# Detecting Traffic Incidents Using Persistence Diagrams
## 4 Results
### Data without Sensor Locations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Incident Timestamp** & **Percentile by Avg** & **Percentile by SD** & **Percentile by Median** \\ \hline
06-Feb-2017 11:42 & 0.875 & 0.89583 & 0.95833 \\
12-Jun-2017 16:47 & 1 & 1 & 0.91667 \\
26-Sep-2017 00:17 & 0.89831 & 0.72881 & 0.83051 \\
05-Sep-2017 01:45 & 0.86441 & 0.86441 & 0.64407 \\
30-May-2017 05:51 & 1 & 0.97222 & 0.94444 \\
18-Jul-2017 12:08 & 0.86458 & 0.91667 & 0.875 \\
05-Sep-2017 16:56 & 1 & 1 & 1 \\
26-Jul-2017 05:23 & 0.80556 & 0.80556 & 1 \\
22-Mar-2017 06:31 & 0.97222 & 1 & 1 \\
05-Apr-2017 10:45 & 0.97917 & 0.72917 & 0.94792 \\
12-Jan-2017 11:05 & 0.79167 & 0.82292 & 0.78125 \\
01-Dec-2017 08:30:00 & 1 & 1 & 0.95833 \\
05-May-2017 09:21 & 1 & 0.90625 & 1 \\
10-Feb-2017 12:44 & 1 & 1 & 1 \\
28-Oct-2017 16:14 & 0.5 & 0.5 & 0.83333 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Thresholds used for classifying windows by percentile score in phase 1. \({}^{\star}\) indicates the largest threshold, \(\tau_{1}\) \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Precision** & **Recall** & **F-Score** \\ \hline S312425 & 0.00022 & 0.17143 & 0.00043 \\ S312520 & 0.00022 & 0.23810 & 0.00043 \\ S312694 & 0.00076 & 0.90476 & 0.00152 \\ S312942 & 0.00030 & 0.32 & 0.00060 \\ S314147 & 0.00218 & 0.56122 & 0.00435 \\ S315017 & 0.00217 & 0.78125 & 0.00434 \\ S315938 & 0.00078 & 0.35593 & 0.00155 \\ S317814 & 0.00184 & 0.88889 & 0.00376 \\ S318180 & 0.00078 & 0.43478 & 0.00156 \\ S318566 & 0 & 0 & NaN \\ _Total_ & 0.00088 & 0.50116 & 0.00175 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Quality of fit for the Phase 1-Train data using standard normal deviates. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Precision** & **Recall** & **F-Score** \\ \hline S312425 & 0.00116 & 0.62 & 0.00232 \\ S312527 & 0.00217 & 0.61856 & 0.00432 \\ S312694 & 0.00079 & 0.81481 & 0.00157 \\ S312771 & 0.00133 & 0.57813 & 0.00264 \\ S313172 & 0 & 0 & NA \\ S314147 & 0.00192 & 0.76056 & 0.00383 \\ S314899 & 0.00130 & 0.70833 & 0.00259 \\ S314982 & 0.00168 & 1 & 0.00335 \\ S315017 & 0.00081 & 0.4 & 0.00162 \\ S318721 & 0.00076 & 0.95652 & 0.00153 \\ S3188859 & 0.00049 & 0.60870 & 0.00098 \\ _Total_ & 0.00112 & 0.62706 & 0.00223 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Quality of fit for the Phase 1-Test data based on the thresholds determined by the Phase 1-Train incidents using Algorithm 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Precision** & **Recall** & **F-Score** \\ \hline S312425 & 0.00100 & 0.56 & 0.00201 \\ S312527 & 0.00189 & 0.46392 & 0.00377 \\ S312694 & 0.00056 & 0.51852 & 0.00112 \\ S312771 & 0.00117 & 0.42188 & 0.00234 \\ S313172 & 0.00125 & 0.76923 & 00250 \\ S314147 & 0.00179 & 0.63380 & 0.00356 \\ S314899 & 0.00130 & 0.75 & 0.00259 \\ S314982 & 0.00175 & 0.97778 & 0.00350 \\ S315017 & 0.00209 & 0.8 & 0.00417 \\ S318721 & 0.00036 & 0.39130 & 0.00072 \\ S3188859 & 0.00044 & 0.60609 & 0.00245 \\ _Total_ & 0.00123 & 0.61609 & 0.00245 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Quality of fit for the Phase 1-Test data using standard normal deviates.