# Based on AFC Data Calculation of Walking Time in Metro Stations Considering the Impact of Passenger Flows

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## Abstract

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## 1. Introduction

- (1)
- Travel time chain analysis. Usually, the travel time chain consists of walking time in and out of the station, waiting time, and on-board time, and for the travel time that requires transfer, this also includes transfer walking time and transfer waiting time. Python crawler technology can obtain the train departure interval and on-train time from the official website, which provides the basic data for the following walking time model construction.
- (2)
- Influence of passenger flow density. The walking time in the same station is mainly affected by the density of passenger flow, and there is a spatial and temporal unevenness in the distribution of passenger flow in the station. On this basis, the influence of passenger flow on walking time at stations is analyzed to determine the threshold value of passenger flow, which provides judgment data for the following walking time model constraints.
- (3)
- Design of walking time projection method. Based on the multi-station combination of passenger travel time chain splitting to initially build a regression model, and combined with the impact of passenger flow density to add walking time constraints to solve the problem of unsatisfactory rank of the model, so as to obtain the walking time imputation model.
- (4)
- Example validation. Based on the swipe data of the top five lines of daily average passenger flow in Guangzhou in 2018, the example validation analysis is conducted and the model is verified in terms of the accuracy of the results and the validity of the constraints.

## 2. Metro Passenger Time Chain Analysis

#### 2.1. Passenger Travel Time Chain Building

#### 2.2. Component Element Analysis

## 3. Impact of Station Passenger Flow on Walking Time

#### 3.1. Metro Traffic Data Selection and Pre-Processing

#### 3.2. Analysis of the Relationship between Station Passenger Flow and Walking Time

#### 3.2.1. Distribution Characteristics of Passenger Flow in Time and at Access and Exit Stations

#### 3.2.2. Total Distribution Characteristics of Passenger Flow at All Stations

#### 3.2.3. Identify Passenger Flow Thresholds That Affect Walking Time

## 4. Method of Calculating the Walking Time of Passengers in the Station

#### 4.1. Model Construction Ideas

#### 4.1.1. Construction of Walking Time Models for Multi-Site Combinations

#### 4.1.2. Adding Constraints to Solve the Model Discontent Rank Problem

#### 4.1.3. Calculation of Transfer Walking Time

#### 4.2. Design of Calculating Equations

#### 4.3. Selection Stations for Model

#### 4.3.1. To Access and Exit Walking Time Model

#### 4.3.2. To Transfer Walking Time Calculation

## 5. Example Validation Analysis

#### 5.1. Accuracy Validation of Model Results

#### 5.2. Model Validity Verification

#### 5.2.1. Model Validity Verification

#### 5.2.2. Comparison of the Effectiveness of Multiple Passenger Flow Thresholds

## 6. Discussion

## 7. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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Number of Station | Number of Variables | Rank of Coefficient Matrix |
---|---|---|

3 | 6 | 5 |

4 | 8 | 7 |

5 | 10 | 9 |

6 | 12 | 11 |

… | … | … |

24 | 48 | 47 |

Time Tag | Transfer Walking Time | Time Tag | Transfer Walking Time | Time Tag | Transfer Walking Time |
---|---|---|---|---|---|

14 | 23.21 | 24 | 91.04 | 34 | 30.78 |

15 | 19.21 | 25 | 89.32 | 35 | 44.55 |

16 | 37.28 | 26 | 68.37 | 36 | 46.48 |

17 | 18.97 | 27 | 67.16 | 37 | 49.54 |

18 | 21.93 | 28 | 49.26 | 38 | 28.43 |

19 | 44.38 | 29 | 30.79 | 39 | 63.18 |

20 | 21.69 | 30 | 26.07 | 40 | 55.24 |

21 | 53.28 | 31 | 19.11 | 41 | 40.1 |

22 | 65.82 | 32 | 40.39 | 42 | 42.07 |

23 | 82.32 | 33 | 35.34 |

Type | Time Tag Number | Effective Rate | Delay Value (s) | Day Type |
---|---|---|---|---|

Access-Large | 29 | 0.83 | −44.54 | weekdays |

Exit-Large | 4 | 1.0 | 113.66 | weekdays |

Access-Large | 16 | 0.75 | −13.78 | weekends |

Exit-Large | 4 | 0.25 | −3.82 | weekends |

Passenger Flow Standard | Station Number | Time Tag Number | Effective Rate |
---|---|---|---|

1500 | 53 | 254 | 0.65 |

2000 | 42 | 71 | 0.66 |

2500 | 18 | 58 | 0.73 |

3000 | 11 | 33 | 0.92 |

3500 | 7 | 18 | 0.97 |

4000 | 2 | 9 | 1.0 |

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## Share and Cite

**MDPI and ACS Style**

Zou, L.; Hu, S.; Zhu, L.
Based on AFC Data Calculation of Walking Time in Metro Stations Considering the Impact of Passenger Flows. *Sustainability* **2023**, *15*, 6660.
https://doi.org/10.3390/su15086660

**AMA Style**

Zou L, Hu S, Zhu L.
Based on AFC Data Calculation of Walking Time in Metro Stations Considering the Impact of Passenger Flows. *Sustainability*. 2023; 15(8):6660.
https://doi.org/10.3390/su15086660

**Chicago/Turabian Style**

Zou, Liang, Suiying Hu, and Lingxiang Zhu.
2023. "Based on AFC Data Calculation of Walking Time in Metro Stations Considering the Impact of Passenger Flows" *Sustainability* 15, no. 8: 6660.
https://doi.org/10.3390/su15086660