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Modeling and reproducing human daily travel behavior from GPS data: A Markov Decision Process approach

Published: 07 November 2017 Publication History

Abstract

Understanding the daily movement of humans in space and time on different granularity levels is of critical value for urban planning, transport management, health care and commercial services. However, population's daily travel behavior data was collected by travel surveys that are infrequent, expensive, and disable to reflect changes in transportation. The demand for capturing, modeling and reproducing human travel behavior in different scenarios pose a challenge on the significant delays. In this study, we propose an inverse reinforcement learning based formulation for training an agent model that enables modeling complex decision-making with consideration of a dynamic environment on the urban granularity level. The modeling framework is based on the Markov decision process to represent an individual's decision making. To obtain the travel behavior characteristics of real humans, we apply the proposed approach to a real-time GPS dataset collected via a smart phone application with more than 2 million daily users to model the people travel behavior for different daily scenarios (i.e., weekdays, weekends, and national holidays) in the Tokyo metropolitan area. It is found that the developed model can generate individual's daily travel plan. In addition, by aggregating the agent travel behavior on the city-wide scale, the urban daily travel demand can be obtained and used for estimate the hourly population distribution. The result of this work can also be regarded as a synthetic mobility dataset, avoiding many of the privacy concerns surrounding real GPS data.

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  • (2024)Comparative Analysis of Different Machine Learning Techniques for Travel Mode Prediction2024 Smart City Symposium Prague (SCSP)10.1109/SCSP61506.2024.10552724(1-4)Online publication date: 23-May-2024
  • (2024)Vehicle-to-grid for car sharing - A simulation study for 2030Applied Energy10.1016/j.apenergy.2024.123731372(123731)Online publication date: Oct-2024
  • (2023)Generative Models for Synthetic Urban Mobility Data: A Systematic Literature ReviewACM Computing Surveys10.1145/361022456:4(1-37)Online publication date: 10-Nov-2023

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  1. Modeling and reproducing human daily travel behavior from GPS data: A Markov Decision Process approach

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    cover image ACM Conferences
    PredictGIS'17: Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility
    November 2017
    51 pages
    ISBN:9781450355018
    DOI:10.1145/3152341
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 07 November 2017

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    Author Tags

    1. Daily travel behavior
    2. human mobility
    3. urban dynamic

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    • (2024)Comparative Analysis of Different Machine Learning Techniques for Travel Mode Prediction2024 Smart City Symposium Prague (SCSP)10.1109/SCSP61506.2024.10552724(1-4)Online publication date: 23-May-2024
    • (2024)Vehicle-to-grid for car sharing - A simulation study for 2030Applied Energy10.1016/j.apenergy.2024.123731372(123731)Online publication date: Oct-2024
    • (2023)Generative Models for Synthetic Urban Mobility Data: A Systematic Literature ReviewACM Computing Surveys10.1145/361022456:4(1-37)Online publication date: 10-Nov-2023

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