Travel time is one of the most important inputs in many Intelligent Transport Systems (ITS). As a result, this information needs to be accurate and dynamic in both spatial and temporal dimensions. For the estimation of travel time, data from fixed sensors such as Inductive Loop Detectors (ILD) and cameras have been widely used since the 1960 s. However, data from fixed sensors may not be sufficiently reliable to estimate travel time due to a combination of limited coverage and low quality data resulting from the high cost of implementing and operating these systems. Such issues are particularly critical in the context of Less Developed Countries, where traffic levels and associated problems are increasing even more rapidly than in Europe and North America, and where there are no pre-existing traffic monitoring systems in place. As a consequence, recent developments have focused on utilising moving sensors (i.e. probe vehicles and/or people equipped with GPS: for instance, navigation and route guidance devices, mobile phones and smartphones) to provide accurate speed, positioning and timing data to estimate travel time. However, data from GPS also have errors, especially for positioning fixes in urban areas. Therefore, map-matching techniques are generally applied to match raw positioning data onto the correct road segments so as to reliably estimate link travel time. This is challenging because most current map-matching methods are suitable for high frequency GPS positioning data (e.g. data with 1 second interval) and may not be appropriate for low frequency data (e.g. data with 30 or 60 second intervals). Yet, many moving sensors only retain low frequency data so as to reduce the cost of data storage and transmission. The accuracy of travel time estimation using data from moving sensors also depends on a range of other factors, for instance vehicle fleet sample size (i.e. proportion of vehicles equipped with GPS); coverage of links (i.e. proportion of links on which GPS-equipped vehicles travel); GPS data sampling frequency (e.g. 3, 6, 30, 60 seconds) and time window length (e.g. 5, 10 and 15 minutes). Existing methods of estimating travel time from GPS data are not capable of simultaneously taking into account the issues related to uncertainties associated with GPS and spatial road network data; low sampling frequency; low density vehicle coverage on some roads on the network; time window length; and vehicle fleet sample size. Accordingly this research is based on the development and application of a methodology which uses GPS data to reliably estimate travel time in real-time while considering the factors including vehicle fleet sample size, data sampling frequency and time window length in the estimation process. Specifically, the purpose of this thesis was to first determine the accurate location of a vehicle travelling on a road link by applying a map-matching algorithm at a range of sampling frequencies to reduce the potential errors associated with GPS and digital road maps, for example where vehicles are sometimes assigned to the wrong road links. Secondly, four different methods have been developed to estimate link travel time based on map-matched GPS positions and speed data from low frequency data sets in three time windows lengths (i.e. 5, 10 and 15 minutes). These are based on vehicle speeds, speed limits, link distances and average speeds; initially only within the given link but subsequently in the adjacent links too. More specifically, the final method draws on weighted link travel times associated with the given and adjacent links in both spatial and temporal dimensions to estimate link travel time for the given link. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-Berkeley s Mobile Century Project. The original GPS dataset which was broadcast on a 3 second sampling frequency has been extracted at different sampling frequencies such as 6, 30, 60 and 120 seconds so as to evaluate the performance of each travel time estimation method at low sampling frequencies. The results were then validated against reference travel time data collected from 4,126 vehicles by high resolution video cameras, and these indicate that factors such as vehicle sample size, data sampling frequency, vehicle coverage on the links and time window length all influence the accuracy of link travel time estimation.

