Quantifying the explanatory power of mobility-related attributes in explaining vehicle ownership decisions

How Personal Accessibility and Frequency of Travel Affect Ownership Decisions on Mobility Resources

“…Le Vine et al [1] has estimated the importance of different travel activity purpose types (e.g., to work, leisure, shopping). Astegiano et al [9] also used the PAS concept, but the importance of a particular travel type was set a priori, being proportional to their frequency. The current study also employs the PAS concept and the importance is estimated for the different travel activity types.…”

“…Upon estimation of the coefficients β tt * , β OPC , and ASCm, Le Vine et al [1] assumed that the transport mode choices for the PAS are independent. Astegiano et al [9] relaxed the assumption, considering the fact the PAS data involve repeated transport mode choices of the same individual (panel data) and hence perception errors are correlated. Therefore, a mixed logit (MXL) model was introduced, accounting for correlations in error term and relaxing the independence of assumed irrelevant alternatives considered by the Multinomial Logit (MNL).…”

“…Explicitly recognizing the number of PAS travel activities and the personal accessibility to PAS in Equation (5), compared to the original utility definition in [1,9] shown as Equation (2), allows for making a more refined distinction between choice situations. Let us consider two individuals, one typically accomplishes many short-distance trips, and the other does only a few long-distance trips.…”

“…The original study used the MNL for the RCS choice (the second step) assuming that the ε i d of the RCS alternatives are mutually independent [1]. [9] estimated a cross-nested logit (CNL) and a nested generalized extreme values (NGEV) model for the second step. The CNL captures the correlations of both, the travel and the non-travel utility perception among RCS alternatives having the same mobility resources.…”

“…[1] used a web-scraping technique that extracts the requested data in batch from websites, for example, travel times from a PT operator website. [9] augmented the observed travel records by the non-observed travel times downloaded through Google API. In both cases, the original surveys contained the geographical references.…”