We demonstrate the impact of such algorithmic bias and explore the best practices to address it using three different representative supervised learning models of varying levels of complexity. The increasing use of new data sources and machine learning models in transport modelling raises concerns with regards to potentially unfair model-based decisions that rely on gender, age, ethnicity, nationality, income, education or other socio-economic and demographic data. In contrast, both machine learning models failed to match the observed distribution. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand.
Agreement is found in the variable's effects identified through the machine learning and econometric models. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land use factors influence home delivery demand. We used both classical econometric and machine learning techniques to obtain the best model. This study developed models to predict household' weekly home delivery frequencies. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. The COVID-19 pandemic dramatically catalyzed the proliferation of e-shopping. The prediction results show that the two data mining models offer comparable but slightly better performances than the MNL model in terms of the modeling results, while the DT model demonstrates the highest estimation efficiency and most explicit interpretability, and the NN model gives a superior prediction performance in most cases. Diary data sets from the San Francisco, California, Bay Area Travel Survey 2000 were used for model estimation and evaluation. Two performance measures, individual prediction rate and aggregate prediction rate, which represent the prediction accuracies for individual and mode aggregate levels, respectively, were applied to evaluate and compare the performances of the models. The similarities and differences of the models' mechanisms and structures are identified, and the mechanisms and structures in the models' specifications and estimations are compared. For comparison, a unique three-layer formulation of the MNL model is presented. Models based on these two techniques are specified, estimated, and comparatively evaluated with a traditional multinomial logit (MNL) model. The capability and performance of two emerging pattern recognition data mining methods, decision trees (DT) and neural networks (NN), for work travel mode choice modeling were investigated. Alternatively, mode choice modeling can be regarded as a pattern recognition problem in which multiple human behavioral patterns reflected by explanatory variables determine the choices between alternatives or classes. Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Spot the data hungry apps.Among discrete choice problems, travel mode choice modeling has received the most attention in the travel behavior literature.
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