Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach
Abstract
:1. Introduction
2. Literature Review
2.1. Novice and Experienced Driver in Traffic Accident Analysis
2.2. Traffic Accident Severity Modeling
3. Data Preparation
- Group 1: driving experience ≤ 3 years (i.e., novice driver).
- Group 2: 3 years < driving experience ≤ 10 years (i.e., experienced driver).
- Group 3: driving experience > 10 years (i.e., experienced driver).
4. Methodology
4.1. Data Resampling
4.2. Gradient Boosting
4.3. CatBoost
4.3.1. Ordered TS
4.3.2. Ordered Boosting
4.4. SHAP
4.5. Performance Measures
5. Results and Discussions
5.1. Model Parameters
5.2. Feature Analysis
5.3. Feature Dependency Analysis
5.4. Feature Interaction Analysis
6. Conclusions
- In the analysis of influencing factors of accident severity, CatBoost generates the best result (AUC: 0.86, 0.79, and 0.87; F1 score: 0.70, 0.67, and 0.70), indicating the application potential of the model in traffic safety.
- Accident cause, age, visibility, light condition, season, road alignment, and terrain are the key factors affecting the severity of traffic accident. Pavement surface condition, overload condition, accident pattern, and gender have the least impact on accident severity. The importance of these features varies for drivers with different driving experience in terms of accident severity.
- The impact of age on fatal accidents is different for drivers with different driving experience. Novice drivers younger than 30 or older than 55 are prone to suffer fatal accidents, but for experienced drivers, the risk of fatal accident decreases when they are young and increases when they are old.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Variable | Description | Group1 | Group2 | Group3 | |||
---|---|---|---|---|---|---|---|
N | % | N | % | N | % | ||
Day of Week | Weekday = 1 | 1928 | 73.93% | 2626 | 72.90% | 1627 | 72.73% |
Weekend = 2 | 680 | 26.07% | 976 | 27.10% | 610 | 27.27% | |
Season | Spring: Match to May = 1 | 680 | 26.07% | 922 | 25.60% | 578 | 25.84% |
Summer: June to August = 2 | 659 | 25.27% | 904 | 25.10% | 553 | 24.72% | |
Autumn: September to November = 3 | 649 | 24.88% | 867 | 24.07% | 526 | 23.51% | |
Winter: December to February = 4 | 620 | 23.77% | 909 | 25.24% | 580 | 25.93% | |
Hour | 0:00~06:59 = 1 | 216 | 8.28% | 314 | 8.72% | 199 | 8.90% |
07:00~09:59 = 2 | 414 | 15.87% | 550 | 15.27% | 368 | 16.45% | |
10:00~15:59 = 3 | 923 | 35.39% | 1280 | 35.54% | 774 | 34.60% | |
16:00~19:59 = 4 | 738 | 28.30% | 982 | 27.26% | 589 | 26.33% | |
20:00~23:59 = 5 | 317 | 12.15% | 476 | 13.21% | 307 | 13.72% | |
Accident Cause | Overloaded or oversized = 1 | 54 | 2.07% | 63 | 1.75% | 57 | 2.55% |
Driving a vehicle that does not satisfy normal driving requirements = 2 | 70 | 2.68% | 68 | 1.89% | 84 | 3.76% | |
Speeding = 3 | 620 | 23.77% | 791 | 21.96% | 368 | 16.45% | |
Drowsy driving = 4 | 30 | 1.15% | 33 | 0.92% | 77 | 3.44% | |
Traffic signal violation = 5 | 31 | 1.19% | 51 | 1.42% | 59 | 2.64% | |
Driving without license = 6 | 46 | 1.76% | 94 | 2.61% | 55 | 2.46% | |
