In order to support efficient transportation planning decisions, household travel survey data with high levels of accuracy are essential. Due to a number of issues associated with conventional household travel surveys, including high cost, low response rate, trip misreporting, and respondents’ self-reporting bias, government and private agencies are desperately searching for alternative data collection methods. Recent advancements in smart phones and Global Positioning System (GPS) technologies present new opportunities to track travelers’ trips. Considering the high penetration rate of smartphones, it seems reasonable to use smartphone data as a reliable source of individual travel diary. Many studies have applied GPS-Based data in planning and demand analysis but mobile phone GPS data has not received much attention. The Google Location History (GLH) data provide an opportunity to explore the potential of these data. This research presents a study using GLH data, including the data processing algorithm in deriving travel information and the potential applications in understanding travel patterns. The main goal of this study is to explore the potential of using cell phone GPS data to advance the understanding in mobility and travel behavior. The objectives of the study include: a) assessing the technical feasibility of using smartphones in transportation planning as a substitute of traditional household survey b) develop algorithms and procedures to derive travel information from smartphones; and c) identify applications in mobility and travel behavior studies that could take advantage of these smartphones GPS data, which would not have been possible with conventional data collection methods. This research aims to demonstrate how accurate travel information can be collected and analyzed with lower cost using smartphone GPS data and what analysis applications can be made possible with this new data source. Moreover, the framework developed in this study can provide valuable insights for others who are interested in using cell phone data. GLH data are obtained from 45 participants in a two-month period for the study. The results show great promise of using GLH data as a supplement or complement to conventional travel diary data. It shows that GLH provides sufficient high resolution data that can be used to study people’s movement without respondent burden, and potentially it can be applied to a large scale study easily. The developed algorithms in this study work well with the data. This study supports that transportation data can be collected with smartphones less expensively and more accurately than by traditional household travel survey. These data provide the opportunity to facilitate the investigation of various issues, such as less frequent long-distance travel, hourly variations in travel behavior, and daily variations in travel behavior.

Swedish football is something that develops continuously every year and with the help of various tools within the sport, the sport also gets better based on the quality of the football field. Swedish football teams are getting further out in Europe and playing European championships in both the Champions League and the Europa League. There are small points that are addressed in this study and that is how tools such as biofeedback systems can help players in Swedish football clubs to develop in order for Swedish football standers to be raised and thus make Swedish football become remarkable. In this study, 10 people who work in football are interviewed as either leaders or players from two different clubs, a team where biofeedback systems are used and a team where they use the traditional method of analysis. This distinguishes the clubs because the possibility of better insight into why a system benefits players and the team more should be shown more clearly. They got interview questions they had to answer from their own perspective and how they see it all in biofeedback systems and IT in football and how it has affected football. The football clubs that the respondents come from are BK Häcken and Utsikten BK.

Macroscopic fundamental diagrams (MFD) reveal the relations among flow, speed and density in a large geographic region. After literature review on macroscopic analysis, the similar methodology is applied in this thesis. The purpose of the thesis is to find the evidence that is able to prove MFD existing in Stockholm urban region. Both floating car data (FCD) based on global positioning system (GPS) data from taxis travelling in Stockholm region and traffic data from fixed detectors data source are used to construct the fundamental diagrams. Geographically, the usage of data is extended from single link to multiple links, then to the entire study region. The temporal phase is restricted in one weekday and weekend. The diagram of flow vs. speed based on single detector is found disordered, by contrast, the diagrams of cumulative flow, speed and density for all detectors represent orderly. MFD diagrams proposed in Yokohama case study by Geroliminis and Daganzo are reproduced with cumulative data in this thesis.  Therefore, it can be proved that MFD exists when using data from multiple links. However, the cumulative data from fixed detectors only represents the traffic on links where they locate, not the entire region. To overcome it, GPS data from taxis, which covers the whole region, is analyzed with same method. Because full taxis travel in the same manner as normal vehicles, they are selected to approximate traffic in whole region. A neat curve of flow vs. speed is produced and it coincides with corresponding diagram in the reference paper. It enhances the conclusion that MFD exists in the entire study region. Moreover, based on the constant ratio between average link flow and region exit flow, a controlling density policy is discussed in aiming for maximizing trip completion.

