Critical commuters and staggered working hours strategies: A two-stage framework integrating machine learning and social equity

Congestion remains one of the most prevalent transport problems in major cities, with social equity being a pivotal aspect in shaping solutions. This study presents a two-stage framework that combines machine learning with social justice principles to identify critical commuters who can adjust their workplace arrival times. This framework addresses both traffic management and social equity concerns, considering synchronisation needs at the employer, household, and individual levels. Our proposed framework can identify critical commuters based on their basic information collected by the employer. We benchmark multiple machine learning approaches to model and predict an individual’s ability to shift their workplace arrival times. Ultimately, we frame the problem as a classification task and select gradient boosting due to its superior performance. Using employee survey data from Rennes Metropole in France, we identify the key factors that influence individual’s flexibility in their arrival times. Regular school drop-offs are the most significant factor, followed by theoretical arrival time contracts with employers and, to a lesser extent, age and income. Building on these findings, we apply the Rawlsian “Min-Max” fairness principle rooted in social science to refine the subpopulation of commuters with theoretical shift abilities and assess their practical likelihood of shifting within a socially equitable framework. By integrating machine learning insights with social equity considerations, this framework offers an interdisciplinary approach to potentially mitigate congestion, ensuring that policies not only address traffic demand management but also promote fairness and inclusion, supporting their long-term effectiveness. • Develop an interdisciplinary framework that combines social justice theory with arrival-time demand management to identify ’Critical Commuters’, commuters whose time shifts can alleviate peak-hour congestion in a fair and socially acceptable manner. • Apply machine learning techniques to identify the key determinants of shift flexibility and characterize ’Critical Commuters,’ demonstrating how targeting this group can effectively reduce peak-hour congestion. • Apply a social justice perspective, using frameworks such as Min-Max theory, to assess the socio-economic and institutional barriers that influence commuters’ ability to shift their schedules equitably. • Conduct a comprehensive analysis and discussion on the characterization of ‘Critical Commuters’ from multiple perspectives, including social, economic, and gender-based factors.