Bike sharing is a service, spread over the world and often finds recognition by being made available to almostanyone, providing affordable access in urban space and becoming an alternative to motorized transportationand vehicles.But does bike sharing now answer our needs and desiresin our every day practice in town? Are there possibilitiesto enhance the sharing experience?How to adress the ones that do not make use of the service yet?By keeping close sight to users throughout my project and involving them into my process, it helped me understandhow they want to make use of the bicycles and the service to meet their expectations.These understandings and keyfindings I translated towardsbike sharing and its conclusion defines my proposal.Resulted is a link to StorStockholms Lokaltrafik and bike sharing, a “muscle driven individual public tranportalternative” to the already established public networksystem in Stockholm‘s urban space.Driven by values such as accessibility, flexibility and reliability, I envisoned in a fictional collaboration with SL, the SL Cykel.The SL Cykel covers a Product Service System, embededin the tbana areas and accessed by SL‘s smart card.Based on different modes of traveling, this proposal is meant to be a self-regulating system, which encouragesits customer to become active part in the service and its flexibility.

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Submitted by Fernando Souza (fernandoafsou@gmail.com) on 2017-08-15T11:46:12Z No. of bitstreams: 1 arquivototal.pdf: 884446 bytes, checksum: 92314027dddef8365b4a2e655b65bd78 (MD5) / Made available in DSpace on 2017-08-15T11:46:13Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 884446 bytes, checksum: 92314027dddef8365b4a2e655b65bd78 (MD5) Previous issue date: 2016-05-20 / The Static Bike Rebalancing Problem (SBRP) is a recent problem motivated by the task of repositioning bikes among stations in a self-service bike-sharing systems. This problem can be seen as a variant of the one-commodity pickup and delivery vehicle routing problem, where multiple visits are allowed to be performed at each station, i.e., the demand of a station is allowed to be split. Moreover, a vehicle may temporarily drop its load at a station, leaving it in excess or, alternatively, collect more bikes (even all of them) from a station, thus leaving it in default. Both cases require further visits in order to meet the actual demands of such station. This work deals with a particular case of the SBRP, in which only a single vehicle is available and the objective is to nd a least-cost route that meets the demand of all stations and does not violate the minimum (zero) and maximum (vehicle capacity) load limits along the tour. Therefore, the number of bikes to be collected or delivered at each station should be appropriately determined in order to respect such constraints. This is a NP-Hard problem since it contains other NP-Hard problems as special cases, hence, using exact methods to solve it is intractable for larger instances. Several methods have been proposed by other authors, providing optimal values for small to medium sized instances, however, no work has consistently solved instances with more than 60 stations. The proposed algorithm to solve the problem is an iterated local search (ILS) based heuristic combined with a randomized variable neighborhood descent (RVND) as local search procedure. The algorithm was tested on 980 benchmark instances from the literature and the results obtained are quite competitive when compared to other existing methods. Moreover, the method was capable of nding most of the known optimal solutions and also of improving the results on a number of open instances. / O Problema do Rebalanceamento Est atico de Bicicletas (Static Bike Rebalancing Problem, SBRP) e um recente problema motivado pela tarefa de reposicionar bicicletas entre esta c~oes em um sistema self-service de compartilhamento de bicicletas. Este problema pode ser visto como uma variante do problema de roteamento de ve culos com coleta e entrega de um unico tipo de produto, onde realizar m ultiplas visitas a cada esta c~ao e permitido, isto e, a demanda da esta c~ao pode ser fracionada. Al em disso, um ve culo pode descarregar sua carga temporariamente em uma esta c~ao, deixando-a em excesso, ou, de maneira an aloga, coletar mais bicicletas (at e mesmo todas elas) de uma esta c~ao, deixando-a em falta. Em ambos os casos s~ao necess arias visitas adicionais para satisfazer as demandas reais de cada esta c~ao. Este trabalho lida com um caso particular do SBRP, em que apenas um ve culo est a dispon vel e o objetivo e encontrar uma rota de custo m nimo que satisfa ca as demandas de todas as esta c~oes e n~ao viole os limites de carga m nimo (zero) e m aximo (capacidade do ve culo) durante a rota. Portanto, o n umero de bicicletas a serem coletadas ou entregues em cada esta c~ao deve ser determinado apropriadamente a respeitar tais restri c~oes. Trata-se de um problema NP-Dif cil uma vez que cont em outros problemas NP-Dif cil como casos particulares, logo, o uso de m etodos exatos para resolv^e-lo e intrat avel para inst^ancias maiores. Diversos m etodos foram propostos por outros autores, fornecendo valores otimos para inst^ancias pequenas e m edias, no entanto, nenhum trabalho resolveu de maneira consistente inst^ancias com mais de 60 esta c~oes. O algoritmo proposto para resolver o problema e baseado na metaheur stica Iterated Local Search (ILS) combinada com o procedimento de busca local variable neighborhood descent com ordena c~ao aleat oria (randomized variable neighborhood descent, RVND). O algoritmo foi testado em 980 inst^ancias de refer^encia na literatura e os resultados obtidos s~ao bastante competitivos quando comparados com outros m etodos existentes. Al em disso, o m etodo foi capaz de encontrar a maioria das solu c~oes otimas conhecidas e tamb em melhorar os resultados de inst^ancias abertas.

