城市轨道交通精细化客流预测系统设计与实现

针对大部分客流预测系统存在预测客流指标不全,时空粒度较粗,多场景的适用性不足等问题,以大规模网络化运营的城市轨道交通精细化客流预测需求为研究对象,分析适应多场景铁路网客流预测实现方法,利用Hadoop、Spark&Hive、Redis、微服务、H5等先进技术搭建客流预测大数据平台,实现铁路网交通出行量(OD,Origin Destination)的精细化客流功能,为调度指挥和客运管理提供进站、出站、换乘、断面客流量等全指标、精细化时空粒度的客流预测数据支持,提升轨道交通调度指挥针对性、客流组织合理性和客运服务水平。

Refined passenger flow forecasting system for urban rail transit

Aiming at the problems of incomplete passenger flow forecasting indicators, coarse temporal and spatial granularity, and insufficient applicability of multiple scenarios in most passenger flow forecasting systems, this paper took the fine passenger flow forecasting demand of urban rail transit with large-scale network operation as the research object, analyzed the implementation method of railway network passenger flow forecasting suitable for multiple scenarios, and used Hadoop, Spark & Hive, Redis, micro service, H5 and other advanced technologies to build a big data platform for passenger flow forecasting, so as to implement the refined passenger flow function of railway network passenger flow OD, provide passenger flow forecasting data support with full indicators such as inbound, outbound, transfer, cross-sectional flow, refined space-time granularity for dispatching command and passenger transport management, and improve the pertinence of rail transit dispatching command, rationality of passenger flow organization and passenger transport service level.