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    Posted in January 2017

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

    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 Den Haag, the Netherlands, by comparing predicted and realized public transport ridership using smart card data. A two-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. 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. At last, no higher waiting time perception for temporary rail-replacement services could be found, compared to regular tram and bus services. The new parameter set leads to substantially 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.

    Read our TRB paper HERE

    Find the poster HERE

    Investigating potential transit ridership by fusing smartcard and GSM data

    The public transport industry faces challenges to cater for the variety of mobility patterns and corresponding needs and preferences of passengers. Travel habit surveys provide information on the overall travel demand as well as its spatial variation. However, it often does not include information on temporal variations. By means of data fusion of smartcard and Global System for Mobile Communications (GSM) data, spatial and temporal patterns of public transport usage versus the overall travel demand are examined. The analysis is performed by contrasting different spatial and temporal levels of smartcard and GSM data. The methodology is applied to a case study in Rotterdam, the Netherlands, to analyze whether the current service span is adequate. The results suggest that there is potential demand for 10 extending public transport service span on both ends. In the early mornings, right before transit operations are resumed, an hour-on-hour increase in visitor occupancy of 33-88% is observed in several zones, thereby showing potential demand for additional public transport services. The proposed data fusion method showed to be valuable in supporting tactical transit planning and decision making and can easily be applied to other origin-destination transport data.

    Read our TRB paper HERE

    Find our presentation HERE

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