This work investigates the problem of efficiently monitoring and disseminating road traffic information in urban settings using fixed and mobile sensor networks. A key challenge in outdoor urban environments is that bandwidth is a scarce resource. It is thus vital to reduce the communication cost of forwarding traffic data from source sensor nodes through the wireless network to the traffic monitoring center. This thesis proposes two distinct approaches to reducing the communication cost of traffic monitoring: 1) in-network data reduction in the context of fixed sensor networks, and 2) efficient data acquisition and routing in the context of mobile sensor networks. In fixed sensor networks, nodes are deployed in fixed locations and are capable of monitoring local traffic at regular intervals. When users can tolerate long delays in traffic updates, we propose Fourier-based compression techniques that exploit spatio-temporal correlations in traffic data and reduce the cost of data delivery. When users require real-time traffic updates, we investigate the use of model-based approaches, in which sensor nodes use a model to predict traffic data, and only report data that deviates from the predicted values. Our evaluation of in-network reduction techniques for fixed sensor networks is based on a real traffic dataset derived from traffic monitoring sensors in the city of Cambridge, UK. In mobile sensor networks, we utilize traveling vehicles as nodes that can sense local traffic and forward it to the monitoring center. The key challenge in vehicular networks is to minimize the communication cost of traffic monitoring by jointly optimizing the processes of data acquisition and routing. Given user requirements for data freshness, we devise a traffic data acquisition scheme, and propose two routing algorithms, D-Greedy and D-MinCost, that carefully alternate between the multi- hop forwarding and data muling strategies. The proposed algorithms are compared with existing approaches in a simulation environment using realistic vehicular traces from the city of Zurich.

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Congestion on roads is a crucial problem which affects our lives in many ways: As a consequence, there is a strong effort to improve road networks in order to keep the traffic flowing. Flow forecasting models based on the large amount of traffic data, which are now available, can be a very useful tool to support decisions and actions when managing traffic networks. Although many forecasting models have been developed to this end, very few of them capture important features of high dimensional traffic data and, moreover, operating most of these models is a hard task when considering on-line traffic management environments. Dynamic graphical models can be a suitable choice to address the challenge of forecasting high-dimensional traffic flows in real-time. These models represent network flows by a graph, which not only is a useful pictorial representation of multivariate time series of traffic flow data, but it also ensures that model computation is always simple, even for very complex road networks. One example of such a model is the multiregression dynamic model (MDM). This thesis focuses on the development of two classes of dynamic graphical models to forecast traffic flows . Firstly, the linear multiregression dynamic model (LMDM), which is an MDM particular case, is extended to allow important traffic characteristics in its structure, such as the heterocedasticity of daily traffic flows, measurement errors due to malfunctions in data collection devices, and the use of extra traffic variables as predictors to forecast flows. Due to its graphical structure, the MDM assumes independence of flows at the entrances of a road network. This thesis therefore introduces a new class of dynamic graphical models where the correlation across road network entrances is accommodated, resulting in better forecasts when compared to the LMDM. All the methodology proposed in this thesis is illustrated using data collected at the intersection of three busy motorways near Manchester, UK.

