Assessing Modal Shift Readiness for Sustainable Mobility among Potential Private Mobility Adopters Using Machine Learning and Scenario Simulation

Primary data were collected through a structured questionnaire survey in the study area during commute hours (8:30-10:30 a.m. and 6:00-8:00 p.m.) on weekdays, which was electronically delivered (tablets and mobile devices) and collected. This method helped to reduce data entry, transcription errors, and improve data quality. Respondents were selected purposively from residential blocks and workplaces in Sectors I–V to capture diverse socio-economic groups.