# Impact of Pavement Surface Condition on Roadway Departure Crash Risk in Iowa

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## Abstract

**:**

## 1. Introduction

## 2. Data Sources

#### 2.1. Pavement Condition Data

#### 2.2. Crash Records

## 3. Methods

#### 3.1. Crash Rate Models

#### 3.2. Risk Analysis

## 4. Results and Discussion

#### 4.1. Visual Diagnostics

#### 4.2. Crash Rate Models

#### 4.3. Risk Analysis Results

## 5. Conclusions

- Crash records are sparse in nature and require carful modeling using discrete count models, such as the negative binomial repression model.
- For the general crash model, which included all crash events under various severities and environmental surface conditions, higher skid resistance reduced the roadway departure crash rates. This impact is amplified on segments with higher speed limits, which reflects the importance of adequate skid resistance on higher speed segments.
- For the general model, rougher road segments had slightly higher crash rates. This impact is amplified on segments with higher speed limits.
- Higher skid resistance reduced the crash rates for both wet and dry crashes.
- Exploring the distribution of the count number of crashes for a given section, provides extra information that cannot be captured by examining the average crash rates solely.
- The risk analysis of traffic crashes provides a helpful tool for estimating the expected consequences in terms of human injury, social impact, and monetary value.
- There is a need for further investigation to understand the impact of weather condition on traffic crashes, and the interaction between the pavement surface condition and weather conditions.

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**A screenshot of the test output from a locked wheel trailer showing SN, rolling test tire speed, vehicle speed, and other test variables.

**Figure 2.**The histogram for the pavement condition including (

**a**) SN; (

**b**) IRI; and (

**c**) RD; for the segments included in the study.

**Figure 3.**The histogram of (

**a**) the severity levels for the roadway departure crashes and (

**b**) the weather-related surface condition during the crash events.

**Figure 4.**The empirical trends between the average number of crashes and (

**a**) The traffic volume expressed in AADT; and (

**b**) the speed limit for all segments in the study.

**Figure 5.**The empirical trends between the average number of crashes and (

**a**) SN; (

**b**) IRI; and (

**c**) RD.

**Figure 6.**Expected crash rates as a function of SN, AADT, and SL; as explained by the NB regression models.

**Figure 7.**Expected crash rates as a function of SN, AADT, and SL; as explained by the NB regression models for (

**a**) dry and (

**b**) wet environmental surface conditions.

**Figure 8.**Probability mass function using fitted general model parameters for varying SN; (

**a**) 20,000 AADT and (

**b**) 40,000 AADT; and fixed IRI and SL.

**Figure 9.**Probability of having at least one crash as a function of SN and varying AADT for the fitted general model.

**Figure 10.**Probability mass function under varying SN and fixed AADT for (

**a**) dry and (

**b**) wet fitted models.

**Figure 11.**Probability of having (

**a**) one crash a year with various severity levels and (

**b**) two crashes a year with various severity combinations.

Model Name | NB Regression Form | Explanatory Variables Statistical Summary | ||||
---|---|---|---|---|---|---|

Term | Estimate ^{2} | Std. Error | Wald Chi-Square | p-Value | ||

General Model | $\begin{array}{c}{\mu}_{i}={\beta}_{0}\times AAD{T}^{{\beta}_{1}}\times \dots {e}^{SN\left({\beta}_{2}+{\beta}_{4}SL\right)+IRI\left({\beta}_{3}+{\beta}_{5}SL\right)}\\ \alpha =0.74\end{array}$ | ${\beta}_{0}$ | 2.67 × 10^{−3} | 0.225 | 696.93 | <0.01 |

${\beta}_{1}$ | 6.22 × 10^{−1} | 0.019 | 1103.51 | <0.01 | ||

${\beta}_{2}$ | −1.29 × 10^{−2} | 0.002 | 43.62 | <0.01 | ||

${\beta}_{3}$ | 1.57 × 10^{−3} | <0.001 | 19.34 | <0.01 | ||

${\beta}_{4}$ | −5.06 × 10^{−4} | <0.001 | 5.32 | 0.02 | ||

${\beta}_{5}$ | 1.44 × 10^{−4} | <0.001 | 14.64 | <0.01 | ||

Dry Condition ^{1} | $\begin{array}{c}{\mu}_{i}={\beta}_{0}\times AAD{T}^{{\beta}_{1}}\times {e}^{{\beta}_{2}SN}\\ \alpha =0.69\end{array}$ | ${\beta}_{0}$ | 2.67 × 10^{−3} | 0.593 | 99.87 | <0.01 |

${\beta}_{1}$ | 5.83 × 10^{−1} | 0.047 | 153.45 | <0.01 | ||

${\beta}_{2}$ | −1.45 × 10^{−2} | 0.005 | 8.40 | <0.01 | ||

Wet Condition ^{1} | $\begin{array}{c}{\mu}_{i}={\beta}_{0}\times AAD{T}^{{\beta}_{1}}\times {e}^{{\beta}_{2}SN}\\ \alpha =3.60\end{array}$ | ${\beta}_{0}$ | 9.18 × 10^{−3} | 1.497 | 9.82 | <0.01 |

${\beta}_{1}$ | 4.41 × 10^{−1} | 0.122 | 12.99 | <0.01 | ||

${\beta}_{2}$ | −3.66 × 10^{−2} | 0.013 | 7.56 | <0.01 |

^{1}The model was derived using the shifted NB distribution;

^{2}Std. Error: the standard error value of the fitted parameter β

_{j}.

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**MDPI and ACS Style**

Alhasan, A.; Nlenanya, I.; Smadi, O.; MacKenzie, C.A.
Impact of Pavement Surface Condition on Roadway Departure Crash Risk in Iowa. *Infrastructures* **2018**, *3*, 14.
https://doi.org/10.3390/infrastructures3020014

**AMA Style**

Alhasan A, Nlenanya I, Smadi O, MacKenzie CA.
Impact of Pavement Surface Condition on Roadway Departure Crash Risk in Iowa. *Infrastructures*. 2018; 3(2):14.
https://doi.org/10.3390/infrastructures3020014

**Chicago/Turabian Style**

Alhasan, Ahmad, Inya Nlenanya, Omar Smadi, and Cameron A. MacKenzie.
2018. "Impact of Pavement Surface Condition on Roadway Departure Crash Risk in Iowa" *Infrastructures* 3, no. 2: 14.
https://doi.org/10.3390/infrastructures3020014