  Accurate and reliable prediction of individual travel mode choices is crucial
for developing multi-mode urban transportation systems, conducting
transportation planning and formulating traffic demand management strategies.
Traditional discrete choice models have dominated the modelling methods for
decades yet suffer from strict model assumptions and low prediction accuracy.
In recent years, machine learning (ML) models, such as neural networks and
boosting models, are widely used by researchers for travel mode choice
prediction and have yielded promising results. However, despite the superior
prediction performance, a large body of ML methods, especially the branch of
neural network models, is also limited by overfitting and tedious model
structure determination process. To bridge this gap, this study proposes an
enhanced multilayer perceptron (MLP; a neural network) with two hidden layers
for travel mode choice prediction; this MLP is enhanced by XGBoost (a boosting
method) for feature selection and a grid search method for optimal hidden
neurone determination of each hidden layer. The proposed method was trained and
tested on a real resident travel diary dataset collected in Chengdu, China.
