大型活动会引起举办场馆周边区域路网出现交通流短时骤增与消散，导致周边区域路网交通运行呈现偶发性与不确定性波动，而现有预测方法通常难以捕捉特殊事件下交通流受多维因素复杂影响及其演变机理。为充分挖掘路段速度的时间序列和影响因素特征，揭示速度预测中不同影响特征间的耦合作用机理，提出了一种结合可解释机器学习与长短时记忆网络的速度预测模型（MC-LSTM）。结合大型活动的特点构建影响因素集，采用XGBoost算法评价活动规模、性质等因素特征对场馆周边路段速度的影响相对重要度，量化多元因素对场馆周边路网运行状态的协同效用，融合LSTM网络，考虑交通状态的时间依赖关系，捕获不同历史时期的时间相关性，实现对活动期间场馆周边路段速度的精确预测。以北京市连续6个月的大型活动期间周边路网为例进行模型验证，结果表明：所构建的MC-LSTM模型的预测精度可达94.5%以上，优于考虑多因素协同的XGBoost模型、只考虑单因素特征的LSTM模型及未考虑外部特征的LSTM模型，证明该研究所提出的模型有效性与稳定性更优，可为大型活动场馆周边路网交通组织优化和制定针对性交通管控与保障措施提供定量化的决策依据。

Large scale activities can cause a sudden increase and dissipation of traffic flow in the area around the venue, resulting in occasional and uncertain fluctuations of the road network operation in the surrounding area. The existing methods are insufficient to capture the evolution mechanism of traffic flow under the influence of multi-dimensional factors in special events at the prediction scale. In order to fully exploit the information of time series and influencing factor features of road section speed and effectively deal with the coupling mechanism between different influencing features in speed prediction, this paper proposed a speed prediction model (MC-LSTM) combining Interpretable Machine Learning and Long Short-Term Memory network. Firstly, the study combined the characteristics of large scale activities to construct the set of influencing factors. Then it used the XGBoost algorithm to evaluate the relative importance of the impact of activity scale, nature and other factors characteristics on the speed of road sections around the venue. It quantified the synergistic utility of multiple factors on the operation state of the road network around the venue, fused LSTM networks, considered the time-dependent relationship of traffic state, captureed the temporal correlation of different historical periods, and accurately predicted the speed of road sections around the venue during the activity. MC-LSTM was validated by taking the road network around large scale activities venues in Beijing for six consecutive months. The results indicate that the prediction accuracy of the MC-LSTM model can reach more than 94.5%, which is better than that of XGBoost model considering multiple factors synergism, LSTM model considering only single factor features and the LSTM model not considering external features. It proved that the model proposed in this paper has better validity and stability. This study can provide a decision basis for optimizing the traffic organization of the road network around the large scale activities venues and formulating traffic control and security measures.