Introduction: Women soccer has increased in players participation over the last decade. Despite this, data is lacking in the transition between amateur and elite soccer, as most studies include male players. It’s important to monitor and understand the players’ training load, to give them good opportunity to develop and progress in their sport and minimize the injury risk. Method: This study includes 18 female participants from the same elite club in Sweden, 10 senior (23,3 ± 2,5 yrs) and eight U-19 players (18,9 ± 0,6 yrs). Data were collected during one training week and one match in pre- and game season. To measure the external load several GPS parameters were collected, and the internal load was measured using a self-assessment Borg CR10 scale. Thus, session total training- and match load was calculated by multiplying training time with CR10 (sRPE). Results: The senior team had a higher total training (sRPE) than U-19 during pre- and game season during a week’s training. The senior-team covered longer distances in higher speed zones, while U-19 had longer distance in lower speed zones during training and matches. The senior team had more number of sprints and reached a higher maximal speed at trainings and matches than U-19 during game season. Conclusion: To our knowledge this is one of the first studies on training- and match load comparing a senior and a U-19 team within the same elite club in Sweden. Data show that the senior team had higher training load than the U-19 team. This may be interpreted that there is a significant gap in training and game load between a senior and U-19 team. In the future it may important to minimize this gap when young players move up from junior to senior level, to handle the load and possible avoid injuries. / Introduktion: Damfotbollen har ökat i deltagarantal under det senaste decenniet. Trots detta saknas det studier på damsidan gällande övergång från amatör till elitfotboll, eftersom de flesta studier inkluderar manliga spelare. Det är viktigt att monitorera och förstå belastningen för spelarna för att de ska få en så bra träning som möjligt, samt att prestera på topp och minska skaderisken. Metod: I studien deltog 18 kvinnliga spelare från samma elitklubb i Sverige, varav 10 var A-lagsspelare (23,3 ± 2,5 år) och åtta var F-19 spelare (18,9 ± 0,6 år). Data samlades in under en träningsvecka och en match under för- och matchsäsong. För att mäta extern belastning samlades flera GPS parametrar in. Den interna belastningen mättes med hjälp avsjälvskattning via Borg CR10 skalan. Den totala belastningen beräknades genom att multiplicera träningstid med CR10 (sRPE). Resultat: A-laget hade högre totalbelastning (sRPE) än F-19 under träning i för- och matchsäsong. A-laget visar längre distans i de högre hastighetszonerna och F-19 visar längre distans i de lägre hastighetszonerna under både träning och match. A-laget har fler sprinter och kommer upp i en högre maxhastighet än F19 under matchsäsong i både träning och match. Konklusion: Så vitt vi vet, är det här är en av de första studierna om tränings- och matchbelastning mellan ett A-lag och ett F-19 lag inom samma elitförening i Sverige. Datan visar att A-laget hade högre total belastning, vilket kan tolkas som att steget i belastningen mellan F-19 och A-laget är betydelsefullt. Det kan vara viktigt i framtiden att minska det gapet när unga spelare ska ta steget upp till seniorfotboll för att klara belastningen ocheventuellt undvika skador.

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.

This thesis presents a comprehensive exploratory analysis of truck route choice diversity in the state of Florida, for both long-haul and short-haul truck travel segments. We employ six metrics to measure three different dimensions of diversity in truck route choice between any given origin-destination (OD) pair. These dimensions are: (1) number of distinct routes used to travel between the OD pair, (2) the extent of overlap (or lack thereof) among the routes, and (3) the evenness (or the dominance) of the usage of different unique routes. The diversity metrics were utilized to examine truck route choice diversity from over 73,000 truck trips that were derived from over 200 million GPS records of a large truck fleet. Descriptive analysis and statistical modeling of the diversity metrics offered insights on the determinants of various dimensions of truck route choice diversity between an OD pair. The results could be used to improve choice set generation algorithms for truck route choice modeling as well as in planning truck route policies and investments.

