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The communication technology revolution in this era has increased the use ofsmartphones in the world of transportation. In this paper, we propose toleverage IoT device data, capturing passengers' smartphones' Wi-Fi data inconjunction with weather conditions to predict the expected number ofpassengers waiting at a bus stop at a specific time using deep learning models.Our study collected data from the transit bus system at James MadisonUniversity (JMU) in Virginia, USA. This paper studies the correlation betweenthe number of passengers waiting at bus stops and weather conditions.Empirically, an experiment with several bus stops in JMU, was utilized toconfirm a high precision level. We compared our Deep Neural Network (DNN) modelagainst 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 MeanSquared Error (MSE) scores for predictions in favor of the DNN compared to LRand WNN, respectively.

Leveraging IoT and Weather Conditions to Estimate the Riders Waiting for the Bus Transit on Campus