Exploring User Experience in Sustainable Transport with Explainable AI Methods Applied to E-Bikes
Abstract
:1. Introduction
2. Fundamentals and Related Work
3. Methods
3.1. Cycling Study
3.1.1. Participants
3.1.2. Materials and Instruments
3.1.3. Procedure
3.2. Hierarchical Cluster Analysis
3.3. Statistical Analysis
3.4. Gaussian Process Regression
3.4.1. Gaussian Processes
3.4.2. Model Performance
3.4.3. Feature Selection
3.4.4. Model Selection
3.4.5. Feature Importance
4. Results
4.1. Hierarchical Cluster Analysis
4.2. Statistical Analysis
4.2.1. Rider and Rider Behaviour Characteristics
4.2.2. Riding Experience
4.3. Gaussian Process Regression
4.3.1. Feature Selection
4.3.2. Model Performance
4.4. Feature Importance Analysis
4.4.1. Rider Type RT1
4.4.2. Rider Type RT2
4.4.3. Rider Type RT3
4.4.4. Rider Type RT4
5. Discussion
5.1. Predictability
5.2. Impact of Predictors on Rider Types
5.3. Impact of Predictors in Related Research
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Section | Terrain | Path Type | Underground | Slope [%] | Length [m] |
---|---|---|---|---|---|
1 | downhill | forest path | gravel | −4 | 1200 |
2 | steep uphill | limited traffic zone | asphalt | +8 | 1100 |
3 | moderate uphill | forest path | gravel | +5 | 1100 |
4 | hilly | limited traffic zone | asphalt | +/−1/2 | 1600 |
Semantic Differential | |||
---|---|---|---|
Category | Item | Negative Polar | Positive Polar |
Activation | |||
A1 | Activation | boring | exciting |
Comfort | |||
C1 | Comfort | uncomfortable | comfortable |
Rider Type | ||||
---|---|---|---|---|
RT1 | RT2 | RT3 | RT4 | |
N | 15 (30%) | 10.00 (20%) | 15 (30%) | 10 (20%) |
Females nf | 7 (47%) | 0 (0%) | 4 (27%) | 2 (20%) |
Age mean (SD) | 33.6 (13.1) | 34.7 (13.1) | 38.9 (11.8) | 39.3 (14.6) |
Riding style | ||||
Comfortable | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
Rather comfortable | 1 (7%) | 1 (10%) | 2 (13%) | 1 (10%) |
Rather sportive | 12 (80%) | 5 (50%) | 11 (73%) | 4 (40%) |
Sportive | 2 (13%) | 4 (40%) | 2 (13%) | 5 (50%) |
Objective parameters | ||||
Rider cadence mean (SD) [rpm] | 72.49 (8.09) | 77.78 (15.03) | 74.18 (8.21) | 79.18 (8.35) |
Rider torque mean (SD) [Nm] | 15.62 (3.56) | 20.94 (4.01) | 18.72 (3.78) | 19.28 (4.18) |
Heart rate mean (SD) [bpm] | 135.69 (19.60) | 143.77 (19.06) | 138.90 (16.04) | 134.83 (16.26) |
Speed mean (SD) [km/h] | 20.98 (2.56) | 23.56 (3.61) | 22.22 (1.80) | 22.82 (2.08) |
Acceleration mean (SD) [m/s2] | 0.31 (0.05) | 0.35 (0.04) | 0.32 (0.04) | 0.34 (0.03) |
Riding Dynamics | |||
---|---|---|---|
Motor Performance | Rider Behaviour | Longitudinal Dynamics | Lateral Dynamics |
Motor power mean | Torque mean | Speed mean | Roll angle deviation |
Motor power deviation | Cadence mean | Acceleration mean | Yaw rate deviation |
Heart rate mean | Deceleration max | ||
Braking duration | |||
Pitch angle max |
Overall GPR Model | Uphill GPR Model | |||||||
---|---|---|---|---|---|---|---|---|
RT1 | RT2 | RT3 | RT4 | RT1 | RT2 | RT3 | RT4 | |
Cross-val. RMSE | 0.61 | 0.76 | 0.60 | 0.82 | 0.53 | 0.62 | 0.53 | 0.50 |
RMSE SD | 0.04 | 0.11 | 0.11 | 0.10 | 0.07 | 0.19 | 0.11 | 0.13 |
R-squared | 0.60 | 0.37 | 0.61 | 0.27 | 0.70 | 0.27 | 0.80 | 0.64 |
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Laqua, A.; Schnee, J.; Pletinckx, J.; Meywerk, M. Exploring User Experience in Sustainable Transport with Explainable AI Methods Applied to E-Bikes. Appl. Sci. 2023, 13, 11277. https://doi.org/10.3390/app132011277
Laqua A, Schnee J, Pletinckx J, Meywerk M. Exploring User Experience in Sustainable Transport with Explainable AI Methods Applied to E-Bikes. Applied Sciences. 2023; 13(20):11277. https://doi.org/10.3390/app132011277
Chicago/Turabian StyleLaqua, Annika, Jan Schnee, Jo Pletinckx, and Martin Meywerk. 2023. "Exploring User Experience in Sustainable Transport with Explainable AI Methods Applied to E-Bikes" Applied Sciences 13, no. 20: 11277. https://doi.org/10.3390/app132011277
APA StyleLaqua, A., Schnee, J., Pletinckx, J., & Meywerk, M. (2023). Exploring User Experience in Sustainable Transport with Explainable AI Methods Applied to E-Bikes. Applied Sciences, 13(20), 11277. https://doi.org/10.3390/app132011277