scholarly journals Computational Methods for Calculating Multimodal Multiclass Traffic Network Equilibrium: Simulation Benchmark on a Large-Scale Test Case

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Mostafa Ameli ◽  
Jean-Patrick Lebacque ◽  
Ludovic Leclercq
Keyword(s):  
Computational Methods ◽  
Large Scale ◽  
Dynamic Traffic Assignment ◽  
Dynamic Network ◽  
Computation Time ◽  
Test Case ◽  
Network Equilibrium ◽  
Loading Mode ◽  
Network Loading ◽  
Metaheuristic Methods

This study reviews existing computational methods to calculate simulation-based dynamic network equilibrium. We consider a trip-based multimodal approach for the dynamic network loading. Mode and path choices are carried out at the same level; therefore, travel times depend on the travel path and the mode attributes of travelers. This study develops a multiclass model with several parameters per class. Two different categories of algorithms (heuristic and metaheuristic) are considered in order to solve the discrete dynamic traffic assignment (DTA) problem. Finally, we analyze the equilibrium in a large-scale multimodal DTA test case (Lyon 6th + Villeurbanne) in order to investigate the performance of different optimization approaches to solve trip-based DTA. The results show that, in a multimodal and heterogeneous setting, the metaheuristic methods provide better solutions than the heuristic methods in terms of optimality and computation time. These improvements are even more significant than in a homogeneous setting.

scholarly journals Simulation-Based Dynamic Passenger Flow Assignment Modelling for a Schedule-Based Transit Network

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Xiangming Yao ◽  
Baomin Han ◽  
Dandan Yu ◽  
Hui Ren
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Dynamic Traffic Assignment ◽  
Dynamic Network ◽  
Complete Information ◽  
Route Selection ◽  
Transit Networks ◽  
Simulation Based ◽  
Flow Equilibrium ◽  
Passenger Assignment

The online operation management and the offline policy evaluation in complex transit networks require an effective dynamic traffic assignment (DTA) method that can capture the temporal-spatial nature of traffic flows. The objective of this work is to propose a simulation-based dynamic passenger assignment framework and models for such applications in the context of schedule-based rail transit systems. In the simulation framework, travellers are regarded as individual agents who are able to obtain complete information on the current traffic conditions. A combined route selection model integrated with pretrip route selection and entrip route switch is established for achieving the dynamic network flow equilibrium status. The train agent is operated strictly with the timetable and its capacity limitation is considered. A continuous time-driven simulator based on the proposed framework and models is developed, whose performance is illustrated through a large-scale network of Beijing subway. The results indicate that more than 0.8 million individual passengers and thousands of trains can be simulated simultaneously at a speed ten times faster than real time. This study provides an efficient approach to analyze the dynamic demand-supply relationship for large schedule-based transit networks.

Dynamic network equilibrium for daily activity-trip chains of heterogeneous travelers: application to large-scale networks

2016 ◽  
Vol 43 (6) ◽  
pp. 1041-1059 ◽  
Author(s):  
Hooram Halat ◽  
Ali Zockaie ◽  
Hani S. Mahmassani ◽  
Xiang Xu ◽  
Omer Verbas
Keyword(s):  
Daily Activity ◽  
Large Scale ◽  
Dynamic Network ◽  
Network Equilibrium ◽  
Large Scale Networks

Dynamic Network Loading Model with Explicit Traffic Wave Tracking

2008 ◽  
Vol 2085 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Thomas Durlin ◽  
Vincent Henn
Keyword(s):  
Dynamic Traffic Assignment ◽  
Dynamic Network ◽  
Traffic Signal ◽  
Signal Model ◽  
Tracking Method ◽  
Dynamic Network Loading ◽  
Network Loading ◽  
Intersection Model ◽  
Traffic Wave ◽  
First Time

A dynamic network loading (DNL) model is presented: it can be used both for dynamic traffic assignment (DTA) and for an accurate description of traffic. The proposed DNL model is composed of (a) the link model based on the Lighthill-Whitham-Richards macroscopic first-order model solved with the wave-tracking method, (b) a new intersection model that generalizes the Daganzo macroscopic merge model to complex intersections, and (c) a traffic signal model that represents the mean effects of stage alternation on traffic in terms of delays and of capacity restrictions. Both the wave-tracking method and the traffic signal model are applied in a network context for the first time. The model can be consistently solved with various precision scales: low-precision scales to quickly provide a good estimation of travel times on the network for DTA purposes, and high-precision scales for accurate descriptions of traffic with all the desired modeling details.

Double Precision Is Not Needed for Many-Body Calculations: New Conventional Wisdom

2018 ◽  
Author(s):  
Pavel Pokhilko ◽  
Evgeny Epifanovsky ◽  
Anna I. Krylov
Keyword(s):  
Large Scale ◽  
Computation Time ◽  
Coupled Cluster ◽  
Double Precision ◽  
Many Body ◽  
Single Precision ◽  
Parallel Performance ◽  
Point Representation ◽  
Electron Repulsion Integrals ◽  
Cluster Methods

