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Context-aware inverse reinforcement learning for modeling individuals’ daily activity schedules
16 references·0 citing·52 second hop
Context-aware inverse reinforcement learning for modeling individuals’ daily activity schedules (2025)Context-aware inverse reinforcement learn…A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards (2023)A deep inverse reinforcement …Proceedings of the 30th International Conference on Advances in Geographic Information Systems (2022)Proceedings of the 30th Inter…Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior (2021)Cumulative prospect theory co…Machine learning applications in activity-travel behaviour research: a review (2020)Machine learning applications…Activity-based travel demand generation using Bayesian networks (2020)Activity-based travel demand …Theory of mind as inverse reinforcement learning (2019)Theory of mind as inverse rei…Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (2019)Proceedings of the 27th ACM S…A Dynamic Discrete Choice Activity-Based Travel Demand Model (2019)A Dynamic Discrete Choice Act…Data-driven activity scheduler for agent-based mobility models (2018)Data-driven activity schedule…A time-use activity-pattern recognition model for activity-based travel demand modeling (2017)A time-use activity-pattern r…Activity-based models of travel demand: promises, progress and prospects (2013)Activity-based models of trav…Modelling the scaling properties of human mobility (2010)Modelling the scaling propert…Limits of Predictability in Human Mobility (2010)Limits of Predictability in H…Framework for the development of the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model (2009)Framework for the development…Florida Activity Mobility Simulator (2005)Florida Activity Mobility Sim…Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns (2004)Comprehensive Econometric Mic…Reality mining: sensing complex social systems (2005)The origin of bursts and heavy tails in human dynamics (2005)Understanding individual human mobility patterns (2008)Friendship and mobility (2011)A universal model for mobility and migration patterns (2012)Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems (1992)A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions (2004)Generating complete all-day activity plans with genetic algorithms (2005)MATSim-T (2009)ALBATROSS : a learning based transportation oriented simulation system (2000)Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model (2012)Uncertainty in travel demand forecasting models: literature review and research agenda (2012)Incorporating space–time constraints and activity-travel time profiles in a multi-state supernetwork approach to individual activity-travel scheduling (2013)Transportation Research Board (2014)Annals of the Association of American Geographers (1921)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? 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References

  • 2010Limits of Predictability in Human Mobility3,230 cited
  • 2010Modelling the scaling properties of human mobility1,286 cited
  • 2004Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns400 cited
  • 2013Activity-based models of travel demand: promises, progress and prospects320 cited
  • 2019Theory of mind as inverse reinforcement learning207 cited
  • 2009Framework for the development of the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model141 cited
  • 2020Machine learning applications in activity-travel behaviour research: a review140 cited
  • 2021Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior97 cited
  • 2017A time-use activity-pattern recognition model for activity-based travel demand modeling90 cited
  • 2023A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards89 cited
  • 2019Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems86 cited
  • 2005Florida Activity Mobility Simulator83 cited
  • 2018Data-driven activity scheduler for agent-based mobility models71 cited
  • 2019A Dynamic Discrete Choice Activity-Based Travel Demand Model65 cited

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2022
Proceedings of the 30th International Conference on Advances in Geographic Information Systems
55 cited
  • 2020Activity-based travel demand generation using Bayesian networks49 cited
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