# Evaluation of Accuracy of Traffic Flow Generation in SUMO
## 5 Conclusions  
In this paper, we built a traffic simulation of the Jianghan Zone in Wuhan, China. For a district with 683,500 inhabitants, traffic situations are relatively complex and traffic jams occur often in this busiest area in Wuhan. We simulate the traffic with ActivityGen in SUMO, using geographic population distribution data gathered from public information. To find out the accuracy of the simulated traffic, real-time road condition and traffic prediction based on previous data from the same time or each road in this area was compared. In the selected eight representative road sections, most simulated results are close to the predicted traffic based on real data (sections 2-4, 7 and 8). In particular areas like hospitals (sections 5 and 6), train station (section 1), the simulated traffic is relatively smoother than real ones. Therefore, further work in enhancing the accuracy of simulated data of ActivityGen can be building new models for special public areas like hospitals and train stations. With more detailed model classification, simulated traffic data can be closer to real conditions.  
This article uses a macroscopic verification method for a microscopic traffic simulation in Jianghan Zone of the city of Wuhan. Comparing with our previous experiences of building traffic simulations [12; 13; 25; 26] with more reliable traffic data sources including induction loops data, camera data, OD matrix data, etc., in this article, all the data sources which have been used in building the scenario are all public information gathered from the Internet. Comparing with accurate traffic estimation models using complicated calculation method such as neural network [27; 28] and machine learning [29], the estimation method in this work can simulate the average speed of each intersection in the entire city with easy-to-obtain data, and does not require the actual speed of each simulated road section of the whole road network. Moreover, the generated traffic models can be used for assisting road planning, traffic light planning, large-scale event planning, etc.  
In an age when privacy information is getting more and more attention and traditional methods of gathering real traffic data are harder to implement and more costly, this paper may offer a new way in traffic flow generation and verification from 100% public information from the Internet. Further research is planned to improve the accuracy of using geographic population information to generate traffic demand by improving more urban models, including hospitals and railway stations.  
X.M.: conceptualization, data curation, formal analysis, methodology, software, writing and editing. X.H., T.W. and D.S.: conceptualization, project administration and supervision and critical revision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
We acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen.  
The authors declare no conflict of interest.