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.