# Evaluation of Accuracy of Traffic Flow Generation in SUMO
###### Abstract  
A traffic simulation of the Jianghan Zone in Wuhan, China was carried out. In order to simulate genuine traffic flow without traditional hard-to-implement data collection methods, geographic population distribution data were gathered from the public information and traffic flow was generated by ActivityGen in SUMO (Simulation of Urban Mobility). For the sake of discovering the accuracy of the simulated traffic, real-time road condition and traffic prediction based on previous data on same time of each road in this area was compared. The results show that traffic flow generated from geographic population distribution data has referential meanings and with more detailed model classification, simulated traffic data can be closer to real conditions. This may offer a new way to generate traffic flow for researchers working in traffic simulation area. Further improvement of the accuracy in traffic flow generation by geographic population needs to pay more attention on special places like hospital and train stations.  
traffic simulation; geographic population distribution; SUMO +
Footnote †: journal: Applied Sciences  
0021  
How to collect real traffic data is always a problem for lots of researchers. Most research works related to the establishment of traffic scenarios benefit from cooperation with the government to obtain data sources. [PERSON] et al. built a real time traffic condition prognose platform in Upper Austria and Salzburg with traffic counters owned by the Upper Austrian government [10]. [PERSON] et al. established a traffic scenario of the city of Bologna based on a project in cooperation with the European Commission [11]. Our previous work was supported by a cooperation with the administration of the city of Duisburg and had access to traffic data like the origin-destination (OD) matrix and detector data collected from induction loops and cameras [12; 13]. [PERSON] et al. built a simulation-based dynamic traffic assignment model of Melbourne, Australia with detector data and OD matrix data [14]. However, it is difficult for most researchers to build and analyze traffic scenarios because the real traffic data are rarely open to the public.  
Simulation of Urban Mobility (SUMO) [15] is an open-source microscopic traffic simulation software, developed by the German Aerospace Center (DLR). Compared to similar simulation software, SUMO has more possibilities of model extension with less complexity. Moreover, SUMO provides a possibility to simulate traffic through geography data. There is an attachment program of SUMO called ActivityGen. The main idea of ActivityGen is to simulate people going on and coming off duty or school. So, quite detailed data such as where the residents live and where they work or the location of schools, are indispensable. Previous studies focus on using geographical population distribution to create traffic demand. [PERSON] et al. [16] rebuilt a 24-h scenario of Luxembourg City with geographical population distribution, the scenario contains morning and evening rush hours, also with buses and bus stations. However, without real data comparison and adjustment the accuracy cannot be proved. [PERSON] et al. [17] built a scenario of Kobe city in Japan using ActivityGen to generate the OD data and found, by changing the speed limit of the road, the simulated data will be closer to the traffic census data than using the default settings.  
In this study, we focus on a region of 683,500 residents in Wuhan, China as a particular example, where the traffic problems are greatest in Wuhan. The traffic simulation of this region was carried out. The purpose of this work is to reproduce the actual traffic using public information on the Internet. The road network, the traffic demand and the real traffic status used for verification were all collected from public information in the Internet. In addition, the reliability of the traffic generated by geographical population distribution will be discussed in the summary and the aspects of enhancing reliability will be given.