Using best–worst scaling to identify barriers to walkability: a study of Porto Alegre,Brazil

This paper pursues three goals: (1) determining the relative importance of built environment barriers limiting walkability, (2) analyzing the existence of an asymmetry in the way people evaluate positive and negative built environment characteristics, and (3) identifying solutions to tackle the main barriers and quantify their impact in walkability. A best–worst scaling survey was developed to compare the importance of eight different attributes of the built environment regarding walkability. Model results show an asymmetry negative–positive in the judgment and choice of built environment characteristics that promote and impede walkability. The most important barriers, obtained from worst responses, are connectivity, topography, sidewalk surface and absence of policemen. Walkability scores were computed for different neighbourhoods and different policy scenarios were forecasted. Simulation results from the worst responses indicate that improvements in sidewalk quality, along with an increase in the number of police officers, lead to an 85% increase in the walkability score for the lower income neighbourhoods.

     

Estimation of a constrained multinomial logit model

Identifying the set of available alternatives in a choice process after considering an individual’s bounds or thresholds is a complex process that, in practice, is commonly simplified by assuming exogenous rules in the choice set formation. The Constrained Multinomial Logit (CMNL) model incorporates thresholds in several attributes as a key endogenous process to define the alternatives choice/rejection mechanism. The model allows for the inclusion of multiple constraints and has a closed form. In this paper, we study the estimation of the CMNL model using the maximum likelihood function, develop a methodology to estimate the model overcoming identification problems by an endogenous partition of the sample, and test the model estimation with both synthetic and real data. The CMNL model appears to be suitable for general applications as it presents a significantly better fit than the MNL model under constrained behaviour and replicates the MNL estimates in the unconstrained case. Using mode choice real data, we found significant differences in the values of times and elasticities between compensatory MNL and semi-compensatory CMNL models, which increase as the thresholds on attributes become active.