A Spatial Econometrics Approach to Integration of Behavioral Biases in Travel Demand Analysis

Random utility models customarily assume strict independence of individual decision-makers. Overwhelming evidence of crowding, peer pressure, herd behavior and other instances of spontaneous discrete choice coordination indicate that decision-makers interact and thus affect choices made by others. Socially influenced individual choices become biased toward either agreeing with or contradicting the choices made by peers. Presuming that many social interdependencies are spatial in nature, this paper presents a basic spatial discrete choice model obtained by extending random utility theory to discrete choices made by heterogeneous spatially dependent individuals. While interdependencies are unobserved, the model allows us to study behavioral biases arising from spatial interdependencies. The spatial discrete choice model is shown to address the effects of behavioral biases on conditional choice probabilities, the marginal effects of exogenous variables on revealed preferences, and the spatial patterns of discrete choices. A pseudo maximum likelihood (PML) estimator for the model is developed and closed-form expressions for conditional choice probability estimates are derived. The PML estimator is shown to be consistent and computationally feasible for large spatial datasets. The performance of the pseudo maximum likelihood estimator for the spatial discrete choice model is illustrated using simulated data