## 4 Simulation and Verification
### Verification Points  
To verify the precision of the simulation of each case, real traffic data are used. The verification data are from detectors equipped on the roads in Duisburg, supported and operated by the Wirtschaftsbetriebe Duisburg and the city of Duisburg. The detectors contain induction loops and cameras. The induction loops are installed beneath the road surface, mainly composed of induction coils and communication devices. When a metallic object (such as a vehicle) drives over the induction loops, the metallic parts of the vehicle inverse the inductance of the loop and the voltage of the circuit is decreased. Therefore, the induction loops can directly measure the time each vehicle passes. With double loops separated by a fixed distance, the detector can measure the speed of the vehicle [16,17]. Figure 3(a) shows the position of detectors of an intersection on the city planning map. The cameras are shown with the dotted line circle and named with K plus number. The induction loops are shown with rectangles and named with D plus number. The detectors in the city Duisburg are always located near the intersections. To compare the real data with simulated traffic, we put detectors in the simulation. In SUMO, the detectors are \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Simulation Scope** & **Case A** & **Case B** & **Case C** & **Case D** \\ \hline \multirow{4}{*}{Traffic} & Inner-city & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \cline{2-6}  & In to out/out to in & x & \(\surd\) & \(\surd\) & \(\surd\) \\ \cline{2-6}  & Out to out & x & \(\surd\) & x & \(\surd\) \\ \hline \multirow{2}{*}{Map} & Inner-city & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \cline{2-6}  & Outer city & x & x & x & \(\surd\) \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of different simulations. added in additional files. The cameras are shown each as a yellow rectangle with a white border and middle cutting line, the induction loops are shown in pink, as can be seen in Figure 4.  
In the selection of verification points, eight roads in different areas of the city Duisburg are chosen. Intersection No. 116, 903, 834, 656, and 751 represent the roads in the north, south, west, east, and middle part of city Duisburg, respectively. Intersection No. 721, 723, 726, and 742 represent the different parts of the inner ring of city Duisburg. The locations of the selected eight road sections are shown in Figure 5. Each intersection has 2 to 12 detectors, in total, 46 detectors are used in verification. Figure 4: Position of detectors of an intersection. (**a**) on map, (**b**) in SUMO. Figure 5: Selected road section for verification. The detectors keep recording traffic volume during 24 h, for each detector, the data of a whole week are provided for this work. Figure 6 shows data from an induction loops detector near the intersection of Schwanenstrasse and Poststrasse of the city Duisburg. Figure 6 shows that the morning peak (around 8 a.m.) and the afternoon peak (around 5 p.m.) are the busiest time of the day for this road section. In the four simulation cases in Table 1, it is hard for the traffic demand of Case D to realize in the network because of the large-scale simulation scope. Because some routes have no access to their destinations, the traffic demand assigned to this route cannot be reflected in the simulation. Therefore, the simulation results of Case D are not representative. Other three cases are simulated separately. Figure 7 shows the simulation result of Case A, B, and C in black, blue, and red lines, and separately the real data in green box plots in four verification points. From the comparison of the real data and simulated traffic volume, the traffic volume of Case B at the beginning time is much more than other cases and the real data for intersection No. 751 and No. 656. However, the large traffic volume in Case B causes traffic jam and makes a decrease of traffic volume in the following simulation.