Automated Assessment of Public Transit Bus Stops Using Computer Vision Methods

Abstract

Transit bus systems are an essential part of public transportation systems and they improve the accessibility of the communities and economical activities. On-street assets, namely bus stops, are key elements of public bus transit systems, but their condition assessment mostly depends on manual efforts that are labor-intensive and expensive. This research proposes a novel system for detection and assessment of the public transit bus stops using captured videos by on-board cameras. This system utilizes deep convolutional neural networks (DCNNs) to detect bus stop assets and semantically segment pixels in the corresponding video frames. Then, the results of these two modules are further processed to assess the connectivity of the bus stop mobility pads and accessibility level of the bus stops on snowy days. The experimental results showed promising performance of the proposed methods, which have potentials for practical applications.