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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.

Sistemas de Bicicletas Compartilhadas (SBCs) têm sido implantados e aprimorados nos últimos anos nas principais cidades do mundo. Neste tipo de sistema, usuários podem retirar e devolver bicicletas em qualquer estação da rede, desde que haja bicicleta e vaga disponível, respectivamente. Porém, devido às características de ocupação do solo em grandes centros urbanos, existe uma tendência natural de desbalanceamento nos fluxos dos usuários, fazendo com que em determinados horários certas estações fiquem lotadas de bicicletas enquanto outras estações estão vazias. Para mitigar este problema, gestores de SBCs utilizam veículos de carga para rebalancear o sistema (reposicionar as bicicletas entre as estações). Entretanto, usualmente, esse processo na prática não é realizado com auxílio de ferramentas quantitativas que tornem o processo racional ou maximizem sua eficácia. Nesse sentido, no presente trabalho é proposto um modelo híbrido de simulação com otimização, aplicado ao rebalanceamento de um SBC brasileiro e com potencial para utilização em sistemas reais com o objetivo de melhorar seus níveis de serviço. Além disso, apresenta-se uma análise de dados e a caracterização de uso deste SBC, um histórico de evolução de SBCs ao redor do mundo e sua bibliografia pertinente, a fim de registrá-los na literatura e de se obter maior compreensão deste tipo de sistema. / Bike Sharing Systems (BSSs) have been implemented and enhanced in several major cities around the world, during the past few years. In such systems, users can take off a bike and return it at any network\'s station, provided that there is a bike and a dock available, respectively. However, these systems face an operational problem, caused by the fact that users\' flows are not balanced, bringing on that, at some point in time, some stations will be completely full while others will be empty. To tackle this issue, cargo vehicles are used by BSS\'s operators to rebalance the system (relocate bicycles through the stations). However, in most cases this process is not supported by quantitative tools that make the process rational or maximize its effectiveness. In this sense, this work proposes a hybrid model of simulation with optimization, applied to the rebalance of a Brazilian BSS and with potential for use in real systems with the aim of improving their service levels. In addition, is presented a data analysis and a usage study of this specific BSS, a BSSs evolutionary study and its relevant literature with the purpose of registering them in the literature and achieving a superior understanding of the problem.

O tamanho dos conjuntos de dados se tornou um grande problema atualmente. À medida que o sensoriamento urbano ganha popularidade, os conjuntos de dados de natureza espacial e temporal se tornam ubíquos, e levantam uma série de questões relacionadas ao armazenamento e gerenciamento destes. Isso também cria uma mudança no paradigma de análise, uma vez que os conjuntos de dados que antes representavam uma única série de medições ordenadas no tempo, agora são compostos por centenas dessas séries, com uma taxa de amostragem que está aumentando constantemente. Além disso, uma vez que os dados urbanos normalmente apresentam disposição geográfica inerente, a maioria das das tarefas requerem o suporte de representações espaciais apropriadas. Este se torna outro problema, visto que as tecnologias de exibição de imagens não avançam na mesma velocidade das tecnologias de sensoriamento, de modo que consequentemente acaba-se tendo mais dados do que espaço visual para representa-los. Após conduzir uma pesquisa exaustiva a respeito de análise de dados temporais e visualização, nós melhoramos uma visualização compacta de series temporais para auxiliar a exploração de grandes conjuntos de dados espaçotemporais. Nossa proposta aproveita a compacticidade de tal representação para permitir o uso de um mapa para representar os atributos espaciais dos dados, de modo coordenado, enquanto representação, de forma compreensível, centenas de series simultaneamente, com total contexto temporal. Nós apresentamos nossa proposta como sendo capaz de auxiliar várias tarefas de caráter exploratório de forma intuitiva. Para defender essa afirmação, nós mostramos como essa ideia foi desenvolvida e melhorada ao longo do desenvolvimento de dois estudos de design visual em diferentes domínios de aplicação, e validamos com a implementação de protótipos que foram usados na análise exploratória de vários conjuntos de dados com 3 representações diferentes. Palavras- / The size of datasets became the major problem in data analysis today. As urban sensing becomes popular, datasets of spatial and temporal nature become ubiquitous, leading to several concerns regarding storage and management. It also creates a shift of paradigm in data analysis, as datasets that once represented a single series of measurements ordered in time are now composed of hundreds of series with ever increasing sampling rates. Also, as urban data usually presents inherent geographic disposition, most analysis tasks requires the support of proper spatial views. It becomes another problem, once that displaying technologies do not advance at the same of pace that sensing technologies do, and consequently, there is usually more data than visual space to represent it. After conducting exhaustive research on temporal data analysis and visualization, we improved a compact visual representation of time series to support the exploration of large spatio-temporal datasets. Our proposal exploits the compactness of such representation to allow the use of a map to represent the spatial properties of the data in a coordinate scheme while presenting, in a comprehensible manner, hundreds of series simultaneously, with full temporal context. We argue that such solution can effectively support many exploratory tasks in an intuitive manner. To support this claim, we show how the idea was conceived, and improved along the development of two design studies from different application domains, and validated by the implementation of prototypes used in the exploratory analysis of several datasets with 3 different data structures.

