# Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

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## Abstract

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## 1. Introduction

#### 1.1. Definition and Background of PU Crash

#### 1.2. Importance of Real-Time Traffic Characteristics in Safety

#### 1.3. Application of Machine Learning and Interpretation Methods

#### 1.4. Aims

- Exploring the interacting effects of real-time traffic parameters and environmental conditions on the severity of PU crashes to address these rare and complex aspects of traffic incidents.
- Utilizing ML models and the SHAP method, proficient in identifying complex patterns and interpreting influence and interactions, in order to present results that are easily interpretable for policymakers.

## 2. Data

#### 2.1. Real-Time Traffic Characteristics

#### 2.2. Crash Characteristics

#### 2.3. Environmental Factors

## 3. Methodology

#### 3.1. Categorical Boosting Method (CatBoost)

#### 3.2. Resampling

#### 3.3. Hyperparameter Tuning

#### 3.4. Model Evaluation

#### 3.5. Model Interpretation

## 4. Results and Discussion

#### 4.1. Model Fitting and Evaluation Results

^{−20}, ‘leaf_estimation iterations’: 10, ‘logging_level’: ‘Silent’, ‘loss_function’: ‘Logloss’.

#### 4.2. Importance and Global Interpretation of Risk Factors

#### 4.3. Main and Interacting Effects of Risk Factors

#### 4.3.1. Real-Time Traffic Factors

#### 4.3.2. Crash Characteristics

#### 4.3.3. Environmental Factors

## 5. Conclusions

## 6. Limitations and Future Direction

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Study routes: 1: Tehran–Qom (150 km), 2: Qom–Isfahan (360 km), 3: Tehran–Qazvin (120 km), 4: Tehran–Saveh (95 Km), 5: Qazvin–Tabriz (490 km), 6: Qazvin–Rasht (160 km), 7: Saveh–Hamadan (175 km), 8: Saveh–Salafchegan (80 Km), 9: Qom–Garmsar (150 km), 10: Khoramabad–Andimeshk (145 km), 11: Ahvaz–BandarImam (90 km).

**Figure 4.**Distribution and F&IN rate of PU crashes by number of vehicles involved: (

**a**) all vehicles; (

**b**) heavy vehicles.

**Figure 7.**Main effects of real-time traffic variables: (

**a**) Avg.speed; (

**b**) $\mathrm{T}\mathrm{V}/\mathrm{C}$; (

**c**) $\mathrm{H}\mathrm{V}\mathrm{V}/\mathrm{T}\mathrm{V}$.

**Figure 8.**SHAP main and interaction effect plots: (

**a**) main effect of $\mathrm{T}\mathrm{V}/\mathrm{C}$; (

**b**) interaction with avg.speed; (

**c**) interaction with $\mathrm{H}\mathrm{V}\mathrm{V}/\mathrm{T}\mathrm{V}$; (

**d**) interaction with light condition.

**Figure 9.**SHAP main and interaction effect plots of $\mathrm{H}\mathrm{V}\mathrm{V}/\mathrm{T}\mathrm{V}$: (

**a**) main effect; (

**b**) interaction with avg.speed.

**Figure 10.**SHAP main effect plots of crash characteristics: (

**a**) main effect of no. of vehicles involved; (

**b**) main effect of no. of heavy vehicles involved.

**Figure 11.**SHAP main and interaction effect plots of environmental factors: (

**a**) main effects of road geometry; (

**b**) interaction effects of road geometry with avg.speed; (

**c**) main effects of light conditions; (

**d**) interaction effects of light conditions with road geometry.

Variable | Description | PDO | F & IN | Total | Mean | Std. Dev | Min | Max |
---|---|---|---|---|---|---|---|---|

