The Swedish government’s objective regarding information technology is to be world-leading in using the possibilities that digitization provides. In some aspects this has already been done, but in some areas much is yet to be accomplished. Although sports and recreational fitness activities are amongst the fastest growing areas of personal and consumer-oriented cloud computing-based technologies around the globe, there are areas within the genre that’s not evolving as quickly. In Sweden, football is the biggest sport but it lacks in the use of technology. Related research shows that usable technology exists, but the using of it is hampered by its accessibility. Meanwhile, Urban Computing is an growing, interdisciplinary field which pertains to the study and application of computing technology in urban areas. This made us raise the question whether the technology used in Urban Computing can influence the field of Sport-IT and provide valuable insight in designing the next generation football coaching tool. The purpose of this study is therefore to examine the outcome of integrating IT-solutions from Urban Computing into environments suited for team sports and combining them with the individualized technologies that already exists in Sport-IT. We also explore what technology requirements football coaches have and how the weather and the climate effects designing the next generation coaching tool. The result of the study indicates that the needs of football coaches, as of today, is not met and that Urban Computing very well may influence the development of the next generation coaching tool.

Emergence of autonomous vehicles (AVs) offers the potential to fundamentally transform the way how urban transport systems be designed and deployed, and alter the way we view private car ownership. In this thesis we advocate a forward-looking, ambitious and disruptive smart cloud commuting system (SCCS) for future smart cities based on shared AVs. Employing giant pools of AVs of varying sizes, SCCS seeks to supplant and integrate various modes of transport -- most of personal vehicles, low ridership public buses, and taxis used in today€™s private and public transport systems -- in a unified, on-demand fashion, and provides passengers with a fast, convenient, and low cost transport service for their daily commuting needs. To explore feasibility and efficiency gains of the proposed SCCS, we model SCCS as a queueing system with passengers' trip demands (as jobs) being served by the AVs (as servers). Using a 1-year real trip dataset from Shenzhen China, we quantify (i) how design choices, such as the numbers of depots and AVs, affect the passenger waiting time and vehicle utilization; and (ii) how much efficiency gains (i.e., reducing the number of service vehicles, and improving the vehicle utilization) can be obtained by SCCS comparing to the current taxi system. Our results demonstrate that the proposed SCCS system can serve the trip demands with 22% fewer vehicles and 37% more vehicle utilization, which shed lights on the design feasibility of future smart transportation systems.

Due to the rapid growth of urban areas, sustainable urbanization is an inevitable task for city planners to address major challenges in resource management across different sectors. Sustainable approaches of energy production, distribution, and consumption must take the place of traditional methods to reduce the negative impacts of urbanization such as global warming and fast consumption of fossil fuels. In order to enable the transition of cities to sustainable ones, we need to have a precise understanding of the city dynamics. The prevalence of big data has highlighted the importance of data-driven analysis on different parts of the city including human movement, physical infrastructure, and economic activities. Sustainable urban mobility (SUM) is the problem domain that addresses the sustainability issues in urban areas with respect to city dynamics and people movements in the city. Hence, to realize an integrated solution for SUM, we need to study the problems that lie at the intersection of energy systems and mobility. For instance, electric vehicle invention is a promising shift toward smart cities, however, the impact of high adoption of electric vehicles on different units such as electricity grid should be precisely addressed. In this dissertation, we use data analytics methods in order to tackle major issues in SUM. We focus on mobility and energy issues of SUM by characterizing transportation networks and energy networks. Data-driven methods are proposed to characterize the energy systems as well as the city dynamics. Moreover, we propose anomaly detection algorithms for control and management purposes in smart grids and in cities. In terms of applications, we specifically investigate the use of electrical vehicles for personal use and also for public transportation (i.e. electric taxis). We provide a data-driven framework to propose optimal locations for charging and storage installation for electric vehicles. Furthermore, adoption of electric taxi fleet in dense urban areas is investigated using multiple data sources. / Ph. D.

