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<p>In the midwestern United States, nitrogen (N) pollution of surface and groundwaters is a substantial threat to water quality because of its ecological and human health effects. Hypoxia in the Gulf of Mexico is primarily caused by N runoff within the Mississippi River basin, and nitrate in drinking water may negatively impact human health in both adults and children. </p><p>Agricultural tile drainage is a common practice that facilitates the transport of N from fields to streams. While the impacts of tile drainage have been studied extensively at the field scale, the impacts on hydrology, nutrient transport, and groundwater recharge are still uncertain at the watershed and landscape scales. </p><p>The overall goal of this thesis work is to assess how tile drainage affects landscape-scale connectivity, hydrologic travel times, and N transport across a large catchment in west-central Indiana using 10 years of bi-annual water chemistry and stable isotope data from a community science education event. Land use data and a previously developed travel time distribution (TTD) model were also incorporated to accomplish this goal. A secondary goal is to estimate seasonal differences in groundwater recharge in west-central Indiana using stable water isotope data from precipitation and groundwater samples. </p><p>Qualitative travel times derived from δ²H and δ¹⁸O variability support the idea that short travel times have greater nitrate concentrations than long travel times. Greater N concentrations are also observed during wetter conditions with increased connectivity. The results of the GIS TTD model support the hypothesis that increasing drainage intensity reduces travel times. Groundwater recharge appears negligible in Tippecanoe County using a traditional water balance approach, but an isotope mass balance approach suggests that about 55-65% of annual recharge occurs during the summer and may be linked to intense precipitation events. </p><p>This knowledge improves our understanding of N transport and hydrologic connectivity in tile drained landscapes. The results of this thesis also demonstrate the importance of drainage density for travel times and provide additional insight into the seasonality of groundwater recharge in west-central Indiana. </p>

The purpose of this research is to develop methodologies for applying traffic flow theories to various ITS categories through the utilization of evolving surveillance technologies. This integration of theory, measurement and application has been overlooked since the advent of ITS because of the number of disciplines involved. In this context, the following illustrative methodologies are selected, developed and presented in this study: - a methodology for automatic measurement of major spatial traffic variables for the present and the future implementation of various ITS functional areas, in general; and - a methodology for real-time link and incident specific freeway diversion in conjunction with freeway incident management, in particular. The first methodology includes the development of a dynamic flow model based on stochastic queuing theory and the principle of conservation of vehicles. An inductive modeling approach adapted here utilizes geometric interpretations of cumulative arrival-departure diagrams which have been drawn directly from surveillance data. The advantages of this model are real-time applicability and transportability as well as ease of use. Analysis results show that the estimates are in qualitative and quantitative agreement with the empirical data measured at 30-second intervals. The analytical expression for link travel times satisfies traffic dynamics where the new form of the equation of conservation of vehicles has been derived. This methodology has potential applicable to automatic traffic control and automatic incident detection. The methodology is then applied to freeway diversion in real-time in conjunction with freeway incident management. The proposed new form of the equation of conservation of vehicles is applied to detect recurring or non-recurring congestion analytically. The principle of conservation of vehicles is applied to develop the concept of progression and retrogression of incident domain, which turns out to be compatible with traditional shock wave traffic mechanism during incidents. The link and incident specific diversion methodology is achieved by using a delay diagram and volume-travel time curves, which can be plotted per link per incident. The use of such graphic aids makes problem solving much easier and clearer. The dynamic traffic flow model developed here can also be applied to estimate travel times during incidents as a function of time. The development of a computer program for freeway diversion concludes this research. / Ph. D.

