In order to explore the impact of Corona Virus Disease 2019 (COVID-19) on the travel choice behavior of urban traffic, the travel cost model was used to quantify the various factors affecting travelers' travel mode choice, and a complete information dynamic game model was established to optimize the total utility value of travel mode. By solving the model with nonlinear Gauss Seidel algorithm, the optimal travel sharing amount and sharing rate of urban transportation mode are obtained. Taking Xi'an as an example, the change rule of urban traffic trip before and after the epidemic is analyzed. The results showed that: after the outbreak of COVID-19, the proportion of public transport travel decreased from 41.8% to 32.2%, while the share rate of car travel increased from 27.3% to 33.8%. The share rate of slow traffic is basically the same. The results of this paper provide the basis for the fine prevention and control of epidemic situation, travel management and control in the post epidemic period.

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