Traffic congestion has become one of the major challenging problems of modern life in many urban areas. This growing problem leads to negative environmental impacts, wasted fuel, lost productivity, and increased travel time. In big cities, trains and buses bring riders to transit stations near shopping and employment centers, but riders then need another transportation mode to reach their final destination, which is known as the last mile problem. A smart bike-sharing system (BSS) can help address this problem and encourage more people to ride public transportation, thus relieving traffic congestion. At the strategic level, we start with proposing a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms. In the transportation sector, researchers can use smartphones to track and obtain information of multi-mode trips. These data can be used to recognize the user's transportation mode, which can be then utilized in several different applications; such as planning new BSS instead of using costly surveys. Next, a new method is proposed to quantify the effect of several factors such as weather conditions on the prediction of bike counts at each station. The proposed approach is promising to quantify the effect of various features on BSSs in cases of large networks with big data. Third, these resulted significant features were used to develop state-of-the-art toolbox algorithms to operate BSSs efficiently at two levels: network and station. Finally, we proposed a quality-of-service (QoS) measurement, namely Optimal Occupancy, which considers the impact of inhomogeneity in a BSS. We used one of toolbox algorithms modeled earlier to estimate the proposed QoS. Results revealed that the Optimal Occupancy is beneficial and outperforms the traditionally-known QoS measurement. / PHD / A growing population, with more people living in cities, has led to increased pollution, noise, congestion, and greenhouse gas emissions. One possible approach to mitigating these problems is encouraging the use of bike-sharing systems (BSSs). BSSs are an integral part of urban mobility in many cities and are sustainable and environmentally friendly. As urban density increases, it is likely that more BSSs will appear due to their relatively low capital and operational costs, ease of installation, pedal assistance for people who are physically unable to pedal for long distances or on difficult terrain, and the ability to track bikes in some cases. This dissertation is a building block for a smart BSS in the strategic level, which could be used in real and different applications. The main aims of the dissertation are to boost the redistribution operation, to gain new insights into and correlations between bike demand and other factors, and to support policy makers and operators in making good decisions regarding planning new or existing BSS. This dissertation makes many significant contributions. These contributions include novel methods, measurements, and applications using machine learning and statistical learning techniques in order to design a smart BSS. We start with proposing a novel framework that increases the accuracy of traditional transportation mode classification algorithms. In the transportation sector, researchers can use smartphones to track and obtain information of multi-mode trips. These data can be used to recognize the user’s transportation mode, which can be then used in planning new BSS. Next, a new method is proposed to quantify the effect of several factors such as weather conditions on the prediction of bike station counts. Third, we use state-of-the-art data analytics to develop a toolbox to operate BSSs efficiently at two levels: network and station. Finally, we propose a quality-of-service (QoS) measurement, which considers the impact of inhomogeneity of BSS properties.

The rapid increase in urbanization poses challenges in diverse areas such as energy, transportation, pandemic planning, and disaster response. Planning for urbanization is a big challenge because cities are complex systems consisting of human populations, infrastructures, and interactions and interdependence among them. This dissertation focuses on a synthetic information-based approach for modeling human activities and behaviors for two urban science applications, epidemiology and disaster planning, and with associated analytics. Synthetic information is a data-driven approach to create a detailed, high fidelity representation of human populations, infrastructural systems and their behavioral and interaction aspects. It is used in developing large-scale simulations to model what-if scenarios and for policy making. Big cities have a large number of visitors visiting them every day. They often visit crowded areas in the city and come into contact with each other and the area residents. However, most epidemiological studies have ignored their role in spreading epidemics. We extend the synthetic population model of the Washington DC metro area to include transient populations, consisting of tourists and business travelers, along with their demographics and activities, by combining data from multiple sources. We evaluate the effect of including this population in epidemic forecasts, and the potential benefits of multiple interventions that target transients. In the next study, we model human behavior in the aftermath of the detonation of an improvised nuclear device in Washington DC. Previous studies of this scenario have mostly focused on modeling physical impact and simple behaviors like sheltering and evacuation. However, these models have focused on optimal behavior, not naturalistic behavior. In other words, prior work is focused on whether it is better to shelter-in-place or evacuate, but has not been informed by the literature on what people actually do in the aftermath of disasters. Natural human behaviors in disasters, such as looking for family members or seeking healthcare, are supported by infrastructures such as cell-phone communication and transportation systems. We model a range of behaviors such as looking for family members, evacuation, sheltering, healthcare-seeking, worry, and search and rescue and their interactions with infrastructural systems. Large-scale and complex agent-based simulations generate a large amount of data in each run of the simulation, making it hard to make sense of results. This leads us to formulate two new problems in simulation analytics. First, we develop algorithms to summarize simulation results by extracting causally-relevant state sequences - state sequences that have a measurable effect on the outcome of interest. Second, in order to develop effective interventions, it is important to understand which behaviors lead to positive and negative outcomes. It may happen that the same behavior may lead to different outcomes, depending upon the context. Hence, we develop an algorithm for contextual behavior ranking. In addition to the context mentioned in the query, our algorithm also identifies any additional context that may affect the behavioral ranking. / Ph. D.

