This thesis explores the innovative integration of Discrete Choice Modeling (DCM) and Machine Learning (ML), specifically Neural Networks (NN), within the transportation sector. DCM, with its highly interpretable and hand-designed models grounded in robust mathematics, contrasts with the data-driven and predictive capabilities of NN. By assessing the existing landscape of DCM and ML at the time, we identified a critical gap: while comparative studies of both fields were prevalent, few ventured into integrating their respective advantages. Our research participates in bridging this gap showcasing the complementary potential of DCM and NN.

Central to our work is the development of a novel hybrid modeling framework, blending the structured clarity of DCM with the adaptive, data-driven nature of NNs. We elaborate a detailed set of conditions to preserve the interpretability of DCM parameters while leveraging the predictive power of NNs. Our research lays the groundwork for several studies building upon our methodology. We present the newfound literature as well as various parallel directions integrating ML methods for DCM, sharing our expert insights for hybrid modeling.

We then proceed by studying our framework for Neural Network applications. Through three distinct investigative approaches, we have enhanced the explainability of NN predictions. Our topics include areas where a high degree of understanding may be critical, such as pedestrian trajectory forecasting and pedestrian crossing behavior. These methods demonstrate how the strategic incorporation of DCM into NNs can elucidate the various weighing factors in pedestrian decision-making.

Finally, we contribute to initiating the era of high-dimensional data within Discrete Choice Modeling. We believe that by leveraging the strength of Neural Networks in handling various types of input, we can steer Discrete Choice Modeling towards promising new directions. To set the stage for future works, we study the case of multi-modal datasets with overlapping information. We discuss in which cases the integrity of interpretable parameters is at risk and how to circumvent this. Our research ends by sharing our future views and insights on this novel field.

In summary, this thesis not only introduces a groundbreaking framework but also provides critical insights for the advancement of DCM and NN integration. It lays a foundation for future research, aimed at understanding and enhancing decision-making models in an age where data complexity and volume continue to grow exponentially.