Improving predictions of public transport usage during disturbances based on smart card data

Abstract

The availability of smart card data from public transport travelling the last decades allows analyzing current and predicting future public transport usage. Public transport models are commonly applied to predict ridership due to structural network changes, using a calibrated parameter set. Predicting the impact of planned disturbances, like temporary track closures, on public transport ridership is however an unexplored area. In the Netherlands, this area becomes increasingly important, given the many track closures operators are confronted with the last and upcoming years. We investigated the passenger impact of four planned disturbances on the public transport network of The Hague, the Netherlands, by comparing predicted and realized public transport ridership using smart card data. A three-step search procedure is applied to find a parameter set resulting in higher prediction accuracy. We found that in-vehicle time in rail-replacing bus services is perceived ≈1.1 times more negatively compared to in-vehicle time perception in the initial tram line. Waiting time for temporary rail-replacement bus services is found to be perceived ≈1.3 times higher, compared to waiting time perception for regular tram and bus services. Besides, passengers do not seem to perceive the theoretical benefit of the usually higher frequency of rail-replacement bus services compared to the frequency of the replaced tram line. For the different case studies, the new parameter set results in 3% up to 13% higher prediction accuracy compared to the default parameter set. It supports public transport operators to better predict the required supply of rail-replacement services and to predict the impact on their revenues.

Smart Card Data Mining of Public Transport Destination: A Literature Review

“…Although the main purpose is to make charging and management more convenient [2], massive and continuous smart card data also can be recorded and served, which can provide lots of precious opportunities for researchers. The data can be used in various fields such as analysis of transit riders' travel patterns [3][4][5], behavior analysis [6][7][8][9], performance assessment of bus transport reform [10][11][12][13] and planning of the public transportation system [14][15][16][17]. In the study of smart card data, the spatio-temporal information on boarding and alighting is very important [18][19][20].…”

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

“…Pereira et al 58 developed method to predict PT arrivals on the time of special events using Twitter data. In addition, to ensure prediction accuracy of the impact of planned, temporary disturbances (such as temporary track closures) of PT usage, Yap et al 72 proposed a method using smart card data.…”

Workshop 8 report: Big data in the digital age and how it can benefit public transport users

“…Van Oort et al (2015) developed an elasticity-based public transport ridership prediction model based on smart card data, to predict public transport mode and route choice after network changes. Yap et al (2018) further calibrate the parameters of this model to predict passenger behaviour specifically during planned track closures and disruptions, based on AFC data obtained during several previous track closures. Several studies adopt machine learning approaches for short-term passenger predictions.…”