ML surrogates for location problems

Electric vehicle charging infrastructure has become a central component of transportation planning as adoption of electric vehicles accelerates worldwide. Modern research models how drivers choose a station using discrete choice models, especially multinomial logit, which capture realistic preferences such as distance, queues, and charging speed. These models are computationally expensive, making large-scale location planning challenging. In this project we aim to address this complexity by approximating the logit model via surrogate models, such as neural networks, in order to both realistically model the driver preferences uncertainty and to allow the model to scale to larger instances.

Faculty Supervisor:

Tommaso Schettini

Student:

Partner:

University of Milano Bicocca

Discipline:

Computer science

Sector:

Artificial Intelligence; Information and Communications Technology (ICT); Transportation (excluding aerospace)

University:

Concordia University

Program: