De senaste årens krav på hållbarhet, har tillsammans med en önskan att göra städerna renare, tystare och mer tillgängliga, resulterat i att cykeln kommit att prioriteras inom samhällsplaneringen. I syfte att främja en hållbar stadsutveckling och uppmuntra fler människor att cykla har allt fler städer valt att upprätta hyrcykelsystem. En av dessa städer är Gävle som under våren 2016 genomför en pilotstudie för att undersöka möjligheten att införa ett regionalt hyrcykelsystem riktat till pendlare. Syftet med denna studie är därför att applicera en metod för att optimera lokaliseringen av cykelstationer i ett hyrcykelsystem i Gävle. För att uppnå detta har en GIS-baserad multikriterieanalys (MKA) upprättats. Multikriterieanalysen resulterade i att ett antal platser pekades ut som lämpliga för etablering av hyrcykelsstationer. Genom vidare analys och diskussion av resultatet prioriterades antalet platser ned ytterligare. Detta mynnade ut i en rekommendation över de två lämpligaste platserna i Gävle för etablering av hyrcykelstationer. Dessa platser är Södermalm och Rådhustorget. / The desire to make our cities cleaner, quieter and more accessible has given the bicycle a higher priority in urban planning in recent years. In order to promote sustainable urban development and encourage people to cycle an increasing number of cities have chosen to set up bike-share systems. During the spring of 2016 the city of Gävle is conducting a pilot study to examine the possibility of introducing a bike-share system. The purpose of this study is therefore to apply a method to optimize the location of bike stations in a bike-share system in Gävle. To achieve this, a GIS-based multi-criteria analysis (MCA) was established. The MCA resulted in a number of sites identified as suitable for the establishment of bike-share stations. Through further analysis and discussion the number of stations was prioritized even further. This resulted in a recommendation of the two most suitable locations for establishment of bike-share stations in Gävle. These places are Södermalm and Rådhustorget.

Bike share system (BSS) has received increasing attention in research for its potential economic and environmental benefits. However, some research has pointed out the negative sustainability impacts of BSS from rebalancing activity, due to its greenhouse gas (GHG) emissions and additional vehicle travels. Additionally, bike and station manufacturing also bring considerable emissions to the system. Therefore, it is important to analyze the current rebalancing efficiency and sustainability of BSSs, and to assist the BSS operators in optimizing the BSS design. Existing studies lack tools to estimate the real-world rebalancing activities and vehicle usage for system sustainability evaluation and improvements. To address this gap, this research first proposed a framework to estimate rebalancing activities and applied a clustering-based method to estimate the rebalancing vehicle use. Applying the framework to the BSSs in Chicago, Boston, and Los Angeles, this study estimated the rebalancing operation and compared the rebalancing efficiencies among the three systems. The analysis results show that 1) only a small proportion of stations and bikes were involved in the daily rebalancing activities; 2) most rebalancing activities were operated during the daytime, while the overnight rebalancing was limited; 3) the system scale, trip demand, and station types are critical for the rebalancing efficiency; and 4) reducing the rebalancing activities at self-rebalance stations could help to improve the rebalancing efficiency and benefits system sustainability. Additionally, the sustainability performance (e.g., carbon emissions) of BSS is not only decided by the rebalance, but also the manufacturing of bikes and stations. It is important to consider all these factors when optimizing a BSS. The existing literature on system improvement for the BSSs lacks an integrated view, and a well-designed integrated model for current BSS improvement is needed. The second part of this thesis built a simulation-based optimization model and generated 2400 scenarios for evaluation. This model aims to minimize the expansion investment, rebalancing mileage, and maximize the system demand and service rate. A Weight Sum Model is applied to solve the multi-criteria decision analysis. The model results show that the best system improvement is to build a new station with a small capacity and initial bikes. The investment and location impacts are discussed to find the tradeoff among expansion strategies. A sensitivity analysis is conducted to evaluate how different weight combinations (refer to different preferences in decision making) impact the preferred station configuration (docks and bikes) and new station locations.

While there has been considerable research regarding the role of Mobility Hubs in cities and transport networks, significant investment is required to develop these facilities. It is the correlation between investment, new users’ attraction and revenue generation that is the key for a sustainable development of Mobility Hubs and this investment must, therefore, be correctly assessed and targeted. This study aims to develop a methodology to determine the viability of investing in Mobility Hub features, weighing the investment on different Hub features and services against expected potential benefits and revenue generation, addressing the question: Can investment in Mobility Hub features be justified and, if so, which features maximize its expected positive impact? Based on a review of literature and definition of possible Hub features as variables, secondary research data was compiled to enable the analysis of expected impacts of each variable/feature in terms of new user’s attraction and revenue generation, which was then used to develop individual Net Present Value analysis of each feature. The result of these analysis demonstrates and concludes that different Hub features have the potential to generate substantially different investment outcomes, and that each feature should be analyzed individually prior to investment decision. It was also concluded by this research that the proposed assessment methodology can be used for future research on other listed Hub features, albeit with the constraint that primary data will be required when secondary research data is not available.