Commuting is widely regarded as a recurrent and prolonged stressor, that triggers both acute and chronic physiological and psychological stress responses (Rüger et al., 2017). Key commuting-related factors such as impedance, lack of control, and low predictability have been found to significantly elevate stress levels. Stressful commutes characterized by long durations, motorized travel, congested public transit systems, and limited active travel options not only increase perceived psychological stress among workers but also lead to elevated salivary cortisol levels and heightened physiological arousal, indicating acute stress responses (Avila-Palencia et al., 2017; Gottholmseder et al., 2009; Loo and Tsoi, 2024; Rissel et al., 2014; Sattler et al., 2020; Sharif et al., 2025; Wener and Evans, 2011). Furthermore, the impact of commuting stress extends well beyond the trip itself. It often spills over into work and family life, reducing productivity, impairing family cohesion, and diminishing overall life satisfaction (Calderwood and Mitropoulos, 2021; Gimenez-Nadal and Molina, 2019; Murphy et al., 2022; Zhou et al., 2017). As a direct stressor, commuting has been associated with mental health risks such as depression, while cumulative stress exposure can contribute to anxiety, cognitive impairment, irritability, fatigue, depression, social isolation, low self-esteem, immune dysfunction, cardiovascular disease, and many other health conditions (Sharif et al., 2022; Wang and Liu, 2022).

These challenges are particularly pronounced for white-collar workers in large metropolitan regions such as Beijing, where high urban density, job-housing imbalance, and overloaded transport systems intensify commuting stress (Engelfriet and Koomen, 2018; Huang and Loo, 2023; Lee, 2024; Loschiavo, 2021). White-collar professionals face elevated psychological demands and sedentary lifestyles, further increasing their vulnerability to stress-related health problems (Hansen et al., 2010). In the post-pandemic era, traditional commuting has increasingly been perceived as a burdensome, time-consuming, and mentally taxing obligation, with many employees preferring telecommuting for its flexibility and reduced stress (Erdoğdu and Watson, 2023). Against this backdrop, commuting stress represents an increasingly relevant yet underestimated urban public health issue, demanding systematic scholarly attention.

Despite the growing interest in commuting stress, prior studies often rely on static, subjective assessments, neglecting the nuanced spatiotemporal dynamics of stress across different commuting stages. Conceptual frameworks such as the one proposed by Lim et al. (2024) highlights the need to consider stress across pre-, in-, and post-commute phases, but empirical studies rarely capture this full temporal continuum. The spatial dimension—including distribution of jobs and residences, as well as the direction of commuting flows—are also crucial factors but underexplored empirically (Lim et al., 2024; Maheshwari et al., 2023). The emergence of wearable biological sensors and GPS tracking offers an opportunity to continuously track stress variations across both spatial and temporal dimensions, overcome the limitations of traditional survey methods by providing non-invasive, fine-grained, continuous, and objective data of monitoring commuting stress.

This study investigates the spatiotemporal dynamics and determinants of commuting stress using time-series stress data and GPS-based commuting trajectories. Focusing on white-collar workers in Beijing, we analyze commuting stress patterns at aggregated commuting time windows chain, identify clusters of stress pattern, and examine the commuting-related, behavioral, and demographic factors associated with high-stress patterns. By applying a mixed-methods approach combining k-means clustering and multi-level statistical modeling, this study offers a more nuanced understanding of how commuting stress evolves across temporal and spatial dimensions.

Theoretically, this study contributes to the literature by moving beyond static and subjective measures of stress to offer a dynamic, empirical model of commuting stress, thereby empirically validating the practical relevance of the PIP model. Practically, by distinguishing different dynamic patterns of commuting stress through visualization techniques and investigating the determinants of the most extreme high-stress patterns, our findings offer actionable insights to inform stress-sensitive transportation policies, promote telecommuting and flexible work arrangements, and address health disparities among white-collar workers in large cities. In doing so, this research directly responds to emerging urban challenges and supports the development of more resilient, equitable, and commuter-friendly cities.