The study of information extraction and knowledge exploration in the urban environment is gaining popularity. Ubiquitous sensors and a plethora of statistical reports provide an immense amount of heterogeneous urban data, such as traffic data, crime activity statistics, social media messages, and street imagery. The development of methods for heterogeneous urban data-based event identification and impacts analysis for a variety of event topics and assumptions is the subject of this dissertation. A graph convolutional neural network for crime prediction, a multitask learning system for traffic incident prediction with spatiotemporal feature learning, social media-based transportation event detection, and a graph convolutional network-based cyberbullying detection algorithm are the four methods proposed. Additionally, based on the sensitivity of these urban sensor data, a comprehensive discussion on ethical issues of urban computing is presented. This work makes the following contributions in urban perception predictions: 1) Create a preference learning system for inferring crime rankings from street view images using a bidirectional convolutional neural network (bCNN). 2) Propose a graph convolutional networkbased solution to the current urban crime perception problem; 3) Develop street view image retrieval algorithms to demonstrate real city perception. This work also makes the following contributions in traffic incident effect analysis: 1) developing a novel machine learning system for predicting traffic incident duration using temporal features; 2) modeling traffic speed similarity among road segments using spatial connectivity in feature space; and 3) proposing a sparse feature learning method for identifying groups of temporal features at a higher level. In transportation-related incidents detection, this work makes the following contributions: 1) creating a real-time social media-based traffic incident detection platform; 2) proposing a query expansion algorithm for traffic-related tweets; and 3) developing a text summarization tool for redundant traffic-related tweets. Cyberbullying detection from social media platforms is one of the major focus of this work: 1) Developing an online Dynamic Query Expansion process using concatenated keyword search. 2) Formulating a graph structure of tweet embeddings and implementing a Graph Convolutional Network for fine-grained cyberbullying classification. 3) Curating a balanced multiclass cyberbullying dataset from DQE, and making it publicly available. Additionally, this work seeks to identify ethical vulnerabilities from three primary research directions of urban computing: urban safety analysis, urban transportation analysis, and social media analysis for urban events. Visions for future improvements in the perspective of ethics are addressed. / Doctor of Philosophy / The ubiquitously deployed urban sensors such as traffic speed meters, street-view cameras, and even smartphones in everybody's pockets are generating terabytes of data every hour. How do we refine the valuable intelligence out of such explosions of urban data and information became one of the profitable questions in the field of data mining and urban computing. In this dissertation, four innovative applications are proposed to solve real-world problems with big data of the urban sensors. In addition, the foreseeable ethical vulnerabilities in the research fields of urban computing and event predictions are addressed. The first work explores the connection between urban perception and crime inferences. StreetNet is proposed to learn crime rankings from street view images. This work presents the design of a street view images retrieval algorithm to improve the representation of urban perception. A data-driven, spatiotemporal algorithm is proposed to find unbiased label mappings between the street view images and the crime ranking records. The second work proposes a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework. Such functionality provided by this model is helpful for the transportation operators and first responders to judge the influences of traffic incidents. In the third work, a social media-based traffic status monitoring system is established. The system is initiated by a transportation-related keyword generation process. A state-of-the-art tweets summarization algorithm is designed to eliminate the redundant tweets information. In addition, we show that the proposed tweets query expansion algorithm outperforms the previous methods. The fourth work aims to investigate the viability of an automatic multiclass cyberbullying detection model that is able to classify whether a cyberbully is targeting a victim's age, ethnicity, gender, religion, or other quality. This work represents a step forward for establishing an active anti-cyberbullying presence in social media and a step forward towards a future without cyberbullying. Finally, a discussion of the ethical issues in the urban computing community is addressed. This work seeks to identify ethical vulnerabilities from three primary research directions of urban computing: urban safety analysis, urban transportation analysis, and social media analysis for urban events. Visions for future improvements in the perspective of ethics are pointed out.

