Parking management in mid-sized Latin American cities is often limited by weak enforcement, scarce off-street supply, and widespread irregular parking. This study uses a stated preference experiment to analyze parking choices among 227 drivers in Loja, Ecuador. Six choice tasks evaluated four alternatives—regulated on-street, private off-street, irregular parking, and leaving the vehicle at home—based on cost, walking distance, search time, availability, expected fines, and security. Multinomial logit (MNL) and mixed logit (ML) models were estimated, including income- and gender-based segmentations. Results show that cost (β = −0.332, p < 0.01) and walking distance (β = −0.0026, p < 0.001) are the primary determinants of formal parking choice. The willingness to pay to avoid 100 m of walking is USD 0.77 per 2-h period. Low-income users are 4.8 times more sensitive to cost. Mixed logit results reveal significant heterogeneity in preferences for cost, search time, and enforcement sensitivity. Policy simulations indicate that increasing enforcement (70% probability, USD 250 fine) reduces illegal parking demand by 93%, while lowering regulated tariffs to USD 0.50 raises its share by 4.2 percentage points. These findings support sustainable mobility policies by promoting efficient parking management, reducing illegal parking, and improving equitable access to urban space.

Road safety is a global priority, and driver behavioral factors are among its most critical determinants. Although the literature has advanced in characterizing driving styles using psychometric instruments such as the Multidimensional Driving Style Inventory (MDSI), an empirical gap persists in the simultaneous integration of latent behavioral profiles, driving exposure, and road safety outcomes, particularly in Latin American contexts and across generational extremes. This study examined the relationship between latent driving style profiles and road safety outcomes among young (18–25 years) and older (≥65 years) licensed drivers in Ecuador, while evaluating the moderating role of driving exposure. A structured survey based on the MDSI was administered to 833 active drivers, and data were analyzed using Latent Profile Analysis (LPA) and binary logistic regression. The six-profile solution was selected according to the Bayesian Information Criterion (BIC = 11,655.07), with acceptable classification quality (entropy = 0.860; minimum posterior probability = 0.808); for inferential parsimony, these profiles were subsequently consolidated into three analytically interpretable categories: Predominantly Careful, Predominantly Risky, and Distress-Reduction. The Predominantly Risky profile was significantly associated with higher odds of traffic accident involvement (OR = 2.76, 95% CI [1.55, 4.93]), whereas the Distress-Reduction profile showed substantially higher odds of receiving traffic infraction fines (OR = 4.74, 95% CI [1.69, 13.34]). The composite driving exposure index was a robust predictor across both models (accident model: OR = 2.82, 95% CI [1.60, 5.14]; fine model: OR = 1.87, 95% CI [1.29, 2.74]). In addition, a significant interaction was observed between the Predominantly Risky profile and driving exposure in the model predicting traffic infraction fines, suggesting that exposure amplified sanction risk within this behavioral category. Older drivers showed a substantially higher representation of the Distress-Reduction profile than young drivers. These findings underscore the utility of person-centered approaches for identifying heterogeneous driver configurations and for designing profile-differentiated road safety interventions; from a practical perspective, these results support the development of targeted road safety programs that integrate behavioral profile screening with exposure-based risk management for young and older drivers.

University mobility in medium-sized cities faces increasing challenges arising from traffic congestion, urban sprawl, and the limited availability of sustainable transport options. In this context, the bicycle represents an efficient and environmentally low-impact alternative, provided that safe and connected infrastructure exists to facilitate its adoption. This study assesses the potential for bicycle use in the Andean city of Loja, Ecuador, taking as a case study the university community of the Universidad Técnica Particular de Loja (UTPL). Geographic Information Systems (GIS) tools, origin–destination (OD) matrices, and logistic models were integrated to analyze the relationship between three key variables: terrain slope, minimum travel time, and the percentage of protected cycling infrastructure. The results show that protected cycling infrastructure shows the strongest positive association with the modeled probability of use, while slopes greater than 15% and trips longer than twenty minutes are associated with lower modeled probabilities. The geospatial analysis identified priority corridors where improvements in cycling protection would yield higher modeled modal returns. It is concluded that strengthening cycling connectivity and the continuity of protected routes may inform scenario-based planning to support active university mobility, offering a replicable framework for medium-sized cities with similar topographic conditions.

Parking management has become an increasingly important challenge in rapidly growing urban areas, yet evidence from intermediate Latin American cities remains scarce. This study analyzes parking choice behavior across Ecuadorian cities using a national stated preference (SP) experiment validated through revealed preference (RP) data and estimated via discrete choice models. Data were collected between 1 April and 3 May 2026 from 2150 active drivers across 12 cities spanning the Coast, Highlands, and Amazon regions. Multinomial Logit (MNL) and Mixed Logit (MIXL) models were estimated to evaluate the effects of parking cost, walking distance, search time, security, and surveillance on parking decisions. Results showed that all three cost-related attributes significantly reduced parking utility, while security improvements increased the probability of selecting formal parking alternatives. The MIXL model outperformed MNL specifications (ΔAIC = 211.84), revealing significant unobserved heterogeneity in cost (SD = 0.45) and security preferences (SD = 0.29). Willingness-to-pay estimates reached USD 0.64 per 100 m reduction in walking distance and USD 0.69 per 10-min reduction in search time, with substantial inter-city variability. Despite low current adoption (18.4%), willingness to use digital reservation systems exceeded 75% across income groups. The findings underscore the need for locally calibrated, context-sensitive parking policies and support differentiated smart parking strategies in developing urban systems.