  The communication technology revolution in this era has increased the use of
smartphones in the world of transportation. In this paper, we propose to
leverage IoT device data, capturing passengers' smartphones' Wi-Fi data in
conjunction with weather conditions to predict the expected number of
passengers waiting at a bus stop at a specific time using deep learning models.
Our study collected data from the transit bus system at James Madison
University (JMU) in Virginia, USA. This paper studies the correlation between
the number of passengers waiting at bus stops and weather conditions.
Empirically, an experiment with several bus stops in JMU, was utilized to
confirm a high precision level. We compared our Deep Neural Network (DNN) model
against two baseline models: Linear Regression (LR) and a Wide Neural Network
(WNN). The gap between the baseline models and DNN was 35% and 14% better Mean
Squared Error (MSE) scores for predictions in favor of the DNN compared to LR
and WNN, respectively.
