# Effect of Built Environment Factors on Pedestrian Safety in Portuguese Urban Areas
## 4 Results
### Modeling Temporal Disaggregated Crash Data  
In this section, the disaggregated data were used to construct the models, and therefore, the modeling technique used was GEE. The dependent variable is the total number of pedestrian crashes per year in each street, and the models considered the following three exposure variables:  
1. Model 1: LnVveh, in addition to the offset variable LnVped;
2. Model 2: LnSPV, in addition to the offset variable LnVped;
3. Model 3: LnPVQ, in addition to the offset variable LnVped;  
As shown in Table 4, Model 1 and Model 2 have the same significant variable set and corresponding parameter estimates. The exposure variables LnVveh and LnSPV were not statistically significant and, therefore, these models will be referred to as Model 1/2.  
The results obtained with models based on disaggregated data show many similarities with the previous model obtained with the aggregated data (Model 0). The variables  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Model 1 and Model 2**} & \multicolumn{3}{c}{**Model 3**} \\ \cline{3-8}  & & \multicolumn{1}{c}{**Parameter**} & \multicolumn{3}{c}{**Parameter Estimate**} & \\  & & **Estimate** & \multirow{2}{*}{**S.E.**} & \multirow{2}{*}{\(p\)-Value**} & \multicolumn{3}{c}{**Parameter Estimate**} & \\  & & & & & \(\beta\)**j** & \(\gamma\)** & \\ \hline Built & ROAD\_L & 0.0013 & 0.0003 & \textless{}0.0001 & 0.0013 & & 0.0003 & 0.0001 \\ environment & PARKS & \(-\)0.2722 & 0.1423 & 0.0558 & & & & \\  & NET\_C & & & & 0.4311 & 0.2475 & 0.0815 \\  & DIST & 0.0025 & 0.0003 & \textless{}0.0001 & 0.003 & & 0.0004 & \textless{}0.0001 \\  & SLOPE & 9.7511 & 2.3645 & \textless{}0.0001 & 6.9419 & & 2.5352 & 0.0062 \\ Road & WIDTH\_W & 0.5105 & 0.1817 & 0.0050 & 0.5329 & & 0.1657 & 0.0013 \\ infrastructure & DIRECTIONS & & & \(-\)0.456 & & 0.2528 & 0.0713 \\  & LnVveh & & & & & & \\ Exposure & LnSPV & & & & & & & \\  & LnPVQ & & & & & & & \\  & Intercept (\(\beta_{0}\)) & \(-\)9.0026 & 0.7090 & \textless{}0.0001 & \(-\)7.7187 & & 1.1768 & \textless{}0.0001 \\  & QIC & & 455.5955 & & & & 461.7280 & \\  & MAD & & 0.97362 & & & & 0.97769 & \\  & RMSE & & 1.25250 & & & & 1.24312 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: GEE models—disaggregated crash data (Models 1, 2 and 3).  
such as ROAD_L, DIST, SLOPE, and WIDTH_W show a negative impact on pedestrian safety. Similarly, the variables with a positive impact on pedestrian safety were PARKS and DIRECTIONS.  
The LnPVQ exposure variable was significant in Model 3, with a negative parameter. This suggests that high volumes of vehicular and pedestrian traffic have a positive impact on pedestrian safety. The explanation might be that in urban areas with intense movements of both vehicles and pedestrians, all road users proceed with a much higher level of attention and the vehicles are often obliged to slow down. There is a need for complementary studies to confirm this, but it cannot be discarded a priori, especially given the fact that there is no consensus in the literature regarding the influence of vehicular traffic on crash rates. Indeed, some authors like [PERSON] and [PERSON] [42] state that greater traffic volumes are associated with a reduction in crash rates, whereas others like [PERSON] et al. [9] report just the opposite.