Abstract This dissertation seeks to identify and discuss challenges related to the engagement process of multipurpose public displays (MPD) in urban spaces. MPD is a public display concept based on the current emergence of passive public displays, which again is part of the growth of digital signage as a medium for commercial and non-commercial content. MPDs are separated from contemporary public displays by two traits: interactivity and new use cases. Due to these traits, a better understanding of the potential of the MPD concept is needed, and this, in its turn, necessitates both a systematic and a multidisciplinary approach. The investigation on the MPD concept and its related engagement process carried out in this thesis is divided into two phases. First, the theoretical phase is based on an extensive and analytical literature review and results in a theoretical framework based on two contributions: a layered design space for capturing the challenges related to design of MPDs in a systematic way, as well as formulation of a three-phase engagement process to model the engaging of MPDs in practice. These two formalizations facilitate reasoning on different aspects of MPD design, and thus scaffold future designs and deployments. Second, the empirical phase is based on a collection of case studies each of which investigates selected sections of the overall theoretical framework along with serving to illustrate how the sections under investigation operationalize in practice. The overall contribution of this dissertation is thus both to lay out a framework for a wider research area, as well as to raise selected findings as part of the framework through the case studies. The findings derived on the basis of the design space, as well as the engagement process indicate the complexity of the design process for MPDs, even in cases where only the aspects of human-computer interaction (HCI) are considered. They also serve to raise the importance of non-functional issues in real-world MPD deployments, most notably, the mental models embodied by current public displays that citizens implicitly transfer over to MPDs as well. For future designs, careful leveraging of existing practices and mental models is crucial to facilitate the adoption of MPDs and to fully realize their potential as flexible urban computing tools. / Tiivistelmä Tämä väitöskirja pyrkii tunnistamaan ja analysoimaan monikäyttöisten julkisten näyttöjen (multipurpose public display, MPD) käyttöön liittyviä haasteita. MPD on uusi kaupunkitiloissa olevien julkisten näyttöjen konsepti, joka perustuu nykyisten passiivisten julkisten näyttöjen sekä niissä esitettävän digitaalisen kyltityksen (digital signage) pohjalle. MPD eroaa konseptitasolla nykyisistä julkisista näytöistä pääasiassa kahdella tavalla: interaktiivisuudella sekä uusilla käyttötarkoituksilla. Näistä eroavaisuuksista sekä kaupunkitilojen yleisemmästä luonteesta johtuen MPD-konseptin parempi ymmärrys ja sitä kautta hyödyntäminen tulevaisuudessa edellyttää sekä järjestelmällistä että tieteidenvälistä tutkimusotetta. Tässä työssä tehty tutkimus jakaantuu ylimmällä tasollaan kahteen vaiheeseen. Ensimmäinen teoreettinen vaihe pohjautuu laajaan kirjallisuuskatsaukseen ja kulminoituu teoreettiseen viitekehykseen, joka koostuu kahdesta osasta. Ensimmäinen osa on kerroksittainen suunnitteluavaruus (design space), jossa pyritään MPD-konseptiin liittyvien haasteiden ja mahdollisuuksien kartoittamiseen tutkimuksen nykytila huomioonottaen. Toinen osa on teorisoitu esitys MPD-konseptin käyttöprosessista (engagement process) kaupunkilaisten näkökulmasta koostuen kolmesta osittain limittyvästä vaiheesta. Nämä kaksi teoreettista osaa tarjoavat pohjaa MDP-konseptiin pohjautuvalle suunnittelulle tulevaisuudessa. Toinen empiirinen vaihe rakentuu kolmen tapaustutkimuksen kokoelmasta, jossa jokainen yksittäinen tapaustutkimus pohjautuu tiettyihin esitetyn teorian osa-alueisiin ja näin ollen myös esittelee, miten suunitteluavaruus sekä käyttöprosessin malli voivat realisoitua käytännössä. Työn kontribuutio koostuu siis laajemman teoreettisen kehyksen muodostamisesta sekä tämän kehyksen määrittämässä fokuksessa tehdyistä tapaustutkimuksista. Työssä saavutetut tulokset auttavat hahmottamaan MPD-konseptiin liittyvän suunnittelun kompleksisuutta tilanteissa, joissa on keskitytty pääasiassa ihminen-kone vuorovaikutuksen (human-computer interaction, HCI) tutkimiseen. Tapaustukimukset nostavat esille myös ns. non-funktionaalisten tekijöiden roolin autenttisissa kaupunkitiloissa tapahtuvassa empiirisessä ja konstruktiivisessa tutkimuksessa. Tässä tärkeään rooliin nousevat etenkin niin kutsutut mentaalimallit, joiden kautta kaupunkilaiset hahmottavat MPD-konseptia. Työn tulosten perusteella voidaan todeta, että MPD-konseptiin pohjautuvassa suunnittelussa tulee korostaa olemassa olevien urbaanien sosiokulttuuristen käytäntöjen roolia. Näin MPD-konseptin käytöstä voidaan tulevaisuudessa saada sujuvampaa ja luontevampaa, ja MPD-konsepti voisi tulevaisuudessa olla keskeisempi osa urbaania sosiokulttuurista rakennetta.

