# Predicting Pedestrian Crashes in Texas' Intersections and Midblock Segments
###### Abstract
[10] analyzed two years of crash information from Michigan and found that the average midblock pedestrian crash took place while pedestrians were crossing 130 feet from the nearest intersection/crosswalk. Key predictors of more pedestrian deaths in these settings were a lack of lighting during a nighttime crash, crashes involving an older pedestrian, and crashes along corridors that carry higher traffic volumes. [PERSON] and [PERSON] [11] developed a Poisson regression using 21 midblock crosswalks and found that pedestrian crash counts rise with the presence of busways and bus stops, road widths, more traffic lanes, and higher volumes of pedestrians and vehicles. [PERSON] et al. [12] and [PERSON] et al. [13] used Texas pedestrian crashes summarized in 700,000+ midblock segments using Texas roadway inventory geometry. However, the authors did not divide the crashes into midblock and intersections, and instead combined all of them at a segment level. Analyses at both intersections and midblock segments for the same locations are limited and tend to focus on small areas [14] or are based on comparisons of crash characteristics [15]. Table 1 summarizes previous research in the field and provides a description of the geometric aggregation used in the studies. As described previously, the use of large samples is often limited due to the lack of information at the network level (e.g., at the state level). Therefore, the contribution of this research is relevant to the field because it provides analysis across 600,000+ intersections and 500,000+ midblock segments comprising the entire network of the state of Texas. This geometry preserves roadway inventory characteristics, such as traffic control variables (signalized intersections), highway design details, traffic attributes, and land use information. To the authors' knowledge, no published work has yet developed crash count models for both intersections and midblock segments across thousands of locations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Intersections** & **Midblock Segments** \\ \hline [PERSON] et al. [7] & 262 & None \\ [PERSON] and [PERSON] [8] & 176 & None \\ [PERSON] and [PERSON] [11] & None & 21 \\ [PERSON] et al. [14] & 31 & 25 \\ [PERSON] et al. [13] & None & 700,000+ \\ [PERSON] et al. [12] & None & 700,000+ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample sizes used in past pedestrian crash studies. The main goal of this research is to develop a large-scale micro-level analysis of pedestrian crashes at intersections and midblock segments using historical pedestrian crash information from police reports in Texas. To this end, a three-pronged analytical framework is developed, including:  
1. A geometry estimation step where data from multiple sources are used to estimate the geometric description and characteristics, such as traffic control variables (signalized intersections), highway design details, traffic attributes, and land use information of intersections and uniform roadway segments in the state of Texas. 2. A data processing step where pedestrian crash counts are obtained from over ten years of Texas police reports. The geographic location and crash report information are used to classify them as midblock or intersection crashes and to map them to the estimated geometry. Figure 1: Description of pedestrian crashes at intersections and midblock [16]. (**a**) Number of pedestrian crashes in the state Texas; (**b**) Number of pedestrian crashes in the city of Austin; (**c**) Intersection crashes severity levels; (**d**) Midblock crashes severity levels. 3. A modeling step where crash counts are used to develop predictive models at both intersection and roadway segment levels, which allows micro-level estimates to be used in studies, such as BCAs, at a large scale. Information from multiple sources is used to obtain a wide variety of variables for the model, including the use of WMT estimated at the state-wide level.