A Traffic Flow Evolution Process toward Mixed Equilibrium with Multicriteria of Route Choice Behaviour

Abstract

Based on the price-quantity adjustment behaviour principle of disequilibrium theory, the route choices of travellers are also affected by a quantity signal known as traffic flow, while the route cost is considered as a price signal in economics. Considering the quantity signal’s effect among travellers, a new route comfort choice behaviour criterion and its corresponding equilibrium condition are established. The network travellers are classified into three groups according to their route choice behaviour: travellers in the first group choose the shortest route following the route rapidity behaviour criterion with complete information forming the UE (user equilibrium) pattern, travellers in the second group choose the most comfortable route following the route comfort behaviour criterion with complete information forming the QUE (quantity adjustment user equilibrium) pattern, and travellers in the third group choose a route according to their perceived travel time with incomplete information forming the SUE (stochastic user equilibrium) pattern. The traffic flows of all three groups converge to a new UE-QUE-SUE mixed equilibrium flow pattern after interaction. To depict the traveller-diversified choice behaviour and the traffic flow interaction process, a mixed equilibrium traffic flow evolution model is formulated. After defining the route comfort indicator and the corresponding user equilibrium state, the equilibrium conditions of the three group flows are given under a mixed equilibrium pattern. In addition, an equivalent mathematical programming of the mixed equilibrium traffic flow evolution model is proposed to demonstrate that the developed model converges to the mixed equilibrium state. Finally, numerical examples are examined to evaluate the effect of route comfort proportions on the traffic network flow evolution and analyse the performance of the proposed model.

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

“…In the QUE state, the route surplus capacities of all used routes between each OD pair are equal to the maximum surplus capacity and greater than (or equal to) the other routes with no flows [51].…”

“…We apply the route rapidity and comfort adjustment criteria stimulatingly to individual travel route selection behaviour, which means that individuals will comprehensively consider the route cost and the route surplus capacity to make a route decision. When applied to the traveller group, the aggregated effect is performed the way that some travellers choose the shortest route and some travellers choose the most comfortable route, as discussed in this study [51]. Specifically, travellers with route rapidity requirement are likely to choose the shortest route with the minimum route cost under the current traffic condition for their trips in the next day, and their traffic flow evolution process converges to the traditional UE state, while travellers with route comfort requirement are supposed to follow the route comfort behaviour criterion based on the current traffic condition, and their traffic flow evolves to a stable state, which is defined as QUE (quantity adjusting user equilibrium).…”

Research on the influence factors of intercity highway bus flow distribution based on random forest algorithm

“…Qing Keyan [2] and others screened travel distance, employment attraction, slow moving environment and traveler characteristics as the main characteristic variables to conduct research on slow traffic distribution prediction from the perspective of slow travel generation and travel distribution. Based on the 2013 and 2019 highway toll data of Jinan metropolitan area, Jiang Yong [3] and others used the null model method to analyze the spatial distribution pattern of highway traffic flow and the influencing factors through the distance decay function, and the results showed that the distance decay coefficient of highway traffic flow in Jinan metropolitan area was significantly negatively and linearly correlated with four factors, namely, economic density, population density, Gross Domestic Product (GDP), and the population, and the degree of influence decreased in order. And the degree of influence decreases in order.…”