In this dissertation, new algorithms that address three traffic problems of major importance are developed. First automatic identification and prediction algorithms are developed to identify and predict the occurrence of traffic congestion. The identification algorithms concoct a model to identify speed thresholds by exploiting historical spatiotemporal speed matrices. We employ the speed model to define a cutoff speed separating free-flow from congested traffic. We further enhance our algorithm by utilizing weather and visibility data. To our knowledge, we are the first to include weather and visibility variables in formulating an automatic congestion identification model. We also approach the congestion prediction problem by adopting an algorithm which employs Adaptive Boosting machine learning classifiers again something novel that has not been done previously. The algorithm is promising where it resulted in a true positive rate slightly higher than 0.99 and false positive rate less than 0.001. We next address the issue of travel time modeling. We propose algorithms to model travel time using various machine learning and statistical learning techniques. We obtain travel time models by employing the historical spatiotemporal speed matrices in conjunction with our algorithms. The algorithms yield pertinent information regarding travel time reliability and prediction of travel times. Our proposed algorithms give better predictions compared to the state of practice algorithms. Finally we consider driver safety at signalized intersections and uncontrolled intersections in a connected vehicles environment. For signalized intersections, we exploit datasets collected from four controlled experiments to model the stop-run behavior of the driver at the onset of the yellow indicator for various roadway surface conditions and multiple vehicle types. We further propose a new variable (predictor) related to driver aggressiveness which we estimate by monitoring how drivers respond to yellow indications. The performance of the stop-run models shows improvements after adding the new aggressiveness predictor. The proposed models are practical and easy to implement in advanced driver assistance systems. For uncontrolled intersections, we present a game theory based algorithm that models the intersection as a chicken game to solve the conflicts between vehicles crossing the intersection. The simulation results show a 49% saving in travel time on average relative to a stop control when the vehicles obey the Nash equilibrium of the game. / Ph. D.

published_or_final_version / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

The use of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place, with significant number of mobile sensors moving around covering expansive areas of the road network. Many travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Vehicles in commercial fleets are now routinely equipped with GPS. Travel time is important information for various actors of a transport system, ranging from city planning, to day to day traffic management, to individual travelers. They all make decisions based on average travel time or variability of travel time among other factors. AVI (Automatic Vehicle Identification) systems have been commonly used for collecting point-to-point travel time data. Floating car data (FCD) -timestamped locations of moving vehicles- have shown potential for travel time estimation. Some advantages of FCD compared to stationary AVI systems are that they have no single point of failure and they have better network coverage. Furthermore, the availability of opportunistic sensors, such as GPS, makes the data collection infrastructure relatively convenient to deploy. Currently, systems that collect FCD are designed to transmit data in a limited form and relatively infrequently due to the cost of data transmission. Thus, reported locations are far apart in time and space, for example with 2 minutes gaps. For sparse FCD to be useful for transport applications, it is required that the corresponding probes be matched to the underlying digital road network. Matching such data to the network is challenging. This thesis makes the following contributions: (i) a map-matching and path inference algorithm, (ii) a method for route travel time estimation, (iii) a fixed point approach for joint path inference and travel time estimation, and (iv) a method for fusion of FCD with data from automatic number plate recognition. In all methods, scalability and overall computational efficiency are considered among design requirements. Throughout the thesis, the methods are used to process FCD from 1500 taxis in Stockholm City. Prior to this work, the data had been ignored because of its low frequency and minimal information. The proposed methods proved that the data can be processed and transformed into useful traffic information. Finally, the thesis implements the main components of an experimental ITS laboratory, called iMobility Lab. It is designed to explore GPS and other emerging data sources for traffic monitoring and control. Processes are developed to be computationally efficient, scalable, and to support real time applications with large data sets through a proposed distributed implementation. / <p>QC 20150525</p>

