A bi-criterion dynamic user equilibrium traffic assignment model and solution algorithm for evaluating dynamic road pricing strategies

2008 ◽  
Vol 16 (4) ◽  
pp. 371-389 ◽  
Author(s):  
Chung-Cheng Lu ◽  
Hani S. Mahmassani ◽  
Xuesong Zhou
Keyword(s):  
Traffic Assignment ◽  
Road Pricing ◽  
User Equilibrium ◽  
Solution Algorithm ◽  
Pricing Strategies ◽  
Dynamic User Equilibrium ◽  
Assignment Model

Probit-Based Time-Dependent Stochastic User Equilibrium Traffic Assignment Model

2008 ◽  
Vol 2085 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Kuilin Zhang ◽  
Hani S. Mahmassani ◽  
Chung-Cheng Lu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Spatial Correlation ◽  
Traffic Assignment ◽  
User Equilibrium ◽  
The Other ◽  
Time Dependent ◽  
Stochastic User Equilibrium ◽  
Assignment Model ◽  
Temporal And Spatial

This study presents a time-dependent stochastic user equilibrium (TDSUE) traffic assignment model within a probit-based path choice decision framework that explicitly takes into account temporal and spatial correlation (traveler interactions) in travel disutilities across a set of paths. The TDSUE problem, which aims to find time-dependent SUE path flows, is formulated as a fixed-point problem and solved by a simulation-based method of successive averages algorithm. A mesoscopic traffic simulator is employed to determine (experienced) time-dependent travel disutilities. A time-dependent shortest-path algorithm is applied to generate new paths and augment a grand path set. Two vehicle-based implementation techniques are proposed and compared in order to show their impact on solution quality and computational efficiency. One uses the classical Monte Carlo simulation approach to explicitly compute path choice probabilities, and the other determines probabilities by sampling vehicles’ path travel costs from an assumed perception error distribution (also using a Monte Carlo simulation process). Moreover, two types of variance-covariance error structures are discussed: one considers temporal and spatial path choice correlation (due to path overlapping) in terms of aggregated path travel times, and the other uses experienced (or empirical) path travel times from a sample of individual vehicle trajectories. A set of numerical experiments are conducted to investigate the convergence pattern of the solution algorithms and to examine the impact of temporal and spatial correlation on path choice behavior.

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

2019 ◽  
Vol 2673 (1) ◽  
pp. 447-459 ◽  
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.

Trial-and-Error Procedure for Implementing a Road-Pricing Scheme

2005 ◽  
Vol 1923 (1) ◽  
pp. 103-109 ◽  
Author(s):  
Qiang Meng ◽  
Wei Xu ◽  
Hai Yang
Keyword(s):  
Travel Time ◽  
Assignment Problem ◽  
Traffic Assignment ◽  
Road Pricing ◽  
User Equilibrium ◽  
Trial And Error ◽  
Traffic Flows ◽  
The Road ◽  
Traffic Assignment Problem ◽  
Pricing Scheme

This paper deals with a road-pricing scheme that aims at alleviating congestion phenomena or air and noise pollution in some areas of a transportation network in such a way that the volumes of traffic flow on entry links to these areas do not exceed their respective predetermined thresholds by levying appropriate tolls at these links. This paper begins to show that the road-pricing scheme is equivalent to a problem that determines optimal Lagrangian multipliers for a user equilibrium traffic assignment problem with link capacity constraints. It then proceeds to devise a novel trial-and-error procedure requiring observed traffic flows at the entry links only, to identify a solution for the road-pricing scheme when link travel time functions, origin–destination demand functions, and users’ value of travel time are unknown. The procedure is as follows. A trial on a set of given tolls is conducted, and then the resultant link flows are observed. According to these observed traffic flows, a new set of tolls for the next trial is adjusted by executing a simple projection operation. The trial-and-error procedure is, in fact, a variation of a gradient projection method for dual formulation of the traffic assignment problem, and its convergence can be guaranteed under mild conditions. Accordingly, a conjecture for the convergence of trial-and-error implementation of the congestion pricing proposed by economists is rigorously proved. Furthermore, the iterative procedure presented in this paper in practice can facilitate the estimation of such tolls by land transport authorities.

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