  Transit ridership flow and origin-destination (O-D) information is essential
for enhancing transit network design, optimizing transit route and improving
service. The effectiveness and preciseness of the traditional survey-based and
smart card data-driven method for O-D information inference have multiple
disadvantages due to the insufficient sample, the high time and energy cost,
and the lack of inferring results validation. By considering the ubiquity of
smart mobile devices in the world, several methods were developed for
estimating the transit ridership flow from Wi-Fi and Bluetooth sensing data by
filtering out the non-passenger MAC addresses based on the predefined
thresholds. However, the accuracy of the filtering methods is still
questionable for the indeterminate threshold values and the lack of
quantitative results validation. By combining the consideration of the assumed
overlapped feature space of passenger and non-passenger with the above
concerns, a three steps data-driven method for estimating transit ridership
flow and O-D information from Wi-Fi and Bluetooth sensing data is proposed in
this paper. The observed ridership flow is used as ground truth for calculating
the performance measurements. According to the results, the proposed approach
outperformed all selected baseline models and existing filtering methods. The
findings of this study can help to provide real-time and precise transit
ridership flow and O-D information for supporting transit vehicle management
and the quality of service enhancement.
