Pedestrian trajectory modelling in an urban complex is challenging because pedestrians can have many possible destinations, such as shops, escalators, and attractions. Moreover, weather and time-of-day may affect pedestrian behavior. In this paper, a new weather-time-trajectory fusion network (WTTFNet) is proposed to improve the performance of baseline deep neural network architecture. By incorporating weather and time-of-day information as an embedding structure, a novel WTTFNet based on gate multimodal unit is used to fuse the multimodal information and deep representation of trajectories. A joint loss function based on focal loss is used to co-optimize both the deep trajectory features and final classifier, which helps to improve the accuracy in predicting the intended destination of pedestrians and hence the trajectories under possible scenarios of class imbalances. Experimental results using the Osaka Asia and Pacific Trade Center (ATC) dataset shows improved performance of the proposed approach over state-of-the-art algorithms by 23.67% increase in classification accuracy, 9.16% and 7.07% reduction of average and final displacement error. The proposed approach may serve as an attractive approach for improving existing baseline trajectory prediction models when they are applied to scenarios with influences of weather-time conditions. It can be employed in numerous applications such as pedestrian facility engineering, public space development and technology-driven retail.

翻译：城市综合体中的行人轨迹建模具有挑战性，因为行人可能前往众多潜在目的地，如商店、自动扶梯和景点。此外，天气与时段因素亦可能影响行人行为。本文提出一种新颖的天气-时间-轨迹融合网络（WTTFNet），旨在提升基线深度神经网络架构的性能。通过将天气与时段信息编码为嵌入结构，基于门控多模态单元构建的WTTFNet实现了多模态信息与轨迹深度表征的融合。采用基于焦点损失的联合损失函数共同优化深度轨迹特征与最终分类器，这有助于提升在类别不平衡可能场景下对行人预期目的地及相应轨迹的预测精度。基于大阪亚太贸易中心（ATC）数据集的实验结果表明：所提方法相较于前沿算法，分类准确率提升23.67%，平均位移误差与最终位移误差分别降低9.16%和7.07%。该方法为改进现有基线轨迹预测模型提供了有吸引力的解决方案，尤其适用于受天气-时间条件影响的场景，可广泛应用于行人设施工程、公共空间开发及技术驱动零售等领域。