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 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.

Les systèmes complexes à longue durée de service sont des systèmes de grande taille qui ont généralement un comportement stochastique. Dans cette thèse, nous étudions, particulièrement, un type de ces systèmes : le système de vélos en libre-service. Le principe de fonctionnement de ce service de transports est de disposer des vélos dans diverses stations de la ville. Les usagers viennent prendre des vélos pour effectuer un trajet et puis les déposent dans des stations quelconques.Comme la durée d’exploitation de ces systèmes est longue, de nouveaux besoins (par exemple l’attractivité de station) et une dégradation de performance peuvent survenir. Un outil d’aide à la décision est ainsi nécessaire pour analyser et améliorer la performance par des opérations de pilotage (p.ex. changement de la taille de flotte) ou de re-conception (p.ex. changement de la capacité d’une station). L’approche suivie, pour cette finalité, est la modélisation stochastique en utilisant un réseau de files d’attente possédant des files à capacités limitées et un mécanisme de blocage. La méthode de résolution du modèle proposé est définie dans les travaux de Kouvatsos (1994). Notre cas d’étude est un sous-réseau de 20 stations du système Vélib’ de Paris. L’analyse de la performance suite aux changements exogènes et aux opérations d’amélioration (pilotage et re-conception), nous a permis de déduire un ensemble de préconisations qui peuvent améliorer les performances du système. Comme la méthode de résolution de ce modèle possède une complexité importante, nous proposons une méthode d’agrégation des stations pour réduire la taille du problème en ayant des erreurs maîtrisables. Cette méthode est implémentée et évaluée pour un système particulier où tous les paramètres sont homogènes. Enfin, l’étude de cette méthode pour un système non-homogène et d’autres perspectives sont proposées pour étendre ces travaux de recherche. / Complex systems having a long period of service are large scale systems that typically have stochastic behavior. In this thesis, we study, in particular, one type of these systems: the Bike Sharing System. The operating principle of this transport service consists of a fleet of bikes disposed in various stations. The users come to take bicycles to use them for their trip and then bring them back in any stations.As these systems are supposed to operate for long periods, new requirements can overcome (eg. station attractiveness) and performance degradation may occur. A decision support tool is thus required to analyze and improve the performance by control operations (eg. fleet size change) or re-design (eg. changing the capacity of a station).The stochastic modeling approach is used through a network of queues with limited capacity queues and a blocking mechanism. The resolution method of the proposed model is defined in the research work of Kouvatsos (1994).The case study is a sub-network of 20 Vélib's stations in Paris. The performance analysis according to exogenous changes and improvement operations (control and re-design) allowed us to deduce recommendations that can improve the performance of the system.As the method of solving this model has a great complexity, we propose a method of aggregation of the stations to reduce the size of the problem by having controllable errors. This method is implemented and evaluated for a particular system where all the parameters are homogeneous. Finally, the study of this method for a non-homogeneous system and other perspectives are proposed to extend this research work.

No description available.

Este trabalho teve como objetivo analisar a demanda potencial para um sistema de compartilhamento de bicicletas pedelecs no campus da USP de São Carlos, com foco nos deslocamentos de estudantes entre as duas áreas do campus. Para tanto, foi elaborado um conjunto de procedimentos, que constituem duas etapas: caracterização do público-alvo e análise da demanda potencial pelas bicicletas pedelecs compartilhadas. Na primeira etapa foi aplicado um questionário, elaborado com a técnica de preferência declarada, para verificar as preferências dos usuários em relação às pedelecs compartilhadas e ao ônibus operado pela USP. Os resultados desta consulta, que envolveu variáveis de condições meteorológicas, situação de ciclovias/ciclofaixas entre as áreas do campus e lotação do ponto de ônibus USP, foram posteriormente utilizados para calibrar um modelo logit e treinar uma Rede Neural Artificial (RNA). Na segunda etapa foi elaborada uma planilha eletrônica com os dados obtidos na coleta, a fim de analisar as probabilidades de escolha da pedelec (ao invés do ônibus USP). Nesta planilha também foram utilizados dados do histórico meteorológico de São Carlos no período entre 2011 e 2015. Alguns dos resultados obtidos são destacados na sequência. A probabilidade de escolha das pedelecs é, em média, três vezes maior quando existem ciclovias/ciclofaixas (em relação à ausência da referida infraestrutura cicloviária). A ocupação do ponto de ônibus USP também é impactante, pois as probabilidades de uso da bicicleta pedelec praticamente dobram quando o ponto está cheio. No caso da meteorologia, foi constatado que as maiores probabilidades ocorrem no Outono e no Inverno, ou seja, nas épocas em que se concentram os dias mais secos e com menores temperaturas. Para o período letivo de 2011 a 2015, considerando a situação atual (isto é, sem ciclovias/ciclofaixas entre as áreas), os valores de probabilidade de uso da pedelec correspondem a 9% com o ponto vazio e 19% com o ponto cheio. Se houvesse ciclovias/ciclofaixas, a probabilidade seria de até 54%. Desse modo, a estratégia de análise desenvolvida conceitualmente, bem como implantada em planilha eletrônica, se constitui em importante ferramenta de auxílio para a condução da política de transportes que a Prefeitura do campus irá adotar para os anos futuros. Além disso, evidencia uma possível demanda potencial para um sistema com pedelecs compartilhadas. / The objective of this study was to analyze the potential demand for a pedelec sharing system at the São Carlos campus of the University of São Paulo (USP), aiming at the displacements of students between the two campus Areas. The set of procedures developed to reach the objective has involved two steps: characterization of the target audience and analysis of the potential demand for shared pedelecs. The first step was accomplished with a questionnaire designed with a stated preference approach for identifying users\' preferences regarding shared pedelecs and the bus system operated by the university. The survey results, which involved variables of weather conditions, existence of bike paths/bike lanes between the campus Areas, and occupancy rates at the USP bus stop, were subsequently used to calibrate a logit model and to develop an Artificial Neural Network (ANN). The survey data were also used in the second step of the process, in which an electronic spreadsheet was created to analyze the probabilities of choosing the pedelec alternative (instead of the bus route operated by university). The spreadsheet was also fed with meteorological data of São Carlos in the period between 2011 and 2015. Some of the obtained outcomes are highlighted in the sequence. The probability of a pedelec being chosen is almost three times higher if bike paths/bike lanes do exist than if they do not exist. The occupancy rates of the bus stop are also particularly relevant. The probability of someone choosing a pedelec nearly doubles when the bus stop is crowded. Regarding the weather conditions, the highest probabilities are observed in the Fall and Winter seasons, i. e. in the driest and coldest days. For the entire academic period comprised between 2011 and 2015, the probabilities range from 9% (empty bus stop) to 19% (full bus stop), considering the current situation (i. e. no cycleways connect the two campus Areas). In the presence of this cycling infrastructure, however, the probability goes up to 54%. Thus, the strategy of analysis conceptually developed, and made available through an electronic spreadsheet, may be an important support tool for the implementation of transport policies by the campus administration. In addition, it highlights a likely potential demand for a system of shared pedelecs.

