Highlights Measuring crowding through archived and real-time data using object detection tools. Object detection tools with surveillance video to quantify transit performance. Logistic regression models with automatic passenger counts and train operations data. Estimation of left-behind passengers at stations with farecard use only at entrance. Estimation and evaluation of waiting time as a transit service reliability measure.
Abstract 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. Trains are not currently equipped with automated passenger counters, and farecard data is only collected on entry to the system. An analysis of crowding from inferred origin–destination data was used to identify stations with high likelihood of passengers being left behind during peak hours. Results from North Station during afternoon peak hours are presented here. Image processing and object detection software was used to count the number of passengers that were left behind on station platforms from surveillance video feeds. Automatically counted passengers and train operations data were used to develop logistic regression models that were calibrated to manual counts of left behind passengers on a typical weekday with normal operating conditions. The models were validated against manual counts of left behind passengers on a separate day with normal operations. The results show that by fusing passenger counts from video with train operations data, the number of passengers left behind during a day’s rush period can be estimated within of their actual number.
Estimation of left behind subway passengers through archived data and video image processing
2020-07-11
Article (Journal)
Electronic Resource
English
The Autonomous Recognition of Left Behind Passengers in Parked Vehicles
SAE Technical Papers | 2011
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