BCE: A Behavior-Learning-Based Crowdedness Estimation Mechanism for Crowdsensing Buses

This paper aims to develop a method that can accurately estimate the crowdedness level for crowdsensing buses. Multiple related features are reflected by passengers’ moving trajectories at the bus stops. Many state-of-the-art posture recognition approaches have high accuracy, which can ensure that the passengers motion monitoring results are reliable. Through these observations, we propose an improved behavior-learning-based crowdedness estimation mechanism, named BCE, to obtain the crowdedness level of a bus. The motion sequence and gait information of a passenger is obtained via sensors in smartphones and is described by feature vectors. Then the feature vectors are classified as bus crowdedness levels based on gcForest classifier for single-person bus’s crowdedness level estimation and on Recurrent Neural Network (RNN) for multipeople bus’s crowdedness level estimation. Additionally, the moving trajectories and the corresponding crowdedness of the passengers who do not involve in our system can be recognized passively through the motion information of adjacent involved passengers on the bus. The experiments prove that our mechanism achieves an accuracy of 92% overall.