Este trabalho teve como objetivo analisar a demanda potencial para um sistema de compartilhamento de bicicletas pedelecs no campus da USP de São Carlos, com foco nos deslocamentos de estudantes entre as duas áreas do campus. Para tanto, foi elaborado um conjunto de procedimentos, que constituem duas etapas: caracterização do público-alvo e análise da demanda potencial pelas bicicletas pedelecs compartilhadas. Na primeira etapa foi aplicado um questionário, elaborado com a técnica de preferência declarada, para verificar as preferências dos usuários em relação às pedelecs compartilhadas e ao ônibus operado pela USP. Os resultados desta consulta, que envolveu variáveis de condições meteorológicas, situação de ciclovias/ciclofaixas entre as áreas do campus e lotação do ponto de ônibus USP, foram posteriormente utilizados para calibrar um modelo logit e treinar uma Rede Neural Artificial (RNA). Na segunda etapa foi elaborada uma planilha eletrônica com os dados obtidos na coleta, a fim de analisar as probabilidades de escolha da pedelec (ao invés do ônibus USP). Nesta planilha também foram utilizados dados do histórico meteorológico de São Carlos no período entre 2011 e 2015. Alguns dos resultados obtidos são destacados na sequência. A probabilidade de escolha das pedelecs é, em média, três vezes maior quando existem ciclovias/ciclofaixas (em relação à ausência da referida infraestrutura cicloviária). A ocupação do ponto de ônibus USP também é impactante, pois as probabilidades de uso da bicicleta pedelec praticamente dobram quando o ponto está cheio. No caso da meteorologia, foi constatado que as maiores probabilidades ocorrem no Outono e no Inverno, ou seja, nas épocas em que se concentram os dias mais secos e com menores temperaturas. Para o período letivo de 2011 a 2015, considerando a situação atual (isto é, sem ciclovias/ciclofaixas entre as áreas), os valores de probabilidade de uso da pedelec correspondem a 9% com o ponto vazio e 19% com o ponto cheio. Se houvesse ciclovias/ciclofaixas, a probabilidade seria de até 54%. Desse modo, a estratégia de análise desenvolvida conceitualmente, bem como implantada em planilha eletrônica, se constitui em importante ferramenta de auxílio para a condução da política de transportes que a Prefeitura do campus irá adotar para os anos futuros. Além disso, evidencia uma possível demanda potencial para um sistema com pedelecs compartilhadas. / The objective of this study was to analyze the potential demand for a pedelec sharing system at the São Carlos campus of the University of São Paulo (USP), aiming at the displacements of students between the two campus Areas. The set of procedures developed to reach the objective has involved two steps: characterization of the target audience and analysis of the potential demand for shared pedelecs. The first step was accomplished with a questionnaire designed with a stated preference approach for identifying users\' preferences regarding shared pedelecs and the bus system operated by the university. The survey results, which involved variables of weather conditions, existence of bike paths/bike lanes between the campus Areas, and occupancy rates at the USP bus stop, were subsequently used to calibrate a logit model and to develop an Artificial Neural Network (ANN). The survey data were also used in the second step of the process, in which an electronic spreadsheet was created to analyze the probabilities of choosing the pedelec alternative (instead of the bus route operated by university). The spreadsheet was also fed with meteorological data of São Carlos in the period between 2011 and 2015. Some of the obtained outcomes are highlighted in the sequence. The probability of a pedelec being chosen is almost three times higher if bike paths/bike lanes do exist than if they do not exist. The occupancy rates of the bus stop are also particularly relevant. The probability of someone choosing a pedelec nearly doubles when the bus stop is crowded. Regarding the weather conditions, the highest probabilities are observed in the Fall and Winter seasons, i. e. in the driest and coldest days. For the entire academic period comprised between 2011 and 2015, the probabilities range from 9% (empty bus stop) to 19% (full bus stop), considering the current situation (i. e. no cycleways connect the two campus Areas). In the presence of this cycling infrastructure, however, the probability goes up to 54%. Thus, the strategy of analysis conceptually developed, and made available through an electronic spreadsheet, may be an important support tool for the implementation of transport policies by the campus administration. In addition, it highlights a likely potential demand for a system of shared pedelecs.