Real-Time Traffic Variables | ||||||||

TV/C | 0.469 | 0.297 | 0.005 | 0.98 | ||||

Avg.speed | 84.70 | 12.77 | 60 | 120 | ||||

HVV/TV | 0.143 | 0.102 | 0.014 | 0.634 | ||||

Crash Characteristics | ||||||||

No. of vehicles involved | 3.24 | 0.574 | 3 | 12 | ||||

No. of heavy vehicles involved | 0.57 | 0.791 | 0 | 3 | ||||

No. of injuries | 0.15 | 0.537 | 0 | 6 | ||||

No. of fatalities | 0.02 | 0.203 | 0 | 5 | ||||

Environmental Characteristics | ||||||||

NO. Lanes | 2 | 24 (23.07%) | 80 (76.92%) | 104 (4.80%) | ||||

3 | 393 (19.06%) | 1668 (80.93%) | 2061 (95.1%) | |||||

Light Condition | Day | 191 (14.81%) | 1098 (85.18%) | 1289 (59.5%) | ||||

Night | 213 (27.41%) | 564 (72.58%) | 777 (35.8%) | |||||

Sunrise | 7 (23.33%) | 23 (76.66%) | 30 (1.38%) | |||||

Sunset | 6 (8.695%) | 63 (91.30%) | 69 (3.18%) | |||||

Road Surface Condition | Dry | 321 (18.03%) | 1459 (81.96%) | 1780 (82.2%) | ||||

Ice and snow | 15 (24.19%) | 47 (75.80%) | 62 (2.86%) | |||||

Wet | 81 (25.07%) | 242 (74.92%) | 323 (14.9%) | |||||

Land Use | Agriculture | 96 (39.18%) | 149 (60.81%) | 245 (11.3%) | ||||

Industrial | 9 (21.42%) | 33 (78.57%) | 42 (1.93%) | |||||

Other | 305 (16.38%) | 1556 (83.61%) | 1861 (85.9%) | |||||

Residential | 7 (41.17%) | 10 (58.82%) | 17 (0.78%) | |||||

Weather Condition | Cloudy and foggy and dusty | 15 (33.33%) | 30 (66.66%) | 45 (2.07%) | ||||

Rainy | 73 (24.74%) | 222 (75.25%) | 295 (13.6%) | |||||

Smooth | 309 (17.60%) | 1446 (82.39%) | 1755 (81.0%) | |||||

Snow | 19 (27.53%) | 50 (72.46%) | 69 (3.18%) | |||||

Storm | 1 (100%) | 0 (0%) | 1 (0.04%) | |||||

Road Geometry | Curve and longitudinal slope | 101 (95.28%) | 5 (4.716%) | 106 (4.89%) | ||||

Curve and plain | 21 (95.45%) | 1 (4.545%) | 22 (1.01%) | |||||

Straight and longitudinal slope | 19 (22.35%) | 66 (77.64%) | 85 (3.92%) | |||||

Straight and plain | 276 (14.13%) | 1676 (85.86%) | 1952 (90.1%) |

Predicted | ||
---|---|---|

Observed | Positive | Negative |

Positive | TP | FN |

Negative | FP | TN |

Total | P | N |

Model (Oversampled Data) | Accuracy | Recall | Precision | F1 Score | ROC-AUC |
---|---|---|---|---|---|

CART | 73.1% | 0.0% | 0.0% | 0.0% | 50.0% |

RF | 95.0% | 87.1% | 93.8% | 90.3% | 92.5% |

CatBoost | 95.6% | 87.6% | 95.6% | 91.4% | 93.1% |

XGBoost | 95.3% | 85.7% | 96.3% | 90.7% | 92.2% |

LightGBM | 95.3% | 86.7% | 95.2% | 90.8% | 92.6% |

AdaBoost | 95.3% | 86.0% | 96.3% | 90.9% | 92.4% |

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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Samerei, S.A.; Aghabayk, K.; Montella, A.
Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method. *Safety* **2024**, *10*, 22.
https://doi.org/10.3390/safety10010022

**AMA Style**

Samerei SA, Aghabayk K, Montella A.
Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method. *Safety*. 2024; 10(1):22.
https://doi.org/10.3390/safety10010022

**Chicago/Turabian Style**

Samerei, Seyed Alireza, Kayvan Aghabayk, and Alfonso Montella.
2024. "Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method" *Safety* 10, no. 1: 22.
https://doi.org/10.3390/safety10010022