Increase of cycling in cities has many positive effects: increased traffic safety, better public health, decreased noise, and air, and climate impact. All these effects contribute to reach the set sustainability goals, both on global and national and local level. The attractiveness of cycling affects whether to choose the bicycle as a transport mode or not. One way to increase the attractiveness of cycling is to improve the mobility of cyclists. The aim of this study was to improve methods that evaluates mobility of cyclists, methods that contributes to understand how bicycle facilities with good mobility are best designed, which thus increases the attractiveness of cycling. In this study a method that combines a visual analysis and a statistical analysis has been developed. The method compare bicycle facilities’ geometric factors with cyclists’ deviations from their desired speeds, which in this study is assumed to be every cyclists’ mean speed of each trip. This method gives a visual picture of how cyclists’ mobility differs between different streets in the city, and the big amount of data allows results with high significance level. An improvement of the method, that would better combine speeds’ exact location with the street segments, could improve the level of description of geometric factors’ effects on cyclists’ speeds. The results from this method, which evaluates cyclists’ mobility on bicycle facilities, shows that there are significant relations between bicycle facilities’ geometric factors and cyclists’ mobility. Down slope seem to have the biggest positive effect, where cyclists allows to drive closer to their desired speed. Whether bicycle facilities are single or double directional also seem to affect cyclists’ speed. However, the reason to the different speeds might be the width combined with the actual use of direction of these bicycle facilities. / Att öka cykeltrafiken i städer har många positiva effekter: ökad trafiksäkerhet, bättre folkhälsa, minskad buller-, luft- och klimatpåverkan. Alla dessa nämna effekter bidrar till att uppnå satta hållbarhetsmål både på global, national och lokal nivå. Attraktiviteten på cyklandet påverkar om man väljer cykeln som transportsätt eller inte. Ett sätt att öka attraktiviteten för cykling är att förbättra framkomligheten för cyklister. Syftet med detta arbete har varit att förbättra metoder som utvärderar framkomligheten för cyklister, metoder som i sin tur bidrar till att förstå hur framkomliga cykelvägar bäst utformas, som i sin tur ökar attraktiviteten för cykling. I detta arbete har det utvecklats en metod som kombinerar en visuell analys med en statistisk analys. Metoden jämför utformningsmässiga faktorer med cyklisters avvikelser från sina önskade hastigheter, vilket i detta arbete antas vara varje enskild cyklists medelhastighet av en cykelrutt. Denna metod ger en visuell bild på hur cyklisters framkomlighet skiljer sig åt på olika gator i staden, och den stora mängden data möjliggör resultat med hög signifikansnivå. En förbättring av metoden, som bättre kombinerar hastigheters exakta läge med cykelbanesegmenten, skulle kunna öka beskrivningsgraden av utformningsmässiga faktorers påverkan på cyklisters hastigheter. Resultaten från denna metod, som utvärderar cyklisters framkomlighet på cykelbanor, visar att det finns signifikanta samband mellan utformningsmässiga faktorer och cyklisters framkomlighet. Störst positiv effekt verkar nedförsbacke ha, där cyklister i stor grad tillåts köra nära sin medelhastighet. Huruvida cykelbanor är enkelriktade eller dubbelriktade verkar också påverka cyklisters hastigheter, dock beror antagligen skillnaderna på bredd kombinerat med hur dessa cykelbanor faktiskt används.

With the recent upsurge in mental health concerns and ongoing isolation regulations brought about by the COVID-19 pandemic, it is important to understand how an individual's daily travel behavior can affect their mental health. Before finding any correlations to mental health, researchers must first have individual travel behavior information: an accurate number of activities and locations of those activities. One way to obtain daily travel behavior information is through the interpretation of cellular Global Positioning System (GPS) data. Previous methods that interpret GPS data into travel behavior information have limitations. Specifically, rule-based algorithms are structured around subjective rule-based tests, clustering algorithms include only spatial parameters that are chosen sequentially or require further exploration, and imputation algorithms are sensitive to provided context (input parameters) and/or require lots of training data to validate the results of the algorithm. Due to the lack of provided training data that would be required for an imputation algorithm, this thesis uses a previously adopted clustering method. The objective of this thesis is to determine which spatial, entropy, and time parameters cause the clustering algorithm to give the most accurate travel behavior results. This optimal set of parameters was determined using a comparison of two non-linear optimization methods: simulated annealing and a limited-memory Broyden-Fletcher-Goldfarb-Shanno Bound (L-BFGS-B) optimizer. Ultimately, simulated annealing optimization found the best set of clustering parameters leading to 91% clustering algorithm accuracy whereas L-BFGS-B optimization found parameters that were only able to produce a maximum of 79% accuracy. Using the most optimal set of parameters in the clustering algorithm, an entire set of GPS data can be interpreted to determine an individual's daily travel behavior. This resulting individual travel behavior sets the groundwork to answer the question of how individual travel behavior can affect mental health.

The fast development in telecommunication is producing a huge amount of data related to how people move and behave over time. Nowadays, travel data are mainly collected through Global Positioning Systems (GPS) and can be used to identify human mobility patterns and travel behaviors. Transport mode detection (TMD) aims to identify the means of transport used by an individual and is a field that has become more popular in recent years as it can be beneficial for various applications. However, developing travel models requires different types of information that can be extracted from raw travel data. Although many useful features like speed, acceleration and bearing rate can be extracted from raw GPS data, detecting transport modes requires further processing. Some previous studies have successfully applied machine learning algorithms for detecting the transport mode. Despite achieving high performance in their models, many of these studies have used rather small datasets generated from a limited number of users or identified a small number of different transport modes. Furthermore, in most of these studies more complex methodologies have been applied, where extra information like GIS layers or road and railway networks were required. The purpose of this study is to propose a simple supervised learning model to identify five common transport modes on large datasets by only using raw GPS data. In total, six commonly used supervised learning algorithms are tested on seven selected features (extracted from raw GPS data). The Random Forest (RF) algorithm proves to perform better in detecting five transport modes from the dataset utilized in this study, with an overall accuracy of 82.7%.

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.