# Interday Stability of Taxi Travel Flow in Urban Areas

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## Abstract

**:**

## 1. Introduction

## 2. Related Studies

## 3. Methodology

#### 3.1. OD Matrix Construction

_{j}, we assigned the value of the spatial structure as 1; if not, then we assigned it as 0. Figure 1b shows the travel flow of the OD matrix, the value of each ${O}_{i}$ and ${D}_{j}$ assigned by their travel flow characteristic.

#### 3.2. Stability Measurement of the Travel Spatial Structure and Flow

- (1)
- Structural similarity

**X**and

**Y**is transformed into the calculation of the edit distance $SLD$ between the sets of the geocode of the destination location in different matrixes for the same original location code. $\mathit{g}{\mathit{x}}_{i}$ and $\mathit{g}{\mathit{y}}_{i}$ indicate the descending sorted geocodes of destination locations that started from the i-th geocode. To reduce the impacts of ODs with low traffic volume on structural similarity, the destination geocodes of the ODs with a flow number smaller than $N0$ are removed in generating $\mathit{g}{\mathit{x}}_{i}$ and $\mathit{g}{\mathit{y}}_{i}$.

- (2)
- Flow similarity

#### 3.3. Stability Measurement of Each OD Flow

## 4. Data

#### 4.1. Dataset of Shenzhen

#### 4.2. Dataset of New York

## 5. Results

#### 5.1. Volume and Distance Characteristics of Interday Taxi Travel

#### 5.2. Influence of the Flow Threshold on the Similarity in the Travel OD Flow Matrix

#### 5.3. Stability of Travel Spatial Structure and Flow between Weekdays and Weekends

#### 5.4. Stability of OD Flows between Days

#### 5.5. Representative Data Analysis

## 6. Conclusions and Discussions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A. Original Data Feature Analysis and Screening

- (1)
- Interday period characteristics of positioning points

- (2)
- Interday period characteristics of the number of vehicles

## Appendix B. Daily OD Distribution

## References

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**Figure 1.**Travel spatial structure and travel flow of OD matrix. (

**a**) Travel structrue of OD matrix. (

**b**) Tavel flow of OD matrix.

**Figure 8.**Distribution of taxi travel flow volume. (

**a**) OD proportion distribution in Shenzhen. (

**b**) Volume proportion distribution in Shenzhen. (

**c**) OD proportion distribution in New York. (

**d**) Volume proportion distribution in New York.

**Figure 10.**Normalized difference of OD volume. (

**a**) Normalized difference between weekday and weekend in Shenzhen. (

**b**) Normalized difference between weekend and weekday in Shenzhen. (

**c**) Normalized difference between weekday and weekend in New York. (

**d**) Normalized difference between weekend and weekday in New York.

**Figure 11.**Normalized difference of OD distribution. (

**a**) Normalized difference between weekday and weekend of OD distribution in Shenzhen. (

**b**) Normalized difference between weekend and weekday of OD distribution in Shenzhen. (

**c**) Normalized difference between weekday and weekend of OD distribution in New York. (

**d**) Normalized difference between weekend and weekday of OD distribution in New York.

**Figure 12.**Changes in the stability of the internal travel flow for geographic units. (

**a**) Low travel flow on weekdays in Shenzhen. (

**b**) High travel flow on weekdays in Shenzhen. (

**c**) Low travel flow on weekends in Shenzhen. (

**d**) High travel flow on weekends in Shenzhen. (

**e**) Low travel flow on weekdays in New York. (

**f**) High travel flow on weekdays in New York. (

**g**) Low travel flow on weekends in New York. (

**h**) High travel flow on weekends in New York.

**Figure 13.**Changes in the stability of taxi travel flow for geographic units. (

**a**) Low travel flow on weekdays in Shenzhen. (

**b**) High travel flow on weekdays in Shenzhen. (

**c**) Low travel flow on weekends in Shenzhen. (

**d**) High travel flow on weekends in Shenzhen. (

**e**) Low travel flow on weekdays in New York. (

**f**) High travel flow on weekdays in New York. (

**g**) Low travel flow on weekends in New York. (

**h**) High travel flow on weekends in New York.

**Figure 14.**The degree of travel spatial structure and flow recovery. (

**a**) Travel spatial structure in Shenzhen. (

**b**) Travel spatial structure in NYC. (

**c**) Travel spatial flow in Shenzhen. (

**d**) Travel spatial flow in NYC.

**Figure 15.**The degree of representation of high- and low-travel structures and flows. (

**a**) Travel spatial structure in Shenzhen. (

**b**) Travel spatial structure in New York. (

**c**) Travel flow in Shenzhen. (

**d**) Travel flow in New York.

Device Number | Longitude | Latitude | Positioning Time | Working Status |
---|---|---|---|---|

104***3 | 113.****05 | 22.****18 | 26 September 2011 11:18:55 | 0 |

104***3 | 113.****49 | 22.****07 | 26 September 2011 11:19:32 | 1 |

104***3 | 113.****32 | 22.****56 | 26 September 2011 11:20:02 | 1 |

104***3 | 113.****07 | 22.****95 | 26 September 2011 11:21:00 | 1 |

PickUp_Datetime | DropOff_Datetime | PULocationID | DPLocationID |
---|---|---|---|

