列车车厢乘客装载水平预测：一种贝叶斯方法
Predicting passenger loading level on a train car: A Bayesian approach
AI总结：
本文提出一种贝叶斯方法，用于预测列车车厢的乘客装载水平，以解决拥挤问题带来的负面影响，提高公共交通效率和乘客满意度。
摘要：
列车车厢内的拥挤问题已成为公共交通机构日益关注的焦点。从乘客和公共交通机构的角度来看，车厢拥挤会导致多个负面后果，例如乘客上下车时间延长，导致停站时间增加，进而破坏发车间隔和调度，以及乘客不满（如压力增加和隐私缺失）。此外，高峰时段的拥挤还表明基础设施无法满足乘客需求。鉴于拥挤问题的重要性，公共交通机构已开发出评估拥挤水平的措施。《公共交通容量和服务质量手册》提供了美国公共交通系统中拥挤阈值的指导方针。
英文摘要：
Crowding in train cars is increasingly a major concern for transit agencies. From the perspective of the passengers and the transit agencies, overcrowding of the train cars has several negative consequences such as: (i) extended duration of passengers boarding and alighting which leads to longer dwell times, (ii) subsequent disruption of the headway and the schedule, and (iii) passenger dissatisfaction (e.g. increased stress and lack of privacy). Moreover, overcrowding during peak service hours also indicates inadequate infrastructure to meet the passenger demands. Realizing the importance of the crowding issue, transit agencies have developed measures to assess the crowding levels. The Transit Capacity and Quality of Service Manual provides guidelines on thresholds for crowding in transit systems in the United States.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
