Studying recurrent mobility perturbations, such as traffic congestions, is a major concern of engineers, planners, and authorities as they not only bring about delay and inconvenience but also have consequent negative impacts like greenhouse gas emission, increase in fuel consumption, or safety issues. In this dissertation, we proposed using the resilience concept, which has been commonly used for assessing the impact of extreme events and disturbances on the transportation system, for high-probability low impact (HPLI) events to (a) provide a performance assessment framework for transportation systems' response to traffic congestions, (b) investigate the role of transit modes in the resilience of urban roadways to congestion, and (c) study the impact of network topology on the resilience of roadways functionality performance. We proposed a multi-dimensional approach to characterize the resilience of urban transportation roadways for recurrent congestions. The resilience concept could provide an effective benchmark for comparative performance and identifying the behavior of the system in the discharging process in congestion. To this end, we used a Data Envelopment Analysis (DEA) approach to integrate multiple resilience-oriented attributes to estimate the efficiency (resilience) of the frontier in roadways. Our results from an empirical study on California highways through the PeMS data have shown the potential of the multi-dimensional approach in increasing information gain and differentiating between the severity of congestion across a transportation network. Leveraging this resilience-based characterization of recurrent disruptions, in the second study, we investigated the role of multi-modal resourcefulness of urban transportation systems, in terms of diversity and equity, on the resilience of roadways to daily-based congestions. We looked at the physical infrastructure availability and distribution (i.e. diversity) and accessibility and coverage to capture socio-economic factors (i.e. equity) to more comprehensively understand the role of resourcefulness in resilience. We conducted this investigation by using a GPS dataset of taxi trips in the Washington DC metropolitan area in 2017. Our results demonstrated the strong correlation of trips' resilience with transportation equity and to a lesser extent with transportation diversity. Furthermore, we learned the impact of equity and diversity can mostly be seen at the recovery stage of resilience. In the third study, we looked at another aspect of transportation supply in urban areas, spatial configuration, and topology. The goal of this study was to investigate the role of network topology and configuration on resilience to congestion. We used OSMnx, a toolkit for street network analysis based on the data from OpenStreetMap, to model and analyze the urban roadways network configurations. We further employed a multidimensional visualization strategy using radar charts to compare the topology of street networks on a single graphic. Leveraging the geometric descriptors of radar charts, we used the compactness and Jaccard Index to quantitatively compare the topology profiles. We use the same taxi trips dataset used in the second study to characterize resilience and identify the correlation with network topology. The results indicated a strong correlation between resilience and betweenness centrality, diameter, and Page Rank among other features of a transportation network. We further looked at the capacity of roadways as a common cause for the strong correlation between network features and resilience. We found that the strong correlation of link-related features such as diameter could be due to their role in capacity and have a common cause with resilience. / Doctor of Philosophy / Transportation infrastructure systems are among the most fundamental facilities and systems in urban areas due to the role they play in mobility, economy, and environmental sustainability. Due to this importance, it is crucial to ensure their resilience to regular disruptions such as traffic congestions as a priority for engineers and policymakers. The resilience of transportation systems has often been studied when disasters or extreme events occur. However, minor disturbances such as everyday operational traffic situations can also play an important part in reducing the efficiency of transportation systems and should be considered in the overall resilience of the systems. Current literature does not consider traffic performance from the lens of resilience despite its importance in evaluating the overall performance of roads. This research addresses this gap by proposing to leverage the concept of resilience for evaluation of roadways performance and identifying the role of urban characteristics in the enhancement of resilience. We first characterized resilience considering the performance of the roadways over time, ranging from the occurrence of disruptions to the time point when the system performance returns to a stable state. Through a case study on some of the major highways in the Los Angeles metropolitan area and by leveraging the data from the Performance Measurement System (PeMS), we have investigated how accounting for a proposed multi-dimensional approach for quantification of resilience could add value to the process of road network performance assessment and the corresponding decision-making. In the second and third parts of this dissertation, we looked at the urban infrastructure elements and how they affect resilience to regular disruptive congestion events. Specifically, in the second study, we focused on alternative transit modes such as bus, metro, or bike presence in the urban areas. We utilized diversity and equity concepts for assessing the opportunities they provide for people as alternative mobility modes. The proposed metrics not only capture the physical attributes of the multi-modal transportation systems (i.e. availability and distribution of transit modes in urban areas) but also consider the socio-economic factors (i.e. the number of people that could potentially use the transit mode). In the third study, we investigated how urban road networks' form and topology (i.e., the structure of roadway networks) could affect its resilience to recurrent congestions. We presented our findings as a case study in the Washington DC area. Results indicated a strong correlation between resilience and resourcefulness as well as topology features. The findings allow decision-makers to make more informed design and operational decisions and better incorporate the urban characteristics during the priority setting process.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / The development and qualification of personnel and cargo aerial delivery parachute systems present unique challenges to the instrumentation and data analysis engineers. Some of the areas that must be addressed include: a) system must be low in cost, b) system often has to be operated on ranges that have limited telemetry or other range instrumentation and support (i.e. commercial skydiving centers), c) system is often rigged and operated by parachute support personnel and test jumpers rather than instrumentation engineers, and d) system must be able to be reconfigured in the field to support a variety of test card requirements during a typical test day, e) data must be available for review and the system be prepared for the next test within a few minutes of parachute recovery, and f) system must withstand ground impact velocities as high as 50 ft/sec (15.24 m/sec) without damage. This paper describes such a system as it is being used for the development and qualification testing of a number of parachute systems for sport skydiving, military personnel, as well as cargo parachute systems. This modular system has been developed as a result of previous experience in other parachute development and qualification projects to address the need for a flexible Data Acquisition System (DAS) system that meets the above requirements. This paper describes some of the tools used to meet these requirements.

