Advanced traveller information systems under recurrent traffic conditions: Network equilibrium and stability

Bifulco, Gennaro Nicola; Cantarella, Giulio Erberto; Simonelli, Fulvio; Velonà, P.

doi:10.1016/j.trb.2015.12.008

Cited by 77 publications

(33 citation statements)

References 36 publications

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“…The interaction between day-to-day dynamic route flows and other components of the transportation system is widely studied in the analytical way, which includes the traffic information system (Bifulco et al, 2016;Cantarella, 2013;Cho and Hwang, 2005;Friesz et al, 1994), fixed or responsive signal control strategies (Cantarella et al, 2012;Huang et al, 2016;Liu and Smith, 2015;Smith et al, 2015;Smith and Mounce, 2011;Xiao and Lo, 2015), congestion pricing (Friesz et al, 2004;Farokhi and Johansson, 2015;Guo, 2013;Guo et al, 2016;Tan et al, 2015;Wang et al, 2015;Xu et al, 2016;Yang, 2007;Yang and Szeto, 2006;Yang et al, 2007;Ye et al, 2015), and tradable credit scheme (Ye and Yang, 2013). Day-to-day dynamics in other travel modes are also studied, such as in railway (Wu et al, 2013) and transit (Bar-Yosef et al, 2013;Cantarella et al, 2015;Li and Yang, 2016).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Exploration of day-to-day route choice models by a virtual experiment

Xiao

Yang

2018

Transportation Research Part C: Emerging Technologies

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This paper examines existing day-to-day models based on a virtual day-to-day route choice experiment using the latest mobile internet techniques. With the realized day-to-day path flows and path travel times in the experiment, we calibrate several welldesigned path-based day-to-day models who take the Wardrop's user equilibrium as (part of) their stationary states. The nonlinear effects of path flows and path time differences on the path swapping are then investigated. Participants' path preferences, time-varying sensitivity and learning behavior in the day-to-day process are also examined. The prediction power of various models with various settings (nonlinear effects, time-varying sensitivity, and learning) is compared. Assumption of rational behavior adjustment process in Yang and Zhang (2009) is further verified. Finally, evolutions of different Lyapunov functions used in the literature are plotted and no obvious diversity is observed.

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

Exploration of day-to-day route choice models by a virtual experiment

Xiao

Yang

2018

Transportation Research Part C: Emerging Technologies

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“…Rambha and Boyles (2016) propose a stochastic day-to-day dynamic route choice model and then present an average cost Markov decision process to minimize the total network travel time by dynamic pricing. Bifulco et al (2016) develop a DTD DTA model to capture the effects of advanced traveler information system to departure time choices under recurrent network conditions. Guo and Szeto (2018) propose a DTD dynamic system to model the decision-making processes of travelers and transportation authority as well as their interactions under both private and public transport system.…”

Section: Dtd Traffic Assignment Modelsmentioning

confidence: 99%

Day-to-day dynamic traffic assignment with imperfect information, bounded rationality and information sharing

Han

Ochieng

2020

Transportation Research Part C: Emerging Technologies

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This paper presents a doubly dynamic day-to-day (DTD) traffic assignment model with simultaneous route-and-departure-time (SRDT) choices while incorporating incomplete and imperfect information as well as bounded rationality. Two SRDT choice models are proposed to incorporate imperfect travel information: One based on multinomial Logit (MNL) model and the other on sequential, mixed multinomial/nested Logit model. These two variants, serving as based models, are further extended with two features: bounded rationality (BR) and information sharing. BR is considered by incorporating the indifference band into the random utility component of the MNL model, forming a BR-based DTD stochastic model. A macroscopic model of travel information sharing is integrated into the DTD dynamics to account for the impact of incomplete information on travelers' SRDT choices. These DTD choice models are combined with within-day dynamics following the Lighthill-Whitham-Richards (LWR) fluid dynamic network loading model. Simulations on large-scale networks (Anaheim) illustrate the interactions between users' adaptive decision making and network conditions (including local disruption) with different levels of information availability and user behavior. Our findings highlight the need for modeling network transient and disequilibriated states, which are often overlooked in equilibrium-constrained network design and optimization.

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“…Recently, some studies further extended the day-to-day model by considering departure time choice, stochastic context, bounded rationality (e.g., Cantarella and Watling, 2016;Guo et al, 2017;Ye et al, 2017) and even multimodal traffic (e.g., Cantarella, et al 2015;Li and Yang, 2016;Liu and Geroliminis, 2017). In addition, some studies incorporated the impacts of travel information in the day-to-day traffic dynamics (e.g., Bifulco et al, 2016;Li et al, 2018;Zhang et al, 2018). All these models satisfy the requirement of RAP, and focus on the day-today evolution process of traffic flows from non-equilibria to equilibria.…”

Section: Day-to-day Traffic Dynamicsmentioning

confidence: 99%

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

Liu

Liao

Qiu

et al. 2020

Transportation Research Part B: Methodological

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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.

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Advanced traveller information systems under recurrent traffic conditions: Network equilibrium and stability

Cited by 77 publications

References 36 publications

Exploration of day-to-day route choice models by a virtual experiment

Exploration of day-to-day route choice models by a virtual experiment

Day-to-day dynamic traffic assignment with imperfect information, bounded rationality and information sharing

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

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