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

TL;DR

This study uses IoT data from passengers' smartphones and weather info to accurately predict the number of people waiting at bus stops on a university campus, improving prediction accuracy with deep learning.

Contribution

It introduces a novel approach combining IoT device data and weather conditions with deep learning to estimate bus waiting passengers, validated on real campus data.

Findings

Deep Neural Network outperforms baseline models in prediction accuracy.

High correlation found between weather conditions and passenger waiting numbers.

DNN achieved 35% and 14% lower MSE than linear regression and WNN.

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.