<div>Bike sharing system (BSS) is growing worldwide. Although bike sharing is viewed as a sustainable transportation mode, it still has environmental footprints from its operation (e.g., bike rebalancing using automobiles) and upstream impacts (e.g., bike and docking station manufacturing). Thus, evaluating the environmental impacts of a BSS from the life cycle perspective is vital to inform decision making for the system design and operation. In this study, we conducted a comparative life cycle assessment (LCA) of station-based and dock-less BSS in the U.S. The results show that dock-less BSS has a greenhouse gas (GHG) emissions factor of 118 g CO2-eq/bike-km in the base scenario, which is 82% higher than the station-based system. Bike rebalancing is the main source of GHG emissions, accounting for 36% and 73% of the station-based and dock-less systems, respectively. However, station-based BSS has 54% higher total normalized environmental impacts (TNEI), compared to dock-less BSS. The dock manufacturing dominants the TNEI (61%) of station-based BSS and the bike manufacturing contributes 52% of TNEI in dock-less BSS. BSS can also bring environmental benefits through substituting different transportation modes. Car trip replacement rate is the most important factor. The results suggest four key approaches to improve BSS environmental performance: 1) optimizing the bike distribution and rebalancing route or repositioning bikes using more sustainable approaches, 2) incentivizing more private car users to switch to using BSSs, 3) prolonging lifespans of docking infrastructure to significantly reduce the TNEI of station-based systems, and 4) increasing the bike utilization efficiency to improve the environmental performance of dock-less systems.</div><div>To improve the design of current BSS from the life cycle perspective, we first proposed a simulation framework to find the minimal fleet size and their layout of the system. Then we did a tradeoff analysis between bike fleet size and the rebalancing frequency to investigate the GHG emission if we rebalance once, twice and three times a day. The optimal BSS design and operation strategies that can minimize system GHG emission are identified for a dock-less system in Xiamen, China. The results show that at most 15% and 13% of the existing fleet size is required to serve all the trip demand on weekday and weekend, if we have a well-designed bike layout. The tradeoff analysis shows that the GHG emission may increase if we continue to reduce the fleet size through more frequent rebalancing work. Rebalancing once a day during the night is the optimal strategy in the base scenario. We also tested the impacts of other key factors (e.g., rebalancing vehicle fleet size, vehicle capacity and multiple depots) on results. The analysis results showed that using fewer vehicles with larger capacity could help to further reduce the GHG emission of rebalancing work. Besides, setting 3 depots in the system can help to reduce 30% of the GHG emission compared with 1-depot case, which benefits from the decrease of the commuting trip distance between depot and the serve region.</div>

