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Dynamic Public Resource Allocation Based on Human Mobility Prediction

Authors:

Yu ZhengAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 1

Article No.: 25, Pages 1 - 22

https://doi.org/10.1145/3380986

Published: 18 March 2020 Publication History

Abstract

The objective of public resource allocation, e.g., the deployment of billboards, surveillance cameras, base stations, trash bins, is to serve more people. However, due to the dynamics of human mobility patterns, people are distributed unevenly on the spatial and temporal domains. As a result, in many cases, redundant resources have to be deployed to meet the crowd coverage requirements, which leads to high deployment costs and low usage. Fortunately, with the development of unmanned vehicles, the dynamic allocation of those public resources becomes possible. To this end, we provide the first attempt to design an effective and efficient scheduling algorithm for the dynamic public resource allocation. We formulate the problem as a novel multi-agent long-term maximal coverage scheduling (MALMCS) problem, which considers the crowd coverage and the energy limitation during a whole day. Two main components are employed in the system: 1) multi-step crowd flow prediction, which makes multi-step crowd flow prediction given the current crowd flows and external factors; and 2) energy adaptive scheduling, which employs a two-step heuristic algorithm, i.e., energy adaptive scheduling (EADS), to generate a scheduling plan that maximizes the crowd coverage within the service time for agents. Extensive experiments based on real crowd flow data in Happy Valley (a popular theme park in Beijing) demonstrate the effectiveness and efficiency of our approach.

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Cited By

Zeng JZhang GYuan JLi YJin D(2024)Empowering Predictive Modeling by GAN-based Causal Information LearningACM Transactions on Intelligent Systems and Technology10.1145/365261015:3(1-19)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3652610
Wang JHao QHuang WFan XTang ZWang BHao JLi YBaeza-Yates RBonchi F(2024)DyPS: Dynamic Parameter Sharing in Multi-Agent Reinforcement Learning for Spatio-Temporal Resource AllocationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672052(3128-3139)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672052
Wang SPan XRuan SHan HWang ZYuan HZhu JLi QBaeza-Yates RBonchi F(2024)DiffCrime: A Multimodal Conditional Diffusion Model for Crime Risk Map InferenceProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671843(3212-3221)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671843
Show More Cited By

Index Terms

Dynamic Public Resource Allocation Based on Human Mobility Prediction
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 1

March 2020

1006 pages

EISSN:2474-9567

DOI:10.1145/3388993

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Copyright © 2020 ACM.

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Publication History

Published: 18 March 2020

Published in IMWUT Volume 4, Issue 1

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National Key Research and Development Program of China
Zhejiang Provincial Natural Science Foundation
National Natural Science Foundation of China-Zhejiang Joint Found for the Integration of Industrialization and Informatization
DiDi Chuxing Inc.
National Science Foundation
National Natural Science Foundation of China

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Cited By

Zeng JZhang GYuan JLi YJin D(2024)Empowering Predictive Modeling by GAN-based Causal Information LearningACM Transactions on Intelligent Systems and Technology10.1145/365261015:3(1-19)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3652610
Wang JHao QHuang WFan XTang ZWang BHao JLi YBaeza-Yates RBonchi F(2024)DyPS: Dynamic Parameter Sharing in Multi-Agent Reinforcement Learning for Spatio-Temporal Resource AllocationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672052(3128-3139)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672052
Wang SPan XRuan SHan HWang ZYuan HZhu JLi QBaeza-Yates RBonchi F(2024)DiffCrime: A Multimodal Conditional Diffusion Model for Crime Risk Map InferenceProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671843(3212-3221)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671843
Zhang YLi TYuan YXu FYang FSun FLi Y(2024)Demand-driven Urban Facility Visit PredictionACM Transactions on Intelligent Systems and Technology10.1145/362523315:2(1-24)Online publication date: 22-Feb-2024
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Sun LLiu MGuo JYu XWang S(2024)Deep Reinforcement Learning Empowered Resource Allocation in Vehicular Fog ComputingIEEE Transactions on Vehicular Technology10.1109/TVT.2023.333857873:5(7066-7076)Online publication date: May-2024
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Cai ZJiang RLian XYang CWang ZFan ZTsubouchi KKobayashi HSong XShibasaki R(2024)Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2023.3310789(1-13)Online publication date: 2024
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Kong XShen ZWang KShen GFu Y(2024)Exploring Bus Stop Mobility Pattern: A Multi-Pattern Deep Learning Prediction FrameworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.334587225:7(6604-6616)Online publication date: 1-Jul-2024
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Wang STang SRuan SLong CLiang YLi QYuan ZBao JZheng Y(2024)Urban Sensing for Multi-Destination Workers via Deep Reinforcement Learning2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00318(4167-4179)Online publication date: 13-May-2024
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Shukla PShukla S(2024)Exploring the potential of deep regression model for next-location predictionKnowledge and Information Systems10.1007/s10115-024-02082-x66:7(4093-4124)Online publication date: 1-Jul-2024
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