N days average volume based ARIMA forecasting model for Shanghai metro passenger flow

Urban railway transport that connects suburbs and city areas is characterized by uneven temporal and spatial distribution in terms of passenger flow and underutilized carrying capacity. This paper aims to develop methodologies to measure the carrying capacity of the urban railway by introducing a concept of the express-slow mode. We first explore factors influencing the carrying capacity under the express-slow mode and the interactive relationships among these factors. Then we establish seven different scenarios to measure the carrying capacity by considering the ratio of the number of the express trains and the slow trains, the station where overtaking takes place, and the number of overtaking maneuvers. Taking Shanghai Metro Line 16 as an empirical study, the proposed methods to measure the carrying capacity under different express-slow mode are proved to be valid. This paper contributes to the literature by remodifying the traditional methods to measure the carrying capacity when different express-slow modes are applied to improve the carrying capacity of the suburban railway.

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Modified Bus Passenger Flow Forecasting Model Based on Integrating ARIMA with Neural Network

CICTP 2015 ◽

10.1061/9780784479292.119 ◽

2015 ◽

Cited By ~ 1

Author(s):

Jing Teng ◽

Sijin Chen

Keyword(s):

Neural Network ◽

Forecasting Model ◽

Passenger Flow ◽

Model Based

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Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow

PLoS ONE ◽

10.1371/journal.pone.0222365 ◽

2019 ◽

Vol 14 (9) ◽

pp. e0222365 ◽

Cited By ~ 5

Author(s):

Quanchao Chen ◽

Di Wen ◽

Xuqiang Li ◽

Dingjun Chen ◽

Hongxia Lv ◽

...

Keyword(s):

Neural Network ◽

Empirical Mode Decomposition ◽

Short Term Memory ◽

Forecasting Model ◽

Short Term ◽

Term Memory ◽

Passenger Flow ◽

Mode Decomposition ◽

Long Short Term Memory

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Shanghai Metro Constructs an “Intelligent Metro”

New Metro ◽

10.37819/nm.001.01.0080 ◽

2020 ◽

Vol 1 (1) ◽

pp. 64-66

Author(s):

Print Journalist

Keyword(s):

Artificial Intelligence ◽

Normal State ◽

Line Planning ◽

Passenger Flow ◽

The World ◽

The Future ◽

Passenger Flows ◽

Planning And Construction ◽

Shanghai Metro ◽

Metro Network

The World Artificial Intelligence (AI) Conference 2020 opened in Shanghai on July 9, 2020. The AI application scenarios presented by Shanghai Shentong Metro Group Co., Ltd. (Shentong Metro Group) have attracted a lot of attention of visitors. On July 9, 2020, the total passenger flow of Shanghai Metro exceeded 10 million, reaching 10.048 million, and Shanghai will enter the normal state of “ten million passenger flow” every day. At present, the operating mileage of Shanghai Metro reaches 705km, ranking first in the world. According to the new line planning and construction trend, the Shanghai Metro network will continue to expand, and there will be even more passenger flows in the future.

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