Proceedings ArticleDOI
Pedestrian-movement prediction based on mixed Markov-chain model
Akinori Asahara,Kishiko Maruyama,Akiko Sato,Kouichi Seto +3 more
- pp 25-33
TLDR
This pedestrian-movement prediction based on MMM using tracking data will make it possible to provide so-called "adaptive mobile services" with proactive functions, and is substantially more accurate than other methods based on a Markov-chain model.Abstract:
A method for predicting pedestrian movement on the basis of a mixed Markov-chain model (MMM) is proposed. MMM takes into account a pedestrian's personality as an unobservable parameter. It also takes into account the effects of the pedestrian's previous status. A promotional experiment in a major shopping mall demonstrated that the highest prediction accuracy of the MMM method is 74.4%. In comparison with methods based on a Markov-chain model (MM) and a hidden-Markov model (HMM) (i.e., prediction rates of about 45% and 2%, respectively), the proposed MMM-based prediction method is substantially more accurate. This pedestrian-movement prediction based on MMM using tracking data will make it possible to provide so-called "adaptive mobile services" with proactive functions.read more
Citations
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Next place prediction using mobility Markov chains
TL;DR: This work extends a mobility model called Mobility Markov Chain in order to incorporate the n previous visited locations and develops a novel algorithm for next location prediction based on this mobility model that is coined as n-MMC.
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Approaching the Limit of Predictability in Human Mobility
TL;DR: The findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.
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Predicting future locations with hidden Markov models
TL;DR: This paper presents an hybrid method for predicting human mobility on the basis of Hidden Markov Models, and reports on a series of experiments with a real-world location history dataset from the GeoLife project, showing that a prediction accuracy of 13.85% can be achieved when considering regions of roughly 1280 squared meters.
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A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models
TL;DR: A self-adaptive parameter selection algorithm called HMTP * is proposed, which captures the parameters necessary for real-world scenarios in terms of objects with dynamically changing speed and has higher positioning precision than HMTP due to its capability of self-adjustment.
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QoI-Aware Multitask-Oriented Dynamic Participant Selection With Budget Constraints
TL;DR: Real and extensive trace-based simulations show that the proposed dynamic participant selection strategy can achieve far better QoI satisfactions for all tasks than selecting participants randomly or through the reversed-auction-based approaches.
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