Failing to give way to pedestrians or vehicles as required = 7 | 488 | 18.71% | 670 | 18.60% | 404 | 18.06% | |
Reversing illegally = 8 | 38 | 1.46% | 75 | 2.08% | 51 | 2.28% | |
Improper backing = 9 | 158 | 6.06% | 224 | 6.22% | 135 | 6.03% | |
Illegal parking = 10 | 38 | 1.46% | 49 | 1.36% | 71 | 3.17% | |
Affecting normal driving when changing lanes = 11 | 117 | 4.49% | 186 | 5.16% | 126 | 5.63% | |
Improper operation = 12 | 178 | 6.83% | 237 | 6.58% | 95 | 4.25% | |
Illegal overtaking = 13 | 121 | 4.64% | 149 | 4.14% | 170 | 7.60% | |
Driving in a place not for traffic = 14 | 257 | 9.85% | 410 | 11.38% | 192 | 8.58% | |
Illegal vehicle meeting = 15 | 191 | 7.32% | 288 | 8.00% | 148 | 6.62% | |
Illegally cut in = 16 | 97 | 3.72% | 118 | 3.28% | 57 | 2.55% | |
Illegal U-turn = 17 | 74 | 2.84% | 96 | 2.67% | 88 | 3.93% | |
Accident Pattern | The occupants dropped or thrown = 1 | 3 | 0.12% | 6 | 0.17% | 3 | 0.13% |
Crushing pedestrians = 2 | 53 | 2.03% | 68 | 1.89% | 50 | 2.24% | |
Vehicle falling = 3 | 23 | 0.88% | 29 | 0.81% | 19 | 0.85% | |
Vehicle rolled or rolled over = 4 | 71 | 2.72% | 96 | 2.67% | 56 | 2.50% | |
Vehicle crashes into a non-fixed object = 5 | 3 | 0.12% | 2 | 0.06% | 2 | 0.09% | |
Vehicle crashes into a fixed object = 6 | 48 | 1.84% | 94 | 2.61% | 55 | 2.46% | |
Crashing into a stationary vehicle = 7 | 50 | 1.92% | 100 | 2.78% | 84 | 3.76% | |
Other vehicle-to-vehicle accidents = 8 | 21 | 0.81% | 24 | 0.67% | 25 | 1.12% | |
Scratch pedestrians = 9 | 317 | 12.15% | 500 | 13.88% | 281 | 12.56% | |
Other vehicle-pedestrian accidents = 10 | 8 | 0.31% | 7 | 0.19% | 5 | 0.22% | |
Crashing into a moving vehicle = 11 | 2011 | 77.11% | 2676 | 74.29% | 1657 | 74.07% | |
Weather | Sunny = 1 | 1882 | 72.16% | 2618 | 72.68% | 1607 | 71.84% |
Cloudy = 2 | 346 | 13.27% | 476 | 13.21% | 324 | 14.48% | |
Foggy = 3 | 6 | 0.23% | 8 | 0.22% | 8 | 0.36% | |
Rainy = 4 | 347 | 13.31% | 469 | 13.02% | 279 | 12.47% | |
Snowy = 5 | 27 | 1.04% | 31 | 0.86% | 19 | 0.85% | |
Pavement Surface Condition | Dry = 1 | 2172 | 83.28% | 2994 | 83.12% | 1874 | 83.77% |
Wet = 2 | 379 | 14.53% | 519 | 14.41% | 309 | 13.81% | |
Water standing = 3 | 38 | 1.46% | 53 | 1.47% | 33 | 1.48% | |
Flooding = 4 | 2 | 0.08% | 3 | 0.08% | 3 | 0.13% | |
Muddy = 5 | 2 | 0.08% | 9 | 0.25% | 1 | 0.04% | |
Icy or snowy = 6 | 15 | 0.58% | 24 | 0.67% | 17 | 0.76% | |
Visibility | < 50 m = 1 | 411 | 15.76% | 516 | 14.33% | 349 | 15.60% |
50~99 m = 2 | 768 | 29.45% | 1063 | 29.51% | 661 | 29.55% | |
100~200 m = 3 | 513 | 19.67% | 698 | 19.38% | 429 | 19.18% | |
> 200 m = 4 | 916 | 35.12% | 1325 | 36.79% | 798 | 35.67% | |
Traffic Control | Without signal control = 1 | 729 | 27.95% | 1049 | 29.12% | 602 | 26.91% |
With signal control = 2 | 1879 | 72.05% | 2553 | 70.88% | 1635 | 73.09% | |
Light Condition | Day = 1 | 1731 | 66.37% | 2365 | 65.66% | 1453 | 64.95% |
Dawn = 2 | 21 | 0.81% | 41 | 1.14% | 24 | 1.07% | |
Dusk = 3 | 40 | 1.53% | 80 | 2.22% | 53 | 2.37% | |
Dark: streetlight on = 4 | 355 | 13.61% | 493 | 13.69% | 301 | 13.46% | |
