Estimation of left behind subway passengers through archived data and video image processing.

Crowding is one of the most common problems for public transportation systems worldwide, and extreme crowding can lead to passengers being left behind when they are unable to board the first arriving bus or train. This paper combines existing data sources with an emerging technology for object detection to estimate the number of passengers that are left behind on subway platforms. The methodology proposed in this study has been developed and applied to the subway in Boston, Massachusetts.