Beyond the Limits of Predictability in Human Mobility Prediction: Context-Transition Predictability

Urban human mobility prediction is forecasting how people move in cities. It is crucial for many smart city applications including route optimization, preparing for dramatic shifts in modes of transportation, or mitigating the epidemic spread of viruses such as COVID-19. Previous research propose th...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 35; no. 5; pp. 4514 - 4526
Main Authors: Zhang, Chao, Zhao, Kai, Chen, Meng
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Context
Context modeling
COVID-19
Entropy
Forecasting
human mobility
Human motion
Limits of predictability
location prediction
Mobility
Optimization
Prediction algorithms
predictive algorithm
Predictive models
Route optimization
Source code
Trajectory
Urban areas
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:Urban human mobility prediction is forecasting how people move in cities. It is crucial for many smart city applications including route optimization, preparing for dramatic shifts in modes of transportation, or mitigating the epidemic spread of viruses such as COVID-19. Previous research propose the maximum predictability to derive the theoretical limits of accuracy that any predictive algorithm could achieve on predicting urban human mobility. However, existing maximum predictability only considers the sequential patterns of human movements and neglects the contextual information such as the time or the types of places that people visit, which plays an important role in predicting one's next location. In this paper, we propose new theoretical limits of predictability, namely Context-Transition Predictability, which not only captures the sequential patterns of human mobility, but also considers the contextual information of human behavior. We compare our Context-Transition Predictability with other kinds of predictability and find that it is larger than these existing ones. We also show that our proposed Context-Transition Predictability provides us a better guidance on which predictive algorithm to be used for forecasting the next location when considering the contextual information. Source code is at https://github.com/zcfinal/ContextTransitionPredictability .
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2022.3148300