Observing individual dynamic choices of activity chains from location-based crowdsourced data |
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Institution: | 1. School of Data and Computer Science, Sun Yat-sen University;2. State Key Laboratory of Integrated Services Networks, School of Electronic Engineering, Xidian University, Xi''an 710071, People''s Republic of China;3. School of Automation Science and Engineering, South China University of Technology;4. Guangzhou Key Laboratory of Brain Computer Interface and Applications, Guangzhou 510640, People''s Republic of China;5. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510275, People''s Republic of China |
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Abstract: | The existing efforts on studying human mobility and activity using location-based crowdsourced data mainly focus on obtaining the activity chain pattern in a region at an aggregate level. To observe individual dynamic choices of activity chains, this paper presents a data-driven approach to estimating individual-specific activity chain set and corresponding choice probabilities for a given person over a 24-h period using crowdsourced data from location-based service apps. We detect an individual-specific stochastic activity set using a contextual-parcel data analysis. Based on the time geography theory, we refine a space-time bicone concept to construct an activity-travel space-time-state network from the stochastic activity set. These space-time bicone constraints define a set of potential activity choices to reduce the search space of activity location and duration choices. We construct an activity state transition graph from the space-time-state network and calculate a Markov matrix for activity choice probabilities. Furthermore, we calculate the probabilities of activity chain choices using the Markov matrix. We also visualize individual-specific activity chain set in a space-time-state network to show the dynamic choices of individual daily mobility and activity. We demonstrate the proposed approach through conducting numerical analyses using crowdsourced data from location-based service apps - Foursquare and Twitter to construct individual-specific activity choice sets and corresponding choice probabilities. |
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Keywords: | Human mobility and activity Location-based crowdsourced data Contextual analysis Time geography Activity-travel space-time-state network Individual-specific activity chain set |
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