Dynamic User Equilibrium Departure Time and Route Choice on Idealized Traffic Arterials

1984 ◽  
Vol 18 (4) ◽  
pp. 362-384 ◽  
Author(s):  
Hani Mahmassani ◽  
Robert Herman
Keyword(s):  
Route Choice ◽  
User Equilibrium ◽  
Dynamic User Equilibrium ◽  
Departure Time

scholarly journals A Dynamic Day-To-Day Departure Time and Route Choice Model for Bounded-Rational Individuals

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lingjuan Chen ◽  
Yu Wang ◽  
Dongfang Ma
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Choice Model ◽  
Route Choice ◽  
User Equilibrium ◽  
Navigation Systems ◽  
Dynamic Learning ◽  
Traffic State ◽  
Departure Time ◽  
Navigation Information

Accurate prediction of travellers’ day-to-day departure time and route choice is critical in advanced traffic management systems. There have been several related works about route choice with the assumption that the departure time for individual travellers is known beforehand. With real-time traffic state information provided by navigation systems and previous historical experience, travellers will dynamically update their departure time, which is neglected in existing works. In this study, we aim to describe travellers’ spatial-temporary choice behaviour taking navigation information into account and propose a bounded-rational day-to-day dynamic learning and adjustment model. The new model contains three steps. First, the real-time navigation guidance on each discrete day is obtained, and the self-learned experience of travellers’ choices with navigation information is presented; then, the day-to-day revision process of the choices is derived to maximize departure and route choice prospect; next, by aggregating each individual’s behaviour and calculating route choice probability, a bounded-rational continuous day-to-day dynamic model is provided. Numerical experiments suggest that the proposed model converges to a spatial-temporal oscillating equilibrium not a fixed-point stable status, and the final equilibrium trend is different from classical user equilibrium. The findings of the study are helpful to improve the prediction accuracy of traffic state in urban street networks.

Dynamic user equilibrium for departure time choice in the basic trip-based model

2021 ◽  
Vol 128 ◽  
pp. 103190
Author(s):  
Renxin Zhong ◽  
Jianhui Xiong ◽  
Yunping Huang ◽  
Nan Zheng ◽  
William H.K. Lam ◽  
...  
Keyword(s):  
User Equilibrium ◽  
Dynamic User Equilibrium ◽  
Departure Time ◽  
Departure Time Choice

A cell-based multiple vehicle type dynamic user equilibrium model with physical queues

2016 ◽  
Vol 43 (1) ◽  
pp. 1-12 ◽  
Author(s):  
ShuGuang Li
Keyword(s):  
Equilibrium Model ◽  
User Equilibrium ◽  
Transmission Model ◽  
Single Type ◽  
Cell Transmission Model ◽  
Dynamic User Equilibrium ◽  
Vehicle Type ◽  
Time Space ◽  
Departure Time ◽  
A Cell

This paper proposes a cell-based multiple vehicle type dynamic user equilibrium model with physical queues. A single-type traffic flow model is extended to a general case with multiple vehicle types that can be partly solved by the time-space discretization method. Then, a network version of the multiple vehicle type cell transmission model is given. An integrated variational inequality (VI) formulation is presented to capture the complex traveler choice behaviors such as route and departure time choices. Furthermore, a genetic algorithm with a flow-swapping method is adopted to solve the VI problem. Two examples are used to evaluate the properties of this formulation. The results show that the model can reflect dynamic phenomena, such as multiple vehicle type speed consistent under congested conditions, queue formation and dissipation and so on. Moreover, the solutions can approximately follow the multiple vehicle type dynamic route and departure time user equilibrium conditions.

Application of Stochastic Learning Automata for Modeling Departure Time and Route Choice Behavior

2002 ◽  
Vol 1807 (1) ◽  
pp. 154-162 ◽  
Author(s):  
Kaan Ozbay ◽  
Aleek Datta ◽  
Pushkin Kachroo
Keyword(s):  
Learning Automata ◽  
Route Choice ◽  
User Equilibrium ◽  
Learning Behavior ◽  
Reinforcement Scheme ◽  
Route Choice Behavior ◽  
Long Run ◽  
Stochastic Learning ◽  
Departure Time ◽  
Departure Time Choice

Stochastic learning automata (SLA) theory is used to model the learning behavior of commuters within the context of the combined departure time route choice (CDTRC) problem. The SLA model uses a reinforcement scheme to model the learning behavior of drivers. A multiaction linear reward-ϵ-penalty reinforcement scheme was introduced to model the learning behavior of travelers based on past departure time choice and route choice. A traffic simulation was developed to test the model. The results of the simulation are intended to show that drivers learn the best CDTRC option, and the network achieves user equilibrium in the long run. Results indicate that the developed SLA model accurately portrays the learning behavior of drivers, while the network satisfies user equilibrium conditions.

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