Studien har för avsikt att förbättra kunskapen om vilka faktorer som påverkar cykelflödeten viss dag i Malmö. Vi har huvudsakligen undersökt frågor om, hur många grupperandekluster är optimalt för att kunna identifiera avvikande dagar och vilka är dess faktoreri en tidsserie cykelvolymdata? Vår arbetsmetod var att använda ett matchande tillvägagångssätt baserat på ett experiment tillsammans med en utvärderingsmetod. Arbetsmetoden skedde i en iterativ process där experimentet var att hitta rätt antal kluster ochdär utvärderingen var analysen av resultaten som producerades av experimentet. Datanerhållen från en cykelräknare belägen på Kaptensgatan i Malmö fick databearbetas medhjälp av normalisering då volymen av cyklister inte ska ha någon påverkan i studien. Syftetmed vårt arbete är att kunna identifiera avvikande datapunkter och dess faktorer med storinverkan på cykelflöden med hjälp av klusteranalys då detta kan leda till mer välinformerade beslut vid stads- och transportplanering. Om det gick att analysera cyklister där dessafaktorer elimineras så skulle detta leda till vidare utveckling och forskning av stor betydelseför Malmö stad. Genom att använda oss av klusteranalysen K-means och Euklidisk distanssom används som beräkning av distanser inom liknande områden kunde vi finna relevantakluster med avvikande datapunkter och faktorer med stor inverkan på cykelflödet. Vårtresultat visar att 7 kluster varav 2 av de delades upp till 6 mindre kluster, var det mest optimala för studien och faktorerna med en stor inverkan på de antal registrerade cyklisternaunder vissa dagar kunde då identifieras bäst. Faktorerna som identifierades var evenemang,festivaler, fotbollsmatcher, konserter, lovdagar, nederbörd och röda dagar. / This study aims to provide a deeper understanding of the different factors and their impacton the bicycle flow in Malmö during a certain day. We mainly examined the questions,what is the most optimal number of clusters needed in order to identify discrepancies, andwhich key factors have huge impact in a dataset? The choice of the method used in thisstudy is a matching approach based on experiment together with an evaluation method.The work method occurred in an iterative process, where the experiment was conductedto find the right number of clusters and the evaluation was the analysing of the resultsthat were produced by the experiment. The collected data from a bicycle counter, locatedin Kaptensgatan in Malmö, had to be processed with normalization to ensure that thevolume of the bicycles does not affect the study. The purpose of our study is to identifydiscrepancies and key factors that have huge implications on the bicycle flow with thehelp of cluster analysis that might lead to more well-informed decision in urban planningand transportation planning. If it were possible to analyze cyclists where these factorsare eliminated, this would lead to further development and research of great importancefor Malmö City. By using the cluster algorithm K-means, and Euclidean distance, whichis used as calculation of distances in similar areas, we could then find relevant clusterswith deviating data points and key factors with great impact on the bicycle flow. Ourresults shows that 7 clusters, 2 of which were divided up to 6 smaller clusters, were themost optimal for the study and the factors with a large impact on the number-registeredcyclists during certain days could then be best identified. The factors identified wereevents, festivals, football matches, concerts, rainfalls and holidays.

The national road database, NVDB, contains data on Swedish roads, streets, bicycle paths, and their attributes. Ensuring good data quality of bicycle data is important since it can help develop the bicycle infrastructure and strengthen the role of cycling in the transport system. The project aimed to investigate the usage and user-experienced quality of the bicycle data in NVDB. One objective was to identify how customers are using the data, to determine if data products, documentation, and distribution are sufficient for the customers’ usage. The project also aimed to identify problems users of NVDB bicycle data are experiencing, regarding availability, interpretability, completeness, and thematic uncertainty. Nine semi-structured interviews with users of NVDB bicycle data were carried out. Five categories of usage were identified: bikeability mapping, development of bicycle networks in built-up areas, development of recreational routes, network analysis, and cartography. The user-experienced problems identified were mainly related to completeness and interpretability.