Travel Behavior Classification: An Approach with Social Network and Deep Learning
Travel Behavior Classification: An Approach with Social Network and Deep Learning
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DOI：
10.1177/0361198118772723
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发表时间：
2018-06
影响因子：
1.7
通讯作者：
Yu Cui;Qing He;A. Khani
中科院分区：
文献类型：
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作者：
Yu Cui;Qing He;A. Khani
揭示人类的出行行为不仅对出行需求分析至关重要，而且对拼车机会也至关重要。为了对相似的旅行者进行分组，本文开发了一种基于深度学习的方法，根据旅行者的旅行特征（包括旅行的时间和星期几、旅行模式、以前的旅行目的、个人人口统计数据和旅行终点附近的地点类别）对旅行者的行为进行分类。本研究首先分析了2012年至2013年加州家庭旅行调查（CHTS）的数据集。在对原始数据进行预处理和挖掘后，为每个参与者构建一个活动矩阵。采用Jaccard相似系数计算每对个体之间的矩阵相似度。此外，给定矩阵相似度度量，构建了所有参与者的社区社会网络。进一步实现了社区检测算法，将具有相似出行行为的出行者聚到同一组中。共检测到购物活动较多的非工作人群、娱乐活动较多的非工作人群、通勤正常的工作人群、工作时间较短的工作人群、工作时间较晚的工作人群和需要上学的个人。根据每个参与者的活动矩阵构建活动图图像。最后，采用卷积神经网络的深度学习方法，根据出行者的活动图对出行者进行分组。分类准确率达97%以上。该方法为旅游行为分析和旅行者分类提供了新的视角。
Uncovering human travel behavior is crucial for not only travel demand analysis but also ride-sharing opportunities. To group similar travelers, this paper develops a deep-learning-based approach to classify travelers’ behaviors given their trip characteristics, including time of day and day of week for trips, travel modes, previous trip purposes, personal demographics, and nearby place categories of trip ends. This study first examines the dataset of California Household Travel Survey (CHTS) between the years 2012 and 2013. After preprocessing and exploring the raw data, an activity matrix is constructed for each participant. The Jaccard similarity coefficient is employed to calculate matrix similarities between each pair of individuals. Moreover, given matrix similarity measures, a community social network is constructed for all participants. A community detection algorithm is further implemented to cluster travelers with similar travel behavior into the same groups. There are five clusters detected: non-working people with more shopping activities, non-working people with more recreation activities, normal commute working people, shorter working duration people, later working time people, and individuals needing to attend school. An image of activity map is built from each participant’s activity matrix. Finally, a deep learning approach with convolutional neural network is employed to classify travelers into corresponding groups according to their activity maps. The accuracy of classification reaches up to 97%. The proposed approach offers a new perspective for travel behavior analysis and traveler classification.