# Correlation Analysis on Accident Injury and Risky Behavior of Vulnerable Road Users Based on Bayesian General Ordinal Logit Model

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Source

#### 2.2. The General Ordinal Logit/Probit Model

## 3. Descriptive Statistics and Classification

#### 3.1. Accident Classification

#### 3.2. Classification and Reconstruction of Typical Risky Scenarios

## 4. Modelling Statistical Analysis

## 5. Discussion

## 6. Conclusions

- According to the statistics on illegal behaviors of pedestrians and non-motorized cyclists, the main risky behaviors leading to accidents are the violation of traffic lights, riding against traffic, and not walking/riding on the designated road.
- The Bayesian General Ordinal Logit modeling analysis is applied to 168 cases of real accidents, and the effect of different variables on accident injury severity is obtained according to marginal effect analysis. Results indicate that when the driver is middle-aged and the VRU party is riding an e-bicycle, the probability of death in the accident will rise significantly, which highlights the importance of wearing a helmet. Additionally, for young male VRUs, for VRUs undertaking minor responsibility, and for the motor vehicle type being a truck, the probability of death will decrease remarkably.
- The above data analysis results show that accident prevention and injury reduction for VRUs should be implemented. Middle-aged motorists and e-bicycle cyclists should pay more attention to safe driving and accident prevention. Compliance with laws and regulations and wearing helmets for cyclists are more conducive to VRUs to reduce possible injuries.
- Results can provide scenarios for intelligent vehicles to identify illegal/risky behaviors of VRUs, to help prevent and avoid particular scenarios utilizing radar or visual technologies, and eventually reduce the injury severity of VRUs or avoid accidents.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Collision scene reconstruction of typical accident scenarios: (

**a**) Riding against traffic; (

**b**) Riding on motorway; (

**c**) Not slowing down while turning; (

**d**) Jaywalking; (

**e**) Walking on motorway.

Variable | Classification | Quantity (n = 378) | Chi-Square Value |
---|---|---|---|

Time | Daytime (7:00–19:00) | 262 (69.31%) | 56.39 |

Nighttime (19:00–7:00) | 116 (30.69%) | ||

Vulnerable road users | Pedestrian | 102 (26.98%) | 80.10 |

Cyclist | 276 (73.02%) | ||

Accident injury severity | Injury | 317 (83.86%) | 173.38 |

Death | 61 (16.14%) | ||

Location | Intersections | 89 (23.55%) | 105.82 |

Others | 289 (76.46%) | ||

Illegal behavior | Yes | 215 (56.88%) | 7.15 |

No | 163 (43.12%) |

Variable | Classification | Number of Pedestrians (n = 102) | Number of Cyclists (n = 276) | Chi-Square Value |
---|---|---|---|---|

Time | Daytime (7:00–19:00) | 72 (19.05%) | 190 (50.26%) | 0.11 |

Nighttime (19:00–7:00) | 30 (7.94%) | 224 (22.75%) | ||

Accident injury severity | Injury | 74 (19.58%) | 243 (64.29%) | 13.21 |

Death | 28 (7.41%) | 33 (8.73%) | ||

Location | Intersections | 17 (4.50%) | 72 (19.25%) | 3.67 |

Others | 85 (22.49%) | 204 (53.97%) | ||

Illegal behavior | Yes | 56 (14.81%) | 159 (42.06%) | 0.22 |

No | 46 (12.17%) | 117 (30.95%) |

Behaviors | Number of Pedestrians (n = 102) | Number of Cyclists (n = 276) |
---|---|---|

