Analyzing and promoting micro-shared mobility system leveraging big data

Shared micro-mobility systems (SMMS), such as bike/e-bike and e-scooter/e-mopeds sharing, are potential sustainable alternatives to fossil-powered transportation and are being embraced by Swedish cities to promote sustainable mobility, especially during the COVID-19 pandemic. SMMS, as new drivers of sustainable mobility, are operating in over 100 EU cities. Urban policymaker, planner and SMMS operators need analysis about usages patterns and demand prediction in different urban contexts to support scientific planning, operational optimizations, and countermeasures to promote usage of SMMS. This project aims to tackle the current challenges leveraging big data resources

The project is funded by Area of Advance Transport for two years with a total amount of 2.8 million SEK. The project is an interdisciplinary collaboration between three departments: ACE Chalmers (Kun Gao, mobility analysis using big data; Jiaming Wu, machine learning and big data; Xiaobo Qu, transport planning and optimization), M2 Chalmers (Jelena Andric and Majid Astaneh, life-cycle assessment and machine learning) and Department of Psychology University of Gothenburg (Lars-Olof Johansson, policymaking and social science).

Samarbetande organisationer

• Göteborgs universitet (Akademisk, Sweden)