Les travaux présentés dans ce mémoire de thèse s’inscrivent dans le cadre des Systèmes de TransportIntelligents (STI). Bien que les premières études sur ces systèmes ont commencé dans les années 60, leurdéveloppement reposant sur les techniques de l’information et de la communication, a atteint sa maturitédans le début des années 80. Les STI, sont composés de différents systèmes et intègrent différents concepts(systèmes embarqués, capteurs intelligents, autoroutes intelligentes, . . .) afin d’optimiser le rendementdes infrastructures routières et répondre aux problèmes quotidiens des congestions. Ce mémoire présentequatre contributions dans le cadre du trafic routier et aborde les problèmes de l’estimation et de lacommande afin d’éliminer les problèmes de congestions « récurrentes ». Le premier point traite unproblème crucial dans le domaine des STI qui est celui de l’estimation. En effet, la mise en oeuvre delois de commande pour réguler le trafic impose de disposer de l’ensemble des informations concernantl’évolution de l’état du trafic. Dans ce contexte, deux algorithmes d’estimation sont proposés. Le premierrepose sur l’emploi du modèle METANET et les techniques de modes de glissement d’ordre supérieur. Lesecond est basé sur les CTM (Cell Transmission Models). Plusieurs études comparatives avec les filtresde Kalman sont proposées. La seconde contribution concerne la régulation du trafic. L’accent est mis surle contrôle d’accès isolé en utilisant les algorithmes issus du mode de glissement d’ordre supérieur. Cettecommande est enrichie en introduisant une commande intégrée combinant le contrôle d’accès et le routagedynamique. L’ensemble des résultats, validé par simulation, est ensuite comparé aux stratégies classiquesnotamment le contrôle d’accès avec l’algorithme ALINEA. La troisième contribution traite des problèmesde coordination. En effet, l’objectif est d’appliquer le principe de la commande prédictive pour contrôlerplusieurs rampes d’accès simultanément. L’ensemble des contributions ont été validées en utilisant desdonnées réelles issues en grande partie de mesures effectuées sur des autoroutes françaises. Les résultatsobtenus ont montré un gain substantiel en termes de performances tels que la diminution du trajet, dutemps d’attente, de la consommation énergétique, ainsi que l’augmentation de la vitesse moyenne. Cesrésultats permettent d’envisager plusieurs perspectives nouvelles de développement des recherches dansce domaine susceptibles d’apporter des solutions intéressantes. / The works presented in this PhD dissertation fit into the framework of Intelligent TransportationSystems. Although the beginnings of these systems have started since the 60s, their development, basedon information and communication technologies, has reached maturity during the early 80s. The ITS usesthe intelligence of different systems (embedded systems, intelligents sensors, intelligents highways, etc.)in order to optimize road infrastructures performances and respond to the daily problems of congestions.The dissertation presents four contributions into the framework of road traffic flow and tackles theestimation and control problems in order to eliminate or at least reduce the “recurrent" congestionsphenomena. The first point treats the problem of traffic state estimation which is of most importance inthe field of ITS. Indeed, the implementation and performance of any control strategy is closely relatedto the ability to have all needed information about the traffic state describing the dynamic behavior ofthe studied system. Two estimation algorithms are then proposed. The first one uses the “metanet"model and high order sliding mode techniques. The second is based on the so-called Cell TransmissionModels. Several comparative studies with the Kalman filters, which are the most used in road traffic flowengineering, are established in order to demonstrate the effectiveness of the proposed approaches. Thethree other contributions concern the problem of traffic flow control. At first, the focus is on the isolatedramp metering using an algorithm based on the high order sliding mode control. The second contributiondeals with the dynamic traffic routing problem based on the high order sliding mode control. Such controlstrategy is enriched by introducing the concept of integration, in the third contribution. Indeed, integratedcontrol consists of a combination of several traffic control algorithms. In this thesis the proposed approachcombines an algorithm of on-ramp control with a dynamic traffic routing control. The obtained results arevalidated via numerical simulations. The validated results of the proposed isolated ramp metering controlare compared with the most used ramp metering strategy : ALINEA. Finally, the last contributiontreats the coordination problems. The objective is to coordinate several ramps which cooperate andchange information in order to optimize the highway traffic flow and reduce the total travel time in theapplied area. All these contributions were validated using real data mostly from French freeways. Theobtained results show substantial gains in term of performances such as travel time, energetic consumptiondecreasing, as well as the increasing in the mean speed. These results allow to consider several furtherworks in order to provide more interesting and efficient solutions in the ITS field.

