当前位置: X-MOL 学术Transp. Res. Part C Emerg. Technol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Estimation of left behind subway passengers through archived data and video image processing.
Transportation Research Part C: Emerging Technologies ( IF 8.3 ) Pub Date : 2020-07-30 , DOI: 10.1016/j.trc.2020.102727
Charalampos Sipetas 1 , Andronikos Keklikoglou 1 , Eric J Gonzales 1
Affiliation  

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 10% of their actual number.



中文翻译:

通过存档的数据和视频图像处理来估算落后的地铁乘客。

拥挤是全球公共交通系统最常见的问题之一,极端拥挤可能导致无法上车的乘客被甩在后面。本文将现有数据源与新兴的物体检测技术相结合,以估算在地铁站台上留下的乘客数量。本研究中提出的方法已经开发并应用于马萨诸塞州波士顿的地铁。火车当前未配备自动乘客计数器,并且仅在进入系统时才收集车票卡数据。根据推断出的始发地-目的地数据进行的拥挤分析被用来确定车站,在高峰时段乘客被抛弃的可能性很高。这里显示了北站在下午高峰时段的结果。图像处理和目标检测软件用于计算监视视频源中留在站台上的乘客数量。使用自动计数的乘客和火车运行数据来开发逻辑回归模型,该模型被校准为在正常工作条件下的典型工作日中对落后乘客的手动计数。在正常运行的另一天,针对人工留下的乘客的手动计数对模型进行了验证。结果表明,通过将视频中的乘客计数与火车运行数据相融合,可以估算出在一天的高峰时段留下的乘客数量 图像处理和目标检测软件用于计算监视视频源中留在车站月台上的乘客数量。自动计数的乘客和火车运行数据用于开发逻辑回归模型,该模型已校准为在正常工作条件下的典型工作日中对落后乘客的手动计数。该模型已针对正常运行的另一天中遗留乘客的人工计数进行了验证。结果表明,通过将视频中的乘客计数与火车运行数据相融合,可以估算出在一天的高峰时段留下的乘客数量 图像处理和目标检测软件用于计算监视视频源中留在车站月台上的乘客数量。自动计数的乘客和火车运行数据用于开发逻辑回归模型,该模型已校准为在正常工作条件下的典型工作日中对落后乘客的手动计数。该模型已针对正常运行的另一天中遗留乘客的人工计数进行了验证。结果表明,通过将视频中的乘客计数与火车运行数据相融合,可以估算出在一天的高峰时段留下的乘客数量 使用自动计数的乘客和火车运行数据来开发逻辑回归模型,该模型已校准为在正常工作条件下的典型工作日中对落后乘客的手动计数。该模型已针对正常运行的另一天中遗留乘客的人工计数进行了验证。结果表明,通过将视频中的乘客计数与火车运行数据相融合,可以估算出在一天的高峰时段留下的乘客数量在 自动计数的乘客和火车运行数据用于开发逻辑回归模型,该模型已校准为在正常工作条件下的典型工作日中对落后乘客的手动计数。该模型已针对正常运行的另一天中遗留乘客的人工计数进行了验证。结果表明,通过将视频中的乘客计数与火车运行数据相融合,可以估算出在一天的高峰时段留下的乘客数量10 他们的实际数量。

更新日期:2020-07-31
down
wechat
bug