Using TOPSIS as a decision-support tool for basic dynamic pricing in passenger rail transport

Dynamic pricing in passenger rail transport is commonly addressed through revenue-management models based on demand forecasting, passenger-choice modelling, seat-inventory control and mathematical optimisation. This paper proposes an illustrative TOPSIS-based decision-support framework for basic fare adjustment under limited-data conditions. The framework combines time to departure and an assumed four-level occupancy-pressure indicator into a relative-closeness coefficient and calibrates that coefficient to an observed fare range. It is demonstrated on one Turista-class fare series for one Renfe AVE Barcelona–Madrid departure on 2 June 2019. The calibrated series follows the broad upward time trend but is not monotonic: an assumed lower occupancy-pressure value four days before departure produces a temporary fare decrease. The occupancy-pressure sequence is not independently observed and closely tracks the reference fare buckets over part of the booking horizon; consequently, it must not be used as independent evidence that occupancy explains the observed fares. In addition, the calibration forces the modelled series to share the reference series’ linear time trend. The reported in-sample fit measures are therefore descriptive properties of this worked example rather than independent evidence of validity. The method should be interpreted as a transparent and bounded demonstration of a decision-support layer, not as a validated predictive or revenue-maximising pricing model.