In this thesis, I analyze the impacts of the design and implementation of different environmental policy tools from a theoretical and empirical perspective: certificates providing information on the energy performance of buildings (chapter 1); urban road pricing schemes such as congestion charges (chapter 2); quantity-based policy tools to support production with non-polluting technologies (chapter 3).In chapter 1, co-authored with Luisa Dressler, we study how energy performance certificates (EPCs) impact the residential rental market. These certificates can help solve information asymmetries between landlords and tenants about the thermal quality of dwellings for rent, which, in turn, is expected to facilitate investment aimed at improving dwellings' energy performance. However, disclosure of EPCs is often incomplete, which hampers their effectiveness in relieving such information asymmetries. Moreover, even when a certificate is available, landlords do not always disclose it. This contradicts the so-called information unraveling result, according to which all landlords should disclose quality information unless it is costly to do so: in such a setting, information eventually unravels. Using a cross-sectional dataset of residential rental advertisements from the Belgian region of Brussels, we empirically evaluate incentives to disclose energy performance ratings. We find that two fundamental assumptions underlying the unraveling result are not confirmed in our setting: firstly, tenants value energy performance of rental property only when dwellings are of very high quality; secondly, tenants do not appear to rationally adjust their expectations when faced with dwellings that withhold their energy performance rating. Finally, we formulate specific policy advice for reforming EPC mechanisms to increase disclosure rates.In chapter 2, I study how urban congestion pricing impacts the use of sustainable mobility options such as bike sharing, presenting evidence from the city of Milan, Italy.As concern for air pollution grows in cities across the world, policies such as urban road pricing are rolled out to induce urban residents to opt for greener transport options. While several papers have analyzed the impact of urban road pricing on air pollution and on car use, this is the first analysis of its impact on sustainable travel behaviors, such as the use of bike sharing.I extend a stylized theoretical model of travel behavior to formalize the drivers of bike-sharing demand. Then, I exploit a panel dataset covering all bike-sharing trips carried out over an 8-year period in the city of Milan to estimate the impact of congestion pricing on bike-sharing use. The empirical strategy I employ in this study is based on the sudden suspension and reintroduction of congestion pricing, which generate a quasi-experimental setting. Adopting an event study approach, I find that suspending the congestion charge reduces daily bike-sharing traffic by about 5% in the short run. I show that, in Milan, congestion pricing mainly impacts bike-sharing use through the reduction of road traffic congestion, which makes cycling safer and more pleasant. The direct effect of the increased relative cost of car use is secondary in individual decisions to use bike-sharing. The role of these effects is likely to be context-specific, as they may be affected by the baseline level of urban congestion, the broader policy mix affecting the cost of driving and the specific design of the congestion pricing scheme.In chapter 3, co-authored with Renaud Foucart, we study the impact of different quantity-based tools that governments can use to support the production of homogeneous goods through clean rather than polluting inputs in a setting where production costs are uncertain.In recent years, many sectors have been disrupted by clean innovation, as clean inputs have emerged as close substitutes of polluting ones: for example, in the power sector renewable energy sources are increasingly used for electricity generation instead of fossil fuels. Whenever the negative externalities caused by polluting incumbent technologies are not internalized in production costs, emerging clean technologies are left at a disadvantage. For this reason, governments may want to design policy support schemes for emerging clean technologies.We develop a theoretical framework in which well-established polluting technologies entail known production and pollution costs, while using emerging green technologies requires higher, steeper and uncertain production costs. In this context, a government chooses between a range of quantity-based instruments to support the deployment of clean technologies based on cost estimates, as costs of production with green inputs are uncertain.We show that a cap on production with polluting inputs is the least distortionary among quantity instruments; next is a mandatory share of production with green inputs out of total production. Setting a policy objective in terms of a precise level of green inputs for production is the least efficient policy approach. This ranking results from the so-called “technology effect”, which determines the extent to which the market corrects cost estimation errors after real costs are observed. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

Bike Sharing is a sustainable mode of urban mobility, not only for regular commuters but also for casual users and tourists. Free-floating bike sharing (FFBS) is an innovative bike sharing model, which saves on start-up cost, prevents bike theft, and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. Efficient management of a FFBS requires: 1) analyzing its mobility patterns and spatio-temporal imbalance of supply and demand of bikes, 2) developing strategies to mitigate such imbalances, and 3) understanding the causes of a bike getting damaged and developing strategies to minimize them. All of these operational management problems are successfully addressed in this dissertation, using tools from Operations Research, Statistical and Machine Learning and using Share-A-Bull Bike FFBS and Divvy station-based bike sharing system as case studies.

abstract: Bicycle sharing systems (BSS) operate on five continents, and they change quickly with technological innovations. The newest “dockless” systems eliminate both docks and stations, and have become popular in China since their launch in 2016. The rapid increase in dockless system use has exposed its drawbacks. Without the order imposed by docks and stations, bike parking has become problematic. In the areas of densest use, the central business districts of large cities, dockless systems have resulted in chaotic piling of bikes and need for frequent rebalancing of bikes to other locations. In low-density zones, on the other hand, it may be difficult for customers to find a bike, and bikes may go unused for long periods. Using big data from the Mobike BSS in Beijing, I analyzed the relationship between building density and the efficiency of dockless BSS. Density is negatively correlated with bicycle idle time, and positively correlated with rebalancing. Understanding the effects of density on BSS efficiency can help BSS operators and municipalities improve the operating efficiency of BSS, increase regional cycling volume, and solve the bicycle rebalancing problem in dockless systems. It can also be useful to cities considering what kind of BSS to adopt. / Dissertation/Thesis / Masters Thesis Urban and Environmental Planning 2018

