Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study
Abstract
:1. Introduction
2. Data and Methods
2.1. Selection of Study Area
2.2. Data Collection
2.3. GIS-Based Analysis for Violation Hotspots
2.4. Traffic Violation Prediction Using ML
2.4.1. K Nearest Neighbors (KNN)
2.4.2. Support Vector Machine (SVM)
2.4.3. CN2 Rule Inducer
2.5. Performance Evaluation Metrics for ML Models
3. Results and Discussions
3.1. Analysis of Descriptive Statistics
3.2. Mapping of Violation Hotspots
3.3. ML Model’s Comparison for Violation Prediction
3.4. Spearman Correlation Analysis
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
No. | IF Condition | Then Class | Distribution | Probabilities (%) | Rule Quality | Length |
---|---|---|---|---|---|---|
1 | Latitude ≥ 28.85656 | Illegal parking | [12, 0, 0, 0] | 81:6:6:6 | 0.929 | 1 |
2 | Latitude ≤ 28.83151 and Season = Autumn | Illegal parking | [16, 0, 0, 0] | 85:5:5:5 | 0.944 | 2 |
3 | Latitude ≥ 28.83693 and Latitude ≤ 28.84196 and Season ≠ Summer | Illegal parking | [20, 0, 0, 0] | 88:4:4:4 | 0.955 | 3 |
4 | Latitude ≤ 28.83151 and Day of Week = Sunday | Illegal parking | [4, 0, 0, 0] | 62:12:12:12 | 0.833 | 2 |
5 | Lattitude ≤ 28.83151 and Day of Week = Tuesday | Illegal parking | [3, 2, 0, 0] | 44:33:11:11 | 0.571 | 2 |
6 | Lattitude ≤ 28.83151 and Season = Winter | Overspeeding | [0, 60, 0, 0] | 2:95:2:2 | 0.984 | 2 |
7 | Month ≥ 12.0 and Lattitude ≥ 28.84942 | Overspeeding | [0, 22, 0, 0] | 4:88:4:4 | 0.958 | 2 |
8 | Lattitude ≥ 28.83693 and Minute ≥ 55.0 | Overspeeding | [0, 3, 0, 0] | 14:57:14:14 | 0.8 | 2 |
9 | Day of Week = Sunday and Hour ≥ 14.0 | Violation of prohibited markings | [0, 0, 76, 5] | 1:1:91:7 | 0.928 | 3 |
10 | Month ≤ 2.0 and Vehicle type = Taxi/Passenger Car and Day of Week = Monday | Violation of prohibited markings | [0, 0, 35, 12] | 2:2:71:25 | 0.735 | 3 |
11 | Month ≤ 2.0 and Day of Week = Monday | Violation of prohibited markings | [0, 0, 100, 79] | 1:1:55:44 | 0.558 | 2 |
12 | Month ≤ 2.0 and Day of Week = Sunday | Violation of prohibited markings | [0, 0, 35, 36] | 1:1:48:49 | 0.493 | 2 |
13 | Month ≤ 2.0 and Day of Week = Tuesday | Violation of prohibited markings | [0, 0, 16, 23] | 2:2:40:56 | 0.415 | 2 |
14 | Season = Autumn and Month ≥ 10.0 | Violation of prohibited markings | [0, 0, 4, 0] | 12:12:62:1 | 0.833 | 3 |
15 | Hour ≥ 17.0 and Season = Spring | Wrongway driving | [0, 0, 2, 151] | 1:1:2:97 | 0.981 | 2 |
16 | Day of Week = Wednesday and Season = Spring | Wrongway driving | [0, 0, 4, 113] | 1:1:4:94 | 0.958 | 2 |
17 | Day of Week = Saturday and Peak/Off Peak ≠ Peak | Wrongway driving | [1, 0, 7, 121] | 2:1:6:92 | 0.931 | 2 |
18 | Season ≠ Winter and Day of Week = Friday | Wrongway driving | [3, 0, 6, 141] | 3:1:5:92 | 0.934 | 2 |
19 | Day of Week = Thursday and Season = Spring | Wrongway driving | [0, 0, 7, 104] | 1:1:7:91 | 0.929 | 2 |
20 | Day of Week = Sunday and Hour ≥ 10.0 | Wrongway driving | [0, 0, 2, 105] | 1:1:3:95 | 0.972 | 3 |
