Because two decades have almost passed since the introduction of activity-based models of
travel demand, this seems the right time to evaluate progress made in the development and …

Inverse optimization describes a process that is the “reverse” of traditional mathematical
optimization. Unlike traditional optimization, which seeks to compute optimal decisions given …

Abstract Evaluation of the demand for emerging transportation technologies and policies
can vary by time of day due to spillbacks on roadways, rescheduling of travelers' activity …

In classical inverse linear optimization, one assumes that a given solution is a candidate to
be optimal. Real data are imperfect and noisy, so there is no guarantee that this assumption …

A hydrogen refueling station siting model that considers scheduling and routing decisions of
individual vehicles is presented. By coupling a location strategy of the set covering problem …

Informed Urban Transport Systems examines how information gathered from new
technologies can be used for optimal planning and operation in urban settings …

Many production systems have acquisition and merge operations to increase productivity.
This paper proposes a novel method to anticipate whether a merger in a market is …

Over the past decades, many activity-based travel behaviour models have been proposed
based on individuals' independent decision making. The modelling of individuals' joint …

We propose an agent-based mixed-logit model (AMXL) that is estimated with inverse
optimization (IO) estimation, an agent-level machine learning method theoretically …

An understanding of the interaction between individuals' activities and travel choice
behaviour plays an important role in long-term transit service planning. In this paper, an …