iBus is Transport for London (TfL)’s GPS-enhanced Automatic Vehicle Location System, which has been rolled out to the entire contracted fleet of over 8,500 buses across London, and resulted in efficiencies in fleet management, improvements in bus waiting times, and provided improved real-time information for passengers. The System resides on board each vehicle, as well as in operators’ bus garages and at the main TfL Control Centre, and records a number of on-street events relating to for example buses’ entry and exit into stop zones, when their doors opened and closed, and their location and speed in real-time. This information, which is collected in the ‘log’ files of every vehicle, provided an opportunity to develop further uses for the System, including an alternative method for measuring bus stop dwell times. Historically, dwell times in London have been obtained using manual road-side surveys, which are relatively expensive, and therefore occur infrequently. However, dwell times and their variability are important to bus operations, network planning and traffic management, and they can affect the ability of urban traffic control systems such as SCOOT to provide buses with priority at traffic signals, which reduces their effectiveness. An alternative method for measuring dwell times using iBus therefore offers many benefits for TfL, provided a process could be determined and largely automated, as the dwell values are not recorded directly by the System, which is relatively complex. A knowledge base of the bus log files therefore had to be developed, and was tested to allow different algorithms, flow charts and programs to be produced for deriving dwell times, based on a sequence of different vehicle speed, stop zone and door events. An experiment was also conducted to validate the dwell times obtained through this method against video data obtained of vehicles stopping on street, which showed a close match between when the vehicle speeds are zero and roadside dwell, although another method, using the duration between when doors opened and closed, provided a close approximation, particularly when an offset value is accounted for. The dwell times obtained through the ‘speed zero’ method in the experiment were then analysed, and this showed wide variations between different bus stops and routes, which are consistent with previous surveys in London, and suggests that generalised values of dwell are inadequate for most applications. The analysis also showed that the dwell time variation by time-of-day is more complex than a traditional morning and afternoon peak, which may reflect changes in ticketing, vehicle modernisation, and the demand made by different types of bus passengers in recent years.

Ziel des Projektes war die Entwicklung und Anwendung einer Methodik für die Ermittlung von Fahrmustern als Grundlage für eine qualifizierte Emissions- und Immissionsprognose in Straßen. In dem Projekt wurde ein Verfahren entwickelt, mit dessen Hilfe unter Verwendung von Floating-Car-Data (FCD) die Fahrmuster ohne aufwändige Messfahrten erhoben und aktualisiert werden können. Die Ergebnisse sind Grundlage zur Modellierung der Immissionssituation z. B. in Luftreinhalteplänen, zur Ermittlung von Vorbelastungswerten, zur Erfüllung von Berichtspflichten an die EU sowie als Eingangsgrößen in Forschungsvorhaben. Das Verfahren kann mit geringem Aufwand für andere Städte angepasst werden, wodurch diese Kosten sparen.

Ziel des Projektes war die Entwicklung und Anwendung einer Methodik für die Ermittlung von Fahrmustern als Grundlage für eine qualifizierte Emissions- und Immissionsprognose in Straßen. In dem Projekt wurde ein Verfahren entwickelt, mit dessen Hilfe unter Verwendung von Floating-Car-Data (FCD) die Fahrmuster ohne aufwändige Messfahrten erhoben und aktualisiert werden können. Die Ergebnisse sind Grundlage zur Modellierung der Immissionssituation z. B. in Luftreinhalteplänen, zur Ermittlung von Vorbelastungswerten, zur Erfüllung von Berichtspflichten an die EU sowie als Eingangsgrößen in Forschungsvorhaben. Das Verfahren kann mit geringem Aufwand für andere Städte angepasst werden, wodurch diese Kosten sparen.