O tamanho dos conjuntos de dados se tornou um grande problema atualmente. À medida que o sensoriamento urbano ganha popularidade, os conjuntos de dados de natureza espacial e temporal se tornam ubíquos, e levantam uma série de questões relacionadas ao armazenamento e gerenciamento destes. Isso também cria uma mudança no paradigma de análise, uma vez que os conjuntos de dados que antes representavam uma única série de medições ordenadas no tempo, agora são compostos por centenas dessas séries, com uma taxa de amostragem que está aumentando constantemente. Além disso, uma vez que os dados urbanos normalmente apresentam disposição geográfica inerente, a maioria das das tarefas requerem o suporte de representações espaciais apropriadas. Este se torna outro problema, visto que as tecnologias de exibição de imagens não avançam na mesma velocidade das tecnologias de sensoriamento, de modo que consequentemente acaba-se tendo mais dados do que espaço visual para representa-los. Após conduzir uma pesquisa exaustiva a respeito de análise de dados temporais e visualização, nós melhoramos uma visualização compacta de series temporais para auxiliar a exploração de grandes conjuntos de dados espaçotemporais. Nossa proposta aproveita a compacticidade de tal representação para permitir o uso de um mapa para representar os atributos espaciais dos dados, de modo coordenado, enquanto representação, de forma compreensível, centenas de series simultaneamente, com total contexto temporal. Nós apresentamos nossa proposta como sendo capaz de auxiliar várias tarefas de caráter exploratório de forma intuitiva. Para defender essa afirmação, nós mostramos como essa ideia foi desenvolvida e melhorada ao longo do desenvolvimento de dois estudos de design visual em diferentes domínios de aplicação, e validamos com a implementação de protótipos que foram usados na análise exploratória de vários conjuntos de dados com 3 representações diferentes. Palavras- / The size of datasets became the major problem in data analysis today. As urban sensing becomes popular, datasets of spatial and temporal nature become ubiquitous, leading to several concerns regarding storage and management. It also creates a shift of paradigm in data analysis, as datasets that once represented a single series of measurements ordered in time are now composed of hundreds of series with ever increasing sampling rates. Also, as urban data usually presents inherent geographic disposition, most analysis tasks requires the support of proper spatial views. It becomes another problem, once that displaying technologies do not advance at the same of pace that sensing technologies do, and consequently, there is usually more data than visual space to represent it. After conducting exhaustive research on temporal data analysis and visualization, we improved a compact visual representation of time series to support the exploration of large spatio-temporal datasets. Our proposal exploits the compactness of such representation to allow the use of a map to represent the spatial properties of the data in a coordinate scheme while presenting, in a comprehensible manner, hundreds of series simultaneously, with full temporal context. We argue that such solution can effectively support many exploratory tasks in an intuitive manner. To support this claim, we show how the idea was conceived, and improved along the development of two design studies from different application domains, and validated by the implementation of prototypes used in the exploratory analysis of several datasets with 3 different data structures.

