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.

Modeling User Equilibrium in Microscopic Transportation Simulation

2013 ◽  
Author(s):  
Liang-Chieh (Victor) Cheng ◽  
Heng Wang
Keyword(s):  
Equilibrium State ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
User Equilibrium ◽  
Microscopic Model ◽  
Dynamic Traffic ◽  
Dynamic User Equilibrium ◽  
Equilibrium Study ◽  
Assignment Method ◽  
Transportation Simulation

User equilibrium refers to the network-wide state where individual travelers cannot gain improvement by unilaterally changing their behaviors. The Wardropian Equilibrium has been the focus of a transportation equilibrium study. This paper modifies the dynamic traffic assignment method through utilizing the TRANSIMS system to reach the dynamic user equilibrium state in a microscopic model. The focus of research is developing three heuristics in a Routing-Microsimulation-Equilibrating order for reaching system-wide equilibrium while simultaneously minimizing the computing burden and execution. The heuristics are implemented to a TRANSIMS model to simulate a subarea of Houston, TX.

Simulation-Based Dynamic Traffic Assignment with Continuously Distributed Value of Time for Heterogeneous Users

2021 ◽  
pp. 036119812110447
Author(s):  
Ye Tian ◽  
Yi-Chang Chiu
Keyword(s):  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
User Equilibrium ◽  
Successful Implementation ◽  
Value Of Time ◽  
Good Representation ◽  
Dynamic Traffic ◽  
Pricing Scheme ◽  
Simulation Based ◽  
The Individual

The value of time (VOT) attribute is usually utilized to represent the trade-off between time and monetary expenses in transportation problems. A good representation of VOT is essential for evaluation of any road pricing scheme. Conventionally, in dynamic traffic assignment models, VOT is considered as either constant or finite discrete among travelers because of memory and computational limitations, which in turn could introduce bias in the results. This research explicitly models the individual bi-criteria dynamic user equilibrium (IBDUE) problem and presents a distinct simulation-based solution algorithm that enables individual-based traffic assignment within reasonable run time with a successful implementation of variable and continuously distributed VOT in a simulation-based dynamic traffic assignment package. Numerical analysis reveals that the constant and discrete VOT models tend to overestimate toll road usage compared with the continuous VOT model when the toll charge is low, and underestimate it when the toll charge is high, which reflects previous studies. In the meantime, an experiment on a real-world congestion pricing scheme demonstrates the capability of the proposed algorithm on evaluating flow-dependent pricing schemes.

Estimating Path Travel Costs for Heterogeneous Users on Large-Scale Networks: Heuristic Approach to Integrated Activity-Based Model–Dynamic Traffic Assignment Models

2017 ◽  
Vol 2667 (1) ◽  
pp. 119-130 ◽  
Author(s):  
Xiang (Alex) Xu ◽  
Fatemeh Fakhrmoosavi ◽  
Ali Zockaie ◽  
Hani S. Mahmassani
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Heuristic Approach ◽  
Dynamic Traffic ◽  
Travel Costs ◽  
Vehicle Trajectories ◽  
Optimal Paths ◽  
Large Scale Networks ◽  
Generalized Cost

Integrating activity-based models (ABMs) with simulation-based dynamic traffic assignment (DTA) have gained attention from transportation planning agencies seeking tools to address the arising planning challenges as well as transportation policies such as road pricing. Optimal paths with least generalized cost are needed to route travelers at the DTA level, while at the ABM level, only the least generalized cost information is needed (without fully specified paths). Thus, rerunning (executing) the least generalized cost path-finding algorithm at each iteration of ABM and DTA does not seem to be efficient, especially for large-scale networks. Furthermore, storing the dynamic travel cost skims for multiclass users as an alternative approach is not efficient either in regard to memory requirements. In this study, the aim was to estimate the least generalized cost so as to be used in destination and mode choice models at the ABM level. A heuristic approach was developed to use the simulated vehicle trajectories that were assigned to the optimal paths in the DTA level to estimate different cost measures, including distance, time, and monetary cost associated with the least generalized cost path for any given combination of the origin, destination, and departure time (ODT) and value of time. The proposed approximation method presented in this study used vehicle trajectories, aligned with the origin–destination direction and located in a specific boundary shaping an ellipse around the origin and destination zones at a certain time window, to estimate travel costs for the given ODT and user class. Numerical results for two real-world networks suggest the applicability of the method in large-scale networks in addition to its lower computational burden, including solution time and memory requirements, relative to other alternative approaches.

scholarly journals Dynamic traffic assignment with a node-based cell transmission model satisfying the link-level first-in-first-out principle

2019 ◽  
Author(s):  
Sai Kiran Mayakuntla ◽  
Ashish Verma
Keyword(s):  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
User Equilibrium ◽  
Standard Test ◽  
Transmission Model ◽  
Efficient Computation ◽  
Cell Transmission Model ◽  
Dynamic Traffic ◽  
Cell Transmission ◽  
First In First Out

This paper develops node-based formulations for user equilibrium (UE) and system optimum (SO) dynamic traffic assignment (DTA) problems with departure time choice and route choices for general multiple origin-destination networks. Both the formulations are embedded with a new cell transmission model that satisfies the link-level First-In-First-Out (FIFO) principle. Because the formulations are node-based, the need for path enumeration is obviated, which results in considerable computational efficiency compared to the existing path-based models. While this advantage of node-based (or bush-based) models has been widely accepted in the literature of static traffic assignment, the formulations of dynamic traffic assignment models have mostly been path-based. The present work first describes a node-based cell transmission model that satisfies the link-level FIFO principle, which is fit within a DTA framework that facilitates efficient computation of UE and SO solutions. Further contributions of the work include the introduction of a backpropagation algorithm to efficiently compute marginal costs and complementarity formulations of the problems. Finally, numerical results are presented to demonstrate the performance of the proposed models using two standard test networks, along with a discussion of their convergence.

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