Les systèmes de transport intelligents ont acquis un grand intérêt pour la recherche ces dernières années. Alors que la simulation réaliste du trafic joue un rôle important, elle n'a pas reçu suffisamment d'attention. Cette thèse est consacrée à l'étude de la simulation du trafic au niveau microscopique et propose des modèles de mobilité des véhicules correspondants. À l'aide de méthodes d'apprentissage profond, ces modèles de mobilité ont fait leurs preuves avec une crédibilité prometteuse pour représenter les véhicules dans le monde réel. D'abord, un modèle de mobilité basé sur un réseau de neurones piloté par les données est proposé. Ce modèle provient de données de trajectoires du monde réel et permet de mimer des comportements de véhicules locaux. En analysant les performances de ce modèle de mobilité basé sur un apprentissage de base, nous indiquons qu’une amélioration est possible et proposons ses spécifications. Un MMC est alors introduit. La préparation de cette intégration est nécessaire, ce qui comprend un examen des modèles de mobilité traditionnels basés sur la dynamique et l’adaptation des modèles « classiques » à notre situation. Enfin, le modèle amélioré est présenté et une simulation de scénarios sophistiqués est construite pour valider les résultats théoriques. La performance de notre modèle de mobilité est prometteuse et des problèmes de mise en œuvre sont également discutés / The intelligent transportation systems gain great research interests in recent years. Although the realistic traffic simulation plays an important role, it has not received enough attention. This thesis is devoted to studying the traffic simulation in microscopic level, and proposes corresponding vehicular mobility models. Using deep learning methods, these mobility models have been proven with a promising credibility to represent the vehicles in real-world. Firstly, a data-driven neural network based mobility model is proposed. This model comes from real-world trajectory data and allows mimicking local vehicle behaviors. By analyzing the performance of this basic learning based mobility model, we indicate that an improvement is possible and we propose its specification. An HMM is then introduced. The preparation of this integration is necessary, which includes an examination of traditional dynamics based mobility models and the adaptation method of “classical” models to our situation. At last, the enhanced model is presented, and a sophisticated scenario simulation is built with it to validate the theoretical results. The performance of our mobility model is promising and implementation issues have also been discussed

La maintenance de flottes de systèmes mobiles dans le monde du transport et de la logistique revêt de nos jours une importance croissante de par l’augmentation des attentes des exploitants et opérateurs en termes de sécurité, de fiabilité, de suivi, de diagnostic et de maintenance de ces systèmes. Dans ce contexte, Les mainteneurs des flottes doivent souvent faire face à d'énormes quantités de données brutes, informations et événements de surveillance liés aux contexte de leurs systèmes. De plus, ces événements, données et informations manquent souvent de précision et sont souvent contradictoires ou obsolètes. Enfin, le degré d'urgence des décisions de maintenance est rarement pris en compte. Ce travail est consacré à la proposition et à la mise au point d’une architecture de surveillance pour l’aide à la maintenance d’une flotte de systèmes mobiles. Cette architecture, appelée EMH², est destinée à faciliter le diagnostic et le suivi de ce type de flotte. Elle est construite sur les principes holoniques, des plus bas (capteurs) aux plus hauts niveaux (ensemble d’une flotte de systèmes mobiles). Elle se base également sur une standardisation des événements traités afin de traiter les données de manière générique. Cette architecture, indépendante des types de systèmes surveillés et de leur niveau hiérarchique, peut devenir l'épine dorsale d’une stratégie efficace de maintenance proactive d’une flotte. Une méthodologie de déploiement est ainsi proposée. Une étude en simulation et une application sur une flotte de 10 trains actuellement en service est présentée. / The maintenance of mobile systems fleets in the world of transport and logistics is of increasing importance today due to the increasing expectations of operators in terms of safety, reliability, monitoring, diagnosis and maintenance of these systems. In this context, fleet maintainers often have to deal with huge amounts of raw data, information and monitoring events related to the context of their systems. Moreover, these events, data and information are often lack precision and often contradictory or obsolete. Finally, the urgency of maintenance decisions is rarely taken into account. This work is devoted to the proposal and the development of a monitoring architecture to help maintain a fleet of mobile systems. This architecture, called EMH², is intended to facilitate the diagnosis and monitoring of this type of fleet. It is built on holonic principles, from the lowest (sensors) to the highest levels (a whole fleet of mobile systems). It is also based on a standardization of processed events in order to process the data generically. This architecture, independent of the types of systems monitored and their hierarchical level, can become the backbone of an effective strategy for proactive fleet maintenance. A deployment methodology is thus proposed. A simulation study and an application on a fleet of 10 trains currently in service is presented.

