# Research on the Impact of Flexible Working Hours on Reducing Traffic Delays in the City
## 3 Results and Discussion
### Outputs of the Simulation on the Traffic Network for the Investigated Variant 1  
Figure 7 shows the change in traffic intensity on the modeled network (a 10% change in intensity). The figure illustrates that Variant 1 has a temporal extension of the traffic peak and a reduction in extreme intensity values during the peak period.  
As shown in Table 3, the total delay time from 7:15 to 8:15 is shorter (by 14.05%), but the delay time from 8:15 to 9:15 is longer (by 18.29% compared to the current state). However, the total delay time is shorter. In the first half of the day, this results in a reduction of 0.19 h, and in the second half of the day, a reduction of 0.64 h. Therefore, from an overall perspective, this represents only a slight reduction in total delay time. These results suggest that the temporal distribution of the traffic peak in the studied period has a relatively significant impact on traffic during the peak period. However, the overall reduction in travel time is minimal.  
In Figure 8, the reduction in total time during the traffic peak from 7:15 to 8:15 and the increase in delay times from 8:15 to 9:15 can be observed. Outside of these periods, the differences in delay times are minimal. From this, it can be inferred that if 10% of vehicles are shifted from the MPH to the period from 8:15 to 9:15, the deterioration in the traffic situation will only occur during this period and will not create congestion that would affect traffic beyond this period. It is also evident that total delay times will be longer in the period from 8:15 to 9:15.  
Figure 7: Comparison of intensity change between Variant 1 (10%) and Variant 0 (current state). Source: processed by authors.  
The following graphs show the difference in delay times on the modeled network for individual vehicles. The graphs represent the percentage of vehicles that fall into 5-s intervals according to their total delay time on the traffic network. In Figure 9, it can be seen that with a 10% shift in start time, the number of vehicles delayed on the network from 50 to 60 s decreases, and the number of vehicles delayed on the network for 40 s/km increases.  
A similar trend can be observed in the graphs showing vehicles in the morning and afternoon (Figure 10). Although simulations indicate more vehicles with reduced delay times, the maximum delay values increase by approximately 4 s (from 60.58 to 64.65 s/km). These maximum values will be reached in the morning hours. In the afternoon, the maximum delay time will also increase by approximately 3 s (from 57.7 to 60.45 s/km). These extreme delay times were simulated for only about 400 vehicles-approximately 1% of vehicles will experience longer delay times than the maximum delay times currently observed.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Time Period** & **Variant 0** & **Variant 1** & **Absolute** & **Relative Change** \\  & **(h)** & **(h)** & **Change (h)** & **(\%)** \\ \hline
5:30–18:30 & 488 & 487 & \(-\)0.83 & \(-\)0.17\% \\
5:30–12:00 & 239 & 238 & \(-\)0.19 & \(-\)0.08\% \\
12:00–18:30 & 249 & 248 & \(-\)0.64 & \(-\)0.26\% \\
7:15–8:15 & 50 & 43 & \(-\)7.04 & \(-\)14.05\% \\
8:15–9:15 & 39 & 46 & +7.12 & 18.29\% \\
14:00–16:15 & 111 & 102 & \(-\)9.78 & \(-\)8.79\% \\
16:15–17:15 & 33 & 39 & +6.75 & 20.63\% \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of total delay time between the current state and Variant 1. Source: processed by authors.  
Figure 8: Comparison of total delay time for Variant 1 (in 15-min intervals). Source: processed by authors.