Comparing Multi-Modal Traffic Assignments in Large-Scale Simulations using the Macroscopic Fundamental Diagram: Mode Shift from Cars to Powered Two Wheelers

This paper investigates traffic dynamics in bimodal urban networks utilizing the macroscopic fundamental diagram (MFD) and the three-dimensional macroscopic fundamental diagram (3D-MFD), which are network-level traffic flow modeling tools. Although the existence and the properties of the MFD have been extensively analyzed with field data in literature, few empirical studies examine these features of the 3D-MFDs for large-scale networks. For this work, GPS data for cars and buses running in the network of Shenzhen city in China are available for analysis and this offers a great opportunity for the investigation. Interestingly, both MFD and 3D-MFD dynamics are reflected in the data. Network partition is performed to reduce the hysteresis on the MFD and the network is split into two regions for further analysis. Then the investigation focuses on the MFD relationship for buses only. The average passenger occupancy is estimated and incorporated to generate a passenger MFD (pMFD) for buses. Moreover, bus operation on dedicated bus lanes is analyzed. Having understood traffic dynamics of cars, buses, and passengers respectively, the 3D-MFDs which illustrate the joint influence of car and bus accumulations on the global network-level traffic performance are presented. Given the scatter plot of the 3D-MFDs for the two partitioned regions, analytical approximations are provided, fitting by exponential functions. These results are promising, as they confirm the traffic features that were found from simulation-based studies in previous work.

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An HPC Framework for Large Scale Simulations and Visualizations of Oil Spill Trajectories

Coastal Hazards ◽

10.1061/9780784412664.002 ◽

2013 ◽

Cited By ~ 1

Author(s):

Jian Tao ◽

Werner Benger ◽

Kelin Hu ◽

Edwin Mathews ◽

Marcel Ritter ◽

...

Keyword(s):

Oil Spill ◽

Large Scale ◽

Large Scale Simulations

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Large-Scale Simulations of Monolithic and Segmented Projectiles Impacting Spaced Armor

10.21236/ada219374 ◽

1990 ◽

Author(s):

Daniel R. Scheffler ◽

Thomas M. Sherrick

Keyword(s):

Large Scale ◽

Large Scale Simulations

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A network-wide anticipatory control of an urban network using macroscopic fundamental diagram

Transportmetrica B Transport Dynamics ◽

10.1080/21680566.2021.1878966 ◽

2021 ◽

Vol 9 (1) ◽

pp. 415-436

Author(s):

Nadia Moshahedi ◽

Lina Kattan ◽

Richard Tay

Keyword(s):

Anticipatory Control ◽

Fundamental Diagram ◽

Urban Network ◽

Macroscopic Fundamental Diagram

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Measuring sleep regularity: Theoretical properties and practical usage of existing metrics

SLEEP ◽

10.1093/sleep/zsab103 ◽

2021 ◽

Author(s):

Dorothee Fischer ◽

Elizabeth B Klerman ◽

Andrew J K Phillips

Keyword(s):

Large Scale ◽

Research Question ◽

Group Differences ◽

Regularity Index ◽

Wake Patterns ◽

Health Related ◽

Phase Deviation ◽

Unbiased Estimates ◽

Large Scale Simulations ◽

Larger Sample

Abstract Study Objectives Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Methods Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: ‘scrambling’ the order of days; daily vs. weekly variation; naps; awakenings; ‘all-nighters’; and length of study. Results SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. Conclusions Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

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Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽

10.3390/a14050154 ◽

2021 ◽

Vol 14 (5) ◽

pp. 154

Author(s):

Marcus Walldén ◽

Masao Okita ◽

Fumihiko Ino ◽

Dimitris Drikakis ◽

Ioannis Kokkinakis

Keyword(s):

Large Scale ◽

Data Driven ◽

Data Sets ◽

Output Constraints ◽

Data Driven Approach ◽

Scientific Simulations ◽

Multiple Metrics ◽

In Transit ◽

Multiple Compression ◽

Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

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Brain stem – from general view to computational model based on switchboard rules of operation

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2019-0059 ◽

2019 ◽

Vol 16 (1) ◽

Author(s):

Włodzisław Duch ◽

Dariusz Mikołajewski

Keyword(s):

Brain Stem ◽

Large Scale ◽

Ionic Channels ◽

General View ◽

The Central Nervous System ◽

Network States ◽

Large Scale Simulations ◽

Influence Of Noise ◽

The Brain ◽

Sufficient Precision

Abstract Despite great progress in understanding the functions and structures of the central nervous system (CNS) the brain stem remains one of the least understood systems. We know that the brain stem acts as a decision station preparing the organism to act in a specific way, but such functions are rather difficult to model with sufficient precision to replicate experimental data due to the scarcity of data and complexity of large-scale simulations of brain stem structures. The approach proposed in this article retains some ideas of previous models, and provides more precise computational realization that enables qualitative interpretation of the functions played by different network states. Simulations are aimed primarily at the investigation of general switching mechanisms which may be executed in brain stem neural networks, as far as studying how the aforementioned mechanisms depend on basic neural network features: basic ionic channels, accommodation, and the influence of noise.

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