Simultaneous Demand Prediction and Planning

Last update: Sep 01, 2022

Related tags

Deep Learning SPAP

Overview

Simultaneous Demand Prediction and Planning

Dependencies

Python packages: Pytorch, scikit-learn, Pandas, Numpy, PyYAML

Data

POI: data/poi
Road network: data/roadnet
Transportation: data/transportation
Station profile: data/station_list
Charging records: data/station
Station profile and charging records can be collected in Star Charge APP.

Experiments

Evaluations of charging demand prediction
python model/run.py --source SOURCE_CITY --target TARGET_CITY --model MODEL_NAME
Evaluations based on real plans
1. Even, CG, Real, TIO: python real_world.py
2. Park: python real_world_parking.py
3. Pop: python real_world_population.py
Evaluations with varied budgets
python evaluation_varied_budget.py
Evaluations with optimal solution
python optimal_comparison_BF_tio.py

Owner

Yizong Wang

GitHub Repository

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Simultaneous Demand Prediction and Planning

Related tags

Overview

Simultaneous Demand Prediction and Planning

Dependencies

Data

Experiments

Owner

Yizong Wang

A blender add-on that automatically re-aligns wrong axis objects.

A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

[CIKM 2021] Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning

Gin provides a lightweight configuration framework for Python

PyTorch code for our paper "Attention in Attention Network for Image Super-Resolution"

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This is the offical website for paper ''Category-consistent deep network learning for accurate vehicle logo recognition''

PromptDet: Expand Your Detector Vocabulary with Uncurated Images

Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch

Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided curriculum Learning Approach

Signals-backend - A suite of card games written in Python

Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

A non-linear, non-parametric Machine Learning method capable of modeling complex datasets

Pre-trained BERT Models for Ancient and Medieval Greek, and associated code for LaTeCH 2021 paper titled - "A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek"

[CVPR2021] The source code for our paper 《Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning》.

PyTorch implementation of DirectCLR from paper Understanding Dimensional Collapse in Contrastive Self-supervised Learning

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