<div>This dissertation focuses on developing a series of mathematical models to understand the role of urban transportation system, urban mobility and information dissemination in the spreading process of infectious diseases within metropolitan areas. Urban transportation system serves as the catalyst of disease contagion since it provides the mobility for bringing people to participate in intensive urban activities and has high passenger volume and long commuting time which facilitates the spread of contagious diseases. In light of significant needs in understanding the connection between disease contagion and the urban transportation systems, both macroscopic and microscopic models are developed and the dissertation consists of three main parts. </div><div></div><div>The first part of the dissertation aims to model the macroscopic level of disease spreading within urban transportation system based on compartment models. Nonlinear dynamic systems are developed to model the spread of infectious disease with various travel modes, compare models with and without contagion during travel, understand how urban transportation system may facilitate or impede epidemics, and devise control strategies for mitigating epidemics at the network level. The hybrid automata is also introduced to account for systems with different levels of control and with uncertain initial epidemic size, and reachability analysis is used to over-approximate the disease trajectories of the nonlinear systems. The 2003 Beijing SARS data are used to validate the effectiveness of the model. In addition, comprehensive numerical experiments are conducted to understand the importance of modeling travel contagion during urban disease outbreaks and develop control strategies for regulating the entry of urban transportation system to reduce the epidemic size. </div><div></div><div>The second part of the dissertation develops a data-driven framework to investigate the disease spreading dynamics at individual level. In particular, the contact network generation algorithm is developed to reproduce individuals' contact pattern based on smart card transaction data of metro systems from three major cities in China. Disease dynamics are connected with contact network structures based on individual based mean field and origin-destination pair based mean field approaches. The results suggest that the vulnerability of contact networks solely depends on the risk exposure of the most dangerous individual, however, the overall degree distribution of the contact network determines the difficulties in controlling the disease from spreading. Moreover, the generation model is proposed to depict how individuals get into contact and their contact duration, based on their travel characteristics. The metro data are used to validate the correctness of the generation model, provide insights on monitoring the risk level of transportation systems, and evaluate possible control strategies to mitigate the impacts due to infectious diseases. </div><div></div><div>Finally, the third part of the dissertation focuses on the role played by information in urban travel, and develops a multiplex network model to investigate the co-evolution of disease dynamics and information dissemination. The model considers that individuals may obtain information on the state of diseases by observing the disease symptoms from the people they met during travel and from centralized information sources such as news agencies and social medias. As a consequence, the multiplex networks model is developed with one layer capturing information percolation and the other layer modeling the disease dynamics, and the dynamics on one layer depends on the dynamics of the other layer. The multiplex network model is found to have three stable states and their corresponding threshold values are analytically derived. In the end, numerical experiments are conducted to investigate the effectiveness of local and global information in reducing the size of disease outbreaks and the synchronization between disease and information dynamics is discussed. </div><div></div>

Access to computers, to mobile phones, and to data connectivity has opened new avenues of interaction and created expectations about the flattening of society brought about by these new modes of production. These technologies have enabled us to recognize many forms of community---from close knit social groups to individuals who merely co-habit public spaces---and to support interaction with each other in novel ways. The notion that modern digital technology holds promises of democratization by expanding access to information and broadening modes of knowledge production often fails to acknowledge that these benefits rely upon devices and infrastructure whose availability reflect socioeconomic contours; that the technologies that enable information access can also reinforce rather than obviate marginality due to barriers to access and suitability. This assessment points to opportunities for better understanding and better designing technologies for the marginalized or dispossessed. The research presented in this dissertation discusses the findings from empirical, theoretical, and design based investigations of technology use with the urban homeless. The empirical work provides a foundation for understanding current technology practices among the homeless and their care providers. The theoretical investigation develops Deweyan publics as a novel frame for participatory design. The design-based investigation presents findings from the design and deployment of the Community Resource Messenger at a shelter for homeless mothers. The results of this research shed light on impact of social computing platforms on social service provision and on the ways the staff and residents used the Community Resource Messenger as a resource for identifying common issues and taking action to contend with those issues.

