A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set

Abstract

We propose an agent-based mixed-logit model (AMXL) that is estimated with inverse optimization (IO) estimation, an agent-level machine learning method theoretically consistent with a utility-maximizing mixed logit model framework. The method provides joint, individual-specific, and deterministic estimation, which overcomes the limitations of discrete choice models (DCMs) given ubiquitous datasets. A case study of the CBD in Shanghai is conducted with mobile phone data of 26,149 anonymous commuters whose whole-day activity schedule on weekdays contains three sub-choices and 1,470 alternatives. AMXL is built to estimate individual tastes and predict the activity scheduling choice in different scenarios. Multinomial logit model (MNL), mixed logit model (MXL), and their dynamic forms (DMNL, DMXL) are built as benchmarks. Prediction accuracies are calculated as the percentage consistency of observed choices and predicted choices, both at individual level (to each commuter) and aggregated level (to each alternative in the choice set). The results show that empirical coefficient distributions in AMXL are neither Gumbel nor Gaussian, i.e. capturing inter-individual heterogeneities in space that are hard for DCMs to capture. The prediction accuracy of AMXL is significantly higher than the best model (DMXL) in benchmarks, improving from 8.66% to 61.68% at aggregated level and from 1.69% to 4.33% at individual level. In a comparison scenario, AMXL predicts different while reasonable change of choices compared with benchmark models. In an optimization scenario, AMXL can be directly integrated into a binary programming (BP) problem, which optimally allocates 10 blocks to send restaurant coupons to increase population consumer surplus by 19%.