Pedestrian modeling remains a formidable challenge in transportation science due to the complicated nature of pedestrian behavior and the irregular movement patterns. To this extent, accurate and reliable positioning technologies and techniques play a significant role in the pedestrian simulation studies. The objective of this research is to predict pedestrian movement in various perspectives utilizing historical trajectory data. The study features considered in this research are pedestrian class, speed and position. The ensemble of these features provides a thorough description of pedestrian movement prediction, whilst contributes to the context of pedestrian modeling and Intelligent Transportation Systems. More specifically, pedestrian movement is grouped into different classes considering gender, walking pace and distraction by employing random forest algorithms. Then, position and speed prediction is computed employing suitable data-driven methods, in particular, the locally weighted regression (LOESS method), taking into account the individual pedestrian’s profile. An LSTM-based (Long Short-Term Memory) model is also applied for comparison. The methodology is applied on pedestrian trajectory data that were collected in a controlled experiment undertaken at the Campus of the National Technical University of Athens (NTUA), Greece. Prediction of pedestrian’s movement is achieved, yielding satisfactory results.

中文翻译：

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行人行为分类和轨迹预测的数据驱动模型

由于行人行为的复杂性和不规则的运动模式，行人建模仍然是交通科学中的一项艰巨挑战。就此而言，准确可靠的定位技术和技术在行人模拟研究中发挥着重要作用。本研究的目的是利用历史轨迹数据从不同角度预测行人运动。本研究考虑的研究特征是行人类别、速度和位置。这些特征的集合提供了对行人运动预测的全面描述，同时有助于行人建模和智能交通系统的背景。更具体地说，考虑到性别，行人运动被分为不同的类别，通过采用随机森林算法的步行速度和分心。然后，使用合适的数据驱动方法计算位置和速度预测，特别是局部加权回归（LOESS 方法），同时考虑到单个行人的轮廓。还应用了基于 LSTM（长期短期记忆）的模型进行比较。该方法应用于在希腊雅典国立技术大学 (NTUA) 校园进行的对照实验中收集的行人轨迹数据。实现了对行人运动的预测，取得了令人满意的结果。还应用了基于 LSTM（长期短期记忆）的模型进行比较。该方法应用于在希腊雅典国立技术大学 (NTUA) 校园进行的对照实验中收集的行人轨迹数据。实现了对行人运动的预测，取得了令人满意的结果。还应用了基于 LSTM（长期短期记忆）的模型进行比较。该方法应用于在希腊雅典国立技术大学 (NTUA) 校园进行的对照实验中收集的行人轨迹数据。实现了对行人运动的预测，取得了令人满意的结果。