The thesis focuses on evaluating and improving the transport efficiency of two types of logistics operations in the supply chain. One research area is the production of raw material in construction operations, specifically earthmoving operations. Methods and tools are developed to provide decision support in improving the transport efficiency of earthmoving at the vehicle and the systems levels. Using known road topography and a GPS unit, an optimal control problem is formulated and solved (Paper III) to determine the optimal gear shift sequence and timing in order to improve the transport efficiency at the vehicle level. For decision support at the systems level, a Fleet Performance Simulation (FPS) model is designed (Paper IV) to evaluate the transport efficiency for a given mix of construction vehicles in earthmoving. The FPS system is integrated with an optimization algorithm to solve the optimal fleet composition problem for earthmoving operations (Paper V &amp; VI). Construction operations are dynamic and the environment is changing constantly, which bring difficulties in decision-making. Using GPS data from construction vehicles, a map inference framework (Papers I &amp; II) is developed to automatically extract relevant input to decision support at the vehicle and the systems levels, which include the locations of various workstations, driving time distributions and road networks. The second research area is the transport efficiency of urban distribution system, which is in the final phase of the supply chain. An off-peak delivery pilot project in Stockholm is used as the background, designed to evaluate the potential for commercial vehicles to make use of off-peak hours for goods delivery. The thesis (Paper VII) evaluates the transport efficiency impacts of the off-peak pilot. An evaluation framework is defined where transport efficiency is studied in a number of dimensions. GPS data, fleet management data, and logistic information are used to assess the impacts. / <p>QC 20170323</p>

Esta pesquisa propõe uma abordagem genérica de processamento de dados de rastreamento de veículos de carga com a finalidade de extrair indicadores de desempenho logístico no contexto urbano. Tais indicadores são importantes para caracterizar como as operações logísticas se desenvolvem nas cidades e subsidiar o processo de tomada de decisão dentro do âmbito do planejamento urbano de cargas, por parte do poder público. Esta abordagem é aplicada a três bases de dados - para a Região Metropolitana de São Paulo (RMSP) - obtidas junto a empresas privadas distintas, sendo duas grandes redes varejistas e a terceira uma provedora de serviços de mapas. Apesar de algumas limitações nos dados disponíveis, foi possível identificar paradas dos caminhões e analisar suas características, como: distribuição espacial e temporal, frequência de paradas por veículo, a localização de clusters de paradas etc. Além disso, foi possível classificar do os veículos que circulam na cidade distinguindo a parcela que corresponde ao \'fluxo de passagem\", veículos que estão apenas cruzando a cidade (São Paulo) e, por fim, apresentou-se também um estudo do perfil de velocidades - por hora e dia da semana - em uma importante via arterial de São Paulo. Os resultados e análises desta pesquisa reforçam que há grande potencial na utilização de dados de rastreamento de veículos de carga no contexto do planejamento de transporte urbano de cargas, embora este dependa em parte de algumas especificidades dos dados, como a frequência dos registros, a precisão da posição geográfica coletada, além da necessidade de um processamento otimizado devido ao grande volume de dados. / The purpose of this research is a general approach for processing tracking data of cargo vehicles in order to extract logistic performance indicators in the urban scenario, essential to characterize how logistic operations are developed and support the decision-making process within the scope of urban freight planning by the public sector. This approach is applied to three data bases - for the São Paulo Metropolitan Region (SPMR) - acquired from different private companies, being two large retailers and the third a great mapping services provider. In spite of available data limitations, it was possible to identity truck stops and analyze its particulars, such as: spatial and temporal distribution, stop frequency per vehicle, the location of stop clusters, etc. Apart from that, it was possible to classify among the flow of vehicles in the city, the ones that are just passing, crossing the city, and ultimately, it was presented a speed profile analysis - per day and week day - of an important arterial way of São Paulo. The results and analysis of this research reinforce that there is great potential in the use of cargo vehicle tracking data for urban freight planning, even though it partially depends on some data specifics, such as frequency of records, location accuracy, besides the need of an optimized data processing due to their great volume.