Dark: streetlight off = 5 | 461 | 17.68% | 623 | 17.30% | 406 | 18.15% | |
Terrain | Plain = 1 | 1561 | 59.85% | 2127 | 59.05% | 1338 | 59.81% |
Hill = 2 | 208 | 7.98% | 265 | 7.36% | 170 | 7.60% | |
Mountain = 3 | 839 | 32.17% | 1210 | 33.59% | 729 | 32.59% | |
Road Alignment | Straight and level = 1 | 1657 | 63.54% | 2322 | 64.46% | 1447 | 64.68% |
Straight with gradient = 2 | 68 | 2.61% | 103 | 2.86% | 65 | 2.91% | |
Curved and level = 3 | 339 | 13.00% | 438 | 12.16% | 258 | 11.53% | |
Curved with gradient = 4 | 544 | 20.86% | 739 | 20.52% | 467 | 20.88% | |
Gender | Male = 1 | 2476 | 94.94% | 3390 | 94.11% | 2127 | 95.08% |
Female = 2 | 132 | 5.06% | 212 | 5.89% | 110 | 4.92% | |
Age | 18~20 = 1 | 110 | 4.22% | 0 | 0.00% | 0 | 0.00% |
21~25 = 2 | 541 | 20.74% | 266 | 7.38% | 0 | 0.00% | |
26~30 = 3 | 485 | 18.60% | 746 | 20.71% | 49 | 2.19% | |
31~35 = 4 | 383 | 14.69% | 711 | 19.74% | 317 | 14.17% | |
36~40 = 5 | 411 | 15.76% | 620 | 17.21% | 514 | 22.98% | |
41~45 = 6 | 334 | 12.81% | 569 | 15.80% | 511 | 22.84% | |
46~50 = 7 | 202 | 7.75% | 345 | 9.58% | 419 | 18.73% | |
51~55 = 8 | 99 | 3.80% | 221 | 6.14% | 247 | 11.04% | |
56~60 = 9 | 36 | 1.38% | 90 | 2.50% | 119 | 5.32% | |
61~65 = 10 | 6 | 0.23% | 31 | 0.86% | 58 | 2.59% | |
>65 = 11 | 1 | 0.04% | 3 | 0.08% | 3 | 0.13% | |
Overload Condition | Overloaded = 1 | 205 | 7.86% | 232 | 6.44% | 149 | 6.66% |
Not overloaded = 2 | 2403 | 92.14% | 3370 | 93.56% | 2088 | 93.34% | |
Vehicle Type | Trailer = 1 | 196 | 7.52% | 208 | 5.77% | 122 | 5.45% |
Tractor = 2 | 43 | 1.65% | 49 | 1.36% | 39 | 1.74% | |
Automobile = 3 | 1955 | 74.96% | 2723 | 75.60% | 1698 | 75.91% | |
Motorcycle = 4 | 394 | 15.11% | 603 | 16.74% | 363 | 16.23% | |
Other = 5 | 20 | 0.77% | 19 | 0.53% | 15 | 0.67% |
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Parameter | Description | Group 1 | Group 2 | Group 3 |
---|---|---|---|---|
l2_leaf_reg | Coefficient at the L2 regularization term of the cost function. | 2 | 5 | 5 |
learning_rate | Used for reducing the gradient step. | 0.15 | 0.3 | 0.25 |
depth | Depth of the tree. | 8 | 10 | 10 |
iterations | The maximum number of trees that can be built. | 1000 | 400 | 500 |
loss_function | The metric to use in training. | MultiClass | MultiClass | MultiClass |
od_wait | The number of iterations to continue the training after the iteration with the optimal metric value. | 12 | 16 | 14 |
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Chen, S.; Shao, H.; Ji, X. Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach. Int. J. Environ. Res. Public Health 2021, 18, 12725. https://doi.org/10.3390/ijerph182312725
Chen S, Shao H, Ji X. Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach. International Journal of Environmental Research and Public Health. 2021; 18(23):12725. https://doi.org/10.3390/ijerph182312725
Chicago/Turabian StyleChen, Shuaiming, Haipeng Shao, and Ximing Ji. 2021. "Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach" International Journal of Environmental Research and Public Health 18, no. 23: 12725. https://doi.org/10.3390/ijerph182312725