# Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments

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## Abstract

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## 1. Introduction

- We proposed a new APS based on VANET and HMM, in which the crash risk was considered as a latent variable.
- Unlike other schemes, besides the velocity, the proposed system also considere other factors that may cause the crash.
- The proposed system was modeled as a weighted multi-observation layer HMM rather than the conventional signal layer HMM.
- The proposed system was validated by means of extensive simulation on a map of London city.
- Simulation results showe the high sensitivity and precision of the proposed system.

## 2. Related Work

#### 2.1. Velocity Based Approaches

#### 2.2. Traffic Density Based Approaches

#### 2.3. Driver Fatigue Based Approaches

#### 2.4. Location Based Approaches

#### 2.5. Weather Based Approaches

## 3. Preliminaries

#### 3.1. Notations

#### 3.2. Observation Evaluation

#### 3.2.1. Forward Procedure

Algorithm 1 Forward Procedure |

for$i=0$ to $N-1$ do${\alpha}_{0}\left(i\right)\leftarrow {\pi}_{i}{b}_{i}\left({O}_{0}\right)$ end forfor $i=0$ to $N-1$ dofor $t=1$ to $t=T-1$ do${\alpha}_{t}\left(i\right)=\left[{{\displaystyle \sum}}_{j=0}^{N-1}{\alpha}_{t-1}\left(j\right){a}_{ji}\right]{b}_{i}\left({O}_{t}\right)$ end forend for |

#### 3.2.2. Backward Procedure

Algorithm 2 Backward procedure |

for$i=0$ to $N-1$ do${\beta}_{T-1}\left(i\right)\leftarrow 1$ end forfor $i=0$ to $N-$1 dofor $t=T-2$ to $0$ do${\beta}_{t}\left(i\right)={\displaystyle \sum}_{j=0}^{N-1}{a}_{ij}{b}_{j}({O}_{t+1)}{\beta}_{t+1}\left(j\right)$ end forend for |

## 4. System Modeling

#### 4.1. HMM Parameters

#### 4.2. Probability Fusion

#### 4.3. Training HMM

## 5. Implementation

#### 5.1. Training Data-Set

- Accident Severity (fatal, serious, slight).
- Accident time (time, date, day of the week,)

- Estimated speed during the accidents
- Whether the road limit was exceeded
- Road speed limit

- Coordinates (Latitude, Longitude)
- Grid reference coordinates (Location Easting OSGR, Location Northing OSGR)
- Weather Conditions (fine no high winds, raining no high winds, snowing no high winds, fine and high winds, raining and high winds, snowing and high winds, fog or mist …etc.)
- Light Conditions (daylight, darkness-lights lit, darkness-lights unlit, darkness-no lighting, darkness-lighting unknown)
- Road surface (dry, wet or damp, snow, frost or ice, flood over 3cm’ deep’, oil or diesel, mud)
- Road Type (roundabout, dual carriageway…etc.)

- Driver’s age
- Journey purpose of driver
- Driver blood alcohol level
- Driver’s health condition

- Number of vehicles involved in the accident
- Vehicle type and propulsion code
- Vehicle reference and engine capacity

- Casualty severity
- Casualties ages
- Casualty type

#### 5.2. Simulation Map

#### 5.3. Training the System

#### 5.3.1. Ranges Mapping

#### 5.3.2. Weights Optimization

#### 5.4. Traffic Simulation

#### 5.5. V2V Communication Parameters

## 6. Performance Evaluation

- True positive (TP): the scenario manager launched the crash and the observed vehicle could detect it.
- False positive (FP): the scenario manager did not launch the crash but the observed vehicle falsely detected a crash.
- False negative (FN): the scenario manager launched the crash but the observed vehicle did not detect it.

#### 6.1. Metrics

- Velocity vs Sensitivity: in this test, we changed the velocity values to test and compared the sensitivity of the schemes.
- Velocity vs Precision: in this test, we changed the velocity values to test and compared the precision of the schemes.
- Velocity vs Performance: to measure the performance of the scheme when changing the velocity value.
- Density vs Sensitivity: in this test, we changed vehicles density to test and compared the sensitivity of the schemes.
- Density vs Precision: in this test, we changed vehicles density to test and compared the precision of the schemes.
- Density vs Performance: to measure the performance of the schemes when changing the vehicles density.

