## 5 Conclusions and Outlook  
This article presents a high-precision microscopic traffic simulation scenario of the city Duisburg. Road network from OSM and traffic demand from OD matrix are combined in four different ways for simulation. To find out the most precise scenario, data from detectors including induction loops and cameras are used for verification. The simulation results show that Case C with part of the traffic demand (contains trips either departure or arrival point is inside the city) and interior city road network are closest to the data collected in the real world. The reproduction rate of the simulation differs from the different simulated range in four cases and the simulation time. The percentage of simulated traffic volume in the real traffic volume range can be up to 72.22%. The traffic volume after 6 o'clock has increased significantly. Meanwhile, the accuracy of the reproduced traffic volume has decreased. The method of generating traffic flow from the OD matrix in SUMO may affect the accuracy under a large amount of traffic and a complex road network.  
Even though we tried our best to achieve the goal of high precision for traffic simulation scenarios, there are still some factors affecting our precision. First, it is hard to balance  
Figure 8: The proportion of simulated data in the real data range.  
Figure 7: Comparison of simulation results and real data.  
the scope of simulation. Second, traffic light data are not available for the entire city, and the lack of other traffic users such as pedestrians, railways, subways, and buses make the roads more convenient for private vehicles. Next, the selection of verification points also has effects on the precision of results. The data of detectors is from one particular week but weather conditions, time of the year, holiday or not. Instead, all have an impact on the traffic volume. Traffic is a special research object that cannot be reproduced 100 percent, many factors impact the final scenario, but with the high precision scenario of the city of Duisburg, lots of research can be carried out. The traffic simulation of the city of Duisburg can provide a reference for the simulations using an OD matrix and traffic plan guidance for the urban transportation departments.  
Our next step will try to decrease traffic demand and then simulate 24 h' traffic of the city of Duisburg. The traffic simulation based on the scenario of city Duisburg will also be the focus on the new technologies of vehicles. Electric vehicles and autonomous vehicles will be the major research objects. What kind of impact these vehicles with new technologies will have on the traffic flow is part of our further research work.  
X.M.: conceptualization, data curation, formal analysis, methodology, software, writing and editing. X.H.: conceptualization and revision. T.W. & D.S.: project supervision co-design of the content and revision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We like to thank Wirtschaftsbetriebe Duisburg and Stadt Duisburg for their support with the traffic data of city Duisburg. A part of this work is based on the researches within this cooperation. Map data copyrighted by OpenStreetMap contributors and available from [[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org)).  
We acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen.  
The authors declare no conflict of interest.