1 July 2019 00:12:33 | 1 July 2019 00:25:00 | 228 | 89 |

1 July 2019 00:41:26 | 1 July 2019 00:51:21 | 97 | 188 |

1 July 2019 00:18:50 | 1 July 2019 00:32:48 | 81 | 220 |

1 July 2019 00:29:01 | 1 July 2019 00:45:50 | 69 | 239 |

Structural Similarity | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday |
---|---|---|---|---|---|---|---|

Monday | 1.000 | 0.810 | 0.808 | 0.811 | 0.804 | 0.796 | 0.793 |

Tuesday | 0.810 | 1.000 | 0.808 | 0.809 | 0.806 | 0.802 | 0.801 |

Wednesday | 0.808 | 0.808 | 1.000 | 0.810 | 0.812 | 0.802 | 0.798 |

Thursday | 0.811 | 0.809 | 0.810 | 1.000 | 0.813 | 0.801 | 0.801 |

Friday | 0.804 | 0.806 | 0.812 | 0.813 | 1.000 | 0.806 | 0.802 |

Saturday | 0.796 | 0.802 | 0.802 | 0.801 | 0.806 | 1.000 | 0.806 |

Sunday | 0.793 | 0.801 | 0.798 | 0.801 | 0.802 | 0.806 | 1.000 |

Structural Similarity | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday |
---|---|---|---|---|---|---|---|

Monday | 1.000 | 0.740 | 0.732 | 0.735 | 0.724 | 0.712 | 0.709 |

Tuesday | 0.740 | 1.000 | 0.735 | 0.733 | 0.730 | 0.723 | 0.719 |

Wednesday | 0.732 | 0.735 | 1.000 | 0.743 | 0.743 | 0.721 | 0.716 |

Thursday | 0.735 | 0.733 | 0.743 | 1.000 | 0.741 | 0.721 | 0.717 |

Friday | 0.724 | 0.730 | 0.743 | 0.741 | 1.000 | 0.729 | 0.721 |

Saturday | 0.712 | 0.723 | 0.721 | 0.721 | 0.729 | 1.000 | 0.734 |

Sunday | 0.709 | 0.719 | 0.716 | 0.717 | 0.721 | 0.734 | 1.000 |

Structural Similarity | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday |
---|---|---|---|---|---|---|---|

Monday | 1.000 | 0.567 | 0.567 | 0.567 | 0.564 | 0.565 | 0.562 |

Tuesday | 0.567 | 1.000 | 0.568 | 0.567 | 0.565 | 0.566 | 0.563 |

Wednesday | 0.567 | 0.568 | 1.000 | 0.569 | 0.567 | 0.566 | 0.563 |

Thursday | 0.567 | 0.567 | 0.569 | 1.000 | 0.565 | 0.563 | 0.561 |

Friday | 0.564 | 0.565 | 0.567 | 0.565 | 1.000 | 0.559 | 0.559 |

Saturday | 0.565 | 0.566 | 0.566 | 0.563 | 0.559 | 1.000 | 0.558 |

Sunday | 0.562 | 0.563 | 0.563 | 0.561 | 0.559 | 0.558 | 1.000 |

Structural Similarity | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday |
---|---|---|---|---|---|---|---|

Monday | 1.000 | 0.590 | 0.576 | 0.576 | 0.538 | 0.462 | 0.489 |

Tuesday | 0.590 | 1.000 | 0.599 | 0.587 | 0.543 | 0.459 | 0.481 |

Wednesday | 0.576 | 0.599 | 1.000 | 0.595 | 0.557 | 0.470 | 0.487 |

Thursday | 0.576 | 0.587 | 0.595 | 1.000 | 0.573 | 0.484 | 0.498 |

Friday | 0.538 | 0.543 | 0.557 | 0.573 | 1.000 | 0.517 | 0.513 |

Saturday | 0.462 | 0.459 | 0.470 | 0.484 | 0.517 | 1.000 | 0.530 |

Sunday | 0.489 | 0.481 | 0.487 | 0.498 | 0.513 | 0.530 | 1.000 |

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## Share and Cite

**MDPI and ACS Style**

Tu, P.; Yao, W.; Zhao, Z.; Wang, P.; Wu, S.; Fang, Z. Interday Stability of Taxi Travel Flow in Urban Areas. *ISPRS Int. J. Geo-Inf.* **2022**, *11*, 590.
https://doi.org/10.3390/ijgi11120590

**AMA Style**

Tu P, Yao W, Zhao Z, Wang P, Wu S, Fang Z. Interday Stability of Taxi Travel Flow in Urban Areas. *ISPRS International Journal of Geo-Information*. 2022; 11(12):590.
https://doi.org/10.3390/ijgi11120590

**Chicago/Turabian Style**

Tu, Ping, Wei Yao, Zhiyuan Zhao, Pengzhou Wang, Sheng Wu, and Zhixiang Fang. 2022. "Interday Stability of Taxi Travel Flow in Urban Areas" *ISPRS International Journal of Geo-Information* 11, no. 12: 590.
https://doi.org/10.3390/ijgi11120590