## 4 Experiments
### From Cyberspace to Physical Space: Flexible Verification  
Although we used a machine-learning method to learn and simulate the relationship between adverse weather topic heat and traffic incidents in social media, we need to answer an additional question to demonstrate the validity of the proposed city-level traffic awareness and alerting model: Does this model reflect real-world situations? In other words, if we detect and track one \"hot traffic incident\" in social media, does this event exist in real life? \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rank** & **Forecasting Event** & \begin{tabular}{c} **Forecasting** \\ **Heat Value** \\ \end{tabular} & **Rank** & **Labeled Event** & \
\begin{tabular}{c} **Labeled** \\ **Heat Value** \\ \end{tabular} &
\begin{tabular}{c} **Labeled Traffic** \\ **Situation** \\ \end{tabular} \\ \hline
1 & \begin{tabular}{c} Shijiazhuang \\ 21 July 12: 00–18: 00 \\ \end{tabular} & 120.13 & 1 & \
\begin{tabular}{c} Shijiazhuang \\ 21 July 12: 00–18: 00 \\ \end{tabular} & 497.0 &
\begin{tabular}{c} traffic interruption, \\ flight delay \\ \end{tabular} \\ \hline
2 & \begin{tabular}{c} Kunming \\ 20 July 6: 00–12: 00 \\ \end{tabular} & 113.61 & 2 & \
\begin{tabular}{c} Kunming \\ 20 July 6: 00–12: 00 \\ \end{tabular} & 224.0 &
\begin{tabular}{c} traffic interruption, \\ highway \\ interruption \\ \end{tabular} \\ \hline
3 & \
\begin{tabular}{c} Beijing \\ 28 July 6: 00–12: 00 \\ \end{tabular} & 45.98 & 3 &
\begin{tabular}{c} Beijing \\ 28 July 6: 00–12: 00 \\ \end{tabular} & 63.0 & traffic congestion \\ \hline
4 & \
\begin{tabular}{c} Chengdu \\ 28 July 6: 00–12: 00 \\ \end{tabular} & 32.77 & 4 &
\begin{tabular}{c} Guangzhou \\ 21 July 18: 00–24: 00 \\ \end{tabular} & 60.0 & road closure \\ \hline
5 & \
\begin{tabular}{c} Shanghai \\ 27 July 6: 00–12: 00 \\ \end{tabular} & 29.24 & 5 &
\begin{tabular}{c} Changsha \\ 25 July 6: 00–12: 00 \\ \end{tabular} & 44.0 & road closure \\ \hline
6 & \
\begin{tabular}{c} Taiyuan \\ 24 July 6: 00–12: 00 \\ \end{tabular} & 26.37 & 6 &
\begin{tabular}{c} Chengdu \\ 28 July 6: 00–12: 00 \\ \end{tabular} & 34.0 & flight delay \\ \hline
7 & \
\begin{tabular}{c} Beijing \\ 28 July 12: 00–18: 00 \\ \end{tabular} & 24.62 & 7 &
\begin{tabular}{c} Shanghai \\ 27 July 6: 00–12: 00 \\ \end{tabular} & 30.0 & traffic congestion \\ \hline
8 & \
\begin{tabular}{c} Changsha \\ 25 July 6: 00–12: 00 \\ \end{tabular} & 23.91 & 8 &
\begin{tabular}{c} Beijing \\ 28 July 12: 00–18: 00 \\ \end{tabular} & 30.0 & traffic congestion \\ \hline
9 & \
\begin{tabular}{c} Shanghai \\ 24 July 6: 00–12: 00 \\ \end{tabular} & 11.80 & 9 &
\begin{tabular}{c} Taiyuan \\ 24 July 6: 00–12: 00 \\ \end{tabular} & 20.0 & traffic interruption \\ \hline
10 & \
\begin{tabular}{c} Haikou \\ 24 July 12: 00–18: 00 \\ \end{tabular} & 7.80 & 10 &
\begin{tabular}{c} Haikou \\ 24 July 12: 00–18: 00 \\ \end{tabular} & 18.0 & traffic control \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of top 10 social traffic events in forecasting and labeled traffic situation. Figure 4: Comparison of (**a**) alerting warning level distribution and (**b**) labeled warning level distribution. Owing to the objectivity and authenticity of news reports, we regarded traffic news reports as objective measurements that indicate real-world hot traffic incidents. Specifically, we assume that all traffic hot incidents are reported in the news. Therefore, we deployed a web crawler to collect the news report data about traffic incidents that took place in Beijing, China between 1 January and 31 July 2017 as a verification dataset. Next, we use the regression models to forecast the heat value of traffic incidents in Beijing. For better visualization, we divided the whole validation time period into time blocks, with each time block containing six hours. We draw all time blocks whose forecasted heat value is larger than zero (i.e., traffic incidents) as in Figure 5. Note that H represents the forecasted heat of each time block.