**5. Conclusions**

In the present study, the Agent-based model is used to simulate traffic condition during commuting hours in a local urban area. First, the commuting demand of residents calculated by mobile phone data is used to simulate congestions on the existing urban road network. Then, data backtracking is used to identify the causes of congestion and to analyze the simulation results. Finally, the results of simulation are proven to be consistent with the actual traffic conditions. Although the data used are simplified for the easiness of processing and modeling, the study is still believed to be a positive

endeavor of combining big data and ABM in an urban study, and it offers a valuable approach to studying residents' commuting and urban traffic.

The approach used in the paper has several limitations that merit future consideration: first, vehicles other than commuter cars, such as buses are not considered. Prospective studies are expected to incorporate other available data sources and machine learning approaches to further specify modes of commuter travels and incorporate buses as a major means of transportation. Second, in the present model's construction, lanes and traffic flow directions on the roads are not specified. In a congestion setting, only the density of vehicles in a certain section is considered while the overlapping of vehicles is neglected. This means that the model cannot sufficiently reflect traffic conditions in reality and also leads to the fact that the simulation results cannot be analyzed on a finer scale for deduction of the processes. Prospective studies are expected to further refine the road and traffic systems of the model.

**Author Contributions:** Conceptualization, Y.Y.; Methodology, L.L.; Validation, H.W. and L.L.; Formal Analysis, H.J.; Data Curation, Z.P.; Writing—Original Draft Preparation, H.W.; Writing—Review & Editing, Y.Y.; Project Administration, H.W.; Supervision, Z.P.; Funding Acquisition, H.W. and Q.N.

**Funding:** The research was funded by the China Postdoctoral Science Foundation (No. 2016M600609), the MOE Layout Foundation of Humanities and Social Sciences (No. 19YJCZH187); the National Natural Science Fund for Young Scholars (No. 51708425); the Natural Science Fund of China (No. 51778503); and the National Natural Science Fund for Young Scholars (No. 51708426).

**Acknowledgments:** The authors acknowledge the contribution of all the anonymous reviewers that improved the quality of the paper.

**Conflicts of Interest:** The authors declare no conflict of interest.
