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A multi-stage fusion network for transportation mode identification with varied scale representation of GPS trajectories
Transportation Research Part C: Emerging Technologies ( IF 8.3 ) Pub Date : 2023-03-15 , DOI: 10.1016/j.trc.2023.104088
Yanli Ma, Xuefeng Guan, Jun Cao, Huayi Wu

Accurate transportation mode identification is essential for traffic management and travel planning. The rapid development of GPS-enabled devices has made it both popular and cost-effective to obtain travel modes from massive GPS trajectory datasets. However, since different transportation modes exhibit significantly different spatial characteristics, varied scale representation can be used to efficiently capture these differences, but existing research have failed to fully exploit this point.

To address these issues, we propose a novel deep learning framework named Multi-Attribute-Scale-Object-based Multi-Stage Fusion Network (MASO-MSF). A MASO structure is constructed to represent the local motion states and spatial characteristics of one GPS trajectory segment at different spatial scales. Subsequently, a multi-stage fusion model (MSF) is designed to perform accurate transportation mode identification in an end-to-end manner. In this model, an attribute channel fusion module is built to fuse local motion states and capture the spatial dependencies. A scale feature fusion module is then established to selectively aggregate features from different spatial scales. Finally, MSF deploys an object decision fusion module to generate the final identification result from different probabilities of multiple objects in a trajectory segment.

To evaluate the performance of the proposed MASO-MSF model, a series of experiments are conducted on a publicly available dataset. The experimental results verify the effectiveness of MASO in characterizing a trajectory segment from three perspectives (i.e., attribute, scale, and object) and demonstrate that MASO-MSF can achieve the state-of-the-art identification performance. In addition, the source code of MASO-MSF is now available at GitHub (https://github.com/MYL23/MASO-MSF).



中文翻译:

一种用于交通方式识别的多级融合网络,具有不同比例的 GPS 轨迹表示

准确的交通方式识别对于交通管理和出行规划至关重要。支持 GPS 的设备的快速发展使得从海量 GPS 轨迹数据集中获取出行模式变得既流行又具有成本效益。然而,由于不同的交通方式表现出显着不同的空间特征,可以使用不同的尺度表示来有效地捕捉这些差异,但现有研究未能充分利用这一点。

为了解决这些问题,我们提出了一种名为基于多属性尺度对象的多阶段融合网络 (MASO-MSF) 的新型深度学习框架。构建了一个 MASO 结构来表示一个 GPS 轨迹段在不同空间尺度下的局部运动状态和空间特征。随后,设计了多阶段融合模型(MSF),以端到端的方式进行准确的运输模式识别。在该模型中,构建了一个属性通道融合模块来融合局部运动状态并捕获空间依赖性。然后建立尺度特征融合模块以选择性地聚合来自不同空间尺度的特征。最后,MSF部署了一个目标决策融合模块,根据轨迹段中多个目标的不同概率生成最终识别结果。

为了评估所提出的 MASO-MSF 模型的性能,在公开可用的数据集上进行了一系列实验。实验结果验证了 MASO 从三个角度(即属性、尺度和对象)表征轨迹段的有效性,并证明 MASO-MSF 可以实现最先进的识别性能。此外,MASO-MSF 的源代码现已在 GitHub ( https://github.com/MYL23/MASO-MSF ) 上提供。

更新日期:2023-03-16
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