# Smart Pedestrian Crossing Management at Traffic Light Junctions through a Fuzzy-Based Approach
## 2 The Analyzed Scenario  
The proposed smart solution is applied on a signalized road intersection located in the urban area of Enna, an Italian town (Figure 1). In particular, the investigated scenario is associated with a four-arm intersection. It is composed of the main road (namely Viale Diaz), represented in Figure 1, with Arms 1 and 3, both two-way and one-lane roads, and a secondary one (namely Via Liberta), indicated in Figure 1 with Arms 2 and 4. In this case, Arm 2 is a two-lane one-way road, while the Arm 4 a two-way road with one lane in each direction. Several business activities are located around this area, such as bars and offices, overlooking Arms 2 and 3, while on the left side of the corner between Arms 1 and 4, there is a primary school (\"De Amicis\"). Other businesses, such as banks, wine bars and restaurants, are closed to the intersection, causing pedestrian movements. The peak of traffic flows, concerning vehicles and pedestrians, is associated with the entrance and exit times of business activities, mainly referring to schools and offices. Figure 2 shows the trend of traffic and pedestrian peak flows during a weekday. For instance, it is possible to note that the increase in vehicles is the highest on Tuesdays due to the open market (i.e., the 5 th, 9 th and 12 th monitoring days). It is valuable to estimate the percentage of vehicles and pedestrians that are involved in the various arms. Data shown in Table 1 are based on about 1000 veh/h, where each percentage of vehicles is related to the maneuvers on the investigated road intersection (Figure 3). The same analysis can be carried out regarding the pedestrians. In fact, the values in Table 2 refer to about 250 pedestrians/hour distributed in the two directions for each arm, considering the pedestrian flows during the peak hours (Figure 4). Figure 1: The analyzed signalized road intersection located in the urban area of Enna. It is useful to note that in real case studies [23], it emerged that 10-13% of pedestrians do not take into account the red interval time of traffic lights. Furthermore, the variability of traffic light settings \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Arm 1** & **Arm 2** & **Arm 3** & **Arm 4** \\ \hline
**Arm 1** & 0\% & 0\% & 40\% & 60\% \\
**Arm 2** & 60\% & 0\% & 14\% & 26\% \\
**Arm 3** & 60\% & 0\% & 0\% & 40\% \\
**Arm 4** & 30\% & 0\% & 70\% & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Vehicle percentage in the various arms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Arm 1** & **Arm 2** & **Arm 3** & **Arm 4** \\ \hline
**Arm 1** & 0\% & 20\% & 50\% & 30\% \\
**Arm 2** & 30\% & 0\% & 20\% & 50\% \\
**Arm 3** & 50\% & 30\% & 0\% & 20\% \\
**Arm 4** & 30\% & 0\% & 50\% & 20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pedestrian percentage in the various arms. Figure 3: Maneuvers on the investigated road intersection: blue = crossing; red = diverging; yellow = merging. Figure 2: The peak of traffic flows: (**a**) vehicles; (**b**) pedestrians. could produce an increased risk for pedestrians who are crossing on average with a reduced time to collision. The age groups that are subject to the higher risk are 15-30, and females behave more aggressively than males. Considering also other findings on the implication of Red Light Running (RLR) proneness when adaptive traffic light operations are employed [32], it is clear that while the daily variability of traffic light settings improves the disposal of vehicles, on the other hand, it has a detrimental effect on pedestrian safety. The variability of vehicle and pedestrian flows is related to the activities that take place in the areas around the traffic light junction throughout the day. In fact, some flows exceed 250 ped/h and 1000 veh/h in peak hours, while there are values of about 1/3 in other hours of the day. This variability is most noticed during the weekend.