Investigating social media spatiotemporal transferability for transport

作     者：Emmanouil Chaniotakis Mohamed Abouelela Constantinos Antoniou Konstadinos Goulias 

作者机构：MaaSLabEnergy InstituteUniversity College LondonLondonWC1E 6BTUK School of Engineering and DesignDepartment of Mobility Systems EngineeringTechnical University of MunichMunich80333Germany University of California Santa BarbaraSanta BarbaraCA93106-4060USA 

出 版 物：《Communications in Transportation Research》 (交通研究通讯（英文）)

年 卷 期：2022年第2卷第1期

页      面：351-362页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

基　　金：partially funded by the DAAD Project(No.57474280)Verkehr-SuTra:Technologies for Sustainable Transportation,within the Programme:A New Passage to India—Deutsch-Indische Hochschulkooperationen ab 2019 the German Federal Ministry of Education and Research,Bundesministerium für Bildung und Forschung(BMBF),project FuturTrans:Indo-German Collaborative Research Center on Intelligent Transportation Systems by the European Union's Horizon 2020 research and innovation programme under grant agreement No.815069(project MOMENTUM(Modelling Emerging Transport Solutions for Urban Mobility)) 

主　　题：Social media Data transferability Transport modelling International comparisons 

摘      要：Social Media have increasingly provided data about the movement of people in cities making them useful in understanding the daily life of people in different *** useful for travel analysis is when Social Media users allow(voluntarily or not)tracing their movement using geotagged information of their communication with these online *** this paper we use geotagged tweets from 10 cities in the European Union and United States of America to extract spatiotemporal patterns,study differences and commonalities among these cities,and explore the nature of user location *** analysis here shows the distinction between residents and tourists is fundamental for the development of city-wide *** of repeated rates of location(recurrence)can be used to define activity *** and similarities across different geographies emerge from this analysis in terms of local distributions but also in terms of the worldwide reach among the cities explored *** comparison of the temporal signature between geotagged and non-geotagged tweets also shows similar temporal distributions that capture in essence city rhythms of tweets and activity spaces.