## 6 Review of Studies Related to Micromobility in the Context of Multi-Modal Mobility
### Review of Studies
The study suggests formulation of policy to increase bicycle owner-ship and improve cycling infrastructure. Question: How to analyze bike sharing travel patterns based on bike sharing trip data? Results: Bike sharing travel patterns can provide insights about the use, design and policy decisions of bike sharing and the associated multi-modal transportation systems. An important research issue is to analyze bike sharing travel patterns based on bike sharing trip data. The study [61] analyzed the distributions of trip distance and trip duration for bike sharing trips for commuting and touristic purposes based on bike sharing trip data from eight cities in the United States. The results show that, for larger bike sharing systems, both the trip distance and duration follows a lognormal distribution. For smaller systems, the distribution varies among Weibull, gamma, and lognormal. For the case of long trips, the analysis shows that the trip distance and duration follows a power law decay in the larger systems. (8) Question: What are the differences of micromobility modes in terms of the characteristics, complementation or competition between them? Results: The combination of shared bikes, shared e-scooters, and public transit enables an efficient and flexible multi-modal transport service. However, differences of micromobility modes were not explored in the literature. An investigation was required to study the characteristics of different modes of micromobility as well as the complementation or competition between them. The study [96] was based on the case of the City of Austin with coexisting public transit, shared bikes, and shared e-scooters. The findings of this study indicate that public transit is closely related to commuting than shared micromobility modes. The results show that e-scooters are more likely to be used for leisure trips than shared bikes and public transit. In comparison with shared e-scooters, shared bikes are more likely to be used by commuters during peak hours. The results suggest that only one shared mode complements public transit at a given area. (9) Question: How to optimize the locations of micromobility stations/service area using geospatial data? Results: Optimization of the locations of micromobility stations/service area poses several challenges for smaller urbanized areas with lower population densities due to fewer resources for system planning, operation, and maintenance. The development of an effective solution method is an important issue. A method based on rasterized geospatial data to capture bikeshare demand indicators was developed in [62] to optimize micromobility stations/service area locations. Priority of inputs with weights calculated by an analytic hierarchy process based on the results of a survey of transportation professionals was used to reflect the overall importance of different inputs. The proposed method identified hotspots for candidate stations/service area locations by combining different factors and creating a spatial index value. The candidate stations/service area locations were further analyzed to choose optimal locations based on the factor of budget and spacing between stations/service areas. The proposed method was applied to the bikeshare station sitting case of Iowa City, Iowa, U.S. for validation. The results show that the introduction of optimal locations found improve multi-modal travel times and job accessibility in the target area. (10) Question: How to assess the effectiveness of integrating shared e-scooters as the feeder to public transit in cities? Results: As a complementary transport mode to solve the first- and last-mile problem to access public transit, the integration of e-scooter sharing with public transit is an important issue. The study reported in [63] was based on vehicle transport data of 124 European cities to analyze the integration between shared e-scooters and public transit.