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In an era of big data and emergence of disrupting mobility technologies, statistical models have been utilized to uncover the influence of significant factors, and machine learning algorithms have been used to explore complex patterns in large datasets. Focusing on discrete choice modeling applications, this research aims to introduce computational graph (CG)-based frameworks for integrating the strengths of econometric models and machine learning algorithms. Specifically, multinomial logit (MNL), nested logit (NL), and integrated choice and latent variable (ICLV) models are selected to demonstrate the performance of the graph-oriented functional representation. Furthermore, the calculation of gradients in the log-likelihood function is accomplished using automatic differentiation (AD). Using the 2017 National Household Travel Survey data and synthetic datasets, we compare estimation results from the proposed methods with those obtained from Biogeme and Apollo. The results indicate that the CG-based choice modeling approach can produce consistent estimates of parameters with substantial computational efficiency.

Ride-hailing services have grown in cities around the world. There are, however, few studies and even fewer publicly available data sources that provide a basis to understand and quantify changes in ride-hailing usage over time. Ride-hailing use may change over time because of socio-demographic shifts, economic and technological changes, and service attribute enhancements, as well as changes in unobserved attributes such as attitudes and perceptions, lifestyle preferences, technology savviness, and social influences. It is important to quantify the effects of these different forces on ride-hailing frequency so that robust forecasts of ride-hailing use can be developed. This paper uses repeated cross-sectional data collected in 2015 and 2017 in the Puget Sound region to analyze the differential effects of socio-demographic variables on the evolution of ride-hailing adoption and usage. By doing so, the study is able to isolate and quantify the pure effect of the passage of time on adoption of ride-hailing services. A joint binary probit-ordered probit model is estimated on the pooled dataset to explicitly account for sample-selection differences between the 2015 and 2017 surveys that may affect estimates of ride-hailing adoption in the two years. Model estimation results are used to compute average treatment effects of different variables on ride-hailing usage over time. It is found that the effects of most demographic variables on individuals’ propensity to use ride-hailing are softening over time, leading to reduced differences in ride-hailing use among market segments. This suggests that there is a “democratization” of ride-hailing services over time.