01629486

Component

Activity-travel patterns in the urban environment become increasingly complex. Activity sequences including trip chains are typically analyzed for understanding the underlying decision procedure by a single model of holistic approach such as logit or state dependent one such as Bayesian. The paper aims to suggest a method that attempts to reproduce activity decision process where nested logit and Markov chain are assumed mixed on the basis of fuzzy membership along the implementation of activities. The premise states that nested logit represents planned and rational behavior, while Markov chain represents myopic and impulsive behavior. The algorithm is designed to attempt dynamic change of activity decisions every 10 minutes. The results show that myopic choice by Markov chain often causes changes in existing plan of nested logit. Simple and complex activity-patterns behave differently in this regard. The suggested method better serves behavioral realism for analyzing activity-travel decisions and shows the potential of improved accuracy.

This paper was sponsored by TRB committee ADB10 Standing Committee on Traveler Behavior and Values. Alternate title: Reproducing Activity Decision Process Using Nested Logit and Markov Chain Monte Carlo Simulation.

01618707

17-02575

English

16p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Activity choices; Decision making; Logits; Markov chains; Monte Carlo method; Simulation; Travel patterns; Trip chaining

Planning and Forecasting; Transportation (General)

Transportation Research Board Annual Meeting 2017 Paper #17-02575

TRIS, TRB, ATRI

Dec 8 2016 10:58AM