Considerable interest has been shown in decreasing car use through planning and policy-making efforts. However, little is known about the interactive effects of built environment (BE) and parking policy on car use, and it is unclear whether and how these effects differ across trip purposes. We conducted a web-based survey in Beijing and collected data from 1036 respondents, including 517 male and 519 female respondents. This study estimates the interactive effects of BE and parking policy on car use for home-based work and non-work trips by employing multilevel logit models. The results show that BE variables at trip origins and destinations are important for shaping car use for both home-based work and non-work trips. Specifically, land use mixture (Coeff. = −0.121), bus stop density (Coeff. = −0.006), and population density (Coeff. = −0.009) at residential locations are negative factors affecting car use for work trips, whereas distance to local center (Coeff. = 0.012) and distance to the city center (Coeff. = 0.019) at residential locations are positive factors. Land use mixture (Coeff. = −0.323), bus stop density (Coeff. = −0.008), road density (Coeff. = −0.002), and population density (Coeff. = −0.007) at residential locations are negative factors of car use for non-work trips. Among BE factors at destinations, land use mixture (Coeff. = −0.319), bus stop density (Coeff. = −0.015), road density (Coeff. = −0.008), distance to the local center (Coeff. = −0.018), and population density (Coeff. = −0.012) are negative factors for car use for work trips, whereas the negative factors for non-work trips are land use mix (Coeff. = −0. 218), bus stop density (Coeff. = −0.038), road density (Coeff. = −0.003), distance to the city center (Coeff. = −0.121), and population density (Coeff. = −0.009). The effects of BE variables can be strengthened or weakened by free parking and parking convenience. Moreover, the results identify significant differences in the effects between work and non-work trips. These findings inform planners and policymakers of how to coordinate the BE and parking policies to decrease car dependence.

Mobility is a fundamental feature of human life, and through it our interactions with the world and people around us generate complex and consequential social phenomena. Social segregation, one such process, is increasingly acknowledged as a product of one's entire lived experience rather than mere residential location. Increasingly granular sources of data on human mobility have evidenced how segregation persists outside the home, in workplaces, cafes, and on the street. Yet there remains only a weak evidential link between the production of social segregation and urban policy. This study addresses this gap through an assessment of the role of the urban transportation systems in shaping social segregation. Using city-scale GPS mobility data and a novel probabilistic mobility framework, we establish social interactions at the scale of transportation infrastructure, by rail and bus service segment, individual roads, and city blocks. The outcomes show how social segregation is more than a single process in space, but varying by time of day, urban design and structure, and service design. These findings reconceptualize segregation as a product of likely encounters during one's daily mobility practice. We then extend these findings through exploratory simulations, highlighting how transportation policy to promote sustainable transport may have potentially unforeseen impacts on segregation. The study underscores that to understand social segregation and achieve positive social change urban policymakers must consider the broadest impacts of their interventions and seek to understand their impact on the daily lived experience of their citizens.

Urban segregation is increasingly understood as a dynamic process reproduced through daily mobility [1–4], yet existing approaches often treat cross-group encounters as occurring within a single or aggregated mobility layer. In reality, sidewalks, roads, buses, and rail often traverse the same physical corridors while carrying distinct residential socioeconomic status (SES) groups, creating parallel layers of mixing and isolation [5–7]. Here we develop a framework to quantify this layered segregation and apply it to a mobile phone dataset covering 11 million individuals in Beijing, with core analyses based on 6.05 million individuals for whom stable residential and workplace anchors could be inferred. We introduce two indices: the mode-specific mixing index ( MMI ) to measure within-mode diversity, and the multimodal uniformity index ( MUI ) to summarize how evenly mixing is distributed across transport layers. We find that segregation varies sharply by transport mode: rail produces the highest observed mixing by linking disparate neighborhoods, while active travel remains locally sorted. An OD-preserving null model further shows that rail has the smallest mixing deficit relative to the level expected under Beijing’s existing origin–destination, timing, routing, and mode structure, whereas other layers show stronger residential SES sorting. This multi-layered segregation also fluctuates with daily rhythms, becoming more pronounced in peripheral zones and during commuting hours where directionally organized mobility flows amplify social divergence. Exploratory mode-choice simulations suggest that changes in generalized travel costs can redistribute residential SES groups unevenly across mobility layers. This research reveals the social dimensions inherent in transport systems, providing metrics to incorporate modal stratification into urban planning and policy evaluation.

Social mixing in cities emerges from encounters between individuals of different backgrounds in shared spaces. These opportunities for inter-group contact are not randomly distributed but shaped by urban transport systems that channel millions of daily trajectories. As cities face rising segregation, understanding and quantifying these opportunity structures has become critical for designing evidence-based policies that foster social inclusion. Yet existing approaches face significant limitations in capturing the probabilistic nature of these opportunities within complex, multimodal cities. This study addresses this gap by developing a computational framework that treats encounters as likelihoods shaped by behavioral uncertainty. Encounter probabilities among individuals are calculated within mode-specific encounter spaces—from individual roads, city blocks to rail and bus service segments—by aggregating potential trajectories across socioeconomic groups. Using city-scale mobility data, the outcomes reveal how infrastructure, daily rhythms, and travel choices interweave to create spatially and temporally varying opportunity structures across multimodal transport systems. We then extend these findings through agent-based simulations, demonstrating how transport policies designed to promote sustainable mobility may produce unintended social consequences. The study underscores that effective policymaking for social inclusion must account for how transport interventions reshape encounter opportunities in citizens' daily mobility.