Using single precision floating point representation reduces the size of data and computation time by a factor of two relative to double precision conventionally used in electronic structure programs. For large-scale calculations, such as those encountered in many-body theories, reduced memory footprint alleviates memory and input/output bottlenecks. Reduced size of data can lead to additional gains due to improved parallel performance on CPUs and various accelerators. However, using single precision can potentially reduce the accuracy of computed observables. Here we report an implementation of coupled-cluster and equation-of-motion coupled-cluster methods with single and double excitations in single precision. We consider both standard implementation and one using Cholesky decomposition or resolution-of-the-identity of electron-repulsion integrals. Numerical tests illustrate that when single precision is used in correlated calculations, the loss of accuracy is insignificant and pure single-precision implementation can be used for computing energies, analytic gradients, excited states, and molecular properties. In addition to pure single-precision calculations, our implementation allows one to follow a single-precision calculation by clean-up iterations, fully recovering double-precision results while retaining significant savings.

scholarly journals A large-scale 2005–2012 flood map record derived from ENVISAT-ASAR data: United Kingdom as a test case

2021 ◽  
Vol 256 ◽  
pp. 112338
Author(s):  
Jie Zhao ◽  
Ramona Pelich ◽  
Renaud Hostache ◽  
Patrick Matgen ◽  
Wolfgang Wagner ◽  
...  
Keyword(s):  
United Kingdom ◽  
Large Scale ◽  
Test Case ◽  
Envisat Asar

Distributed learning with indefinite kernels

2019 ◽  
Vol 17 (06) ◽  
pp. 947-975 ◽  
Author(s):  
Lei Shi
Keyword(s):  
Large Scale ◽  
Substantial Reduction ◽  
Computation Time ◽  
Distributed Learning ◽  
Rates Of Convergence ◽  
Regression Problem ◽  
Data Set ◽  
Regularization Scheme ◽  
Original Algorithm ◽  
Indefinite Kernel

We investigate the distributed learning with coefficient-based regularization scheme under the framework of kernel regression methods. Compared with the classical kernel ridge regression (KRR), the algorithm under consideration does not require the kernel function to be positive semi-definite and hence provides a simple paradigm for designing indefinite kernel methods. The distributed learning approach partitions a massive data set into several disjoint data subsets, and then produces a global estimator by taking an average of the local estimator on each data subset. Easy exercisable partitions and performing algorithm on each subset in parallel lead to a substantial reduction in computation time versus the standard approach of performing the original algorithm on the entire samples. We establish the first mini-max optimal rates of convergence for distributed coefficient-based regularization scheme with indefinite kernels. We thus demonstrate that compared with distributed KRR, the concerned algorithm is more flexible and effective in regression problem for large-scale data sets.

Model and algorithm of stochastic dynamic traffic assignment based on dynamic rainfall intensity

2021 ◽  
pp. 1-11
Author(s):  
Xun Ji ◽  
Chunfu Shao
Keyword(s):  
Cost Function ◽  
Traffic Flow ◽  
Rainfall Intensity ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Network ◽  
Travel Cost ◽  
Stochastic Dynamic ◽  
Dynamic Traffic ◽  
Rainy Weather

Frequent occurrence of urban rainy weather, especially rainstorm weather, affects transportation operation and safety, so it is essential that effective intervention measures to recover disordered traffic be adopted and then analyzed for their influence on the dynamic network. Therefore, models and algorithm to show dynamic traffic flow of traffic network in rainy weather are a fundamental need and have drawn great interest from governments and scholars. In this paper, innovative content contains a travel cost function considering rainfall intensity; considering the travel cost function, a dynamic traffic assignment model based on dynamic rainfall intensity is built. Then a corresponding algorithm is designed. Moreover, this study designs three scenarios under rainfall and analyzes the influence of the rainfall on an example network. The results show that rainfall has a significant effect on traffic flow. The finding proved the proposed models and algorithm can express the development trend of path flow rate on a dynamic network under rainfall.

scholarly journals A study of computational methods for wake structure and base pressure prediction of a generic SUV model with fixed and rotating wheels

2017 ◽  
Vol 231 (9) ◽  
pp. 1222-1238 ◽  
Author(s):  
David Forbes ◽  
Gary Page ◽  
Martin Passmore ◽  
Adrian Gaylard
Keyword(s):  
Experimental Data ◽  
Computational Methods ◽  
Large Scale ◽  
Ground Plane ◽  
Computational Cost ◽  
Rear Wheel ◽  
Base Pressure ◽  
Detached Eddy Simulation ◽  
Wake Structure ◽  
Wake Interactions

This study is an evaluation of the computational methods in reproducing experimental data for a generic sports utility vehicle (SUV) geometry and an assessment on the influence of fixed and rotating wheels for this geometry. Initially, comparisons are made in the wake structure and base pressures between several CFD codes and experimental data. It was shown that steady-state RANS methods are unsuitable for this geometry due to a large scale unsteadiness in the wake caused by separation at the sharp trailing edge and rear wheel wake interactions. unsteady RANS (URANS) offered no improvements in wake prediction despite a significant increase in computational cost. The detached-eddy simulation (DES) and Lattice–Boltzmann methods showed the best agreement with the experimental results in both the wake structure and base pressure, with LBM running in approximately a fifth of the time for DES. The study then continues by analysing the influence of rotating wheels and a moving ground plane over a fixed wheel and ground plane arrangement. The introduction of wheel rotation and a moving ground was shown to increase the base pressure and reduce the drag acting on the vehicle when compared to the fixed case. However, when compared to the experimental standoff case, variations in drag and lift coefficients were minimal but misleading, as significant variations to the surface pressures were present.

Sign in / Sign up

Export Citation Format

Share Document