In recent years, Intelligent Transportation Systems (ITS) have emerged as an efficient way of enhancing traffic flow, safety and management. These goals are realized by combining various technologies and analyzing the acquired data from vehicles and roadways. Among all ITS technologies, computer vision solutions have the advantages of high flexibility, easy maintenance and high price-performance ratio that make them very popular for transportation surveillance systems. However, computer vision solutions are demanding and challenging due to computational complexity, reliability, efficiency and accuracy among other aspects.   In this thesis, three transportation surveillance systems based on computer vision are presented. These systems are able to interpret the image data and extract the information about the presence, speed and class of vehicles, respectively. The image data in these proposed systems are acquired using Unmanned Aerial Vehicle (UAV) as a non-stationary source and roadside camera as a stationary source. The goal of these works is to enhance the general performance of accuracy and robustness of the systems with variant illumination and traffic conditions.   This is a compilation thesis in systems engineering consisting of three parts. The red thread through each part is a transportation surveillance system. The first part presents a change detection system using aerial images of a cargo port. The extracted information shows how the space is utilized at various times aiming for further management and development of the port. The proposed solution can be used at different viewpoints and illumination levels e.g. at sunset. The method is able to transform the images taken from different viewpoints and match them together. Thereafter, it detects discrepancies between the images using a proposed adaptive local threshold. In the second part, a video-based vehicle's speed estimation system is presented. The measured speeds are essential information for law enforcement and they also provide an estimation of traffic flow at certain points on the road. The system employs several intrusion lines to extract the movement pattern of each vehicle (non-equidistant sampling) as an input feature to the proposed analytical model. In addition, other parameters such as camera sampling rate and distances between intrusion lines are also taken into account to address the uncertainty in the measurements and to obtain the probability density function of the vehicle's speed. In the third part, a vehicle classification system is provided to categorize vehicles into \private car", \light trailer", \lorry or bus" and \heavy trailer". This information can be used by authorities for surveillance and development of the roads. The proposed system consists of multiple fuzzy c-means clusterings using input features of length, width and speed of each vehicle. The system has been constructed by using prior knowledge of traffic regulations regarding each class of vehicle in order to enhance the classification performance.

Indiana University-Purdue University Indianapolis (IUPUI) / With the rapid growth in wireless mobile communication technology, Vehicular Ad-hoc Network (VANET) has emerged as a promising method to effectively solve transportation-related issues. So far, most of researches on VANETs have been conducted with simulations as the real-world experiment is expensive. A core problem affecting the fidelity of simulation is the mobility model employed. In this thesis, a sophisticated traffic simulator capable of generating realistic vehicle traces is introduced. Combined with network simulator NS-2, we used this tool to evaluate the general performance of several routing protocols and studied the impact of intersections on simulation results. We show that static nodes near the intersection tend to become more active in packet delivery with higher transferred throughput.

Indiana University-Purdue University Indianapolis (IUPUI) / Pedestrian Automatic Emergency Braking (PAEB) system for avoiding/mitigating pedestrian crashes have been equipped on some passenger vehicles. At present, there are many e orts for the development of common standard for the performance evaluation of PAEB. The Transportation Active Safety Institute (TASI) at Indiana University-Purdue University-Indianapolis has been studying the problems and ad- dressing the concerns related to the establishment of such a standard with support from Toyota Collaborative Safety Research Center (CSRC). One of the important components in the PAEB evaluation is the development of standard testing facili- ties at night, in which 70% pedestrian crash social costs occurs [1]. The test facility should include representative low-illuminance environment to enable the examination of sensing and control functions of di erent PAEB systems. This thesis work focuses on modeling low-illuminance driving environment and describes an approach to recon- struct the lighting conditions. The goal of this research is to characterize and model light sources at a potential collision case at low-illuminance environment and deter- mine possible recreation of such environment for PAEB evaluation. This research is conducted in ve steps. The rst step is to identify lighting components that ap- pear frequently on a low-illuminance environment that a ect the performance of the PAEB. The identi ed lighting components include ambient light, same side/opposite side light poles, opposite side car headlight. Next step is to collect all potential pedes- trian collision cases at night with GPS coordinate information from TASI 110 CAR naturalistic driving study video database. Thirdly, since ambient lighting is relatively random and lack of a certain pattern, ambient light intensity for each potential col- lision case is de ned and processed as the average value of a region of interest on all video frames in this case. Fourth step is to classify interested light sources from the selected videos. The temporal pro le method, which compressing region of interest in video data (x,y,t) to image data (x,y), is introduced to scan certain prede ned region on the video. Due to the fact that light sources (except ambient light) impose distinct light patterns on the road, image patterns corresponding to speci c light sources can be recognized and classi ed. All light sources obtained are stamped with GPS coordinates and time information which are provided in corresponding data les along with the video. Lastly, by grouping all light source information of each repre- sentative street category, representative light description of each street category can be generated. Such light description can be used for lighting construction of PAEB test facility.

No description available.

High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors. In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data. iii At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized iv appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users.

No description available.

A model for estimating travel time on short arterial links of congested urban networks, using currently available technology, is introduced in this thesis. The objective is to estimate travel time, with an acceptable level of accuracy for real-life traffic problems, such as congestion management and emergency evacuation. To achieve this research objective, various travel time estimation methods, including highway trajectories, multiple linear regression (MLR), artificial neural networks (ANN) and K –nearest neighbor (K-NN) were applied and tested on the same dataset. The results demonstrate that ANN and K-NN methods outperform linear methods by a significant margin, also, show particularly good performance in detecting congested intervals. To ensure the quality of the analysis results, set of procedures and algorithms based on traffic flow theory and test field information, were introduced to validate and clean the data used to build, train and test the different models.