## 6 Review of Studies Related to Micromobility in the Context of Multi-Modal Mobility
### Review of Studies
The results presented in [93] showed that the probabilities for dockless bike sharing to replace other transport modes can be quantified by theproposed method at the trip level and were heterogeneous depending on the trips in urban contexts. The average rates that bike-sharing may substitute bus, metro, walking and ride-hailing were projected to be 0.356, 0.116, 0.347 and 0.181, respectively, in Shanghai. The rate for dockless bike sharing to substitute a certain transport mode can be linked to built environment factors such as presence of transit systems. 4. Question: How to assess the interactions between different modes of transport and the influence of one transport mode on the other one from the perspectives of time and region attribute? Results: Exiting studies on transport demand forecasting primarily focus on a single transport mode based on the associated historical data. There lacked a study on co-evolution between different transport modes, e.g., the interactions between different modes of transport and influence of one transport mode on the other one from the perspectives of time and region attribute. In [94], the co-evolution between taxi demand and shared-bike demand was studied. There are two challenges for discovering the co-evolving patterns of different transport modes: (1) diversity of the co-evolving correlation in terms of regions and time and (2) multi-modal data fusion. The authors overcame these challenges by developing a novel method called the coevolving spatial temporal neural network to learn a multi-view demand for each transport mode, extract the co-evolution pattern of the two modes and predict the demand for a specific transport mode. The proposed approach uses a multi-scale representation method, including fine-scale demand information and coarse-scale pattern information. The superiority of the proposed approach over the state-of-art methods was demonstrated by the results of extensive experiments. 5. Question: What are the determinants of station-based round-trip bike sharing demand? Results: A train station-based round-trip bike sharing scheme, which was different from the existing one-way bike sharing schemes, was promoted by the Dutch train station operator to offer bike and train services. However, there lacked a study on the determinants of demand for round-trip bike sharing. The paper [95] identified potential temporal and weather-related determinants for station-based round-trip bike sharing rentals by applying multiple linear regression with an in-depth analysis. The authors compared the results with the findings of one-way bike sharing schemes. The results show that the most important determinants for hourly rentals in the station-based round-trip bike sharing scheme is the number of departing train travelers, followed by temporal and weather-related determinants. In addition, the correlation between the determinants and the hourly demand varies with stations and depends on the underlying demand patterns. 6. Question: How to model public transport users' propensity to shift to the bicycle to access bus stops, train and metro stations? Results: Although the integration of cycling and public transport in developed countries has been studied, the issue to integrate cycling with other main modes such as bus and metro in developing countries is less explored. The goal of the study [60] is to study the propensity for public transport users to shift to the bicycle to access bus stops, train and metro stations in Rio de Janeiro. This study used two binary logit models to predict the factors affecting the propensity to adopt a bicycle to access public transport. The findings are that socio-economic characteristics, barriers and motivators are important factors for users to adopt bike and ride. Personal constraints, distance too close to public transport stops, parking conditions and public safety are the barriers. Changing home location, owning a bicycle, implementing cycle ways and improving parking conditions are motivators.