Over the past few decades, the value and weight of freight shipments have grown steadily in both developed and developing countries. A recent statistic in the U.S. reveals that weight of shipments increased from 18,879 to 19,662 million tons between 2007 and 2012 (1). It is also expected that this amount will increase to 28,520 million tons by 2040 (1). It is worth mentioning that 67 percent of shipments are shipped by truck mode in 2012. The monetary value of freight is expected to escalate even faster than weight. This value is estimated to rise from US$ 882 per ton in 2007 to US$ 1,377 per ton in 2040. As a result, freight transportation management and modeling has aroused the interest of both public sector and groups of firms to improve the efficiency of the business operations. Traffic assignment plays a central role in the current freight modeling, and freight route analysis is of fundamental importance in understanding the truck flows explicitly. In the first part of this thesis, large streams of truck-GPS data from the American Transportation Research Institute (ATRI) are cleaned, processed, and analyzed using easy to implement and practical procedures to study the diversity of observed truck routes between a given origin-destination (OD) pair. This is because, for any given OD pair, the analyst could observe and compare the route choices of a large number of trips, as opposed to observing only one or a few trips. Doing so helps in quantifying the number of different routes taken by trucks between an OD pair and paves the way for a systematic analysis of the “diversity” in route choices between any OD pair. This thesis develops methods to measure the diversity of routes between a given OD pair and identifies unique routes used between the given OD pair. From a practical standpoint, such analysis of the diversity in observed route choices helps in improving the existing route choice set generation algorithms. In the second part of the thesis, the methodologies developed in the first part are implemented in an FDOT sponsored project entitled “GPS Data for Truck-Route Choice Analysis of Port Everglades Petroleum Commodity Flows”. This project aims to use truck-GPS data from ATRI to derive petroleum tanker trucks’ travel path (or route) information, describing the routes that the tanker trucks take to travel from Port Everglades to their final delivery points.

Automation of travel diary collection, an essential input for transport planning, has been a fruitful line of research for the last years; in particular, concerning the problem of automatic inference of transport modes. Taking advantage of technological advance, several solutions based on the collection of mobile devices data, such as GPS locations and variables related to movement (such as speed) and motion (e.g. measurements from accelerometer), have been investigated. The literature shows that many of them rely on explicit initial segmentation of GPS trajectories into trip legs, followed by a segment-based classification problem. In some cases, GIS-related features are included in the classification instance, but usually in terms of distance to transport networks or to specific points of interest (POIs). The aim of this MSc Thesis is to investigate a novel transport mode inference procedure based on the generation of topological features from a multimodal map matching instance. We define topological features as the topological context of each point of a GPS trajectory. Further utilization of these features as part of a sequence classification problem leads to mode prediction and to the implicit definition of the trip legs. In addition to not depending on an explicit segmentation step, the proposed routine also has less requirements in terms of the complexity of the required GIS features: there is no need to consider distance features, and the proposed map matching implementation does not require the usage of one unified multimodal network —as other multimodal map matching approaches do. The procedure was tested with a travel diary data set collected in Stockholm, containing 4246 trips from 368 different commuters. The transport modes considered were walk, subway, commuter train, bus and tram. In order to assess the impact of the topological context, different feature set compositions were investigated, including topological and conventional movement and motion features. Three different classifiers —decision tree, support vector machine and conditional random field— were evaluated as well. The results show that the proposed procedure reached high accuracy, with a performance that is similar to the one offered by current approaches; and that the most performant feature set composition was the one that included both topological and movement and motion features. The best evaluation measures were obtained with decision tree and conditional random field classifiers, but with some differences: while both of the them presented similar recall, the former yielded better precision and the latter achieved a higher segmentation quality.

Cette thèse porte sur le problème de l'estimation des temps de parcours, de véhicules, par section de route dans un contexte urbain, en utilisant les données GPS à faible densité d’échantillon. L'un des défis de cette thèse est d'utiliser ce genre de données. Dans le cadre de ce travail de recherche, j'ai développé une carte numérique avec son nouveau système d'information géographique (SIG), qui traite la problématique du map-matching, où nous avons apporté des améliorations, ainsi que le problème du plus court chemin.La thèse s'inscrit dans le cadre du projet PUMAS (Plate-forme Urbaine de Mobilité Avancée et Soutenable), ce qui est un avantage pour nos recherches en ce qui concerne le processus de collecte de données réelles sur le terrain ainsi que pour faire nos tests. Le projet PUMAS est un projet préindustriel qui a pour objectif d'informer sur la situation du trafic mais également de développer et de mettre en œuvre une plate-forme de mobilité durable afin de l'évaluer dans la région, notamment à Rouen, France. Le résultat offre un cadre pour tout contrôleur de la situation, gestionnaire ou chercheur pour accéder à de vastes réserves de données sur l'estimation du flux du trafic, sur les prévisions et sur l'état du trafic. / This dissertation is concerned with the problem of estimating travel time per links in urban context using sparsely sampled GPS data. One of the challenges in this thesis is use the sparsely sampled data. A part of this research work, i developed a digital map with its new geographic information system (GIS), dealing with map-matching problem, where we come out with an enhancement tecnique, and also the shortest path problem.The thesis research work was conduct within the project PUMAS, which is an avantage for our research regarding the collection process of our data from the real world field and also in making our tests. The project PUMAS (Plate-forme Urbaine de Mobilité Avancée et Soutenable / Urban Platform for Sustainable and Advanced Mobility) is a preindustrial project that has the objective to inform about the traffic situation and also to develop an implement a platform for sustainable mobility in order to evaluate it in the region, specifically Rouen, France. The result is a framework for any traffic controller or manager and also estimation researcher to access vast stores of data about the traffic estimation, forecasting and status.