This thesis seeks to provide a detailed understanding of the introduction of dockless bike-sharing to London. As part of a wave of new smart and shared mobility services that are aiming to transform the way people move around cities, this emerging form of transport has created disruptions in London since its launch in 2017. This study aims to analyse to what extent dockless bike-sharing aligns or conflicts with the aims and objectives of local authorities governing public space in London. In doing so, it also aims to reveal insights into transformations in contemporary mobility by exploring the dynamics of niche innovations within socio-technical transitions, thus contributing to knowledge in the field of transition studies.To do this, a qualitative case study methodology was employed using document analysis and interviews with four stakeholders integrally involved in the case study, representing both public authorities and a private sector dockless bike-sharing operator, Mobike.The findings demonstrate that dockless bike-sharing is well aligned with the city’s explicit objectives to reduce car dependency and encourage active travel. It has particular potential to make cycling more accessible by bringing bike-sharing to parts of the city that do not have access to the pre-existing, docked bike-sharing scheme, operated by the central transport authority, Transport for London. Despite this, dockless bike-sharing, as a niche innovation, has struggled to break into the existing urban mobility regime. This can be seen to result from a variety of factors that include a failure to collaborate and build local legitimacy or pay sufficient regard to local conditions during early implementation. Furthermore, dockless bike-sharing’s demand for flexible parking has resulted in uses and misuses of public space that have created friction and placed the innovation in conflict with the existing physical urban landscape and the authorities that govern it. Its momentum has been further hindered by London’s complex governance structure, a structure which has not proved conducive to the dockless bike-sharing operating model. It is posited that if dockless bike-sharing is to build momentum and achieve its potential to expand the reach of bike-sharing in London, greater support is required from public authorities.

In recent years, the paradigm of personal urban mobility has radically evolved as an increasing number of Mobility-on-Demand (MoD) systems continue to revolutionize urban transportation. Hailed as the future of sustainable transportation, with significant implications on urban planning, these systems typically utilize a fleet of shared vehicles such as bikes, electric scooters, cars, etc., and provide a centralized matching platform to deliver point-to-point mobility to passengers. In this dissertation, we study MoD systems along three operational directions – (1) modeling: developing analytical models that capture the rich stochasticity of passenger demand and its impact on the fleet distribution, (2) economics: devising strategies to maximize revenue, and (3) control: developing coordination mechanisms aimed at optimizing platform throughput. First, we focus on the metropolitan bike-sharing systems where platforms typically do not have access to real-time location data to ascertain the exact spatial distribution of their fleet. We formulate the problem of accurately predicting the fleet distribution as a Markov Chain monitoring problem on a graph representation of a city. Specifically, each monitor provides information on the exact number of bikes transitioning to a specific node or traversing a specific edge at a particular time. Under budget constraints on the number of such monitors, we design efficient algorithms to determine appropriate monitoring operations and demonstrate their efficacy over synthetic and real datasets. Second, we focus on the revenue maximization strategies for individual strategic driving partners on ride-hailing platforms. Under the key assumption that large-scale platform dynamics are agnostic to the actions of an individual strategic driver, we propose a series of dynamic programming-based algorithms to devise contingency plans that maximize the expected earnings of a driver. Using robust optimization techniques, we rigorously reason about and analyze the sensitivity of such strategies to perturbations in passenger demand distributions. Finally, we address the problem of large-scale fleet management. Recent approaches for the fleet management problem have leveraged model-free deep reinforcement learning (RL) based algorithms to tackle complex decision-making problems. However, such methods suffer from a lack of explainability and often fail to generalize well. We consider an explicit need-based coordination mechanism to propose a non-deep RL-based algorithm that augments tabular Q-learning with a combinatorial optimization problem. Empirically, a case study on the New York City taxi demand enables a rigorous assessment of the value, robustness, and generalizability of the proposed approaches.

Today bike sharing systems exists in many cities around the globe after a recent growth and popularity in the last decades. It is attractive for cities and users who wants to promote healthier lifestyles; to reduce air pollution and gas emission as well as improve traffic. One major challenge to docked bike sharing system is redistributing bikes and balancing dock stations. There are studies that propose models that can help forecasting bike usage; strategies for rebalancing bike distribution; establish patterns or how to identify patterns. Some of these studies proposes to extend the approach by including weather data. Some had limitations and did not include weather data. This study aims to extend upon these proposals and opportunities to explore on how and in what magnitude weather impacts bike usage. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed by using a clustering algorithm called k-means. K-means is suitable for discovering patterns within the data by grouping (clustering) similar instances, which literature review also advocated. In this project, the k-means algorithm managed to identify three clusters that corresponds to bike usage depending on weather. The results show that weather impact on bike usage was noticeable between clusters. It showed that temperature followed by precipitation weighted the most, out of five weather variables. Results also supported that the use of k-means was appropriate for this type of study.