Citizen travel habits: a new solution to encourage sustainable commuting and facilitate the assessment of mobility management policies

Urban mobility and sustainable transport have become increasingly important due to their environmental impact and influence on quality of life. Understanding the factors that shape commuting behaviour is crucial for developing effective policies. This study examines the commuting patterns of employees in Italy, focusing on the factors that influence their preferences for various transport modes, including public transport, cycling, and e-scooters. The main objectives of this research are to analyse the factors influencing transport choices, identify distinct user clusters based on mobility preferences, and assess spatial variations in commuting behaviour across cities in Italy. The study combines quantitative analyses with spatial insights to provide a comprehensive understanding of commuting behaviours. A mixed-methods approach is employed, integrating quantitative techniques such as Binary Logistic Regression (BLR), clustering analysis (K-means, K-modes, Two-step), and spatial analysis. These methods explore the relationships between demographic factors, attitudes, and mobility choices. Clustering analysis identifies distinct user groups, while spatial analysis reveals regional differences across Milan, Bari, and Rome. Additionally, participant feedback on transport preferences and barriers is incorporated to offer a deeper understanding of mobility patterns. The findings reveal key factors influencing transport choices, including cost, travel time, and proximity to public transport. Clustering analysis identifies several distinct user groups with varying preferences and barriers to adopting sustainable transport. Spatial analysis uncovers regional variations in mobility patterns across the cities studied. This mixed-methods approach provides a comprehensive understanding of the motivations and challenges employees face in adopting alternative transport modes, highlighting the need for targeted policy interventions tailored to specific user groups and regions.