Yahan Lu at hEART 2026

Yahan Lu presented research on integrated train scheduling and dynamic pricing at hEART 2026 in Paris. The conference took place from 29 September to 1 October 2026.

The paper

Integrated train scheduling and dynamic pricing with endogenous demand under uncertainty: A machine learning-enhanced Benders decomposition approach

The study considers train schedules and fare incentives together. Pricing can influence when passengers choose to travel, while the timetable determines the capacity available to serve them. Treating these choices jointly makes it possible to account for how demand responds to the transport service.

Connecting behavior and operations

The research addresses uncertainty alongside this interaction between supply and demand. It combines optimization with a machine learning-enhanced Benders decomposition approach to tackle the resulting planning problem.

Yahan’s presentation brought these questions to the hEART transport research community. The work connects with TRANSFORM’s interest in traveler behavior, uncertainty and the efficient use of transport resources.

Photo: Yahan Lu presenting her research poster at hEART 2026.