# Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

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## Abstract

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## 1. Introduction

## 2. Related Work

## 3. Hierarchical Agglomerative Clustering Based on Ultra-Light Edge Computing Algorithm

#### 3.1. IoSB Architecture

#### 3.2. Problem Statement

#### 3.3. Ultra-Light Edge Computing Algorithm

- $-1$:
- indicates a negative net arrival, i.e., the number of bicycles that have been returned is lower than the number of bicycles that have been picked up; in this case, the docking station is empting;
- $\phantom{+}0$:
- indicates that the number of deposits compensate the number of withdrawals, thus resulting in a constant occupancy value during time interval t;
- $+1$:
- indicates a positive net arrival, i.e., the number of bicycles that have been returned is higher than the number of bicycles that have been picked up; in this case, the docking station is filling up.

#### 3.4. Hierarchical Agglomerative Clustering

## 4. Results

#### 4.1. Dataset

- Time stamp: it showed the moment when the bicycle was picked up; every time stamp was set with 1 hour definition for privacy and anonymity issues.
- User’s identifier: it was an encrypted identifier which was unique per user and day in order to preserve the privacy of the user.
- Type of user: annual, eventual, staff.
- User’s range of age: it provided six age intervals [0,16], [17,18], [19–26], [27–40], [41–65], [66,∞), and unknown.
- Identifier of the origin docking station.
- Identifier of the destination docking station.
- Travel time: time elapsed between the withdrawal and deposit of the bicycle.
- Track: sequence of geographical coordinates travelled by the bicycle, updated every minute (this information was not always available).

#### 4.2. Dendograms

#### 4.3. Analysis with Two Clusters

#### 4.4. Analysis with 11 Clusters

## 5. Conclusions and Future Research

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Dendrogram (binary tree). Groups numbered from left to right: g#1 (dark blue), 28 stations; g#2 (red), 5 stations; g#3 (orange), 19 stations; g#4 (purple), 26 stations; g#5 (dark green), 3 stations; g#6 (light blue), 7 stations; g#7 (garnet), 16 stations; g#8 (light pink), 33 stations; g#9 (gold yellow), 22 stations; g#10 (light green), 6 stations; g#11 (dark pink), 4 stations.

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**MDPI and ACS Style**

Vinagre Díaz, J.J.; Fernández Pozo, R.; Rodríguez González, A.B.; Wilby, M.R.; Sánchez Ávila, C.
Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing. *Sensors* **2020**, *20*, 3550.
https://doi.org/10.3390/s20123550

**AMA Style**

Vinagre Díaz JJ, Fernández Pozo R, Rodríguez González AB, Wilby MR, Sánchez Ávila C.
Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing. *Sensors*. 2020; 20(12):3550.
https://doi.org/10.3390/s20123550

**Chicago/Turabian Style**

Vinagre Díaz, Juan José, Rubén Fernández Pozo, Ana Belén Rodríguez González, Mark R. Wilby, and Carmen Sánchez Ávila.
2020. "Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing" *Sensors* 20, no. 12: 3550.
https://doi.org/10.3390/s20123550