Adaptive Transportation Systems with Holistic Representation of Supply and Demand

In Adaptive Transportation and Logistics (ADAPT-OR) group we bring together the methodologies of Operations Research, Behavioral Modeling and Machine Learning. Operations Research techniques are supported by Machine Learning algorithms to reach dynamic and predictive optimization models towards robust transportation systems. Behavioral models are incorporated into optimization models and demand-driven (aka choice-driven) decisions on the supply side are obtained where user preferences are incorporated. Machine Learning techniques also support the behavioral models as the preferences of users can be learned continuously as heterogeneous users make choices and the overall system can make use of most up-to-date information. This integrated methodological framework enables to use the right resources at the right time that addresses the long term sustainability of the transport and logistics systems. A better utilization of the available resources is one of the key challenges towards sustainability goals and the methodologies we develop provide promising models and algorithms to address this.