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Flexible Working Arrangements (FWAs) allow workers to adjust their schedules and locations, primarily to support work–life balance, while also indirectly influencing traffic conditions. However, their impact on travel behaviour remains inconclusive. Some studies suggest reductions in traffic congestion and vehicle miles travelled (VMT), whereas others report increases, likely due to latent behavioural differences among workers. Although clustering techniques have been employed to study these dynamics, factors such as trip chaining, peak-hour demand, and VMT are often overlooked, leading to overestimates of the benefits of FWAs. Moreover, limited research has analysed workers’ behavioural shifts under varying levels of FWAs. Therefore, this study focuses on the impacts of FWAs on travel behaviour and peak-hour congestion, examining how FWAs can be used as a Travel Demand Management (TDM) strategy. This study addresses these gaps using data collected from Greater Kuala Lumpur, Malaysia, applying Mutual Information Maximisation Clustering with Random Effects (MIMC-RE) and Deep Neural Network (DNN). This integrated predictive framework identifies latent behavioural clusters and forecasts travel behaviour under FWAs. It represents the first application of this approach to travel behaviour analysis, enabling the exploration of relationships between FWAs and behavioural shifts. The findings reveal that the heterogeneous impacts of FWAs stem from latent behavioural differences across worker groups and underscore the need to account for these patterns in transport planning. Scenario analysis demonstrates that offering flex-time to 40% of fixed-time non-teleworkers can shift 24% of peak-hour home-based work trips to off-peak hours, reduce travel time by 33%, and decrease travel distance by 21% per trip, while increasing public transport use. These results offer evidence-based guidance for developing TDM strategies that optimise travel demand, alleviate peak-hour congestion, and support sustainable transport planning in the post–COVID-19 pandemic era.
Zubair et al. (Sun,) studied this question.