Papers for

transport planners

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Exactly solving cost-aware mass transport on graphs with matrix methods

Cost-augmented Schrödinger bridges on graphs are exactly solvable: a Feynman-Kac tilt replaces learned control

Abstract: The generalized Schrödinger bridge on a graph moves mass between two distributions while charging a cost for the states visited. It has been approached by learning the rates of a controlled continuous-time Markov chain, with a temporal-difference penalty that restores the cost. A state cost folds into the reference process as a Feynman-Kac tilt. The cost-augmented bridge is then a plain bridge against the tilted reference, and the penalty is unnecessary. The bridge is computed exactly by alternating two endpoint rescalings, each one sparse matrix-exponential application; nothing is discretized in time or learned. The alternation converges at a rate set by the endpoint coupling alone. For a quadratic congestion cost on time-averaged occupancies, damped best response around the exact bridge is gradient descent on a strongly convex function, and its residual bounds its error. On a protein-folding model, a free-energy cost lowers the expected barrier of the folding paths. On the learned approach's road network, roll-outs of the exact bridge match the target within sampling error, and on networks with millions of intersections its memory grows linearly.

Thu 1 OctMachine Learning
The gist
Moving resources or things between points on a network often has costs depending on the path taken. The authors showed a new way to find the best paths exactly without learning or approximations by changing the underlying network with a special mathematical tilt. This approach works faster and with less memory, even on huge networks, and can model complicated costs like congestion. Their method matches or improves on previous learned approaches and gives clear error bounds during optimization.
Open → 2610.02195v1

Origin-destination travel data detailed by mode and purpose for england and wales

Travel Mode- and Purpose-Specific Origin-Destination Matrices for England and Wales from Fused Travel Survey and Mobile Network Data

Abstract: Origin-destination (OD) matrices sit behind much of quantitative transport planning, from model calibration and accessibility analysis to the appraisal of new services and development. The increasing emphasis on place-based solutions requires mobility data that can support decision-making not only at the strategic level, but also at finer spatial scales. This requires up-to-date OD evidence at small-area resolution, disaggregated by travel mode and purpose, which remains either inaccessible or unavailable. In this work, we present dense MSOA-to-MSOA OD matrices for England and Wales, segmented by seven travel modes and representative time periods, with eight trip purposes for the weekday morning peak. The matrices are built by calibrating aggregate mobile network data provided by BT against National Travel Survey (NTS), census and trip-rate evidence, preserving the observed spatial structure of movement while referencing its age, mode and purpose composition to the survey. The open-source data processing pipeline is released alongside the matrices, so that the construction of the dataset can be inspected in full and adapted to other years, regions or assumptions.

Tue 29 SeptComputers and Society
The gist
Transport planners need detailed data about how people travel between places to make better decisions about roads, buses, and trains. The authors combined mobile phone movement data with traditional travel surveys to create detailed maps showing trips by different travel modes and travel reasons across small areas in England and Wales. This new data helps reveal not just where people go, but how and why, with updates for current travel patterns. They also shared the full process publicly so others can create similar datasets for other times or regions.
Open → 2609.36466v1