## 5 Simulation Results
### Effects on Other Road Users  
Changing the traffic light timings from a fixed cycle to a dynamic cycle means that the duration of the red and green phases is no longer predictable or balanced, as it is actively determined by the traffic dynamics. Figure 5 shows a snapshot of 15 min of traffic routing with both strategies, which illustrates how the cycle timings generated by the smart routing algorithm can be irregular and unbalanced.  
This variability and unpredictability could affect other users who share the same infrastructure, such as pedestrians. There are two crosswalks at the intersection, concurrent  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Profile**} & **P-Average Speed** & **S-Average Speed** & **P-Average** & **S-Average** & **P-Total CO2** & **S-Total CO2** \\  & **(km/h)** & **(km/h)** & **Waiting Time (s)** & **Waiting Time (s)** & **Emissions (kg)** & **Emissions (kg)** \\ \hline \multirow{2}{*}{Weekday} & \multirow{2}{*}{17.6} & 30.3 & 25.0 & 7.4 & \multirow{2}{*}{1125} & 744 \\  & & (+72\%) & & (\(-70\%\)) & & (\(-34\%\)) \\ \hline \multirow{2}{*}{Weekend} & \multirow{2}{*}{18.3} & 35.3 & 2.9 & \multirow{2}{*}{895} & 533 \\  & & (+93\%) & & (\(-88\%\)) & & (\(-40\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results for a whole day.  
Figure 5: A snapshot of the cycle timing differences between passive and smart routing.  
with each traffic flow, and pedestrians can only use them when the traffic flow is stopped, so they are affected by the frequency and duration of the red phase of the respective traffic flow.  
To investigate the effects of the smart routing algorithm on pedestrians, the cycle times were recorded using the normal traffic profile, and two indicators were calculated: the average waiting time until pedestrians can use the crosswalk (assuming that pedestrians arrive at the crosswalk at arbitrary times) and the average time window to cross it. The results are shown in Table 8; the passive routing and the smart routing are identified in the titles by the P and S prefixes, respectively.  
As can be observed in Table 8, both the indicators for crosswalk B were improved by the smart routing algorithm, with a substantial reduction in waiting time. However, at crosswalk A, both indicators have worsened: the waiting time is just slightly longer, but the average crossing window has been significantly reduced. This can be a problem at intersections with a high number of pedestrians (which is not the case here), but the algorithm can be parameterized to impose larger time windows for pedestrians. However, limiting the algorithm's freedom will have an impact on its performance regarding optimization of CO2 emissions.  
Other road users who could be affected by the intelligent algorithm are cyclists. There are no dedicated lanes for cyclists at this location, so modelling their behaviour is challenging, as they usually move erratically in traffic, especially when the flow of traffic is at a standstill. However, the results from the simulations show that the average waiting time for any vehicle is substantially reduced, regardless of the type of vehicle or lane. This means that an arbitrary vehicle (including users that do not produce CO2 such as cyclists) has a high probability of benefiting from the smart traffic routing algorithm. This is explained by the fact that this optimization problem is not a \"zero sum game\"; the gains are obtained mostly by eliminating the inefficiencies of the traditional fixed cycle traffic lights and not by delaying other vehicles.