Data-driven inference approach for integration between shared micro-mobility and public transit with empirical analysis

Abstract

E-scooters are here to stay, as we see promising growths of about 10% annually by 2030. The industry is envisioned as a prospect to promote environmental and socio-economic sustainability. Integrating it with other forms of public transit since it is a more flexible form of transit, for the first- and last-mile seems to be the most promoted desire presently. However, the challenge lies in the fact that there are very few policies to govern them and also very little research to fully understand the impact of e-scooter’s integration with public transport. With our research aimed at using machine learning and a k-prototype technique to analyse the usage patterns, seasonal effect and effects on POI of the first- and last-mile trips within the city of Gothenburg. From that we found that the closer an e-scooter was to a stop it encouraged it’s usage for integration, especially in the winter with about 62% decline in integrated trips as compared to 70% in non-integrated trips. Indicating that, there is a stronger desire for integrated trips in the winter than in the summer. We also found that the city had 80% of substituted and 20% complementary e-scooter trips with public transit, with the common day and time of usage being on Wednesdays and Thursdays between 12:00 and 14:00 or 14:00 and 16:00. In the city the high counts of integration was found to be in the centre of the city at locations with multi-modal transport and dense activities which included commercial, others and recreational areas but their integration rates mostly occurred in suburban areas which were less dense with less efficient transport. One location stood dominant in both integrated trip count and integration rate which was "Stenpiren". Finally we, found that the weather impact the number of trips but does not affect the perception of usage, with the integration patterns being similar.