En viabilité hivernale, la prévision de l’état de surface des infrastructures s’avère indispensable, et permet une anticipation, une meilleure coordination et une efficacité d’intervention des services d’exploitation. La majorité des pays dispose de modèles de prévision de la température de surface d'infrastructures et des routes en particulier (TSR). La complexité de ces outils d’aide à la décision est croissante, pour servir au mieux les usagers et l’exploitant. Le microclimat urbain influence le bilan énergétique de surface selon différents processus : radiatifs, aérodynamiques et hydrologiques. Néanmoins, d’autres processus physiques anthropiques influencent cette TSR, tel que le trafic. Des travaux ont été menés par le passé concernant l’apport énergétique du trafic dans le bilan thermique de la ville. Celui-ci a fait l’objet d’études sur les périodes estivales et les îlots de chaleur urbains associés. Cependant, dans les cas de dégradations des conditions hivernales de circulation, ces apports énergétiques ont été intégrés de façon marginale dans la modélisation des paramètres de surface de la route. L’absence de cette contribution du trafic dans la modélisation du bilan énergétique de surface explique, dans une certaine limite, la prévision imparfaite de l’état de surface de la route. La bibliographie recense plusieurs études conduites afin d’identifier et de quantifier ces effets du trafic. Elles n'ont pas ou peu traité la perte ou le gain d'énergie causé par le passage des véhicules sur le bilan énergétique de surface, ou sur la modélisation de la TSR. Dans la présente étude, deux approches ont été proposées pour paramétrer le trafic dans le modèle numérique Town Energy Balance (TEB), l'une globale et la seconde détaillée. Leur analyse comparée indique que la seconde a significativement amélioré les résultats de la modélisation de la TSR. Les apports thermiques du trafic ont augmenté la TSR de 2 à 4°C pour la rapprocher des mesures expérimentales (écart de 0.5 à 1°C en moyenne). Elle est le résultat de l’effet cumulatif des différents processus physiques associés au trafic, et varie en fonction de ses paramètres (densité, vitesse de circulation, fluidité, etc.). Une étude de sensibilité a été menée afin d’apprécier les processus physiques responsables de l’amélioration de la TSR. Les résultats indiquent que l’effet turbulent, la densité de flux radiatif ainsi que la densité de flux due aux frottements des pneumatiques contribuent le plus à l'augmentation la TSR. Néanmoins la contribution énergétique de chacun de ces processus dépend à la fois des paramètres du trafic et des conditions météorologiques. Cette étude a présenté également une analyse préliminaire de l’influence de la lame d’eau présente en surface sur la TSR. Cette dernière décroit en fonction de l’épaisseur de la lame d’eau. Les facteurs anthropiques tels que le trafic et l’adjonction de fondants routiers sur la lame d’eau présente en surface sont décrits et discutés, et une paramétrisation proposée en perspectives / In winter maintenance, forecasting the infrastructure surface status is mandatory, to allow anticipation, better coordination and efficiency of services. The majority of countries have forecast models of the infrastructure surface temperature and especially roads one (RST). The complexity of these decision tools is growing, to best serve the users and managers. The urban microclimate influences the surface energy balance according to various processes: radiative, aerodynamic and hydrologic. However, other anthropogenic physical processes influence this RST, such as traffic. Studies have been carried out in the past about the traffic heat input in the town heat balance. These were conducted on the summer periods and associated urban heat islands. However, in case of traffic in adverse winter conditions, these energy contributions were marginally integrated into the modeling of the road surface parameters. The absence of this traffic's contribution in the surface energy balance modeling explains, to a given limit, the imperfect forecasting of road surface status. The literature identifies several studies conducted to identify and to quantify these effects of traffic. They have insufficiently or not treated the loss or gain on energy caused by circulating vehicles on the surface energy balance, or on modeling the RST. In this study, two approaches have been proposed to parameterize the traffic in the Town Energy Balance (TEB) numerical model, this first one being overall and the second one detailed. Their comparative analysis indicates that the second significantly improved the results of the RST modeling. The traffic heat inputs increased RST by 2 to 4°C, results being then closer to experimental measurements (average difference of 0.5 to 1°C). It is the result of the cumulative effect of the various traffic physical processes, and varies according to its parameters (density, vehicle velocity, fluidity, etc.). A sensitivity analysis was conducted to assess the physical processes responsible for the improvement of the RST. The results indicate that the turbulent effect, the radiative heat flux and flux density due to tires friction represent the greatest contribution to RST increase. Nevertheless the energy contribution of each of these processes depends both traffic parameters and weather conditions. This study also presented a preliminary analysis of the influence of a water layer over the surface on the RST. The latter decreases as a function of the thickness of the water layer. Anthropogenic factors such as traffic and the addition of de-icing products into the water layer present on the surface are described and discussed, and a parameterization proposed as a perspective