Observational network analysis reveals structural commuting inequalities across municipal transit networks, indicating persistent infrastructure gaps despite geographic proximity.
Typically, past works on urban mobility have leveraged origin–destination analysis to capture where people travel, often leaving the sequence and structure of their trips unaddressed, thereby limiting the diagnosis of accessibility inequalities. We address this gap by integrating process mining and network science, applying both frameworks to Call Detail Records (CDRs) from the Lisbon Metropolitan Area. From 2.7 million reconstructed trips, we construct case-centric event logs and discover Directly-Follows Graphs (DFGs) for morning commuting in each of the 18 municipalities. We represent these DFGs as weighted directed networks and compare them using eight complementary metrics, including degree-distribution Wasserstein distance, flow correlation, Jensen–Shannon divergence, and a composite complexity index. This reveals systematic structural heterogeneity that aggregated OD analysis cannot detect. Lisbon city emerges as a structural outlier, with the highest modal diversity and most complex DFG topology, whereas most peripheral municipalities are dominated by private car use. Geographic proximity does not guarantee temporal accessibility: median commutes between neighbouring municipalities such as Sintra and Cascais exceed 25 min, indicating infrastructure gaps. Municipalities cluster into spatially coherent groups defined by shared mobility process structures−a northern suburban cluster, a southern Tagus cluster, and an outlying Lisbon cluster−patterns that are consistent across structural and temporal metrics. Inferred commuting patterns align strongly with 2021 Census data (Pearson r = 0.94, Spearman ρ = 0.90), supporting the validity of the approach. We show that process mining and network science are complementary: process mining yields structured event representations and modal sequencing, while network science offers robust tools for structural comparison. However, DFG structures represent only mode–destination transitions, not full multimodal trip chains, a limitation to be addressed in future works.
No takes yet. Share an insight, caveat, or question.
Filonchik et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: