Leveraging IoT and Weather Conditions to Estimate the Riders Waiting for the Bus Transit on Campus
This work provides an incremental improvement in predicting bus transit wait times for campus transportation systems, potentially aiding operational efficiency.
This paper proposes using IoT device data, specifically Wi-Fi data from smartphones, combined with weather conditions to estimate the number of passengers waiting at bus stops. Their Deep Neural Network (DNN) model achieved a 35% and 14% better Mean Squared Error (MSE) score compared to Linear Regression and Wide Neural Network baselines, respectively.
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.