The project was carried out together with OC Off Course who manufactureand distribute mainly electric bikes.In today’s society, alternative sources of transportation becomes more andmore present and several bicycle sharing programs arises in major citiesacross the world every year.Something that doesn’t exist too much as of yet is bike sharing programsthat uses electric bikes. An electric bike is a convenient way oftransportation as well as a good way of avoiding traffic. With an electricbike you avoid having to sweat when biking over hills or when you need totravel over longer distances.The goal of this project is to develop a bike sharing station according tothe functions need to be met.This thesis Is based on Fredy Olsson´s methods principkonstruktion aswell as a couple of concept generating methods from Kenneth Österlin.From this, a number of sketches were made that later was evaluated inseveral steps in conjunction to a set of specifications. A 3D model of theselected concept was later made in CatiaV5 for a complete product.

Indiana University-Purdue University Indianapolis (IUPUI) / Location Allocation, rooted in Operations Research and Mathematical programming, allows real world problems to be solved using optimization (based on mathematics and science) and equity principles (based on ethics). Finding nearest facilities for everyone simultaneously is a task solved by numerical and algebraic solutions. Bikeshare as a public good requires equitable allocation of bikeshare resources. Distance, as an impediment, can be minimized using location allocation algorithms. Since location allocation of this kind involves large numbers, sophisticated algorithms are needed to solve them due to their combinatorically explosive nature (i.e. as ‘n’ rises, solution time rises at least exponentially – sometimes called ‘Non Polynomial Time-Hard’ problems). Every day, researchers are working to improve such algorithms, since faster and better solutions can improve such algorithms and in turn help improve our daily lives.

Current works of bike demand prediction mainly focus on cluster level and perform poorly on predicting demands of a single station. In the first task, we introduce a contextual based bike demand prediction model, which predicts bike demands for per station by combining spatio-temporal network and environment contexts synergistically. Furthermore, since people's movement information is an important factor, which influences the bike demands of each station. To have a better understanding of people's movements, we need to analyze the relationship between different places. In the second task, we propose an origin-destination model to learn place representations by using large scale movement data. Then based on the people's movement information, we incorporate the place embedding into our bike demand prediction model, which is built by using multi-source large scale datasets: New York Citi bike data, New York taxi trip records, and New York POI data. Finally, as deep learning methods have been successfully applied to many fields such as image recognition and natural language processing, it inspires us to incorporate the complex deep learning method into the bike demand prediction problem. So in this task, we propose a deep spatial-temporal (DST) model, which contains three major components: spatial dependencies, temporal dependencies, and external influence. Experiments on the NYC Citi Bike system show the effectiveness and efficiency of our model when compared with the state-of-the-art methods.