Analysis of Rising Tutition Rates in The United States Based on Clustering Analysis and Regression Models

Cheng, Long; You, Chenyu

doi:10.5121/csit.2016.60611

Cited by 2 publications

(2 citation statements)

References 8 publications

(8 reference statements)

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“…Koh et al [46] have used RL for real-time vehicle navigation and routing. Related lines of work [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63] tackled the optimal adaptive routing problem in a variety of stochastic and time-dependent settings. However, none of these studies have considered the traffic signal pre-emption coupling, nor did they took the context of EMV passage into account solving shortest path problems.…”

Section: Literature Reviewmentioning

confidence: 99%

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Su,

Zhong,

Chow

et al. 2022

Preprint

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Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on historical traffic-flow data and design traffic signal pre-emption accordingly; however, we still lack a systematic methodology to address the coupling between EMV routing and traffic signal control. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for joint dynamic EMV routing and traffic signal pre-emption. We adopt the multi-agent advantage actor-critic method with policy sharing and spatial discounted factor. This framework addresses the coupling between EMV navigation and traffic signal control via an innovative design of multi-class RL agents and a novel pressure-based reward function. The proposed methodology enables EMVLight to learn network-level cooperative traffic signal phasing strategies that not only reduce EMV travel time but also shortens the travel time of non-EMVs. Simulation-based experiments indicate that EMVLight enables up to a 42.6% reduction in EMV travel time as well as an 23.5% shorter average travel time compared with existing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Su,

Zhong,

Chow

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This is particularly evident in cases of image [1], smart grids [2], biological data [3] and education statistics [4], where high-dimensional representations are inevitable. However, in most cases, these high-dimensional representations are correlated in a certain fashion, and contain too much redundant information.…”

Section: Introductionmentioning

confidence: 99%

Random Projections for Non-linear Dimensionality Reduction

Cheng¹,

You²,

Guan³

2016

IJMLC

Self Cite

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Abstract-The need to analyze high-dimensional data in various areas, such as image processing, human gene regulation and smart grids, raises the importance of dimensionality reduction. While classical linear dimensionality reduction methods are easily implementable and efficiently computable, they fail to discover the true structure of high-dimensional data lying on a non-linear subspace. To overcome this issue, many non-linear dimensionality reduction approaches, such as Locally Linear Embedding, Isometric Embedding and Semidefinite Embedding, have been proposed. Though these approaches can learn the global structure of non-linear manifolds, they are computationally expensive, potentially limiting their use in large-scale applications involve very high-dimensional data. An innovative method framework that combines random projections and non-linear dimensionality reduction methods is proposed in this paper to increase computational speed and reduce memory usage, while preserving the non-linear data geometry. Illustrations with various combinations of random projections and non-linear dimensionality reduction methods tested on a hand-written digits dataset are also given in this paper to demonstrate that this method framework is both computationally efficient and globally optimal.Index Terms-Random projections, nonlinear dimensionality reduction, manifold learning, high dimensional data.

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Analysis of Rising Tutition Rates in The United States Based on Clustering Analysis and Regression Models

Cited by 2 publications

References 8 publications

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Random Projections for Non-linear Dimensionality Reduction

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