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| # Paper Page Template | ||
| This repo creates a template for an academic paper page. It has been set up so that no HTML coding is required for a paper page. All configuration is done through markdown and yaml files. | ||
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| demo: https://mpslab-asu.github.io/Paper_Page_Template/ | ||
| # IncidentNet: Traffic Incident Detection, Localization and Severity Estimation with Sparse Sensing | ||
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| ## Requirements: | ||
| You need to use docker and docker-compose to run this project. If you don't have them installed, follow the instructions [here](https://docs.docker.com/compose/install/). | ||
| ## Abstract | ||
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| ## Usage: | ||
| The files that need to be edited for any paper are: | ||
| * [_config.yml](./_config.yml) | ||
| * [_data/figures.yml](./_data/figures.yml) | ||
| * [_data/authors.yml](./_data/authors.yml) | ||
| * [paper_components/abstract.md](./paper_components/abstract.md) | ||
| * [paper_components/bibtex_ref.md](./paper_components/bibtex_ref.md) | ||
| * [paper_components/description.md](./paper_components/description.md) | ||
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| Everything else should generate automatically. | ||
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| --- | ||
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| ## Modifing the template for your paper: | ||
| * Any fields that you do not want to use must be commented out in the yaml files. This is done by adding a '#' at the beginning of the line. (Ex: ln 12 in [_config.yml](./_config.yml) which comments out the supplementary_pdf) | ||
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| ## YAML Files: | ||
| ### `_config.yml`: | ||
| The [_config.yaml](./_config.yml) file contains all the information about the paper. The only fields that need to be edited are: | ||
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| **Paper Information** -> | ||
| * **paper_name** : The title of the paper | ||
| * **organization** : The organization that the paper is associated with | ||
| * **sub_organization** : The department that the paper is associated with | ||
| * **preprint** : (Boolean) If the paper is a preprint or not (True/False) | ||
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| **Paper Resources** -> | ||
| * **paper_pdf** : The path to the pdf of the paper | ||
| * **supplementary_pdf** : The path to the supplementary pdf of the paper | ||
| * **code** : The path to the code for the paper (preferable GitHub) | ||
| * **arXiv** : The path to the arXiv page for the paper | ||
| * **IEEE** : The path to the IEEE page for the paper | ||
| * **favicon** : The path to the favicon for the paper page | ||
| * All favicons must be .ico files. You can convert any image to a .ico file [here](https://cloudconvert.com/png-to-ico) | ||
| * Make sure the is in the [assets/images](./assets/images) folder for GitHub pages compatibility | ||
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| ### `figures.yml`: | ||
| The [_data/figures.yml](./_data/figures.yml) file contains all the information about the figures for the paper. The only fields that need to be edited are: | ||
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| * **filename** : The filename of the figure | ||
| * The image needs to be in the [assets/images](./assets/images) folder for GitHub pages compatibility. | ||
| * jpeg/jpg files must be name .JPG (all caps) for GitHub pages compatibility | ||
| * **caption** : The caption for the figure | ||
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| ### `authors.yml`: | ||
| The [_data/authors.yml](./_data/authors.yml) file contains all the information about the authors for the paper. The format for the file is: | ||
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| ```yaml | ||
| - name: Shail Dave | ||
| position: PhD | ||
| github: shail-dave | ||
| email: [email protected] | ||
| linkedin: daveshail | ||
| organization: Arizona State University | ||
| sub_organization: School of Computing and Augmented Intelligence | ||
| google_scholar: https://scholar.google.com/citations?hl=en&user=SVft2R0AAAAJ&view_op=list_works | ||
| website: https://sites.google.com/view/shail/ | ||
| primary: true | ||
| ``` | ||
| Each of these fields is optional. Any fields that are not used should be removed per author. | ||
| #### Additional Notes about author sections: | ||
| * The information here shows up on the cards screen when the author named is clicked on. | ||
| * The only fields represented on the main page are **name** and **primary**. | ||
| * **primary** is meant to indicate major contributors, it shows as "*" on the main page and a badge on the author cards | ||
| --- | ||
| ## Markdown Files: | ||
| ### `abstract.md` : | ||
| You [abstract.md](./paper_components/abstract.md) file should have the abstract for the paper. This is the only file that requires markdown. | ||
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| ### `bibtex_ref.md` : | ||
| The [bibtex_ref.md](./paper_components/bibtex_ref.md) file should have the bibtex reference for the paper. This is the only file that requires markdown. | ||
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| ### `description.md` : | ||
| The [description.md](./paper_components/description.md) file should have the description for the paper or any additional information you want to include. | ||
| * This is the only file that requires markdown. | ||
| * **Latex Math** is supported in this file. | ||
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| --- | ||
| ## Hosting the Website: | ||
| The web hosting is handled by GitHub Pages. The process is rather straight forward. | ||
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| ### **Step 1:** Create a Repository or Branch. | ||
| * If you the paper **does not have code or a repository**, create a new repository. | ||
| * You can use the template option when creating the repo | ||
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| * Edit as per instructions above | ||
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| * If you the paper **has a repository**, create a new branch in the repository. Preferable titled "pages" | ||
| * Clone the repo and that branch and remove all existing code. | ||
| * Copy all files from this repo into the new branch and edit as per instructions above | ||
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| ### **Step 2:** Start git hosting. | ||
| 1. Go to the settings page of the repo: | ||
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| 2. Scroll down to the GitHub Pages section and select the branch you want to host from. | ||
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| 3. Select the host from branch option and save. | ||
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| ## You're Done !!! | ||
| Prior art in traffic incident detection relies on high sensor coverage and is primarily based on decision-tree and random forest models that have limited representation capacity and, as a result, cannot detect incidents with high accuracy. This paper presents IncidentNet - a novel approach for classifying, localizing, and estimating the severity of traffic incidents using deep learning models trained on data captured from sparsely placed sensors in urban environments. Our model works on microscopic traffic data that can be collected using cameras installed at traffic intersections. Due to the unavailability of datasets that provide microscopic traffic details and traffic incident details simultaneously, we also present a methodology to generate a synthetic microscopic traffic dataset that matches given macroscopic traffic data. IncidentNet achieves a traffic incident detection rate of 98%, with false alarm rates of less than 7% in 197 seconds on average in urban environments with cameras on less than 20% of the traffic intersections. |