Time Granularity Setting Principle for Short-Term Passenger Flow Prediction in Urban Rail Transit

Guangyu Zhu, Yansu Gong, Jiacun Ding,Edmond Q. Wu,Rob Law

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS（2024）

Beijing Jiaotong Univ | Shanghai Jiao Tong Univ | Univ Macau

Cited 0|Views42

Abstract

Time granularity is a key parameter necessary for short-time passenger flow prediction of urban rail transit (URT); however, no universal method is available for its setting. This study investigates the time granularity setting principle for short-term passenger flow prediction in URT. First, a method to measure the autocorrelation of passenger flow time series is constructed, focusing on the comparison of time granularities. Second, based on the functional relationship between the first-order autocorrelation coefficients of the passenger flow time series under different time granularities, the time granularity setup principle is obtained for different passenger flow characteristics. Finally, the reasonableness and universality of the time granularity setting principle are verified by analyzing the passenger flow characteristics and autocorrelation magnitude of the actual inbound and origin-destination (OD) passenger flow data under different stations and dates at different time granularities.

Translated text

Key words

Autocorrelation,Time series analysis,Predictive models,Time measurement,Long short term memory,Rails,Data models,Autocorrelation coefficient,passenger flow characteristics,short-term prediction model,time granularity setting,urban rail transit (URT) passenger flow

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper