Network Traffic Flow Evolution Model Based on Disequilibrium Theory

Huang, Zhongxiang; Wu, Jianhui; Huang, Ruqing; Xu, Yan

doi:10.1155/2018/8478910

Cited by 3 publications

(4 citation statements)

References 29 publications

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“…In our model, the weight λ of residual congestion is 50%. Meanwhile, the demand function for an OD pair is (25,20,15,20). The adjusted model parameters are listed in Table 2.…”

Section: Road Network Testmentioning

confidence: 99%

A Day-to-Day Stochastic Traffic Flow Assignment Model Based on Mixed Regulation

Li-xuan

Huang

et al. 2020

IEEE Access

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In many cases, the final path selection of travellers' is not the shortest path, due to the limited computing power and high cost of path search. To solve the problem, this paper proposes a day-today (DTD) stochastic traffic flow assignment model that regulates the traffic flow based on the travel time (travel cost) and residual congestion of optional paths. The regulation mechanism is called the mixed regulation. Then, the authored proved the existence, uniqueness and stability of the model solution. The proposed model was verified through simulation on a Nguyen-Dupuis road network. The results show that traffic flows and travel times of all paths reached the equilibrium state, thanks to the DTD mixed regulation for 20 ∼ 30 days. From the traffic flows and congestion degrees of different sections, it can be seen that our model with mixed regulation diverts the traffic flow to the sections with a low congestion degree, and encourages travellers to drive through the sections with a low traffic flow. In addition, the congestion degrees of the four most congested sections decreased by 5.8%, 4%, 7% and 1.2%, respectively, and the entire road network exhibited a slight downward trend in mean congestion degree. These results prove that our model can uniformize the traffic flow, improve the operation efficiency and alleviate the congestion of the road network. These findings shed new light on the control, guidance and planning of traffic flow in road networks.

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“…In our model, the weight λ of residual congestion is 50%. Meanwhile, the demand function for an OD pair is (25,20,15,20). The adjusted model parameters are listed in Table 2.…”

Section: Road Network Testmentioning

confidence: 99%

A Day-to-Day Stochastic Traffic Flow Assignment Model Based on Mixed Regulation

Li-xuan

Huang

et al. 2020

IEEE Access

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“…en, the car travel demand on OD pair , at day can be expressed as where ( ) is the minimum comprehensive cost of car travel on OD pair , at day , ( ) is the minimum comprehensive cost of bus travel on OD pair , at day , and is the sensitivity of car travelers to the minimum comprehensive cost. Hence, the bus travel demand on OD pair , at day can be written as To depict the day-to-day evolution process of dual-mode network tra c ow, this study uses the method of network tatonnement process to simulate the path (or generalized bus line) choice behavior of car travel (or bus travel) according to user equilibrium principle (Huang et al [30]). e excess travel cost on path on OD pair , at day is expressed as where represents the class of travel modes, = represents the car travel mode ( ∈ ), and = represents the bus travel mode ( ∈ ).…”

Section: Day-to-day Evolution Model Of Dual-mode Network Tra Cmentioning

confidence: 99%

“…Formula (28) is the constraint for bus travel demand. Formulas (29) and (30) are the day-to-day evolution of minimum comprehensive cost and path ow, respectively.…”

Section: Day-to-day Evolution Model Of Dual-mode Network Tra Cmentioning

confidence: 99%

Guidance Optimization of Travelers’ Travel Mode Choice Based on Fuel Tax Rate and Bus Departure Quantity in Two-Mode Transportation System

Sun

et al. 2020

Journal of Advanced Transportation

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This aim of this study is to improve the guidance role of the fuel tax rate and bus departure quantity on travel mode choice. Car and bus travel are chosen as the research object, and a day-to-day evolution model of dual-mode network traffic flow (based on a stochastic user equilibrium model and the method of network tatonnement process) is established. Subsequently, a guidance optimization model of fuel tax rate and bus departure quantity is designed. This guidance optimization model is formulated to determine the comprehensive minimum value among system total travel time of car travel, system total comprehensive cost of bus travel, and the difference between the total operating cost of bus departure increment and the total amount of fuel tax levied on car travelers. Through numerical examples, the validity of this guidance optimization model is verified, and the influence of fuel tax rate and bus departure quantity on the traffic network is analyzed. The results show that a guidance optimization scheme based on fuel tax rate and bus departure quantity can help regulate the proportion of car travel and improve bus service quality.

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“…It is apparent that all existing models in the literature were formulated based on a single route rapidity choice preference, where the route travel time is the core indicator. is approach has been widely applied in various network traffic flow models [14][15][16][17][18]. However, with the rapid economic growth and social development, people-oriented considerations, such as traffic safety and travel satisfaction, have been the dominant research direction [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Proportional-Switch Adjustment Model towards Mixed Equilibrium with Multiroute Choice Behaviour Criterion

Huang

Jiang

Hao

2020

Journal of Advanced Transportation

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Based on the price-quantity adjustment behaviour principle of the non-Walrasian equilibrium theory, this paper adopted a new QUE (quantity adjustment user equilibrium) criterion to formulate the route comfort choice behaviour. The purpose of the present paper is to establish a proportional-switch adjustment model which aims to reflect the route adjustment behaviour interaction between the traditional UE (user equilibrium) travellers and the QUE travellers and converge to a mixed equilibrium state. It is assumed that a group of road network travellers follow the UE criteria by choosing the travel route with the purpose of minimizing their route travel time (travel cost). In addition, the other group of travellers follow the QUE criteria by selecting the route with the largest residual capacity to achieve a more comfortable travel experience. The travel route adjustment behaviour of the two group travellers generates the dynamic traffic flow evolution towards the mixed equilibrium, and the route adjusting flow is proportional to the difference of traveller decision-making variable among the alternative routes. Simple illustrative examples are used to evaluate the performance of the proposed model, and the uniqueness and stability of the solution are demonstrated by applying the variational inequality and Lyapunov stability theorem.

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Network Traffic Flow Evolution Model Based on Disequilibrium Theory

Cited by 3 publications

References 29 publications

A Day-to-Day Stochastic Traffic Flow Assignment Model Based on Mixed Regulation

A Day-to-Day Stochastic Traffic Flow Assignment Model Based on Mixed Regulation

Guidance Optimization of Travelers’ Travel Mode Choice Based on Fuel Tax Rate and Bus Departure Quantity in Two-Mode Transportation System

A Proportional-Switch Adjustment Model towards Mixed Equilibrium with Multiroute Choice Behaviour Criterion

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