Implementation of Scheduling Conflict Resolution Model in ADAPTS: Activity-Based Model Using Linear Programming Approach

Activity scheduling is a very complicated process in which individuals are involved during their daily lives. However, studying this process is very difficult because usually it is the outcome of the decisions that is revealed and surveys often fail to capture key factors influencing the process. Therefore, few researches have been conducted to capture the process of activity scheduling. Consequently, most Activity-based Models (ABMs) do not model this process and use assumptions and predefined set of patterns of activities and priorities to generate enough information for operating their models. Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) is one of the few ABMs that tries to simulate the process of scheduling and resolve the conflicts when they occur. The original model consists of a decision tree system to determine conflict resolution strategy types and a series of rules that determine how to fit the activities in the schedule. Although applying a set of rules is not computationally intensive, when the number of rules becomes large, the implementation becomes very difficult (difficult to develop, understand, and maintain), not to mention that it is also very inflexible. This paper advances the current conflict resolution model of ADAPTS by replacing the series of rules with a powerful, flexible and advance non-linear optimization model. The paper then proposes a series of linear optimization models that together perform the same task as the non-linear model, however they are much easier to implement and maintain, while fast to run and flexible to extend. The new model defines an objective function, which aims to minimize the amount of change (in terms of shifting activities and/or changing their duration) that is needed to fit activities into the schedule. The objective function could be extended to include socioeconomic variables as well as activity attributes if needed. The performance of the proposed approach is compared against TASHA and ADAPTS original schedulers using CHASE scheduling process data.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Javanmardi, Mahmoud
  • Fasihozaman Langerudi, Mehran
  • Shabanpour, Ramin
  • Mahmoudifard, Seyed Mehdi
  • Mohammadian, Abolfazl (Kouros)
• Conference:
  • Transportation Research Board 95th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2016-1-10 to 2016-1-14
• Date: 2016

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 17p
• Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Activity choices; Decision trees; Implementation; Linear programming; Scheduling; Travel demand
• Subject Areas: Planning and Forecasting; Transportation (General); I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01589904
• Record Type: Publication
• Report/Paper Numbers: 16-5406
• Files: TRIS, TRB, ATRI
• Created Date: Feb 8 2016 10:32AM