1366085 - Big-data Technologies for Traditional Public Transport and Emerging Micromobility Services in Tucson, Arizona

Due to the increasing need to improve the city’s sustainability, public transport including bikesharing and buses has attracted attention in advancing smart cities and has been used as a substitute for private vehicles in order to reduce traffic congestion, fuel consumption, and emissions. Despite the popularity of traditional public transport and emerging micromobility services, there are some urgent questions still challenging to be resolved due to the data limitation, which are the barriers to further promoting these public travel systems. For example, it still remains unclear if a bikesharing system could be used to solve the first-mile and last-mile problems, and could be used to connect with existing transit. Therefore, we applied big-data technologies to solve traditional issues in traditional public transport and emerging micromobility services. In this presentation, we will first introduce the applications of data-driven methods for quantitively evaluating the relationship between biksharing and traditional public transit. Then, we will introduce how to estimate the fuel savings of launching a dock-based bikesharing system in Tucson, Arizona, and provide a data-driven evaluation method for the unbiased quantification of fuel savings for varying rates of vehicle trip replacement by considering the traffic condition. In addition, we will introduce the applications of the AI method and crowdsourced data in Tucson for improving the prediction accuracy of transit arrival time at stops to enhance transit service quality.