Le bus est le moins cher des transports en commun. En contrepartie, il est beaucoup plus difficile à exploiter que le tramway ou le métro qui sont mieux protégés des influences extérieures. Un exemple typique est l’apparition de trains de bus, groupes de véhicules appartenant à la même ligne et arrivant ensemble à un arrêt. Ce phénomène augmente le temps d’attente moyen des usagers aux arrêts et induit un mauvais usage des bus disponibles. Cette thèse développe les outils permettant de garantir la régularité des lignes. Les recherches menées au cours de cette thèse s’articulent suivant deux directions.Un premier constat est que les modèles de lignes de bus existants ne prennent pas en compte les éléments extérieurs que sont les feux de circulation et le trafic environnant. L’absence d’une modélisation mixte intégrant aussi bien les dynamiques internes des lignes que les influences extérieures contraint fortement la diversité des stratégies de contrôle qui ont été proposées jusqu’ici. En effet, les régulations s’appliquent principalement au niveau des arrêts par l’intermédiaire des conducteurs et ne cherchent jamais à réguler le trafic à l’aide des feux de circulation. Un premier axe de recherche développé dans cette thèse est le raffinement des modèles de bus pour prendre en compte le trafic.Plusieurs méthodes d’estimation de temps de parcours sur un boulevard à feu sont proposées. Elles sont basées sur le modèle LWR, compromis fort satisfaisant entre simplicité d’usage et robustesse pour reproduire des situations réelles.Un second constat est que les stratégies de régulation classiques ne sont que rarement basées sur une prévision à court-terme de l'état du système. Elles sont donc souvent actionnées une fois que la situation est trop dégradée, ce qui les rend parfois inaptes à compenser l'instabilité des lignes. Le deuxième axe de recherche consiste à appliquer les modèles raffinés dans un contexte d’exploitation en temps-réel. Le modèle prévoit l'évolution des lignes de bus à court terme, ce qui permet d’actionner préventivement une stratégie de régulation adaptée. En particulier, une méthode de prévision à court terme est développée et testée sur des données réelles. Elle est ensuite combinée à une méthode récente de contrôle des bus. / Bus is cheaper than other transport modes. However, maintaining optimal operations is harder than for streetcars or subways since buses are surrounded by traffic flows. Sometimes, buses of the same route bunch and travel together instead of keeping constant time headways. This phenomenon increases the average waiting time of passengers. As a result, they may tend to shift to other transport modes. This thesis proposes some methods to keep bus routes regular. Two main lines of research are investigated.First, classical models of bus routes do not account for external events like traffic signals and traffic flows. Due to this gap, existing control strategies only apply on buses through their drivers.Traffic flows are not controlled to favor buses compared to cars. Thus, the first area of research consists in refining bus models to account for external events. Several travel time estimation methods on urban arterials are proposed. They are based on the kinematic wave model (LWR). It is known to be a fine trade-off between simplicity and robustness to properly reproduce traffic dynamics.Second, control strategies are often applied once the bus route is too disrupted to be restored to regularity. Predictions of future bus route states could improve the efficiency of regulations. The second area of research consists in using the refined bus models in real time operations. The model forecasts the evolution of buses on their route for short-term. The predictions are evaluated thanks to real data to guarantee their quality. Then it enables regulations to be applied before bunching. In particular, height holding control methods are presented and compared in simulation.

The aim of this diploma thesis is to discuss the assessment of changes of the modal split in connection with the construction of high speed railway Prague-Brno. Thesis is aimed on wider context of two theoretical concepts in transportation researches: travel behaviour and the value of travel time. Data collection took place in the form of "Paper and Pencil Interview" method. For a deeper analysis was used the Pivot Table tools. From the collected data was found that economically active people from coaches and trains have higher travel time than students. Among cars users the most important factor influencing the choice of traffic mode is the speed, for coach users it is price and for train users the posibility of work/rest during the journey. Assuming a reduction of travel time between Prague and Brno for about 1 hour while keeping the fare price about CZK 200, high-speed rail connections has the potential to generate more frequent journeys among existing passengers. Keywords: modal split, travel behaviour, the value of travel time savings, competitiveness of railways, high speed railway

