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Modeling activity conflict resolution strategies using scheduling process data

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  • Auld, Joshua
  • Mohammadian, Abolfazl (Kouros)
  • Doherty, Sean T.

Abstract

Developments in activity scheduling process data have recently allowed for much improvement and validation of rule-based activity scheduling models. The use of actual scheduling process data allows for a potentially more realistic model in terms of how the travel episodes are actually derived. Since these types of models are simulations of the scheduling process, scheduling conflicts naturally arise in the creation of the final schedule. The handling of these scheduling conflicts is a critical component of all rule-based activity models. Many of these models rely on the concept of priority of the activities to resolve conflicts. However, recent research has shown that this is often not how conflicts are actually resolved. In order to more accurately model actual conflict resolution behavior, scheduling process data was used to estimate conflict resolution rules using decision trees. The use of a conflict resolution model allows the strategy chosen to depend on the attributes of the individual and the conflicting activities, rather than assuming a prior definition of precedence based on the activity types. The conflict resolution decision showed reasonable ability to predict the resolution strategy chosen in actual scheduling process survey data, and represents an important advance in developing a functional computational process model of activity scheduling.

Suggested Citation

  • Auld, Joshua & Mohammadian, Abolfazl (Kouros) & Doherty, Sean T., 2009. "Modeling activity conflict resolution strategies using scheduling process data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 386-400, May.
  • Handle: RePEc:eee:transa:v:43:y:2009:i:4:p:386-400
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    References listed on IDEAS

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    Cited by:

    1. Jiahua Tang & Du Zhang & Xibin Sun & Haiou Qin, 2022. "Improving Temporal Event Scheduling through STEP Perpetual Learning," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    2. Fang, Zhixiang & Tu, Wei & Li, Qingquan & Li, Qiuping, 2011. "A multi-objective approach to scheduling joint participation with variable space and time preferences and opportunities," Journal of Transport Geography, Elsevier, vol. 19(4), pages 623-634.
    3. Auld, Joshua & Mohammadian, Abolfazl(Kouros), 2012. "Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1386-1403.
    4. Langerudi, Mehran Fasihozaman & Javanmardi, Mahmoud & Shabanpour, Ramin & Rashidi, Taha Hossein & Mohammadian, Abolfazl, 2017. "Incorporating in-home activities in ADAPTS activity-based framework: A sequential conditional probability approach," Journal of Transport Geography, Elsevier, vol. 61(C), pages 48-60.
    5. Li Ping Gan & Will Recker, 2013. "Stochastic Preplanned Household Activity Pattern Problem with Uncertain Activity Participation (SHAPP)," Transportation Science, INFORMS, vol. 47(3), pages 439-454, August.

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