Collecting effective data is a fundamental step in developing transport networks and related research. Social media have become an emerging source of data for traffic analyses. In this paper, we demonstrate that the function of a city influences the utility of social media data in travel demand models by generating models for eight US cities with different functions. Data from Twitter and Foursquare, as well as other socio-demographic information, are considered as independent variables in Origin-Destination trip regression models generated via a Random Forest regression technique. Model performance with and without use of social media data are compared via 10-fold cross-validation. The results indicate that the accuracy of the models for all eight cities improved when independent variables based on social media data were included. The performance was most improved in metropolitan areas, followed by rural and tourist areas. Inspired by this finding, we conclude that the city function influences the utility of social media data in travel demand models. Meanwhile, we create models based on trip purpose and transport mode to explore other factors that may impact the efficiency of applying social media data in transport research.

中文翻译：

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整合家庭旅行调查和社交媒体数据以提高 OD 矩阵的质量：比较案例研究

收集有效数据是发展交通网络和相关研究的基本步骤。社交媒体已成为流量分析的新兴数据来源。在本文中，我们通过为八个具有不同功能的美国城市生成模型，证明城市的功能会影响社交媒体数据在旅行需求模型中的效用。来自 Twitter 和 Foursquare 的数据以及其他社会人口统计信息被视为通过随机森林回归技术生成的起点-目的地旅行回归模型中的自变量。使用和不使用社交媒体数据的模型性能通过 10 倍交叉验证进行比较。结果表明，当包括基于社交媒体数据的自变量时，所有八个城市的模型准确性都得到了提高。大城市地区的表现改善最大，其次是农村和旅游区。受这一发现的启发，我们得出结论，城市功能会影响社交媒体数据在旅行需求模型中的效用。同时，我们根据出行目的和交通方式创建模型，以探索可能影响社交媒体数据在交通研究中应用效率的其他因素。