#### 6.2. Baselines

#### 6.3. Simulation Results

^{2}). In Figure 10, with the exception of the system from Sam et al., all of the other three systems increased in terms of sensitivity within the range of 20 to 300 vehicles/km

^{2}. HMM-Optimized had higher sensitivity (7.2 to 7.55) compared to HMM-Mean and Density-Based that had (0.685–0.74) and (0.659–0.75), respectively, while with high-density values (400–5000 vehicles/km

^{2}) Lv’s scheme surpassed HMM-Mean with a relatively big sensitivity difference, and also surpassed HMM-Optimized with a slight sensitivity difference. However, the superiority of Lv’s scheme in terms of sensitivity came at the cost of its low precision which is obvious in Figure 11. Despite its good precision within low dense environments (20–70 vehicles/km

^{2}), its precision dropped dramatically in highly dense environments (200–5000 vehicles/km

^{2}). That is because it generated more FP alerts at high density regardless of other factors. HMM-Optimized, on the other hand, maintained a stable precision in different densities. HMM-Mean had medium precision, as it considered other factors but with equal weights. In Figure 12, the overall performance is presented: we can clearly observe the robustness of our proposed system compared to its counterparts.

## 7. Conclusions and Future Directions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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Symbol | Description |
---|---|

$\mathsf{\pi}$ | Initial state distribution |

$\mathit{A}$ | State transition probability matrix (transition matrix) |

${\mathit{a}}_{\mathit{i},\mathit{j}}$ | The probability of being in state $\mathit{i}$ at the time $\mathit{t}$ and transfer to state $\mathit{j}$ at the time $\mathit{t}\mathbf{+}\mathbf{1}$ |

$\mathit{B}$ | Observation probability matrix (emission matrix) |

${\mathit{b}}_{\mathit{i},\mathit{j}}$ | The probability of being in state $\mathit{j}$ during the observation ${\mathit{O}}_{\mathit{i}}$ |

$\mathit{\lambda}\left(\mathit{\pi},\mathit{A},\mathit{B}\right)$ | The HMM parameters |

$O=\left({\mathit{O}}_{0},{\mathit{O}}_{1}\dots {\mathit{O}}_{t}\right)$ | The observed sequence |

$\mathit{T}$ | Length of the observation sequence |

$\mathit{N}$ | Number of states in the model |

$\mathit{M}$ | Number of observations |

Factor | Value Range |
---|---|

States | Negligible, Low, Moderate, High, Very high, Deadly |

S | Very Slow, Slow, Medium, High, Very high, Extreme |

L | Safe, Normal, Dangerous, Deadly |

W | Clear, Sunny, Rainy, Foggy, Snowing |

V | Low, Medium, High |

D | Fresh, Medium, Tired |

Factor (Reason) | Weight (Percentage) |
---|---|

Vehicle speed (W_{S}) | 48.3% |

Location dangerous level (W_{L}) | 12.9% |

Weather conditions (W_{W}) | 18% |

Vehicle density (W_{V}) | 15.8% |

Driver fatigue (W_{D}) | 5% |

Parameters | Description |
---|---|

Network Simulator | Omnet++ 5 [30] |

Traffic Simulator | Sumo 0.27.1 [28] |

Map Information | Openstreetmap [27] |

Simulated Location | London |

Simulated area | 3X4 km |

Parameter | Value |
---|---|

PHY model | 802.11 p |

Channel frequency | 5.890e9 Hz |

Propagation model | Two ray model |

MAC model | EDCA |

Propagation distance | 150 m |

Maximum hop count | 10 |

Fading model | Jakes model rayleigh fading |

Shadowing model | LogNormal |

Antenna model | Omnidirectional |

Transmission power | 20 mW |

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**MDPI and ACS Style**

Aung, N.; Zhang, W.; Dhelim, S.; Ai, Y. Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments. *Information* **2018**, *9*, 311.
https://doi.org/10.3390/info9120311

**AMA Style**

Aung N, Zhang W, Dhelim S, Ai Y. Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments. *Information*. 2018; 9(12):311.
https://doi.org/10.3390/info9120311

**Chicago/Turabian Style**

Aung, Nyothiri, Weidong Zhang, Sahraoui Dhelim, and Yibo Ai. 2018. "Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments" *Information* 9, no. 12: 311.
https://doi.org/10.3390/info9120311