This repository contains the source code and dataset for the paper: "Improving Traffic Load Prediction with Multi-modality: A Case Study of Brisbane".
Datasets used for developing models are provided under /dataset
.
This dataset is the traffic load of the Coronation Drive in Brisbane, Australia. For information about the meaning of the column, please refer to this website.
The data provided is pre-processed. Please contact author if you need the raw data.
You can download raw data from this website by using API. Note the data is in real-time.
tweet_all_brisbane.csv
file contains the data retrieved from twitter used in this paper.
main-models-training.ipynb
: main models development (CNN, RNN and LSTM)sentiment-classification-for-tweets-all-brisbane.ipynb
: Sentiment classification for Brisbane tweetstraining-sentiment-analysis-model.ipynb
: Training sentiment classifiervisualizing-main-models.ipynb
: Visualizing the prediction from 3 main modelsscraping-twitter.ipynb
: Collecting twitter data
We also provide pre-trained model used in our paper under folder /trained models
.
CUDA Version: 10.2
Nvidia GeForce RTX 2080 GPU
Python 3.8.10
Tensorflow 2.4.0
@inproceedings{tran2022improving,
title={Improving Traffic Load Prediction with Multi-modality},
author={Tran, Khai Phan and Chen, Weitong and Xu, Miao},
booktitle={Australasian Joint Conference on Artificial Intelligence},
pages={254--266},
year={2022},
organization={Springer}
}