Abstract : Embedded intelligence in vehicular applications is becoming of great interest since the last two decades. Position estimation has been one of the most crucial pieces of information for Intelligent Transportation Systems (ITS). Real time, accurate and reliable localization of vehicles has become particularly important for the automotive industry. The significant growth of sensing, communication and computing capabilities over the recent years has opened new fields of applications, such as ADAS (Advanced driver assistance systems) and active safety systems, and has brought the ability of exchanging information between vehicles. Most of these applications can benefit from more accurate and reliable localization. With the recent emergence of multi-vehicular wireless communication capabilities, cooperative architectures have become an attractive alternative to solving the localization problem. The main goal of cooperative localization is to exploit different sources of information coming from different vehicles within a short range area, in order to enhance positioning system efficiency, while keeping the cost to a reasonable level. In this Thesis, we aim to propose new and effective methods to improve vehicle localization performance by using cooperative approaches. In order to reach this goal, three new methods for cooperative vehicle localization have been proposed and the performance of these methods has been analyzed. Our first proposed cooperative method is a Cooperative Map Matching (CMM) method which aims to estimate and compensate the common error component of the GPS positioning by using cooperative approach and exploiting the communication capability of the vehicles. Then we propose the concept of Dynamic base station DGPS (DDGPS) and use it to generate GPS pseudorange corrections and broadcast them for other vehicles. Finally we introduce a cooperative method for improving the GPS positioning by incorporating the GPS measured position of the vehicles and inter-vehicle distances. This method is a decentralized cooperative positioning method based on Bayesian approach. The detailed derivation of the equations and the simulation results of each algorithm are described in the designated chapters. In addition to it, the sensitivity of the methods to different parameters is also studied and discussed. Finally in order to validate the results of the simulations, experimental validation of the CMM method based on the experimental data captured by the test vehicles is performed and studied. The simulation and experimental results show that using cooperative approaches can significantly increase the performance of the positioning methods while keeping the cost to a reasonable amount. / Résumé : L’intelligence embarquée dans les applications véhiculaires devient un grand intérêt depuis les deux dernières décennies. L’estimation de position a été l'une des parties les plus cruciales concernant les systèmes de transport intelligents (STI). La localisation précise et fiable en temps réel des véhicules est devenue particulièrement importante pour l'industrie automobile. Les améliorations technologiques significatives en matière de capteurs, de communication et de calcul embarqué au cours des dernières années ont ouvert de nouveaux champs d'applications, tels que les systèmes de sécurité active ou les ADAS, et a aussi apporté la possibilité d'échanger des informations entre les véhicules. Une localisation plus précise et fiable serait un bénéfice pour ces applications. Avec l'émergence récente des capacités de communication sans fil multi-véhicules, les architectures coopératives sont devenues une alternative intéressante pour résoudre le problème de localisation. L'objectif principal de la localisation coopérative est d'exploiter différentes sources d'information provenant de différents véhicules dans une zone de courte portée, afin d'améliorer l'efficacité du système de positionnement, tout en gardant le coût à un niveau raisonnable. Dans cette thèse, nous nous efforçons de proposer des méthodes nouvelles et efficaces pour améliorer les performances de localisation du véhicule en utilisant des approches coopératives. Afin d'atteindre cet objectif, trois nouvelles méthodes de localisation coopérative du véhicule ont été proposées et la performance de ces méthodes a été analysée. Notre première méthode coopérative est une méthode de correspondance cartographique coopérative (CMM, Cooperative Map Matching) qui vise à estimer et à compenser la composante d'erreur commune du positionnement GPS en utilisant une approche coopérative et en exploitant les capacités de communication des véhicules. Ensuite, nous proposons le concept de station de base Dynamique DGPS (DDGPS) et l'utilisons pour générer des corrections de pseudo-distance GPS et les diffuser aux autres véhicules. Enfin, nous présentons une méthode coopérative pour améliorer le positionnement GPS en utilisant à la fois les positions GPS des véhicules et les distances inter-véhiculaires mesurées. Ceci est une méthode de positionnement coopératif décentralisé basé sur une approche bayésienne. La description détaillée des équations et les résultats de simulation de chaque algorithme sont décrits dans les chapitres désignés. En plus de cela, la sensibilité des méthodes aux différents paramètres est également étudiée et discutée. Enfin, les résultats de simulations concernant la méthode CMM ont pu être validés à l’aide de données expérimentales enregistrées par des véhicules d'essai. La simulation et les résultats expérimentaux montrent que l'utilisation des approches coopératives peut augmenter de manière significative la performance des méthodes de positionnement tout en gardant le coût à un montant raisonnable.