Sistemas de Bicicletas Compartilhadas (SBCs) têm sido implantados e aprimorados nos últimos anos nas principais cidades do mundo. Neste tipo de sistema, usuários podem retirar e devolver bicicletas em qualquer estação da rede, desde que haja bicicleta e vaga disponível, respectivamente. Porém, devido às características de ocupação do solo em grandes centros urbanos, existe uma tendência natural de desbalanceamento nos fluxos dos usuários, fazendo com que em determinados horários certas estações fiquem lotadas de bicicletas enquanto outras estações estão vazias. Para mitigar este problema, gestores de SBCs utilizam veículos de carga para rebalancear o sistema (reposicionar as bicicletas entre as estações). Entretanto, usualmente, esse processo na prática não é realizado com auxílio de ferramentas quantitativas que tornem o processo racional ou maximizem sua eficácia. Nesse sentido, no presente trabalho é proposto um modelo híbrido de simulação com otimização, aplicado ao rebalanceamento de um SBC brasileiro e com potencial para utilização em sistemas reais com o objetivo de melhorar seus níveis de serviço. Além disso, apresenta-se uma análise de dados e a caracterização de uso deste SBC, um histórico de evolução de SBCs ao redor do mundo e sua bibliografia pertinente, a fim de registrá-los na literatura e de se obter maior compreensão deste tipo de sistema. / Bike Sharing Systems (BSSs) have been implemented and enhanced in several major cities around the world, during the past few years. In such systems, users can take off a bike and return it at any network\'s station, provided that there is a bike and a dock available, respectively. However, these systems face an operational problem, caused by the fact that users\' flows are not balanced, bringing on that, at some point in time, some stations will be completely full while others will be empty. To tackle this issue, cargo vehicles are used by BSS\'s operators to rebalance the system (relocate bicycles through the stations). However, in most cases this process is not supported by quantitative tools that make the process rational or maximize its effectiveness. In this sense, this work proposes a hybrid model of simulation with optimization, applied to the rebalance of a Brazilian BSS and with potential for use in real systems with the aim of improving their service levels. In addition, is presented a data analysis and a usage study of this specific BSS, a BSSs evolutionary study and its relevant literature with the purpose of registering them in the literature and achieving a superior understanding of the problem.

O tamanho dos conjuntos de dados se tornou um grande problema atualmente. À medida que o sensoriamento urbano ganha popularidade, os conjuntos de dados de natureza espacial e temporal se tornam ubíquos, e levantam uma série de questões relacionadas ao armazenamento e gerenciamento destes. Isso também cria uma mudança no paradigma de análise, uma vez que os conjuntos de dados que antes representavam uma única série de medições ordenadas no tempo, agora são compostos por centenas dessas séries, com uma taxa de amostragem que está aumentando constantemente. Além disso, uma vez que os dados urbanos normalmente apresentam disposição geográfica inerente, a maioria das das tarefas requerem o suporte de representações espaciais apropriadas. Este se torna outro problema, visto que as tecnologias de exibição de imagens não avançam na mesma velocidade das tecnologias de sensoriamento, de modo que consequentemente acaba-se tendo mais dados do que espaço visual para representa-los. Após conduzir uma pesquisa exaustiva a respeito de análise de dados temporais e visualização, nós melhoramos uma visualização compacta de series temporais para auxiliar a exploração de grandes conjuntos de dados espaçotemporais. Nossa proposta aproveita a compacticidade de tal representação para permitir o uso de um mapa para representar os atributos espaciais dos dados, de modo coordenado, enquanto representação, de forma compreensível, centenas de series simultaneamente, com total contexto temporal. Nós apresentamos nossa proposta como sendo capaz de auxiliar várias tarefas de caráter exploratório de forma intuitiva. Para defender essa afirmação, nós mostramos como essa ideia foi desenvolvida e melhorada ao longo do desenvolvimento de dois estudos de design visual em diferentes domínios de aplicação, e validamos com a implementação de protótipos que foram usados na análise exploratória de vários conjuntos de dados com 3 representações diferentes. Palavras- / The size of datasets became the major problem in data analysis today. As urban sensing becomes popular, datasets of spatial and temporal nature become ubiquitous, leading to several concerns regarding storage and management. It also creates a shift of paradigm in data analysis, as datasets that once represented a single series of measurements ordered in time are now composed of hundreds of series with ever increasing sampling rates. Also, as urban data usually presents inherent geographic disposition, most analysis tasks requires the support of proper spatial views. It becomes another problem, once that displaying technologies do not advance at the same of pace that sensing technologies do, and consequently, there is usually more data than visual space to represent it. After conducting exhaustive research on temporal data analysis and visualization, we improved a compact visual representation of time series to support the exploration of large spatio-temporal datasets. Our proposal exploits the compactness of such representation to allow the use of a map to represent the spatial properties of the data in a coordinate scheme while presenting, in a comprehensible manner, hundreds of series simultaneously, with full temporal context. We argue that such solution can effectively support many exploratory tasks in an intuitive manner. To support this claim, we show how the idea was conceived, and improved along the development of two design studies from different application domains, and validated by the implementation of prototypes used in the exploratory analysis of several datasets with 3 different data structures.

