Framework to Evaluate Rescheduling due to Unexpected Events in an Activity-Based Model

Abstract

The concept of rescheduling is essential to activity-based modeling in order to calculate effects of both unexpected incidents and adaptation of individuals to traffic demand management measures. When collaboration between individuals is involved or timetable based public transportation modes are chosen, rescheduling becomes complex. This paper describes a new framework to investigate algorithms for rescheduling on a large scale. The framework explicitely models the information flow between traffic information services and travelers. It combines macroscopic traffic assignment with microscopic simulation of agents adapting their schedules. Perception filtering is introduced to allow for traveler specific interpretation of perceived macroscopic data and information going unnoticed; it feeds person specific short term predictions required for schedule adaptation. Individuals are assumed to maximize schedule utility. Initial agendas are created by the FEATHERS activity-based schedule generator for mutually independent individuals using an undisturbed loaded transportation network. The new framework allows both agent behavior and external phenomena to influence the transportation network state; individuals interpret the state changes via perception filtering and start adapting their schedules; again affecting the network via updated traffic demand. The first rescheduler investigated uses marginal utility that monotonically decreases with activity duration and a monotonically converging relaxation algorithm to efficiently determine the new activity timing. The current framework implementation can support re-timing, re-location and activity re-sequencing; re-routing however is the subject of future research.

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