Day-to-day needs-based activity-travel dynamics and equilibria in multi-state supernetworks

Abstract

This study proposes a new framework for studying activity-travel dynamics in multi-state supernetworks based on a needs-based theory. The framework postulates that temporal fluctuations in human needs are the source of dynamically generated activities, which in turn are manifested in the choice of day-to-day activity-travel patterns (ATPs) and dynamics in day-to-day traffic. Specifically, the utility of fulfilling a daily ATP is determined jointly by the degree of satisfying the disaggregate needs and the aggregate effects of traffic conditions when conducting the ATP. The needs are updated after completing the activities and so is the utility of fulfilling the ATP, which means that conducting the same ATP on two consecutive days is likely to derive different utilities. Thus, the choice of a daily ATP is determined not only by the needs based on past ATP choices but also on current traffic conditions and usage of facilities. We formulate these dynamics in terms of an activity-travel adjustment process, through which the proposed needs-based activity-travel dynamic system converges to a steady state, coinciding with the Wardropian user equilibrium and the equivalent mixed-strategy Nash equilibrium. This formalism integrates activity generation, activity-travel scheduling, and traffic flow evolution, and offers appealing explanations for day-to-day traffic dynamics and equilibria. AbstractThis study proposes a new framework for studying activity-travel dynamics in multi-state supernetworks based on a needs-based theory. The framework postulates that temporal fluctuations in human needs are the source of dynamically generated activities, which in turn are manifested in the choice of day-to-day activity-travel patterns (ATPs) and dynamics in day-to-day traffic. Specifically, the utility of fulfilling a daily ATP is determined jointly by the degree of satisfying the disaggregate needs and the aggregate effects of traffic conditions when conducting the ATP. The needs are updated after completing the activities and so is the utility of fulfilling the ATP, which means that conducting the same ATP on two consecutive days is likely to derive different utilities. Thus, the choice of a daily ATP is determined not only by the needs based on past ATP choices but also on current traffic conditions and usage of facilities. We formulate these dynamics in terms of an activity-travel adjustment process, through which the proposed needs-based activity-travel dynamic system converges to a steady state, coinciding with the Wardropian user equilibrium and the equivalent mixed-strategy Nash equilibrium. This formalism integrates activity generation, activity-travel scheduling, and traffic flow evolution, and offers appealing explanations for day-to-day traffic dynamics and equilibria.

A Need-Based Approach for Modeling Recurrent Discretionary Activity Participation Patterns for Travel Demand Analysis

“…Day preference refers to one's inclination toward a specific day of the week for engaging in activities. This need-based theory has been applied in numerous previous studies that scrutinized multi-day activity participation patterns using various methodologies [17][18][19][20][21]. The concept of the need-based theory used herein is presented in Figure 2.…”

A mixed-equilibrium model of individual and household activity–travel choices in multimodal transportation networks

“…To account for the effects of transportation network congestion on individuals' activity-travel scheduling behaviors within a unified framework, several individual-oriented activity-based network equilibrium models have been proposed for congested road networks (Lam and Yin (2001); Lam and Huang (2002); Ramadurai and Ukkusuri (2010); Ouyang et al (2011); Nourinejad et al (2016); Liu et al (2018)), transit networks (Li et al (2010); ; ), multimodal transportation networks (Chow and Djavadian (2015); Liu et al (2015Liu et al ( , 2016; Li et al (2018); Najmi et al (2020); Liu et al (2020)), and bottlenecks (Li et al (2014(Li et al ( , 2017; Li and Zhang (2020); Cantelmo and Viti (2019)). These models can capture the interrelation of various choice facets and aspects of the activity-travel scheduling behavior of travelers (e.g., their activity/trip chain, activity duration, departure time, path/mode at various times of day, day-to-day need-based activity-travel dynamics, and/or network uncertainty), and the endogenous effects of transportation network congestion.…”

“…Bus routes are fixed without consideration of road congestion, and the number of buses is preloaded on the road links relating to their routes. A5 The physical capacity constraints of activities are not considered in this paper; instead, positive and negative congestion effects at activity locations are modeled (Fu and Lam (2018); Liu et al (2016Liu et al ( , 2020). Activity congestion effects have various physical interpretations, which depend on the activity type.…”

Maximizing space-time accessibility in multi-modal transit networks: an activity-based approach

“…Both negative (crowding) and positive (bandwagon) effects should be explicitly formulated in further studies. Different conditions can be given to indicate whether positive or negative effect is dominant on the activity link (Kang et al 2013;Liu et al 2020).…”