Papers for
transport planning teams
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.
Benchmark tests time series forecasts for future what if scenarios
WhatIf-Bench: Benchmarking Intervention-Aware Time Series Forecasting
Abstract: Recent time series foundation models (TSFMs) can consume covariates in-context, extending zero-shot forecasting to problems driven by external factors. This enables scenario analysis: forecasting how a target responds to events that set covariates to chosen future values. Whether they can do this reliably is an open question. We introduce WhatIf-Bench, a benchmark measuring how well forecasters use covariates to forecast a target's response to future events. WhatIf-Bench is semi-synthetic: time series come from mechanistic simulators of data centers, power grids, road traffic, and water networks, with controlled covariate-target relationships grounded in each system. Each task presents a history of events intervening on covariates, then asks to forecast a future event that either replays seen values (Replay) or sets them to unseen ones (Novel). Varying the number of covariates, historical events, and event length yields 37,665 tasks. Evaluating TSFMs, frontier LLMs, and simple regression baselines, we find the LLMs reach the highest skill (improvement over a naive baseline), ahead of the best TSFMs. Every model forecasts well on Replay futures (48 to 85% skill) but drops to -7 to 50% on Novel futures, with the regression baselines falling the most. Skill on Novel futures also falls as events set more covariates jointly. More historical events raise skill on Novel futures across the board, but large differences remain. WhatIf-Bench reveals generalization to Novel futures as the core challenge, and offers a testbed toward arbitrary scenario analysis.
Method establishes fair audit of urban transit station plans with shared data
Freeze, Validate, Report: Auditing Urban Station Plans with Common Evidence
Abstract: Transport authorities comparing urban station plans need to distinguish plan differences from differences in evaluation inputs. Reconstructing spatial units, included trips, or candidate blocks from each plan's access outcomes can make scores incomparable. Freeze--Validate--Report fixes an evaluation contract: target trips, proxy groups, reporting cells, candidate pools, and a lexicographic selection rule. A two-point example shows that plan-specific observation selection can reverse the target mean ordering. We compare seven generators spanning clustering, facility location, fairness-oriented adaptations, and a grid control. All plans are scored on common validation evidence using endpoint distance: the sum of origin and destination distances to their nearest stations. A five-metric rule selects one plan for a single test report. In Porto, it selects IFkCO, an adaptation of individually fair $k$-center with outliers; test overall and worst-group 90th percentiles are 805 m and 872 m. In Chicago, it selects Grid; values in a later test window on the same day are 1404 m and 1656 m. Post hoc checks show input sensitivity: expanding candidates from 300 to 600 changes selection in both cities, while holding the hour fixed across three Chicago dates selects Priority. A proportional mean fairlet diagnostic shows that plan-specific proposal pools can change diagnostic ranks; admissibility and solver bounds apply only to the sampled pool. The package supplies code, hashes, decision logs, and proposal pools. We establish auditability within a declared contract, not stable performance after deployment; proxy definitions and metric priorities remain the authority's choices.