Forecasting current and next trip purpose with social media data and Google Places
Forecasting current and next trip purpose with social media data and Google Places
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DOI：
10.1016/j.trc.2018.10.017
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发表时间：
2018-12-01
影响因子：
8.3
通讯作者：
Gao, Jing
中科院分区：
文献类型：
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作者：
Cui, Yu;Meng, Chuishi;Gao, Jing
出行目的是交通规划和投资决策中出行行为建模和出行需求预测的关键。然而，人类活动的时空复杂性使得出行目的预测成为一个具有挑战性的问题。这项研究是Ermagun et al.（2017）和Meng et al.（2017）工作的延伸，解决了使用Google Places和社交媒体数据预测当前和下一次旅行目的的问题。首先，本文实现了一种新的方法来匹配兴趣点（POI）从谷歌的地方API与历史Twitter数据。因此，可以获得每个POI的受欢迎程度。此外，贝叶斯神经网络（BNN）的模型的行程依赖于每个人的日常出行链和推断的行程目的。与传统模型相比，发现Google Places和Twitter信息可以大大提高某些活动的整体预测准确率，包括“EatOut”，“Personal”，“Recreation”和“Shopping”，但对于“Education”和“Transportation”则没有。此外，行程持续时间被认为是一个重要的因素，在推断活动/行程的目的。此外，为了解决BNN中的计算挑战，在分类任务之前实现弹性网络用于特征选择。我们的研究可以导致三种类型的可能的应用：基于活动的旅游需求建模，调查标签的援助，和在线推荐。
Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual's daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including "EatOut", "Personal", "Recreation" and "Shopping", but not for "Education" and "Transportation". In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity based travel demand modeling, survey labeling assistance, and online recommendations.