Through over forty-five years of development, bike sharing is not a fangle in Europe. But it becomes a popular topic in China in recent two years. The Chinese startups exert IoT technologies and GPS modular in shared bikes and launched the world’s first station-less bike sharing system. This new bike sharing system gains in popularity and develops dramatically all across China. In addition, the leading bike sharing service providers such as Mobike, got over $300 million investment since the start of 2017, which caught the attention of the public. More and more venture capitalists want to touch this new tempting pie. This paper mainly focuses on investigating what are the roles of this new bike sharing system in urban mobility in China especially in Shanghai and its influences in the society. Meanwhile, the socio-technological innovations of the new bike sharing are explored together with the application of different theoretical frameworks, such as Porter’s Five Forces and system thinking. This paper also tempted to fill up the gap in the literature that describing the missing part of smart bike sharing business - using the station less bike sharing business model, involving a discussion of its pros and cons. In order to give more detailed insights about the new bike sharing industry, we choose the world’s first station-less bike sharing service company - Mobike, as our case study object to investigate the revolutionary bike sharing system in Chinese major cities, specifically in Shanghai - the representative megacity of China. Conclusions and future development suggestions are provided at the end of this paper so that the stakeholders could have some references for further development of bike sharing industry.

Syftet med denna studie var tvådelat: Första delen handlade om att undersöka deteventuella sambandet mellan olika väderfaktorer och efterfrågan på låneycklar.Målet med studien var att undersöka hur sambandet mellan dessa förklarandevariabler och responsvariabel ser ut. För att undersöka detta användesregressionsanalys där en multipel regressionsmodell byggdes upp baserat påverklig data. Studien utgick ifrån staden Seoul där stadens lånecykel- ochväderdata användes. Det studien kom fram till var att alla väderfaktorer varstatistiskt signifikanta med efterfrågan på lånecyklar och sambandet mellan dessavar exponentiellt. Sambandet var positivt för väderfaktorerna temperatur ochsikt. Samtidigt som de resterande, luftfuktighet, vindhastighet, solstrålning, regnoch snöfall hade ett negativt samband. Fortsättningsvis, blev sambandet starkastför temperatur, fuktighet och regn, samtidigt svagast för vindhastighet, snöfalloch sikt. I den andra delen av studien genomfördes en marknadsanalys för lånecyklar.Även i denna del var staden Seoul i fokus. Inom denna marknadsanalysanalyserades bland annat marknadsituation, konkurrerande tjänster, styrkor ochsvagheter. Slutsatsen av denna analys är att marknaden för lånecyklar är komplexoch innefattar många olika faktorer. Den har även visat att en stad som Seoulhar lyckats med införandet av lånecyklar trots många utmaningar. Slutligen harstudien visat att lånecyklar är en växande marknad som kommer bli ännu meraktuell i framtiden. / The purpose of this study was twofold: The first part was about analyzing possiblecorrelations between various weather factors and the demand for bike-sharing.The aim of the study was to investigate how the relationship between theseexplanatory variables and the response variable looks like. To investigate this,regression analysis was used where a multivariate linear model was built basedon real data. The study was based on the city of Seoul, where the city’s bike-sharing and weather data were used. What the study found was that all weatherfactors were statistically significant with the demand for bike-sharing and therelationship between them was exponential. The relationship was positive forthe weather factors temperature and visibility while the remaining, i.e. weatherfactors, humidity, wind speed, solar radiation, rain and snowfall, had a negativerelationship. The strongest relationship were temperature, humidity and rainwhile the weakest were wind speed, snowfall and visibility. In the second part of the study a market analysis for bike-sharing was carried out.Also in this part, the city of Seoul was in focus. Within this market analysis, amongother things, the market situation, competing services, strengths and weaknessesswere analyzed. The conclusion based on this analysis is that the market for bike-sharing is complex and includes many factors. It has also shown that a city likeSeoul has succeeded in introducing bike-sharing despite many challenges. Finally,the study has shown that bike-sharing is a growing market that will become evenmore relevant in the future.

