ABSTRACT

Residents’ trajectory data denote their instantaneous locations along their movements. Mobility research that applies trajectory mining techniques to identify the transportation modes of these movements can inform urban transportation planning. Herein, we propose a five-step approach with information entropy and a multi-layer neural network to identify transportation modes from trajectory data. First, this approach extracts the motion features at each time-stamped location based on foundation geospatial data and spatiotemporal trajectory data, including the speed, acceleration, change of direction, rate of change in direction, and distance from each basic transportation facility. The second step uses information entropy to identify the features that play key roles in identifying transportation modes. The third step weighs each attribute in the feature vector consisting of the selected features and normalizes it to prepare it as input data. The fourth step constructs, trains, and tests a multi-layer neural network with seven-fold cross-validation. The final step includes a post-processing method to optimize the identification result. We use F-measure metric to evaluate the performance. Experimental results on a real trajectory dataset show that the proposed approach can identify the transportation mode at each time-stamped location and outperforms existing transportation-mode identification methods in terms of accuracy and stability.

摘要

居民的轨迹数据表示他们在移动过程中的瞬时位置。应用轨迹挖掘技术来识别这些运动的交通方式的流动性研究可以为城市交通规划提供信息。在此，我们提出了一种具有信息熵和多层神经网络的五步方法，以从轨迹数据中识别交通模式。首先，该方法基于基础地理空间数据和时空轨迹数据提取每个时间戳位置的运动特征，包括速度、加速度、方向变化、方向变化率以及与每个基本交通设施的距离。第二步使用信息熵来识别在识别交通方式中起关键作用的特征。第三步对由所选特征组成的特征向量中的每个属性进行加权，并将其归一化以准备作为输入数据。第四步构建、训练和测试一个具有七重交叉验证的多层神经网络。最后一步包括后处理方法以优化识别结果。我们用F-measure指标来评估性能。在真实轨迹数据集上的实验结果表明，所提出的方法可以识别每个时间戳位置的交通方式，并且在准确性和稳定性方面优于现有的交通方式识别方法。