An Iterative Algorithm to Determine the Dynamic User Equilibrium in a Traffic Simulation Model

An iterative algorithm to determine the dynamic user equilibrium with respect to link costs defined by a traffic simulation model is presented. Each driver's route choice is modeled by a discrete probability distribution which is used to select a route in the simulation. After each simulation run, the probability distribution is adapted to minimize the travel costs. Although the algorithm does not depend on the simulation model, a queuing model is used for performance reasons. The stability of the algorithm is analyzed for a simple example network. As an application example, a dynamic version of Braess's paradox is studied.

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Comparison of Morning and Evening Commutes in the Vickrey Bottleneck Model

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1807-04 ◽

2002 ◽

Vol 1807 (1) ◽

pp. 26-33 ◽

Cited By ~ 45

Author(s):

André de Palma ◽

Robin Lindsey

Keyword(s):

Sufficient Conditions ◽

User Equilibrium ◽

Dynamic User Equilibrium ◽

Peak Period ◽

Time Pattern ◽

Travel Costs ◽

Bottleneck Model ◽

Schedule Delay ◽

Deterministic Dynamic ◽

Traveler Heterogeneity

Dynamic user equilibrium has received considerable theoretical attention for morning peak-period travel but very little for the evening peak. In an attempt to redress this imbalance, morning and evening travel are characterized and compared by using Vickrey’s bottleneck model. To focus ideas, it is assumed that morning and evening travel differ in just one respect: scheduling preferences for the morning are defined in terms of arrival time at work, whereas preferences for the evening are defined in terms of departure time from work. Sufficient conditions are identified for the existence and uniqueness of a deterministic dynamic user equilibrium in terms of departure times for the morning and evening peaks. These conditions, which go well beyond previous work, involve relatively general assumptions about the schedule delay cost functions for morning and evening and essentially no restrictions on the degree of heterogeneity in trip-timing preferences of travelers. Plausibility of the conditions is examined in light of the limited empirical evidence. A numerical example is developed at length to illustrate the importance of traveler heterogeneity and the extent of differences between morning and evening in the time pattern of departures and aggregate travel costs.

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Dynamic User Equilibrium of Mobility-on-Demand System with Linear Programming Rebalancing Strategy

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118821629 ◽

2019 ◽

Vol 2673 (1) ◽

pp. 447-459 ◽

Cited By ~ 2

Author(s):

Rongsheng Chen ◽

Michael W. Levin

Keyword(s):

Traffic Congestion ◽

Traffic Assignment ◽

Dynamic Traffic Assignment ◽

User Equilibrium ◽

Dynamic Traffic ◽

Dynamic User Equilibrium ◽

Total Delay ◽

Fleet Size ◽

Travel Costs ◽

On Demand

Mobility-on-demand (MoD) services are provided by multiple competing companies. In their competition for travelers, they need to provide minimum travel costs, or travelers will switch to competitors. This study developed a dynamic traffic assignment of MoD systems. A static traffic assignment (STA) model is first defined. When demand is asymmetric, empty rebalancing trips are required to move vehicles to traveler origins, and the optimal rebalancing flows are found by a linear program. Because of the time-dependent nature of traveler demand, the model was converted to dynamic traffic assignment (DTA). The method of successive averages, which is provably convergent for STA, was used to find dynamic user equilibrium (DUE). The simulation was conducted on two networks. The MoD system was simulated with different fleet sizes and demands. The results showed that the average total delay and travel distance decreased with the increase in fleet size whereas the average on-road travel time increased with the fleet size. The result of traffic assignment of one network with MoD system was compared with a network where all travelers use private vehicles. The results showed that the network with MoD system created more trips but less traffic congestion.

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Intelligent control of complex traffic conditions based on machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219063 ◽

2021 ◽

pp. 1-12

Author(s):

Zhe Li

Keyword(s):

Machine Learning ◽

Simulation Model ◽

Traffic Simulation ◽

Learning Algorithms ◽

Control Model ◽

Machine Learning Algorithms ◽

Transmission Model ◽

Cell Transmission Model ◽

Traffic Conditions ◽

Simulation Control

In order to improve the simulation effect of complex traffic conditions, based on machine learning algorithms, this paper builds a simulation model. Starting from the macroscopic traffic flow LWR theory, this paper introduces the process of establishing the original CTM mathematical model, and combines it with machine learning algorithms to improve it, and establishes the variable cell transmission model VCTM ordinary transmission, split transmission, and combined transmission mathematical expressions. Moreover, this paper establishes a road network simulation model to calibrate related simulation parameters. In addition, this paper combines the actual needs of complex traffic conditions analysis to construct a complex traffic simulation control model based on machine learning, and designs a hybrid microscopic traffic simulation system architecture to simulate all relevant factors of complex road conditions. Finally, this paper designs experiments to verify the performance of the simulation model. The research results show that the simulation control model of complex traffic conditions constructed in this paper has certain practical effects.

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