The large increase in on-road vehicles over the years has resulted in cities facing challenges in providing high-quality transportation services. Traffic jams are a clear sign that cities are overwhelmed, and that current transportation networks and systems cannot accommodate the current demand without a change in policy, infrastructure, transportation modes, and commuter mode choice. In response to this problem, cities in a number of countries have started putting a threshold on the number of vehicles on the road by deploying a partial or complete ban on cars in the city center. For example, in Oslo, leaders have decided to completely ban privately-owned cars from its center by the end of 2019, making it the first European city to totally ban cars in the city center. Instead, public transit and cycling will be supported and encouraged in the banned-car zone, and hundreds of parking spaces in the city will be replaced by bike lanes. As a government effort to support bicycling and offer alternative transportation modes, bike-sharing systems (BSSs) have been introduced in over 50 countries. BSSs aim to encourage people to travel via bike by distributing bicycles at stations located across an area of service. Residents and visitors can borrow a bike from any station and then return it to any station near their destination. Bicycles are considered an affordable, easy-to-use, and, healthy transportation mode, and BSSs show significant transportation, environmental, and health benefits. As the use of BSSs have grown, imbalances in the system have become an issue and an obstacle for further growth. Imbalance occurs when bikers cannot drop off or pick-up a bike because the bike station is either full or empty. This problem has been investigated extensively by many researchers and policy makers, and several solutions have been proposed. There are three major ways to address the rebalancing issue: static, dynamic and incentivized. The incentivized approaches make use of the users in the balancing efforts, in which the operating company incentives them to change their destination in favor of keeping the system balanced. The other two approaches: static and dynamic, deal with the movement of bikes between stations either during or at the end of the day to overcome station imbalances. They both assume the location and number of bike stations are fixed and only the bikes can be moved. This is a realistic assumption given that current BSSs have only fixed stations. However, cities are dynamic and their geographical and economic growth affects the distribution of trips and thus constantly changing BSS user behavior. In addition, work-related bike trips cause certain stations to face a high-demand level during weekdays, while these same stations are at a low-demand level on weekends, and thus may be of little use. Moreover, fixed stations fail to accommodate big events such as football games, holidays, or sudden weather changes. This dissertation proposes a new generation of BSSs in which we assume some of the bike stations can be portable. This approach takes advantage of both types of BSSs: dock-based and dock-less. Towards this goal, a BSS optimization framework was developed at both the tactical and operational level. Specifically, the framework consists of two levels: predicting bike counts at stations using fast, online, and incremental learning approaches and then balancing the system using portable stations. The goal is to propose a framework to solve the dynamic bike sharing repositioning problem, aiming at minimizing the unmet demand, leading to increased user satisfaction and reducing repositioning/rebalancing operations. This dissertation contributes to the field in five ways. First, a multi-objective supervised clustering algorithm was developed to identify the similarity of bike-usage with respect to time events. Second, a dynamic, easy-to-interpret, rapid approach to predict bike counts at stations in a BSS was developed. Third, a univariate inventory model using a Markov chain process that provides an optimal range of bike levels at stations was created. Fourth, an investigation of the advantages of portable bike stations, using an agent-based simulation approach as a proof-of-concept was developed. Fifth, mathematical and heuristic approaches were proposed to balance bike stations. / Doctor of Philosophy / Large urban areas are often associated with traffic congestion, high carbon mono/dioxide emissions (CO/CO2), fuel waste, and associated decreases in productivity. The estimated loss attributed to missed productivity and wasted fuel increased from $87.2 to $115 between 2007 and 2009. Driving in congested areas also results in long trip times. For instance, in 1993, drivers experienced trips that were 1.2 min/km longer in congested conditions. As a result, commuters are encouraged to leave their cars at home and use public transportation modes instead. However, public transportation modes fails to deliver commuters to their exact destination. Users have to walk some distance, which is commonly called the “last mile”. Bike sharing systems (BSSs) have started to fill this gap, offering a flexible and convenient transportation mode for commuters, around the clock. This is in addition to individual financial savings, health benefits, and reduction in congestion and emissions. Resent reports have shown BSSs multiplying over 50 countries. This notable expansion of BSSs also brings daily logistical challenges due to the imbalanced demand, causing some stations to run empty while others become full. Rebalancing the bike inventory in a BSS is crucial to ensure customer satisfaction and the whole system’s effectiveness. Most of the operating costs are also associated with rebalancing. The current rebalancing approaches assume stations are fixed and thus don’t take into account that the demand changes from weekday to weekend as well as from peak to non-peak hours, making some stations useless during specific days of the week and times of day. Furthermore, cities change continually with regard to demographics or structures and thus the distribution of trips also changes continually, leading to re-installation of stations to accommodate the dynamic change, which is both impractical and costly. In this dissertation, we propose a new generation of BSS in which we assume some stations are portable, meaning they can move during the day. They can be either stand-alone or an extension of existing stations with the goal of accommodating the dynamic changes in the distribution of trips during the day. To implement our new BSSs, we developed a BSS optimization framework. This framework consists of two components: predicting the bike counts at stations using fast approaches and then balancing the system using portable stations. The goal is to propose a framework to solve the dynamic bike sharing repositioning problem, aiming at minimizing the unmet demand, leading to increased user satisfaction and reducing repositioning/rebalancing operations. This dissertation contributes to the field in five ways. First, a novel algorithm was developed to identify the similarity of bike-usage with respect to time events. Second, easy-to-interpret and rapid approaches to predict bike counts at stations in a BSS were developed. Third, an inventory model using statistical techniques that provide an optimal range of bike levels at stations was created. Fourth, an investigation of the advantages of portable bike stations was developed. Fifth, mathematical approach was proposed to balance bike stations.