. Their performances are analyzed and compared. The transferability of the developed models is studied using data collected from a road intersection in another city. It was concluded that all NNs provide reliable estimates of pollutant concentrations using limited information and noisy data

Predicting real-time roadside CO and NO2 concentrations using neural networks

The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data. The location of the study site is at a road intersection in Melton Mowbray, which is a town in Leicestershire, U.K. Several NNs, which can be classified into three types, namely, the multilayer perceptron, the radial basis function, and the modular network, were developed to model the nonlinear relationships that exist in the pollutant concentrations. Their performances are analyzed and compared. The transferability of the developed models is studied using data collected from a road intersection in another city. It was concluded that all NNs provide reliable estimates of pollutant concentrations using limited information and noisy data

Realistic wireless communication simulations for VANETS

International audienceVehicular Ad­hoc NETworks (VANETs) are mainly evaluated through simulations in which the choice of a realistic wireless channel model is a central point. Deterministic channel models bring good realism but need huge simulation time, whereas with statistical models the computational effort is reduced, but sadly so is the realism of the model. In this paper, we present a semi­deterministic channel model, called UM­CRT, based on a deterministic channel simulator, CRT (Communication Ray Tracer) and a statistical channel model, SCME-UM (Spatial Channel Model Extended - Urban Microcell). To integrate it into the NS­2 network simulator, we couple it to self­developed fully compliant 802.11p and 802.11n physical layers. Simulations in urban environment show both a good realism and a reduced computation time indicating that UM­CRT is adapted for VANETs simulations

A survey of V2V channel modeling for VANET simulations

International audienceMost Vehicle to Vehicle (V2V) network protocols are evaluated by simulation. However in most network simulators, the physical layer suffers from a lack of realism. Therefore, realistic V2V channel modeling has become a crucial issue in Intelligent Transportation Systems (ITS) networks. V2V channels are known to exhibit specific features which imply the design of new simulation models. In this survey paper, we first recall the main physical features of such wireless time and frequency dispersive channels. Next, three "simulation-ready" V2V channel models found in the literature are reviewed. Finally, two complete VANET simulation frameworks are presented. They illustrate the importance of a realistic channel and physical layer modeling in vehicular networking

Vehicle-Vehicle Channel Models for the 5 GHz Band

In this paper, we describe the results of a channel measurement and modeling campaign for the vehicle-to-vehicle (V2V) channel in the 5-GHz band. We describe measurements and results for delay spread, amplitude statistics, and correlations for multiple V2V environments. We also discuss considerations used in developing statistical channel models for these environments and provide some sample results. Several statistical channel models are presented, and using simulation results, we elucidate tradeoffs between model implementation complexity and fidelity. The channel models presented should be useful for system designers in future V2V communication systems

Estudi comparatiu de la publicació científica de la UPC i l’ETSETB vs. altres universitats (2006-2016)

L'informe es centra en la publicació científica especialitzada en l'àmbit temàtic propi de l'ETSETB: l'enginyeria de telecomunicacions i l'electrònica. Es comparen indicadors bibliomètrics de la UPC i l'ETSETB amb els d'altres universitats nacionals, europees i internacionals amb activitat de recerca notable en l'àrea de les telecomunicacions i l'electrònica.Postprint (published version

Big data analytics and processing for urban surveillance systems

Urban surveillance systems will be more demanding in the future towards smart city to improve the intelligence of cities. Big data analytics and processing for urban surveillance systems become increasingly important research areas because of infinite generation of massive data volumes all over the world. This thesis focused on solving several challenging big data issues in urban surveillance systems. First, we proposed several simple yet efficient video data recoding algorithms to be used in urban surveillance systems. The key idea is to record the important video frames when cutting the number of unimportant video frames. Second, since the DCT based JPEG standard encounters problems such as block artifacts, we proposed a very simple but effective method which results in better quality than widely used filters while consuming much less computer CPU resources. Third, we designed a novel filter to detect either the vehicle license plates or the vehicles from the images captured by the digital camera imaging sensors. We are the first to design this kind of filter to detect the vehicle/license plate objects. Fourth, we proposed novel grate filter to identify whether there are objects in these images captured by the cameras. In this way the background images can be updated from time to time when no object is detected. Finally, we combined image hash with our novel density scan method to solve the problem of retrieving similar duplicate images

Résolution de conflits et séquençage d'avions par algorithmes évolutionnaires multiobjectifs

L'augmentation grandissante du trafic aérien rend le travail des contrôleurs aériens de plus en plus ardu, spécialement en ce qui a trait aux tâches de résolution de conflits et de séquençage d'avions en arrivée. L'automatisation de la résolution de conflits et du séquençage reste toujours un problème ouvert aujourd'hui. L'automatisation de ces deux problèmes permettrait d'une part de mieux modéliser le comportement des contrôleurs aériens dans un simulateur de vol, ou d'améliorer les outils de gestion du trafic aérien. Les caractéristiques combinatoires de ces problèmes conduisent à l'utilisation de techniques numériques stochastiques, plus spécifiquement des algorithmes évolutionnaires. De plus, les nombreux paramètres intervenant dans une situation de gestion de trafic aérien incitent à l'utilisation d'algorithmes multiobjectif. Dans un premier temps, un algorithme génétique multiobjectif (SPEA-MOD) et un algorithme de colonies de particules (PSO-MO) également multiobjectif ont été développés. Ces deux algorithmes ont été comparés à des problèmes multiobjectif contraints et non-contraints. Les résultats ont montré que SPEA-MOD et PSO-MO sont en général supérieurs à ce que l'on rapporte dans la littérature. Dans un deuxième temps, les deux algorithmes ont résolu plusieurs situations conflictuelles de la phase de vol en route (régime de croisière). Les instructions fournies par les algorithmes peuvent être en deux ou en trois dimensions. Les objectifs et les contraintes représentent des paramètres tels que la minimisation d'instructions fournies aux avions et une séparation minimale entre les avions. De ces solutions numériques réalisées, l'algorithme SPEA-MOD s'est avéré particulièrement efficace à des problèmes fortement contraints. Une modélisation novatrice de trajectoires complexes a permis de résoudre des problèmes de séquençage d'avions dans la phase d'arrivée. Le séquençage d'avions en arrivée par un algorithme évolutionnaire fut réalisé pour la première fois dans le cadre de cette recherche. Cette modélisation a également rendu possible la résolution de conflits de deux flux d'avions se croisant