Understanding travel behavior and identifying the mode of transportation are essential for adequate urban devising and transportation planning. Global positioning systems (GPS) tracking data is mainly used to find human mobility patterns in cities. Some travel information, such as most visited location, temporal changes, and the trip speed, can be easily extracted from GPS raw tracking data. GPS trajectories can be used as a method to indicate the mobility modes of commuters. Most previous studies have applied traditional machine learning algorithms and manually computed data features, making the model error-prone. Thus, there is a demand for developing a new model to resolve these methods' weaknesses. The primary purpose of this study is to propose a semi-supervised model to identify transportation mode by using a contemporary machine learning algorithm and GPS tracking data. The model can accept GPS trajectory with adjustable length and extracts their latent information with LSTM Autoencoder. This study adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. Moreover, different case studies are performed to evaluate the proposed model's efficiency. The model results in an accuracy of 93.6%, which significantly outperforms similar studies.

Advance Driver Assistance System (ADAS) are becoming the main issue in today’s automotive industry. The new generation of ADAS aims at focusing on more details and obtaining more accuracy. To achieve this objective, the research and development parts of the automobile industry intend to utilize Global Positioning System (GPS) by integrating it with other existing tools in ADAS. There are several driving assistance systems which are served by a digital map as a primary or a secondary sensor. The traditional techniques of digital map generation are expensive and time consuming and require extensive manual effort. Therefore, having frequently updated maps is an issue. Furthermore, the existing commercial digital maps are not highly accurate. This Master thesis presents several algorithms for automatically converting raw Universal Serial Bus (USB)-GPS and Real Time Kinematic (RTK) GPS traces into a routable road network. The traces are gathered by driving 20 times on a highway. This work begins by pruning raw GPS traces using four different algorithms. The first step tries to minimize the number of outliers. After the traces are smoothed, they tend to consolidate into smooth paths. So in order to merge all 20 trips together and estimate the road network a Trace Merging algorithm is applied. Finally, a Non-Uniform Rational B-Spline (NURBS) curve is implemented as an approximation curve to smooth the road shape and decrease the effect of noisy data further. Since the RTK-GPS receiver provides highly accurate data, the curve resulted from its GPS data is the most sufficient road shape. Therefore, it is used as a ground truth to compare the result of each pruning algorithm based on data from USB-GPS. Lastly, the results of this work are demonstrated and a quality evaluation is done for all methods.

