# Predicting Pedestrian Crashes in Texas' Intersections and Midblock Segments
## 5 Results and Discussion  
The results from the NB model are summarized in Table 4 (Texas) and Table 5 (Austin). Two models are estimated for each location; an intersection model and midblock-level model are described, along with sensitivity analysis summarized in Figures 5 and 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Intersections**} & \multicolumn{3}{c}{**Midblock Segments**} \\ \cline{2-7}  & **Coeff.** & **Std. Error** & \(p\)**-Value** & **Coeff.** & **Std. Error** & \(p\)**-Value** \\ \hline (Intercept) & \(-8.694\) & \(0.216\) & \(0.000\) & \(-8.035\) & \(0.098\) & \(0.000\) \\ Walking density (log WMT/sq mi) & \(0.335\) & \(0.013\) & \(0.000\) & \(0.305\) & \(0.007\) & \(0.000\) \\ Signalized intersection (ind.) & \(1.426\) & \(0.032\) & \(0.000\) & & & \\ Number of approaches & \(0.398\) & \(0.019\) & \(0.000\) & & & \\ Intersections crossed & & & & \(0.093\) & \(0.002\) & \(0.000\) \\ \hline DVMT (log) (major) & \(0.195\) & \(0.008\) & \(0.000\) & \(0.522\) & \(0.006\) & \(0.000\) \\ Speed limit (mph) (major) & \(-0.020\) & \(0.002\) & \(0.000\) & \(-0.013\) & \(0.001\) & \(0.000\) \\ Number of lanes (major) & \(0.132\) & \(0.012\) & \(0.000\) & \(0.217\) & \(0.010\) & \(0.000\) \\ Lane width (ft) (major) & \(0.033\) & \(0.004\) & \(0.000\) & \(0.041\) & \(0.003\) & \(0.000\) \\ Median width (ft) (major) & \(-0.006\) & \(0.001\) & \(0.000\) & \(-0.014\) & \(0.001\) & \(0.000\) \\ One-way road (ind.) (major) & \(0.095\) & \(0.052\) & \(0.068\) & \(-0.906\) & \(0.048\) & \(0.000\) \\ \hline DVMT (log) (minor) & \(0.136\) & \(0.008\) & \(0.000\) & & & \\ Speed limit (mph) (minor) & \(-0.021\) & \(0.002\) & \(0.000\) & & & \\ Number of lanes (minor) & \(-0.004\) & \(0.018\) & \(0.842\) & & & \\ Lane width (ft) (minor) & \(0.040\) & \(0.005\) & \(0.000\) & & & \\ Median width (ft) (minor) & \(-0.027\) & \(0.005\) & \(0.000\) & & & \\ One-way road (ind.) (minor) & \(-0.211\) & \(0.063\) & \(0.000\) & & & \\ \hline AADT per lane (major) & \(1.76\times 10^{-5}\) & \(4.53\times 10^{-6}\) & \(0.000\) & \(-7.67\times 10^{-5}\) & \(4.19\times 10^{-6}\) & \(0.000\) \\ Truck percentage (major) & \(0.020\) & \(0.003\) & \(0.000\) & \(0.003\) & \(0.002\) & \(0.100\) \\ Arterial (ind.) (major) & \(0.444\) & \(0.037\) & \(0.000\) & \(0.198\) & \(0.028\) & \(0.000\) \\ On system roadway (ind.) & \(-0.230\) & \(0.036\) & \(0.000\) & \(0.209\) & \(0.028\) & \(0.000\) \\ \hline Rural (ind.) & \(-0.107\) & \(0.087\) & \(0.218\) & \(-0.339\) & \(0.041\) & \(0.000\) \\ Small urban (ind.) & \(-0.108\) & \(0.055\) & \(0.050\) & \(0.049\) & \(0.034\) & \(0.154\) \\ Large urbanized (ind.) & \(0.171\) & \(0.037\) & \(0.000\) & \(0.170\) & \(0.025\) & \(0.000\) \\ Distance to nearest hospital (mi) & \(-0.023\) & \(0.006\) & \(0.000\) & \(-0.009\) & \(0.003\) & \(0.002\) \\ Transit stops (ind.) & \(0.525\) & \(0.047\) & \(0.000\) & \(0.526\) & \(0.033\) & \(0.000\) \\ Number of transit stops & \(0.042\) & \(0.008\) & \(0.000\) & \(0.049\) & \(0.004\) & \(0.000\) \\ City of Austin (ind.) & \(0.327\) & \(0.047\) & \(0.000\) & \(-0.392\) & \(0.042\) & \(0.000\) \\ \hline
**No. of observations** & **699.954** & & & **574.910** & & \\
**Dispersion Parameter (\(p\)):** & \(0.393\) & & & \(0.575\) & & \\
**McFadden’s R2:** & \(0.483\) & & & \(0.543\) & & \\
**2 \(\times\) log-likelihood** & \(-86\),\(105\) & & & \(-161\),\(539\) & & \\ \hline \end{tabular} Note: DVMT = daily vehicles miles traveled, WMT = walk miles traveled, AADT = annual average daily traffic.  
\end{table} 
Table 4: Estimation results of NB for pedestrian crashes, Texas. The Texas model (Table 4) shows a positive correlation between the walking density (WMT/area) and the number of pedestrian crashes across intersections and midblock-segment models, likely due to increased exposure levels. However, previous research also found that the relationship between crash exposure and crash rates is non-linear.