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Browsing by Author "Willberg, Elias"

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  • Willberg, Elias (2019)
    The number of bike-sharing systems has increased rapidly during the last decade. These systems expand urban mobility options and provide a solution to the so-called “last-mile” problem. While new bike-sharing systems are opened and current ones expanded in Finland and elsewhere in large numbers, it is important to understand how these systems are used and by whom. Despite the wealth of bike-sharing literature, usage patterns by different user groups are still not yet well studied. This knowledge is needed to ensure that the benefits of bike-sharing systems distribute as evenly as possible to the citizens. In this study, I have employed a person-based approach to study mobility patterns of bike-sharing users in Helsinki. The system in Helsinki was opened in 2016 and the urban bikes quickly became popular among citizens. I have aimed to understand how equally the bike-sharing system in Helsinki is serving the citizens and how different user groups have differed from each other in their use. I have also studied how the system is linking to public transport in Helsinki and compared the bike-sharing system usage and users in Helsinki to other systems internationally. These specific questions stem from the systematic literature review on bike-sharing (n=799), which I carried out before the empirical study. In this study, I have used a dataset provided by Helsinki Region Transport, which contained all the bike-sharing trips (~1.5 million) from 2017. Besides the trip information, the dataset contained the basic demographic information of the user. The results of literature review show bike-sharing systems have been an active and extensive study topic even though the study areas are mostly concentrated to certain cities. Based on the empirical data-analysis, majority of bike-sharing users are young adults between 25-35 years old whereas the share of over 50 year olds is only 12 %. Both men and women use urban bikes actively but men are overrepresented both in the number of users and trips. The use of bikes is not equal but a small minority of users have generated the majority of trips. The users who live inside the bike station coverage area make around 80 % of the trips implying that the proximity of a station has a considerable impact on the use. Trip profiles of those living inside the system coverage area differ considerably from those who live outside the area. For example, the users living inside the area seem to combine urban bikes less with public transport and they use urban bikes relatively more on weekends compared to the other group. The subscription type and use activity are also important factors shaping usage patterns. Then again, age and gender are more important in determining whether someone chooses to become a user than in shaping usage patterns. The use of bike-sharing system in Helsinki has been high even when compared internationally. The results of this study show that the high usage rates still do not necessarily mean that the system would be equally used by citizens. Based on the systematic review, equity is a critical topic to address in relation to bike-sharing users. The user profiles in Helsinki seem to follow similar patterns of bike sharing as found in other cities with an overrepresentation of certain population groups. The use of young adults might promise well for the change of urban mobility. However, it is important to keep promoting cycling to a wider range of the population. The bike-sharing system in Helsinki will expand in 2019 to new areas. Based on the results of this study the expansion seems reasonable as a large part of the users live close to a bike-sharing station. The expansion will then bring the full benefits of bike sharing accessible to a larger group of people in Helsinki. The system seems both to replace and extend the public transport system, which is common to bike-sharing systems in many cities. From the data perspective, the origin-destination type of trip data, which was used in this study, provided a great deal of useful information about users and usage profiles. Even when accounting for limitations in this data type, it is still an excellent addition complementing existing cycling data sources.