Riding against traffic | 33 (8.73%) | |

Not slowing down when turning, or giving way to pedestrians | 13 (3.44%) | |

Not walking/riding on a designated road | 2 (0.53%) | 8 (2.12%) |

Jaywalking | 22 (5.82%) | 14 (3.70%) |

Illegal behavior while walking/riding on the road | 4 (1.06%) | 36 (9.52%) |

Violation of traffic light | 5 (3.78%) | 28 (7.41%) |

Drinking | 0 (0%) | 9 (2.38%) |

Others | 23 (6.08%) | 18 (4.76%) |

Total | 56 (14.81%) | 159 (42.06%) |

Variables | Description and Value |
---|---|

Motor vehicle type (MVT) | |

Coach | Bus (reference item) |

Truck | Truck |

Motorcycle | Motorcycle |

Gender of driver (GD) | 1 for male/0 for female |

Age of driver (AD) | |

AD_young | ≤25 |

AD_mature | 26–45 (reference item) |

AD_middle | 46–55 |

AD_old | >55 |

Vulnerable group (VG) | |

Bicycle | Bicycle |

E-bicycle | E-bicycle |

Pedestrian | Pedestrians (reference item) |

Gender of vulnerable group (GVG) | 1 for male/0 for female |

Age of vulnerable group (AVG) | |

AGV_young | ≤25 |

AGV_mature | 26–45 |

AGV_middle | 46–55 |

AGV_old | >55 (reference item) |

Responsibility (RES) | |

VRU_major | VRU: major and full responsibility |

VRU_minor | VRU: minor responsibility, driver: major responsibility |

VRU_none | VRU: no responsibility, driver: full responsibility (reference item) |

Same | Equal responsibility |

Weekend or workday | 1 for weekend, 0 for workday |

Time of day | |

Before dawn | 0 a.m.–6 a.m. |

Morning | 6 a.m.–12 a.m. (reference item) |

Afternoon | 12 a.m.–6 p.m. |

Evening | 6 p.m.–0 a.m. |

Covariates | Beta Latent Propensity | t-Value | p-Value | Alpha Threshold ${\mathit{\alpha}}_{\mathit{k}}$ [28] | t-Value | p-Value |
---|---|---|---|---|---|---|

Truck | −5.049 (1.845) ^{a} | 2.736 | <0.01 | −1.216 (0.275) | 4.421 | <0.01 |

AD_middle | 4.921 (2.514) | 1.957 | 0.05 | 0.5979 (0.2327) | 2.569 | 0.01 |

E-bicycle | 4.545 (2.019) | 2.251 | 0.02 | \ | ||

GVG_male | −3.838 (1.461) | 2.626 | 0.01 | −0.4526 (0.1586) | 2.854 | <0.01 |

AVG_young | \ | 0.7186 (0.2887) | 2.489 | 0.01 | ||

VRU_minor | \ | 1.978 (0.9002) | 2.197 | 0.03 |

^{a}: average (standard deviation).

Covariates | Property Loss (%) | Injury (%) | Death (%) |
---|---|---|---|

AD_middle | −3.9 | −36.7 | 40.5 |

AVG_young | 0 | 27.5 | −27.5 |

GVG_male | 5.7 | 25.4 | −31.1 |

VRU_minor | 0 | 28.1 | −28.1 |

E-bicycle | −5.3 | −26.8 | 32.1 |

Truck | 15.9 | 4.1 | −19.9 |

Covariates | Property Loss (%) | Injury (%) | Death (%) |
---|---|---|---|

AD_middle | −5.0 | −49.0 | 44.8 |

AVG_young | 2.5 | 55.1 | −42.5 |

GVG_male | 7.5 | 57.4 | −35.0 |

VRU_minor | −2.5 | 49.6 | −47.3 |

E-bicycle | −5.1 | −32.4 | 62.7 |

Truck | 82.5 | 17.5 | −52.5 |

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**MDPI and ACS Style**

Yuan, Q.; Zhai, X.; Ji, W.; Yang, T.; Yu, Y.; Yu, S.
Correlation Analysis on Accident Injury and Risky Behavior of Vulnerable Road Users Based on Bayesian General Ordinal Logit Model. *Sustainability* **2022**, *14*, 16048.
https://doi.org/10.3390/su142316048

**AMA Style**

Yuan Q, Zhai X, Ji W, Yang T, Yu Y, Yu S.
Correlation Analysis on Accident Injury and Risky Behavior of Vulnerable Road Users Based on Bayesian General Ordinal Logit Model. *Sustainability*. 2022; 14(23):16048.
https://doi.org/10.3390/su142316048

**Chicago/Turabian Style**

Yuan, Quan, Xianguo Zhai, Wei Ji, Tiantong Yang, Yang Yu, and Shengnan Yu.
2022. "Correlation Analysis on Accident Injury and Risky Behavior of Vulnerable Road Users Based on Bayesian General Ordinal Logit Model" *Sustainability* 14, no. 23: 16048.
https://doi.org/10.3390/su142316048