Estimating Congestion in a Fixed-Route Bus by Using BLE Signals
Abstract
:1. Introduction
- We propose a BLE-based congestion estimation system that protects the privacy of passengers while reducing the cost of installation.
- We propose new features of learning models for estimating congestion of public route buses.
- Our estimation model, which is trained data from real-world experiments, achieves high accuracy for practical use.
2. Related Work
2.1. Estimating the Number of Passengers
2.2. Congestion Estimation Using BLE Signals
2.3. Positioning of This Research
3. Proposed System
3.1. System Requirements
- Collection of data that does not include passenger privacy information
- Reduction of the installation cost of sensing devices
3.2. Overview of the Proposed System
3.3. System Design
- (1)
- Sensing Mechanism
- (2)
- Estimation Mechanism
4. Implementing the Data Collection Device
4.1. Implementation Overview
4.2. Sensing Device
4.3. Sensing Process
5. Data Collection Experiment
5.1. Experiment Overview
5.2. Results of the Experiment
6. Estimation and Evaluation
6.1. Estimation by Threshold
6.2. Estimation by Machine Learning Models
7. Discussion
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Domain | Subject | Sensor | Privacy | Number of Sensors | Location Constraints | Estimate | |
---|---|---|---|---|---|---|---|
[26] | indoor | pedestrian flow | BLE | ◯ | 1 | △ | correlation |
[27] | outdoor | pedestrian flow | Wi-Fi | △ | 3 | △ | correlation |
[28] | outdoor | congestion | BLE | ◯ | 2 | △ | classification |
[29] | outdoor | congestion | BLE | ◯ | 2 | △ | classification |
[30] | bus | onboard devices | BLE | ◯ | 1 | △ | correlation |
Proposed | bus | number of passengers | BLE | ◯ | 1 | ◯ | regression |
BD Address | The Mean Value of RSSI | The Frequency of Occurrence |
---|---|---|
00:00:5e:00:53:1a | −78.5 | 25 |
00:00:5e:00:53:38 | −90.0 | 100 |
00:00:5e:00:53:90 | −56.4 | 75 |
… | … | … |
Departure Time | Bus Stop | Number1 | Number2 |
---|---|---|---|
08:52 | Gakken Nara Tomigaoka | 25 | 4 |
08:54 | Kita Tomigaoka Ittyoume | 25 | 4 |
08:55 | Higashi Tomigaoka Yontyoume | 25 | 5 |
08:56 | Higashi Tomigaoka Gotyoume | 25 | 6 |
08:57 | Higashi Tomigaoka Rokutyoume | 51 | 7 |
08:58 | Tomigaoka Rokutyoume Higashi | 104 | 8 |
09:00 | Oshikuma/Jinkou | 75 | 11 |
09:02 | Seika Sakuragaoka Santyoume | 40 | 15 |
09:03 | Kabutodai Santyoume | 44 | 15 |
09:04 | Kabutodai Nityoume | 51 | 15 |
09:05 | Kabutodai Ittyoume Nishi | 21 | 15 |
09:05 | Kabutodai Ittyoume | 78 | 15 |
Method | MAE | MAPE |
---|---|---|
All | 75.8 | 2182.5 |
Baseline (RSSI ≥ −74) | 3.9 | 77.3 |
Proposed (RSSI ≥ −80, F ≥ 40%) | 3.4 | 61.4 |
ND | ||||
---|---|---|---|---|
Model | MAE | MAPE | MAE | MAPE |
LR | 3.65 | 81.4 | 3.41 | 64.4 |
SVM | 3.09 | 66.5 | 2.97 | 44.7 |
RF | 2.93 | 63.1 | 2.54 | 47.7 |
XGB | 2.98 | 60.0 | 2.49 | 38.8 |
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Kanamitsu, Y.; Taya, E.; Tachibana, K.; Nakamura, Y.; Matsuda, Y.; Suwa, H.; Yasumoto, K. Estimating Congestion in a Fixed-Route Bus by Using BLE Signals. Sensors 2022, 22, 881. https://doi.org/10.3390/s22030881
Kanamitsu Y, Taya E, Tachibana K, Nakamura Y, Matsuda Y, Suwa H, Yasumoto K. Estimating Congestion in a Fixed-Route Bus by Using BLE Signals. Sensors. 2022; 22(3):881. https://doi.org/10.3390/s22030881
Chicago/Turabian StyleKanamitsu, Yuji, Eigo Taya, Koki Tachibana, Yugo Nakamura, Yuki Matsuda, Hirohiko Suwa, and Keiichi Yasumoto. 2022. "Estimating Congestion in a Fixed-Route Bus by Using BLE Signals" Sensors 22, no. 3: 881. https://doi.org/10.3390/s22030881
APA StyleKanamitsu, Y., Taya, E., Tachibana, K., Nakamura, Y., Matsuda, Y., Suwa, H., & Yasumoto, K. (2022). Estimating Congestion in a Fixed-Route Bus by Using BLE Signals. Sensors, 22(3), 881. https://doi.org/10.3390/s22030881