# Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Data and Methodology

#### 3.1. Data Collection

#### 3.2. Community Structure

#### 3.3. Long Short-Term Memory (LSTM)

## 4. Results

#### 4.1. Clustering the Community

#### 4.2. Prediction of Demand

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Unavailable areas, which are the red blocks on the app execution screen (

**left**), and spatial area based on Kakao map in Seocho and Gangnam (

**right**).

**Figure 2.**Modularity of partition results (

**left**) and the result of service cluster based on Kakao map (

**right**).

Variables | Type | Explanation | |
---|---|---|---|

Hourly demand (Pick-up) | Numeric | The number of hourly demand (pick-up) | |

Time variables | Weekday | Binary | 1: a weekday, 0: otherwise |

Weekend | Binary | 1: a weekend, 0: otherwise | |

Hour of day | Numeric | Time window | |

Weather variables | Temperature | Numeric | Temperature in Celsius |

Wind speed | Numeric | Wind speed in meter per second |

Community | The Number of Grids | The Total Demands (Pick-Up) | The Demands for Each Grid | Major Facilities |
---|---|---|---|---|

Red | 144 | 43,384 | 301 | Sinsa-dong garosu-gil road |

Orange | 356 | 57,512 | 162 | Residential area |

Yellow | 199 | 39,346 | 198 | Teheran-ro |

Green | 301 | 37,775 | 125 | Samsung-dong trade center, Residential area |

Blue | 164 | 46,079 | 281 | Apgujeong rodeo street, Cheongdam-dong luxury shopping street |

Total | 1164 | 224,096 | 193 |

Evaluating Indicators | Hidden State Size (The Number of Hidden Layers Is 1) | Number of Hidden Layers (Hidden State Size Is 6) | ||||||
---|---|---|---|---|---|---|---|---|

2 | 4 | 6 | 8 | 1 | 2 | 3 | 4 | |

MSE | 0.0134 | 0.0126 | 0.0065 | 0.0106 | 0.0065 | 0.0139 | 0.0139 | 0.0145 |

MAE | 0.0716 | 0.0753 | 0.0608 | 0.0685 | 0.0608 | 0.0805 | 0.0749 | 0.0804 |

Activation Function | 5 Partitions | 1164 Square Grids | |||||
---|---|---|---|---|---|---|---|

MSE | MAE | Computing Time | MSE | MAE | Computing Time | ||

LSTM | Sigmoid | 0.0091 | 0.0739 | 109 | 0.0512 | 0.1707 | 5998 |

Tanh | 0.0065 | 0.0608 | 53 | 0.0507 | 0.1700 | 6390 | |

ReLU | 0.0085 | 0.0677 | 50 | 0.0509 | 0.1710 | 6002 | |

ELU | 0.0066 | 0.0604 | 78 | 0.0507 | 0.1702 | 5700 | |

HA | 0.0083 | 0.0618 | - | 0.1300 | 0.5440 | - |

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**MDPI and ACS Style**

Kim, S.; Choo, S.; Lee, G.; Kim, S.
Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method. *Sustainability* **2022**, *14*, 2564.
https://doi.org/10.3390/su14052564

**AMA Style**

Kim S, Choo S, Lee G, Kim S.
Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method. *Sustainability*. 2022; 14(5):2564.
https://doi.org/10.3390/su14052564

**Chicago/Turabian Style**

Kim, Sujae, Sangho Choo, Gyeongjae Lee, and Sanghun Kim.
2022. "Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method" *Sustainability* 14, no. 5: 2564.
https://doi.org/10.3390/su14052564