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

Abstract

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.

Improving the Prediction of Passenger Numbers in Public Transit Networks by Combining Short-Term Forecasts With Real-Time Occupancy Data

“…Before operators could automatically collect large quantities of occupancy data, surveyors were tasked with counting passengers at critical locations of the transportation network to assess the passenger numbers and derive the transportation demand [7]. With the emergence of ITS around 2000, data collection has been simplified [17]: Data sources such as the data of automated fare collection (AFC) systems [18], or APC systems with cameras, light barriers, LiDAR [19], weight sensors [20], Wi-Fi data [21,22], or crowdsourcing [23,24] became available. Some of these data sources continually transmit their data to an ITS; thus, real-time passenger information systems became possible.…”