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Online estimation model for passenger flow state in urban rail transit using multi-source data

机译：使用多源数据的城市轨道交通乘客流状态的在线估计模型

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摘要

The estimation of present passenger flow state plays a vital role in the urban rail transit (URT) operation process and it is the basis of passenger flow control and train dispatching. Most of the existing researches used the Automatic Fare Collection (AFC) System data as the single data source for estimating present passenger flow state. In consideration of the delay in the data uploading process, the obtained estimation results based on the single AFC data source are not accurate enough, which could lead to the improper handling and decision failure and cause high risk and low efficiency for the operation and management of URT. To address problems mentioned above, this paper adopted the automatic differentiation method as the framework for the fusion of multi-source heterogeneous data (including uploaded AFC data, the mobile phone signaling data provided by mobile phones, and the historical passenger flow data, etc.). Then, this paper proposed the online estimation model and the error layered optimization algorithm to implement the estimation of present passenger flow state. The calculation results indicate that the proposed model and algorithm can obtain reasonable and reliable URT passenger flow state. Finally, the study developed a present passenger flow state estimation system using the proposed model and algorithm. The system has been deployed in Chengdu Metro that serves about 3 million passengers per day from October 2018 to April 2020, with the overall error within 3% as of now.
机译：目前乘客流状态的估计在城市轨道交通（URT）运行过程中起着至关重要的作用，并且是乘客流量控制和培训调度的基础。大多数现有研究使用自动票价收集（AFC）系统数据作为估计当前乘客流状态的单个数据源。考虑到数据上传过程中的延迟，基于单个AFC数据源的获得结果不够准确，这可能导致处理和决策失败不当，并对操作和管理产生高风险和低效率urt。为了解决上述问题，本文采用了自动差异化方法作为多源异构数据融合的框架（包括上传的AFC数据，由移动电话提供的移动电话信令数据以及历史客流数据等。 ）。然后，本文提出了在线估计模型和误差分层优化算法实现了当前乘客流状态的估计。计算结果表明，所提出的模型和算法可以获得合理且可靠的URT客流状态。最后，该研究开发了一种使用所提出的模型和算法的目前的乘客流状态估计系统。该系统已在成都地铁部署，从2018年10月至2020年10月到4月到2020年的每天为每天提供约300万次乘客，其总体误差在现在的3％内。

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    Tao Zhengping; Tang Jinjin; Hou Kaiwen;

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    Guiyang Urban Rail Transit Grp Co Ltd Guiyang Guizhou Peoples R China;

    Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China;

    Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China;

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