Prediction of railroad user count using number of route searches via bivariate state–space modeling

Conventional demand-prediction methods predominantly rely on past user behaviors to predict regular future transportation demands using acquired user preference data. Nevertheless, predicting unforeseen travel demands arising from bad weather or emergency events remains challenging owing to the absence of data on such future contingencies. This study introduces a method to predict travel demand by leveraging search history data, which potentially signal unforeseen travel requirements. We elucidate the correlation between the search count and integrated circuit (IC) card usage on an aggregate level. Subsequently, we propose a two-stage analytical technique to estimate the number of IC card usages based on route-search counts. Our findings demonstrate that the proposed model has superior accuracy, and the route-search count plays a pivotal role in predicting the number of IC card usages, especially unforeseen shifts in demand.