运筹与模糊学
Operations Research and Fuzziology

《运筹与模糊学》是一本开放获取、关注运筹学与模糊学领域最新进展的国际中文期刊，主要刊登数学规划、数学统筹、模糊信息与工程、模糊管理学相关内容的学术论文和成果评述。本刊支持思想创新、学术创新，倡导科学，繁荣学术，集学术性、思想性为一体，旨在给世界范围内的科学家、学者、科研人员提供一个传播、分享和讨论运筹与模糊学领域内不同方向问题与发展的交流平台。

ISSN Print: 2163-1476
ISSN Online: 2163-1530
中国教图刊号：932B0155
编辑邮箱: orf@hanspub.org 微信号：hansi-zhang
Website: https://www.hanspub.org/journal/ORF.html

期刊菜单

基于时间序列分析的上海地铁16号线客流预测—以临港大道站为例
Prediction of Shanghai Metro Line 16 Passenger Flow Based on Time Series Analysis—with Lingang Avenue Station as a Study Case
陈彦莉, 沙玉五, 朱小林, 张小红
运筹与模糊学 Vol. 6 No. 1 (February 2016)
被下列文章引用：

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[1]	Short-term passenger flow prediction for urban rail systems: A deep learning approach utilizing multi-source big data
	2024

[2]	A separate modelling approach for short-term bus passenger flow prediction based on behavioural patterns: A hybrid decision tree method
	2023

[3]	Short‐Term Passenger Flow Prediction of Urban Rail Transit Based on SDS‐SSA‐LSTM
	2022

[4]	A novel bus scheduling model based on passenger flow and bus travel time prediction using the improved cuckoo search algorithm
	2022

[5]	Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data
	2021

[6]	A framework of vehicular security and demand service prediction based on data analysis integrated with blockchain approach
	2021

[7]	Modelling and Optimisation of Resource Usage in an IoT Enabled Smart Campus
	2021

[8]	Modelling and forecasting bus passenger occupation using data-driven methods
	2021

[9]	A novel deep recurrent neural network for Short-term travel demand forecasting under on-demand ride services
	2019

[10]	A comparison study of short-term passenger flow forecast model of rail transit
	2019

[11]	Applying a multistage of input feature combination to random forest for improving MRT passenger flow prediction
	2019

[12]	A novel hybrid model for short-term high-speed railway passenger demand forecasting
	2019

[13]	A novel bus-dispatching model based on passenger flow and arrival time prediction
	2019

[14]	A novel passenger flow prediction model using deep learning methods
	2017

[15]	Deep Learning Methods to Improve the Public Transport Comfort and Safety: Forecasting Bus Passenger Occupation

[1]	A novel passenger flow prediction model using deep learning methods Transportation Research Part C: Emerging Technologies, 2017 DOI:10.1016/j.trc.2017.08.001

[2]	A novel deep recurrent neural network for Short-term travel demand forecasting under on-demand ride services IOP Conference Series: Materials Science and Engineering, 2019 DOI:10.1088/1757-899X/688/3/033022

[3]	A Comparison Study of Short-Term Passenger Flow Forecast Model of Rail Transit CICTP 2019, 2019 DOI:10.1061/9780784482292.155

[4]	Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data International Journal of Production Economics, 2021 DOI:10.1016/j.ijpe.2020.107920

[5]	A novel deep recurrent neural network for Short-term travel demand forecasting under on-demand ride services IOP Conference Series: Materials Science and Engineering, 2019 DOI:10.1088/1757-899X/688/3/033022

[6]	Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data International Journal of Production Economics, 2021 DOI:10.1016/j.ijpe.2020.107920

[7]	Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM Journal of Advanced Transportation, 2022 DOI:10.1155/2022/2589681

[8]	A separate modelling approach for short-term bus passenger flow prediction based on behavioural patterns: A hybrid decision tree method Physica A: Statistical Mechanics and its Applications, 2023 DOI:10.1016/j.physa.2023.128567

[9]	A separate modelling approach for short-term bus passenger flow prediction based on behavioural patterns: A hybrid decision tree method Physica A: Statistical Mechanics and its Applications, 2023 DOI:10.1016/j.physa.2023.128567

[10]	A novel bus scheduling model based on passenger flow and bus travel time prediction using the improved cuckoo search algorithm 2022 International Conference on Big Data, Information and Computer Network (BDICN), 2022 DOI:10.1109/BDICN55575.2022.00047

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