Nowadays, limited bike parking spaces have been a severe problem to be solved in the Netherlands. Shared bikes are considered ideal solutions, as well as other emerging shared micromobility modes, e.g., shared e-bikes, e-scooters and e-mopeds, since they can help utilise vehicles better and alleviate more occupying land space. Moreover, shared micromobility modes have a great potential to attract private car users and achieve a modal switch from private cars to them. In this case, this research aims to understand how the sum of parking areas changed by the modal switch from private cars and bikes to shared micromobility modes. A calculation approach of parking areas will be proposed and applied in an agent-based simulation model mimicking the mode choice of travellers in Delft. Moreover, shared micromobility modes regarding vehicle characteristics (speed and cost) will be differentiated, so their impacts on mode choice and land space will also be investigated. Additionally, this study made a sensitivity analysis of coefficients of utility functions to test the reliability and validity of the results.

Recent studies have highlighted the significant impact of built environments (BE) in residential neighbourhoods on well-being, focusing on correlations between micro-scale BE features—such as trees, grass, fences, and bikes—and specific well-being aspects like physical health, social interaction, or perceptions of safety. However, these studies often fail to comprehensively explore how these features influence residential preferences or provide clear design guidance. To address this gap, examining residential location choice (RLC) datasets can offer valuable insights into how various micro-scale BE features affect the attractiveness of residential neighbourhoods and residents' well-being. This thesis proposes a semantic Computer Vision-enriched Discrete Choice Model (CV-DCM), which uses a panoptic segmentation model to quantify micro-scale BE features and integrates with traditional discrete choice models. A manual evaluation of 400 masks generated by the semantic computer vision model ensured accurate quantification of BE features, enhancing choice modelling interpretation. The choice modelling results highlight the specific impacts of various micro-scale BE features on RLC, providing valuable insights for urban planners. For instance, restricting unattractive features like motorcycles in residential neighbourhoods and planting or maintaining more trees are the most attractive among vegetation. By pinpointing the most influential elements, the study facilitates the cost-effective restructuring of residential neighbourhoods to boost their attractiveness and enhance residents' well-being.

Recent studies have highlighted the significant impact of built environments (BE) in residential neighbourhoods on well-being, focusing on correlations between micro-scale BE features—such as trees, grass, fences, and bikes—and specific well-being aspects like physical health, social interaction, or perceptions of safety. However, these studies often fail to comprehensively explore how these features influence residential preferences or provide clear design guidance. To address this gap, examining residential location choice (RLC) datasets can offer valuable insights into how various micro-scale BE features affect the attractiveness of residential neighbourhoods and residents' well-being. This thesis proposes a semantic Computer Vision-enriched Discrete Choice Model (CV-DCM), which uses a panoptic segmentation model to quantify micro-scale BE features and integrates with traditional discrete choice models. A manual evaluation of 400 masks generated by the semantic computer vision model ensured accurate quantification of BE features, enhancing choice modelling interpretation. The choice modelling results highlight the specific impacts of various micro-scale BE features on RLC, providing valuable insights for urban planners. For instance, restricting unattractive features like motorcycles in residential neighbourhoods and planting or maintaining more trees are the most attractive among vegetation. By pinpointing the most influential elements, the study facilitates the cost-effective restructuring of residential neighbourhoods to boost their attractiveness and enhance residents' well-being.