# Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data

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## Abstract

**:**

## 1. Introduction

## 2. Previous Work

## 3. Visibility-Based Assessment

#### 3.1. LiDAR Data

#### 3.2. Extraction of Vehicle Trajectory

#### 3.3. Voxelization of LiDAR Point Cloud

#### 3.4. Visual Field Assessment and Visibility Analysis

## 4. Safety-Based Assessment

#### 4.1. Background

#### 4.2. Beta-Binomial (BB) Collision Regression Model

_{i}is defined as the total collision number at location i during a specific time; x

_{i}is the collision number of a specific collision pattern under investigation at location i from the n

_{i}collisions; $\tilde{P}$

_{i}is the ratio of specific collision pattern x

_{i}to n

_{i}as a random variable; $\overline{P}$ is the mean value for $\tilde{P}$; f (p) is the prior distribution with function p in the reference group.

## 5. Results and Discussions

#### 5.1. Visibility Assessment for Un-Signalized Intersection Using a Passenger Vehicle

#### 5.2. Visibility Assessment for Un-Signalized Intersection Using a Heavy Truck

#### 5.3. Impacts of Voxel Size on the Extraction Results

#### 5.4. Beta-Binomial (BB) Regression Model

_{r}value is lower than the 0.05 significance level. For the goodness-of-fit model assessment, Maximum Log-Likelihood Estimation was used. Pearson’s chi-squared measure, shown in the model as the “Pearson Statistic,” must be compared with the tabulated chi-squared value. Because one coefficient is considered in the model and has 26 site observations, the degree of freedom (DF) is equal to 25. Therefore, the tabulated chi-squared distribution value is equal to 14.611, which is greater than the Pearson Statistics output of the regression model. Thus, the model regression coefficients and the goodness of fit are found to be statistically significant.

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 6.**Different Voxel Sizes in Space (

**a**) voxel size 0.1 m; (

**b**) Voxel Size 0.15 m; (

**c**) Voxel Size 0.2 m.

**Figure 9.**Visibility Map (Intersection 85 Ave and 100 St) (

**a**) left sight triangle blockage percentage 63%; (

**b**) right sight triangle blockage percentage 71%.

**Figure 12.**Visibility Map (Intersection 84 Ave and 105 St) (

**a**) left sight triangle blockage percentage 27%; (

**b**) right sight triangle blockage percentage 40%.

**Figure 14.**Visibility Map (Intersection 85 Ave and 101 St) (

**a**) left sight triangle blockage percentage 70%; (

**b**) right sight triangle blockage percentage 77%.

**Figure 15.**Visibility Map (Intersection 84 Ave and 105 St) (

**a**) left sight triangle blockage percentage 20%; (

**b**) right sight triangle blockage percentage 26%.

**Figure 16.**Available Visible Distance Estimated at Different Voxel Size (Intersection 84 Ave and 105 St) (

**a**) 0.1 m voxel size; (

**b**) 0.15 m voxel size; (

**c**) 0.2 m voxel size.

**Table 1.**Intersection Sight Distance (ISD) [3].

US Customary | Metric |
---|---|

$ISD=1.47\text{}{v}_{major}\text{}{t}_{g}$ | $ISD=0.278\text{}{v}_{major}\text{}{t}_{g}$ |

ISD = intersection sight distance (length of the leg of sight triangle along the major road) (ft) | ISD = intersection sight distance (length of the leg of sight triangle along the major road) (m) |

V_{major} = design speed of major road (mph) | V_{major} = design speed of major road (km/h) |

t_{g} = time gap for a minor road vehicle to enter the major road (s) | t_{g} = time gap for a minor road vehicle to enter the major road (s) |

Sight Triangle | Short Leg (m) | Long Leg (m) | |||||
---|---|---|---|---|---|---|---|

Intersection | Major Speed | Minor Speed | Sign | Approach | Left Triangle | Right Triangle | Left & Right Triangles |

(km/h) | (km/h) | ||||||

85 Ave & 101 St | 30 | 30 | Yield | East | 25 | 28.5 | 84 |

84 Ave & 105 St | 50 | 50 | Stop | East | 7.2 | 10.5 | 139 |

Effect | Estimate | Standard Error | z-Value | $\mathbf{P}{\mathbf{r}}_{\text{}}\text{}\left|\mathit{z}\right|$ |
---|---|---|---|---|

Intercept | −2.5822 | 1.2487 | −2.07 | 0.0387 |

B | 2.398 | 1.123 | 2.14 | 0.0327 |

Fit Statistics | −2 Log-Likelihood | Pearson Statistics |
---|---|---|

Value | 23.5001 | 8.0387 |

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**MDPI and ACS Style**

Kilani, O.; Gouda, M.; Weiß, J.; El-Basyouny, K.
Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data. *Sustainability* **2021**, *13*, 9259.
https://doi.org/10.3390/su13169259

**AMA Style**

Kilani O, Gouda M, Weiß J, El-Basyouny K.
Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data. *Sustainability*. 2021; 13(16):9259.
https://doi.org/10.3390/su13169259

**Chicago/Turabian Style**

Kilani, Omar, Maged Gouda, Jonas Weiß, and Karim El-Basyouny.
2021. "Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data" *Sustainability* 13, no. 16: 9259.
https://doi.org/10.3390/su13169259