How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach

Overview

EV-charging-impact

This repository contains the code that has been used for the Queue modelling for the paper "How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach" by Artur Grigorev, Tuo Mao, Adam Berry, Joachim Tan, Loki Purushothaman, Adriana-Simona Mihaita. The paper has been published and presented during the IEEE ITSC 2021 conference. The preprint is available: https://arxiv.org/abs/2110.14064 .

You can find a working queue model in "queue_model.py" file.

This EV charging station queue simulation program reads file "Northern_Sydney_EV_charger_list.csv" and outputs queue simulation results into file "q2080_2016_seq.csv". It relies on multiprocessing package to perform parallel simulation.

Input parameters of the model:

  1. Duration of modeling (day, week, month)
  2. Number of plugs on EV stations
  3. Distribution of time intervals between arrivals
  4. Distribution of charging time: normaly distributed between 20% and 80%.
  5. Max queue size
  6. Power supply at EV charger: KW/h

Model output:

  • (O1) Mean queue length of an EV station [n]'] = HOURQUEUE[i]
  • (O2) Mean waiting time in queue at an EV station [hours]
  • (O3) Mean service time to charge at an EV station [hours]
  • (O4) Total time spent overall at an EV station [hours]
  • (O5) Total energy consumption of an EV station [kWh]
  • (O6) Maximum recorded queue length of an EV station [n]
  • (O7) Maximum waiting time in queue at an EV station [hours]
  • (O8) Maximum time spent overall at an EV station [hours]
  • (O9) Maximal energy consumption of an EV station [kW]
  • Consumed electricity by hour [kWh]
  • Total waiting time (minutes) by hour
  • Overall Mean Service time/day'

queue model

To perform calculations for specific OD traffic flow (2016, OD15, OD30) change the line: DICT['StationFlow'] = float(dt[dt.Name==N]['2016 volume']) at the "Setup" section (to 2016, 15 or 30).

The structure of the framework: framework

The code to produce lineplots is in "lineplots.ipynb":

lineplot

lineplot2

The code to produce supplementary animation is in "anim.ipynb": anim

Code for the paper titled "Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks" (NeurIPS 2021 Spotlight).

Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks This repository contains the code and pre-trained

Hassan Dbouk 7 Dec 05, 2022
Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN

Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN Introduction Image super-resolution (SR) is the process of recovering high-resoluti

8 Apr 15, 2022
Learning Temporal Consistency for Low Light Video Enhancement from Single Images (CVPR2021)

StableLLVE This is a Pytorch implementation of "Learning Temporal Consistency for Low Light Video Enhancement from Single Images" in CVPR 2021, by Fan

99 Dec 19, 2022
Official code of paper: MovingFashion: a Benchmark for the Video-to-Shop Challenge

SEAM Match-RCNN Official code of MovingFashion: a Benchmark for the Video-to-Shop Challenge paper Installation Requirements: Pytorch 1.5.1 or more rec

HumaticsLAB 31 Oct 10, 2022
Multimodal commodity image retrieval 多模态商品图像检索

Multimodal commodity image retrieval 多模态商品图像检索 Not finished yet... introduce explain:The specific description of the project and the product image dat

hongjie 8 Nov 25, 2022
Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

The official implementation of Mask-aware IoU and maYOLACT detector. Our implementation is based on mmdetection. Mask-aware IoU for Anchor Assignment

Kemal Oksuz 46 Sep 29, 2022
An atmospheric growth and evolution model based on the EVo degassing model and FastChem 2.0

EVolve Linking planetary mantles to atmospheric chemistry through volcanism using EVo and FastChem. Overview EVolve is a linked mantle degassing and a

Pip Liggins 2 Jan 17, 2022
A demo of how to use JAX to create a simple gravity simulation

JAX Gravity This repo contains a demo of how to use JAX to create a simple gravity simulation. It uses JAX's experimental ode package to solve the dif

Cristian Garcia 16 Sep 22, 2022
Graph Representation Learning via Graphical Mutual Information Maximization

GMI (Graphical Mutual Information) Graph Representation Learning via Graphical Mutual Information Maximization (Peng Z, Huang W, Luo M, et al., WWW 20

93 Dec 29, 2022
(NeurIPS '21 Spotlight) IQ-Learn: Inverse Q-Learning for Imitation

Inverse Q-Learning (IQ-Learn) Official code base for IQ-Learn: Inverse soft-Q Learning for Imitation, NeurIPS '21 Spotlight IQ-Learn is an easy-to-use

Divyansh Garg 102 Dec 20, 2022
Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).

Weakly Supervised Segmentation with TensorFlow This repo contains a TensorFlow implementation of weakly supervised instance segmentation as described

Phil Ferriere 220 Dec 13, 2022
BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins

BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins Deep learning has brought most profound contributio

Narinder Singh Punn 12 Dec 04, 2022
Neural network for stock price prediction

neural_network_for_stock_price_prediction Neural networks for stock price predic

2 Feb 04, 2022
Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

Update 2019/06/24: A model trained on 10% of the Shepard-Metzler dataset has been added, the following notebook explains the main features of this mod

Jesper Wohlert 313 Dec 27, 2022
Implementation of Basic Machine Learning Algorithms on small datasets using Scikit Learn.

Basic Machine Learning Algorithms All the basic Machine Learning Algorithms are implemented in Python using libraries Acknowledgements Machine Learnin

Piyal Banik 47 Oct 16, 2022
Punctuation Restoration using Transformer Models for High-and Low-Resource Languages

Punctuation Restoration using Transformer Models This repository contins official implementation of the paper Punctuation Restoration using Transforme

Tanvirul Alam 142 Jan 01, 2023
Unofficial implementation of MUSIQ (Multi-Scale Image Quality Transformer)

MUSIQ: Multi-Scale Image Quality Transformer Unofficial pytorch implementation of the paper "MUSIQ: Multi-Scale Image Quality Transformer" (paper link

41 Jan 02, 2023
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing da

MIT CSAIL Computer Vision 4.5k Jan 08, 2023
CARL provides highly configurable contextual extensions to several well-known RL environments.

CARL (context adaptive RL) provides highly configurable contextual extensions to several well-known RL environments.

AutoML-Freiburg-Hannover 51 Dec 28, 2022
In this project, two programs can help you take full agvantage of time on the model training with a remote server

In this project, two programs can help you take full agvantage of time on the model training with a remote server, which can push notification to your phone about the information during model trainin

GrayLee 8 Dec 27, 2022