Modelling public transport disruptions and impact by smart-card data

Evaluating disruptions in public transport (PT) utilisation is challenging due to often stochastic traveller behaviour and missing data information on affected services. This paper proposes a new approach for modelling PT patronage and disruption impact using integrated data-driven modelling and the Fourier transform technique. Firstly, using tap-on and off information of smart-card data, we estimate in-vehicle passenger numbers to integrate as well as trips passing through the incident area. Secondly, considering the PT patronage pattern as a periodic function, we employ the Fourier transform to convert it into a sum of simpler trigonometric functions to filter out the one representing common data noise successfully and generate an accurate profile for a typical day. Thirdly, we introduce an enhanced sensitivity test to improve the model's ability to identify the impact of the disruption. Finally, multiple impact measurement methods are compared to capture the disruption impact. The findings demonstrate the effectiveness of leveraging in-vehicle count to maximise data volume and enhance impact identification. The PT patronage pattern can be effectively modelled using the Fourier transform. The utilisation of the enhanced sensitivity test can effectively filter out unnecessary trigonometric components, resulting in a refined model capable of accurately identifying the impact of disruption.