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This paper presents an approach to estimate mode-choice models from spatially anonymized revealed preference travel survey data. We propose an algorithm to find a feasible sequence of activity locations for each individual that minimizes the maximum error of each trip's Euclidean distance within the activity chain. The synthetic activity locations are then used to create unchosen alternatives within the choice set for each individual. This is followed by the mode-choice model estimation. We test our approach on three large-scale travel surveys conducted in Switzerland, Île-de-France, and São Paulo. We find that our methodological approach can reconstruct activity locations that accurately match trip Euclidean distances but with location errors that still provide location protection. The discrete mode-choice models estimated on the synthetic locations perform similarly, in terms of goodness of fit and prediction, to the ones obtained from the observed activity locations. [ABSTRACT FROM AUTHOR]

In the last years, Urban Air Mobility (UAM) has been receiving increasing attention and even if the first services are expected to be launched shortly, there is still uncertainty about which type of commercial services (e.g., airport shuttles or city-taxis) will be implemented at an early stage, as well as which price point will be perceived as affordable by travelers. Based on data collected through a large-scale survey campaign in the Milan metropolitan area (Italy), in this paper passengers' value of travel time savings for different UAM services are estimated using advanced discrete choice modeling. Estimated mixed logit models allowed to comparatively analyze the differences between the two potential use cases, i.e., airport shuttle and city-taxi services. Results show a willingness to pay for UAM services from/to airports that is greater (in a range of 44%–57%) than for travelling within the metropolitan area, and greater (in a range of 31%–44%) for business travels than for other purposes, indicating that the most financially sustainable UAM services will potentially be available for airport-shuttle connections from/to central business districts. • Willingness to pay for UAM airport shuttles (48–69 €/h) is greater than that for UAM city-taxis (34–44 €/h). • Business travelers are willing to pay 31%–44% more than non-business ones for UAM services. • In the early stages, UAM services connecting airports and central business districts will be the most financially sustainable. [ABSTRACT FROM AUTHOR]

Travel behavior analyses through traditional discrete choice models offer insights into the factors that influence individual choices. However, the literature often neglects the spatial interactions between individuals and attributes in the context of walking trips. This paper investigates the spatial dependencies in walking travel choices with a case study in the city of São Paulo, Brazil. We compared a non-spatial and spatial lag of X (SLX) logit models for walking choices with data derived from a revealed preference survey conducted in the Metropolitan Region of São Paulo (MRSP). Sociodemographic information of individuals and trip-related attributes were included in the models. Overall, the prediction capabilities of the spatial models outperform the non-spatial model in terms of log-likelihood. The results indicate that the SLX with the attribute referring to the lower income group of individuals lagged by the distance of 500 m performed better compared to the non-spatial model. In the studied context, such groups are usually concentrated in more walkable neighborhoods. Therefore, improving the built environment and increasing job and educational opportunities may increase the propensity of walking for work and study trips in areas other than in higher income neighborhoods. • Walking travel choices assessed through Revealed Preference (RP) data. • Spatial lag of X models with sociodemographic and trip-related attributes. • Distance threshold between 200 and 600 m influence walking choice decisions between neighbors. • Lower income individuals are influenced by neighbors in walking travel choices. [ABSTRACT FROM AUTHOR]