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

Abstract

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.

Mobile computing and communications-driven fog-assisted disaster evacuation techniques for context-aware guidance support: A survey

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“…[158] Hybrid algorithm implementation on tripartite graph to rank node importance. Traffic assignment [159] Traffic flow regulation based on daily-basis stochastic model. [160] Traffic light scheduling system using proposed controlling algorithm.…”

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

“…Existing traffic assignment problems studied by researchers can be classified into two categories: (a) Static Traffic Assignment (STA) [3]; (b) Dynamic Traffic Assignment (DTA) [4].…”

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

“…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].…”