The large number of people and vehicles gathered in a short period of time around large-scale events will lead to a differentiated traffic flow.Here,an interpretable machine learning model integrating XGBoost algorithm and partial dependence plots is proposed to capture the nonlinear effects and synergistic influences of large-scale events and their characteristics on the operation of nearby road network,and an empirical study has been conducted in Beijing.The heterogeneity of single factors shows that the distance of road section away from event venue and the event scale have great impact on nearby traffic flow,with relative importance of 27.1% and 25.4%,respectively;time before start and after end of the event has obvious nonlinear characteristics,and the road sections within 3 km from the venue will be significantly affected within 30-60 minutes before the event and 30 minutes after the event.The synergistic effect of two-dimensional factors shows that,if an event attracted more than 30,000 people,holidays and adverse weather have a negative impact on the nearby traffic flow;in rain or haze weather,the road section within 2.5 km from the venue will be affected within 60 minutes before the event and 40 minutes after the event.The findings can provide quantitative data support for identifying the causes of road congestion and formulating reasonable and effective road network control strategies during large events.