A transportation system is a critical infrastructure that is key for mobility in any community. Natural hazards can cause failure in transportation infrastructure and impede its routine performance. Ecological systems are resilient systems that are very similar to transportation systems. Diversity is a fundamental factor in ecological resilience, and it is recognized as an important property of transportation resilience. However, quantifying transportation diversity remains challenging, which makes it difficult to understand the influence of diversity on transportation performance and resilience. Consequently, three studies are undertaken to remedy this circumstance. The first study develops a novel approach – inspired by biodiversity in ecological stability theory – to characterize and measure transportation diversity by its richness (availability) and evenness (distribution). This transportation diversity approach is then applied to New York City (NYC) at the zip code level using the GIS data of transportation modes. The results demonstrate the variation of transportation diversity across the city. The characterized inherent and augmented complementarities start to uncover the dynamics of modal compensation and to demonstrate how transportation diversity contributes to this phenomenon. Moreover, the NYC zip codes with low transportation diversity are mainly in hurricane evacuation zones that are more vulnerable. Consequently, low transportation diversity in these areas could affect their post-disaster mobility. In the second study, the influence of transportation diversity on post-disaster mobility is examined by investigating the patterns of mobility in New York City one month before and after Hurricane Sandy using Twitter data. To characterize pre- and post-Sandy mobility patterns, the locations that individuals visited frequently were identified and travel distance, the radius of gyration, and mobility entropy were measured. Individuals were grouped according to the transportation diversity of their frequently visited locations. The findings reveal that individuals that lived in or visited zip codes with higher transportation diversity mostly experienced less disturbance in their mobility patterns after Sandy and the recovery of their mobility patterns was faster. The results confirm that transportation diversity affects the resilience of individual post-disaster mobility. The approach used in this study is one of the first to examine the root causes of changes in mobility patterns after extreme events by linking transportation infrastructure diversity to post-disaster mobility. Finally, the third study employs the transportation diversity approach to investigate modal accessibility and social exclusion. Transportation infrastructure is a sociotechnical system and transport equity is crucial for access to opportunities and services such as jobs and infrastructure. The social exclusion caused by transport inequity could be intensified after natural disasters that can cause failure in a transportation system. One approach to determine transport equity is access to transportation modes. Common catchment area approaches to assess the equity of access to transportation modes cannot differentiate between the equity of access to modes in sub-regions of an area. The transportation diversity approach overcomes this shortcoming, and it is applied to all transportation modes in NYC zip codes to measure the equity of access. Zip codes were grouped in quartiles based on their transportation diversity. Using the American Community Survey data, a set of important socioeconomic and transport usage factors were compared in the quartile groups. The results indicated the relationship between transportation diversity and income, vehicle ownership, commute time, and commute mode. This relationship highlighted that social exclusion is linked with transport inequity. The results also revealed that the inequity of the transport system in zip codes with low transportation diversity affects poor individuals more than non-poor and the zip codes with a majority of black and Hispanic populations are impacted more. Further consideration of the impacts of Hurricanes Irene and Sandy in NYC shows that people in areas with a lower transportation diversity were affected more and the transport inequity in these areas made it