Public transit is becoming an increasing important field of study to combat global issues such as traffic congestion and climate change. Accurate simulation of public transit is therefore likewise vital, as it is an important tool for understanding potential impacts of public transit policies. The research presented in this thesis describes the implementation of a multimodal, dynamic, agent-based supply-side simulation model of public transit implemented in the open-source platform MATSim for the city of Toronto. Transit schedule data was converted from Google Transit Feed Specification (GTFS) and map-matched to a region-wide road network to obtain a congestion-based multimodal assignment for transit. Volume-based results from the assignment showed under-prediction of subway volumes and slight over-prediction of bus volumes, but were generally comparable with static EMME/3 assignment for the same data. Travel time analysis indicated that further calibration of network specification is needed.

Public transit is becoming an increasing important field of study to combat global issues such as traffic congestion and climate change. Accurate simulation of public transit is therefore likewise vital, as it is an important tool for understanding potential impacts of public transit policies. The research presented in this thesis describes the implementation of a multimodal, dynamic, agent-based supply-side simulation model of public transit implemented in the open-source platform MATSim for the city of Toronto. Transit schedule data was converted from Google Transit Feed Specification (GTFS) and map-matched to a region-wide road network to obtain a congestion-based multimodal assignment for transit. Volume-based results from the assignment showed under-prediction of subway volumes and slight over-prediction of bus volumes, but were generally comparable with static EMME/3 assignment for the same data. Travel time analysis indicated that further calibration of network specification is needed.

It is crucial to find a good balance between positioning accuracy and cost when developing navigation systems for ground vehicles. In open sky or even in a semi-urban environment, a single global navigation satellite system (GNSS) constellation performs sufficiently well. However, the positioning accuracy decreases drastically in urban environments. Because of the limitation in tracking performance for standalone GNSS, particularly in cities, many solutions are now moving toward integrated systems that combine complementary sensors. In this master thesis the improvement of tracking performance for a low-cost ground vehicle navigation system is evaluated when complementary sensors are added and different filtering techniques are used. How the GNSS aided inertial navigation system (INS) is used to track ground vehicles is explained in this thesis. This has shown to be a very effective way of tracking a vehicle through GNSS outages. Measurements from an accelerometer and a gyroscope are used as inputs to inertial navigation equations. GNSS measurements are then used to correct the tracking solution and to estimate the biases in the inertial sensors. When velocity constraints on the vehicle’s motion in the y- and z-axis are included, the GNSS aided INS has shown very good performance, even during long GNSS outages. Two versions of the Rauch-Tung-Striebel (RTS) smoother and a particle filter (PF) version of the GNSS aided INS have also been implemented and evaluated. The PF has shown to be computationally demanding in comparison with the other approaches and a real-time implementation on the considered embedded system is not doable. The RTS smoother has shown to give a smoother trajectory but a lot of extra information needs to be stored and the position accuracy is not significantly improved. Moreover, map matching has been combined with GNSS measurements and estimates from the GNSS aided INS. The Viterbi algorithm is used to output the the road segment identification numbers of the most likely path and then the estimates are matched to the closest position of these roads. A suggested solution to acquire reliable tracking with high accuracy in all environments is to run the GNSS aided INS in real-time in the vehicle and simultaneously send the horizontal position coordinates to a back office where map information is kept and map matching is performed.