Many cities suffer from problems with high traffic flows in the city centers which leads to a desire to get more people to choose public transport over cars. For many car drivers, the main reason to take the car is the convenience and time efficiency; the price is often of less importance. The public transport providers should, therefore, strive to improve their Level of Service (LOS). A general process that can be used by public transport providers or other stakeholders to evaluate the LOS in a public transport system based on Automated Vehicle Location (AVL) data is developed and presented in this thesis.The process values the quality and suitability of the AVL data, propose which KPIs to use and how to use the results to find possible improvements. Four different types of erroneous data were discovered: outliers in position, outliers in speed, outliers in travel time and general errors. KPIs are developed in three main areas: on-time performance, travel time distribution and speed, where each KPI is divided into several sub-areas.

Le transport de contaminants, l’altération des roches ainsi que les grands cycles biogéochimiques sont contrôlés par les temps de séjour de l’eau. Ces temps de séjour représentent le temps de transit durant lequel l’eau « voyage » dans un bassin versant. Contraindre ces temps de transit est donc un enjeu essentiel pour quantifier l’impact de l’homme sur la qualité de l’eau en rivières et dans les aquifères et pour évaluer la résilience des écosystèmes aquatiques continentaux. Cependant, les rivières comme les nappes phréatiques sont constituées d’un mélange d’eau de différents âges (une distribution des temps de transit ou des temps de résidence) qui varie avec le temps, en fonction des aléas météorologiques et climatiques, rendant difficile leur caractérisation ainsi que leur prédiction. Dans cette thèse, nous inférons ces temps de résidence à l’aide de traceurs géochimiques et de modèles guidés par les données. Nous montrons comment cette connaissance permet de quantifier l’altération des roches cristallines. Nous développons ensuite un cadre original de modélisation à base physique, capable de représenter la variabilité saisonnière et interannuelle des débits et des temps de transit mesurés en rivière. Nous montrons comment le processus de battements de nappes et son interaction avec les couches perméables du sol mène à la génération d’un ruissellement qui explique les fluctuations saisonnières de qualité de l’eau en rivières, traduites par des mesures de silice dissoute. Enfin, nous esquissons un cadre général de représentation de la réactivité à l’échelle du versant capable de rendre compte des processus biogéochimiques. En effet, représenter la dégradation des éléments réactifs (oxygène, nitrates, carbone) permettra d’évaluer les mesures de réduction d’intrants agricoles, de prédire l’évolution long terme de ces solutés en rivières, et donc leur potentiel d’eutrophisation ainsi que d’évaluer des mesures pour réconcilier agriculture et environnement. Cette réactivité apparaît comme le dernier maillon manquant pour comprendre, mesurer et prédire, les impacts anthropiques sur la zone critique. / Groundwater travel time controls contaminant transport, weathering processes and biogeochemical cycles. Groundwater travel time is a fundamental descriptor characterizing the transit time of water inside the catchment, from precipitation events to the streams. Quantifying these transit times is pivotal to predict the impact of anthropogenic pressure and assess freshwater ecosystems resilience. However, streamwater and groundwater are a mixture of water of different ages (the transit time and the residence time distribution), which vary according to climatic forcings. This makes difficult its characterization and prediction. Here we infer residence times with geochemical tracers and data-driven models. We show how this can be constrained by silicate weathering at the catchment scale. We then develop a novel process-based framework, which can model discharge and transit time seasonal and interannual variabilities. We identify water table fluctuations, its interaction with permeable soil layers and the resulting subsurface stormflow generation as a key process for seasonal water quality variations described by dissolved silica measurements. Finally, we draw a reactivity framework to represent biogeochemical processes. Indeed, evaluating reactive solute degradation is needed to assess the efficiency of reducing fertilizer loads, to predict the long term evolution of in stream solute concentrations and the eutrophication potential of freshwater bodies. Modeling the reactivity at the catchment scale is the missing link to understand, quantify and predict the effect of anthropogenic pressure on the critical zone.