在铁路运输系统中使用智能卡数据估计路线选择:处理车辆时间表和步行时间的不确定性
Thomas James Tiam-Lee1, Rui Henriques1
1INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
概括
这项研究使用自动收费数据估计了乘客的路线选择,揭示了旅行者往往优先考虑的转机次数较少,但有时选择较短的距离. 这种方法可以提高城市铁路运输规划,而无需侵入性跟踪.
科学领域:
- 城市规划是城市规划.
- 运输工程 运输工程 运输工程
- 数据科学是数据科学.
背景情况:
- 城市铁路交通系统对于日常通勤至关重要,需要准确的乘客位置数据才能有效运行.
- 乘客的路线选择是复杂的,受感知旅行时间,转机和方便的影响.
研究的目的:
- 开发一种可靠的方法来估计乘客的路线选择,仅使用自动收费 (AFC) 数据.
- 为基于传感器的跟踪提供保护隐私的替代方案,以了解乘客行为.
主要方法:
- 从出口门的乘客数量峰值推断火车抵达时间.
- 基于AFC数据,车辆位置和乘客入境/出境时间的路线概率估计.
- 开发了一种对时间表不准确性和运营中断有力的方法.
主要成果:
- 从里斯本的AFC数据成功估计了乘客的航线选择.
- 观察到,乘客通常更喜欢有更少转机的航线.
- 在某些情况下,确定了对更短路线的显著偏好.
结论:
- 拟议的方法提供了一种对隐私有意识的方法来分析乘客的航线选择.
- 这些发现有助于铁路运营商优化列车调度,资源分配和服务规划.
- 这种数据驱动的方法可以帮助解决交通瓶,提高整体运输效率.
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