Urban Mobility and Socio-Environmental Aspects in David, Panama: A Bayesian-Network Analysis

Abstract

Given that urban mobility arises from the interaction between social and environmental conditions, this study constructs a Bayesian network to represent these relationships in David, Panama, using 500 georeferenced household surveys that recorded variables related to demographics, travel behavior, infrastructure, mobility patterns and perceptions of risk, safety, and vulnerability. The Bayesian network was built and validated through a consensus-driven hybrid procedure combining structural learning and expert knowledge, resulting in a directed acyclic graph (DAG) with 127 nodes and 189 arcs; and conditional probability tables (CPTs) were learned from data. The topology of the network was analyzed with Louvain community detection, revealing eleven subsystems that group household economy and mode choice, hydrometeorological mobility barriers, congestion, public-transport quality, and safety in school travel. The inferences show gender-based differences in the risk of harassment on public transport, higher perceived vulnerability on longer trips, and elevated stress among middle-aged drivers. The model highlights potential priority interventions such as reinforcing public-transport safety, promoting self-contained trips, and encouraging short-distance active mobility, based on population perceptions. The resulting DAG functions as both an analytical and communication tool for urban management, is visually understandable to all stakeholders, and provides unprecedented evidence for Panama in a little-studied context.