21 | Season ≠ Winter | Wrongway driving | [39, 9, 70, 209] | 12:3:21:63 | 0.638 | 1 |
22 | Month ≤ 2.0 and Day of Week = Thursday | Wrongway driving | [0, 0, 1, 24] | 3:3:7:86 | 0.926 | 2 |
23 | Month ≤ 2.0 and Month ≥ 2.0 | Wrongway driving | [0, 0, 16, 107] | 1:1:13:85 | 0.864 | 2 |
24 | Month ≤ 5.0 and Day of Week = Friday | Wrongway driving | [2, 0, 21, 29] | 5:2:39:54 | 0.556 | 2 |
25 | Month ≤ 5.0 and Day of Week = Tuesday | Wrongway driving | [0, 0, 23, 23] | 2:2:48:48 | 0.5 | 2 |
26 | Day_of_Week = Monday and Hour ≤ 11.0 | Wrongway driving | [2, 2, 19, 25] | 6:6:38:50 | 0.52 | 2 |
27 | Month ≤ 5.0 and Day of Week = Saturday | Wrongway driving | [0, 0, 15, 12] | 3:3:52:42 | 0.448 | 2 |
28 | Month ≤ 2.0 and Hour ≥ 17.0 | Wrongway driving | [0, 0, 13, 10] | 4:4:52:41 | 0.44 | 2 |
29 | Day of Week = Wednesday | Wrongway driving | [8, 3, 10, 12] | 24:11:30:135 | 0.371 | 1 |
30 | TRUE | Wrongway driving | [57, 91, 358, 1502] | 3:5:18:75 | 0.747 |
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K (Number of Neighbors) | Metric | Weight |
---|---|---|
3 | Euclidean | Distance |
5 | Euclidean | Distance |
7 | Manhattan | Distance |
10 | Euclidean | Uniform |
12 | Mahalanobis | Distance |
Model Parameters | Parameter Value |
---|---|
α1 | 0.04 |
α2 | 0.04 |
Minimum rule coverage | 1 |
Maximum rule coverage | 7 |
Variable | Percentage of Total Violations (%) | Frequency | Variable Type |
---|---|---|---|
Wrongway driving | 74.94 | 1501 | Response |
Violation of Prohibited Markings | 17.87 | 358 | Response |
Overspeeding | 4.54 | 91 | Response |
Illegal Parking | 2.65 | 53 | Response |
Vehicles Type | |||
Private Car | 58.46 | 1171 | Predictor |
Taxi | 23.27 | 466 | Predictor |
Van | 9.54 | 191 | Predictor |
Small Truck | 6.19 | 124 | Predictor |
Bus | 2.55 | 51 | Predictor |
Seasons | |||
Spring | 54.72 | 1096 | Predictor |
Winter | 34.4 | 689 | Predictor |
Summer | 7.79 | 156 | Predictor |
Autumn | 3.1 | 62 | Predictor |
Week | |||
Weekdays | 68.3 | 1368 | Predictor |
Weekends | 31.7 | 635 | Predictor |
Hours of the Day | |||
Peak Hours (9:00 a.m.–11:00 a.m., 15:00 p.m.–17:00 p.m.) | 47.98 | 961 | Predictor |
Off Peak Hours (11:00 a.m.–15:00 p.m., 17:00 p.m.–9:00 a.m.) | 52.02 | 1042 | Predictor |
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Zahid, M.; Chen, Y.; Jamal, A.; Al-Ofi, K.A.; Al-Ahmadi, H.M. Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study. Int. J. Environ. Res. Public Health 2020, 17, 5193. https://doi.org/10.3390/ijerph17145193
Zahid M, Chen Y, Jamal A, Al-Ofi KA, Al-Ahmadi HM. Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study. International Journal of Environmental Research and Public Health. 2020; 17(14):5193. https://doi.org/10.3390/ijerph17145193
Chicago/Turabian StyleZahid, Muhammad, Yangzhou Chen, Arshad Jamal, Khalaf A. Al-Ofi, and Hassan M. Al-Ahmadi. 2020. "Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study" International Journal of Environmental Research and Public Health 17, no. 14: 5193. https://doi.org/10.3390/ijerph17145193