The use of GPS observations for investigating routing behaviors can be a good alternative to using more traditional traffic simulation models. In this paper, a method for inferring paths from GPS observations is proposed. Further, a route set generation algorithm is implemented. The inferred trips are used for the calibration of the parameters in the route set generation algorithm. The investigated network is part of the Interstate 210 freeway east of Los Angeles, USA. The results shows significant differences in number of eastbound travelers choosing to travel north of, south of, and on the freeway during regular days compared with the incident day. The travel times are also higher during the incident day. Different travel times as costs on the links have a large impact on the results from the route set generation algorithm. The conclusion is that the implemented methods can be used to gain a better understanding about routing behavior. However, to use the results for decision making, more input data with better precision should be used.

Orientador: Denis Jose Schiozer / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica e Instituto de Geociencias / Made available in DSpace on 2018-08-13T11:18:36Z (GMT). No. of bitstreams: 1 Risso_ValmirFrancisco_D.pdf: 9435021 bytes, checksum: 278e304092ceeb3f841e585ac7a34e55 (MD5) Previous issue date: 2007 / Resumo: O ajuste de histórico de produção tem como principal objetivo calibrar modelos numéricos de campos de petróleo para que os resultados de produção e de pressão da simulação sejam coerentes com o histórico de produção e de pressão observados e que estes modelos ajustados possam ser usados na previsão de produção com maior confiabilidade. Essa técnica apresenta algumas limitações, principalmente no início do desenvolvimento do campo quando há menos dados observados e as incertezas são maiores, o que torna o processo de ajuste do modelo numérico menos confiável. Com o avanço das técnicas de processamento sísmico e com a sísmica 4D, já é possível a obtenção de mapas de saturação do campo e com essa informação adicional, melhorar a qualidade do modelo em estudo possibilitando realizar previsões de comportamento do campo mais confiáveis, principalmente em campos onde a água proveniente de poços injetores ou de aqüíferos ainda não alcançou os poços produtores. O trabalho atual propõe uma metodologia para aumentar a confiabilidade do modelo numérico através da incorporação dos mapas de saturação no processo de ajuste do histórico do campo, combinando estas informações com os dados de produção de óleo, água e gás, de injeção e de pressão. A utilização dos mapas no processo de ajuste aumenta o número de parâmetros a serem analisados no processo de ajuste, aumentando assim o número de simulações necessárias e dificultando a análise dos resultados. Uma alternativa para tentar minimizar esse problema é a metodologia do planejamento estatístico e da superfície de resposta, a qual permite estudar um número maior de variáveis e regiões críticas ao mesmo tempo possibilitando otimizar ou minimizar várias respostas simultaneamente, estruturando melhor as etapas do processo de ajuste evitando-se o processo usual de tentativa e erro. / Abstract: The main objective of history matching is to improve numerical models of oil fields by incorporating observed data, production and pressure, into the characterization process, in order to obtain more reliable production forecasting. This technique presents some limitations mainly in the beginning of the development of oil fields, when less information is available and higher uncertainties are present. With seismic 4D, it is possible to obtain saturation maps allowing the improvement of the numerical model yielding a more reliable production forecasting. The objective of this work is to developed a methodology to improve the numerical model through the incorporation of the saturation maps in the process of history matching. The process requires a higher number of critical parameters to be analyzed in the adjustment process; therefore, it is necessary to increase the number of simulations yielding a more complex procedure. An alternative to minimize this problem is the statistical design and response surface methodologies which allow to study many variables and regions at the same time. It is possible to optimize some answers simultaneously, improving the process by reducing the manual work yielding better results. / Doutorado / Reservatórios e Gestão / Doutor em Ciências e Engenharia de Petróleo