Probabilistic forecasting of train ticket booking demand with hierarchical correlations

具有层次相关性的火车票预订需求概率预测

• 批准号: 573339-2022
• 负责人: Sun, LijunL
• 金额: $ 4.37万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749492
• 关键词: Probabilistic; forecasting; train; ticket; booking

Forecasting the number of passengers using the data from ticket booking systems is critical for railway companies to manage revenue and allocate resources. The booking demand data have a unique hierarchical correlation structure that cannot be appropriately modeled by conventional time-series approaches. Therefore, this project aims to develop state-of-the-art probabilistic forecasting models for booking demand in train networks. The project will incorporate the hierarchical data correlations, train capacity, and the impact of cancellation into the model to improve forecasting accuracy. In partnership with ExPretio, the research outcomes of this project will benefit passenger transport companies in increasing revenue and reducing waste of resources. In addition, passenger travel demand can also be better matched with the optimized allocation of transport capacities.

使用订票系统的数据预测乘客数量对于铁路公司管理收入和分配资源至关重要。预订需求数据具有独特的层次相关结构，无法通过传统的时间序列方法进行适当建模。因此，该项目旨在开发最先进的火车网络预订需求概率预测模型。该项目将把分层数据相关性、列车容量和取消的影响纳入模型中，以提高预测准确性。通过与 ExPretio 合作，该项目的研究成果将有利于客运公司增加收入并减少资源浪费。此外，旅客出行需求也能与运力优化配置更好匹配。