This thesis delves into the realm of mobile forensics, specifically focusing on the forensic analysis of location-based data derived from smartwatches and smartphones. With the generation of wearable devices and the increasing reliance on personal electronics, understanding the differences and collaborative potential between these devices is imperative for forensic investigators. The study addresses three key research questions:1) How does geolocation differ between a smartphone and a smartwatch?2) How can information from smartphones and smartwatches collaborate to provide a more comprehensive picture in an investigation?3) Is the GPS/activity data from smartphones reliable evidence in legal cases?The research is positioned against existing studies that explore location-based health data, forensic analysis of IoT devices, and the relevance of smartwatch data in legal proceedings.The methodology used in the study is strategically chosen to offer a comprehensive understanding of the role and challenges associated with smartwatch and smartphone data in forensic contexts, such as experiments and non-structured interviews. By aligning with existing studies and leveraging experimental and interview-based approaches, the research aims to contribute valuable knowledge to the evolving field of mobile forensics. The outcomes of this study are expected to shed light on the use of location-based data in legal investigations, providing guidance for future practices and emphasizing the increasing centrality of these technologies in contemporary society.The results show that there is a difference between the devices, not only between the smartphones and smartwatches but also between the different brands. What significance this plays will be discussed and useful conclusions inferred. / Denna studie undersöker området mobil forensik, med särskilt fokus på den forensiskaanalysen av geolokaliserings data från smartklockor och smartphones. På grund av denökade framväxten av bärbara enheter och det ökande beroendet av personlig elektronikär det viktigt för forensiska utredare att förstå de skillnaderna och samarbetsmöjlighetersom finns mellan dessa enheter. Studien behandlar tre centrala forskningsfrågor:- Hur skiljer sig geolokalisering mellan en telefon och en smartklocka?- Hur kan information från smartphones och smartklockor samverka för att ge enmer heltäckande bild i en utredning?- Är GPS-/aktivitetsdata från smarttelefoner tillförlitliga bevis i rättsfall?Forskningen är positionerad mot befintliga studier som undersöker platsbaseradehälsodata, forensisk analys av IoT-enheter och relevansen av data från smartklockor irättsliga sammanhang.Den metodologi som används i studien är strategiskt vald för att erbjuda en omfattandeförståelse av rollen och utmaningarna associerade med data från smartklockor ochsmartphones i forensiska sammanhang. Metoderna inkluderar experiment ochostrukturerad intervju. Genom att anpassa sig till befintliga studier och utnyttjaexperimentella och intervjubaserade tillvägagångssätt, syftar studien till att bidra medvärdefull kunskap till det växande fältet. Resultaten av denna studie förväntas belysaanvändningen av geolokaliserings data i rättsliga utredningar, ge vägledning förframtida praxis och betona den ökande centraliteten av dessa teknologier i dagenssamhälle. Resultaten visar att det finns skillnader mellan enheterna, inte bara mellansmartphones och smartklockor utan också mellan olika märken. Vilken betydelse dettahar kommer att diskuteras och användbara slutsatser dras.

Taxis equipped with GPS sensors are an important sensory device for examining people's movements and activities. They are not constrained to a pre-defined schedule/route. Big taxi GPS data recording the spatio-temporal traces left by taxis provides rich and detailed glimpse into the motivations, behaviours, and resulting dynamics of a city's mobile population through the road network. In this dissertation, we aim to uncover the "hidden facets" regarding social and community dynamics encoded in the taxi GPS data to better understand how urban population behaves and the resulting dynamics in the city. As some "hidden facets" are with regard to similar aspect of social and community dynamics, we further formally define three categories for study (i.e. social dynamics, traffic dynamics, and operational dynamics), and explore them to fill the wide gaps between the raw taxi GPS data and innovative applications and smart urban services. Specifically, 1. To enable applications of real-time taxi fraud alerts, we propose iBOAT algorithm which is capable of detecting anomalous trajectories "on-the-fly" and identifying which parts of the trajectory are responsible for its anomalousness, by comparing them against historically trajectories having the same origin and destination. 2. To introduce cost-effective and environment-friendly transport services to citizens, we propose B-Planner which is a two-phase approach, to plan bi-directional night bus routes leveraging big taxi GPS data. 3. To offer a personalized, interactive, and traffic-aware trip route planning system to users, we propose TripPlanner system which contains both offline and online procedures, leveraging a combination of Location-based Social Network (i.e. LBSN) and taxi GPS data sets. Finally, some promising research directions for future work are pointed out, which mainly attempt to fuse taxi GPS data with other data sets to provide smarter and personalized urban services for citizens

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.

This thesis deals with design and implementation of collecting data from GPS device. The proposed solution provides storage of the current GPS information in NMEA standard, where each sentence of this standard are stored in XML le. Record performs GPS device based on Windows Mobile 6.1 system, which communicates with built-in module and the data are stored in XML format. Thus, stored data is interpreted using Interpret GPS data, which uses Google Maps to display information about the track.