difficult to cope with these disasters and caused post-disaster social exclusion. Therefore, enhancing transportation diversity should support transport equity and reduce social exclusion under normal situations and during extreme events. Together, these three studies illustrate the influence of transportation diversity on the resilience of this infrastructure. They highlight the importance of the provision and distribution of all transportation modes, their influence on mobility during normal situations and extreme events and their contribution toward mitigating social exclusion. Finally, these studies suggest that transportation diversity can contribute to more targeted and equitable transportation and community resilience planning, which should help decision-makers allocate scarce resources more effectively. / Doctor of Philosophy / Transportation systems are very important in every city. Natural disasters like hurricanes and floods can destroy roads and inundate metro tunnels that can cause problems for mobility. Ecological systems like forests are very resilient because they have experienced disturbances like natural disasters for millions of years. Ecological systems and transportation systems are very similar; for example, both have different components (different species in an ecological system and different modes in a transportation system). Because of such similarities, we can learn from ecological resilience to improve transportation resilience. Having a variety of species in an ecological system makes it diverse. Diversity is the most important factor in ecological resilience, and it is also recognized as an important factor in transportation resilience. Current methods cannot effectively quantify transportation diversity – the variety of modes in a system – so determining its impact on transportation resilience remains a challenge. In this dissertation, principles of ecological diversity are adapted to characterize transportation infrastructure to develop a new approach to measure transportation diversity; metrics include the availability of transportation modes and their distribution in a community. The developed approach was applied in New York City (NYC) at the zip code level. Locations with low transportation diversity (fewer modes and/or unequal distribution) were identified, and most of these zip codes are located in hurricane evacuation zones. Consequently, these zip codes with the least diverse transportation systems are the most vulnerable, which can cause serious issues during emergency evacuations and the ability of people to access work or essential services. Therefore, in a city hit by a natural disaster, understanding the relationship between people's mobility and a transportation system's diversity is important. Twitter data was used to find the places that people in NYC visited regularly for one month before and one month after Hurricane Sandy. Subsequently, using different methods, the pre- and post-disaster mobility patterns of these individuals were characterized. The results show that after the disaster, individuals had a higher chance of maintaining their pre-disaster mobility patterns if they were living in and/or visiting areas with high transportation diversity. Based on these findings, we confirmed the influence of transportation diversity on post-disaster mobility. In addition, the transportation infrastructure should provide equitable service to all individuals, during normal operations and extreme events. One of the ways to determine this equality is equity of access to transportation modes. Hence, transportation diversity was used as an indicator for equity of access to transportation modes to overcome the limitations of current methods like catchment area approaches. NYC zip codes were grouped based on their transportation diversity and a set of important socioeconomic and transport related factors were compared among these groups. The comparison of socioeconomic and transport related factors in zip codes showed that the zip codes with lower transportation diversity are also more socioeconomically deprived. This highlights the likely influence of transportation diversity on social exclusion. Further consideration of the impacts of Hurricanes Irene and Sandy in NYC shows that people in areas with a lower transportation diversity were affected more and the transport inequity in these areas made it difficult to cope with these disasters and caused post-disaster social exclusion. Therefore, enhancing transportation diversity should support transport equity and reduce social exclusion under normal situations and during extreme events. The investigations conducted highlight the importance of the provision and distribution of all transportation modes, their influence on mobility during normal situations and extreme events and their contribution toward mitigating social exclusion. Finally, the collective results suggest that transportation diversity can contribute to more targeted and equitable transportation and community resilience planning, which should help decision-makers allocate scarce resources more effectively.