Context

this dataset is sub-set of ' US Accidents (3 million records -- updated) ' dataset. purpose to make this dataset is for beginning EDA practice.

Content

this dataset content [42678 rows and 47 columns] of 16 MB size.

Acknowledgements

original dataset uploaded by sobhan moosavi. Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.

Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. "Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights." In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.

Usage Policy and Legal Disclaimer

This dataset is being distributed only for Research purposes, under Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By clicking on download button(s) below, you are agreeing to use this data only for non-commercial, research, or academic applications. You may need to cite the above papers if you use this dataset.

Description

This is a countrywide car accident dataset that covers 49 states of the USA. The accident data were collected from February 2016 to March 2023, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by various entities, including the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road networks. The dataset currently contains approximately 7.7 million accident records. For more information about this dataset, please visit here.

Acknowledgements

If you use this dataset, please kindly cite the following papers:

• Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.

• Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. "Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights." In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.

Content

This dataset was collected in real-time using multiple Traffic APIs. It contains accident data collected from February 2016 to March 2023 for the Contiguous United States. For more details about this dataset, please visit [here].

Inspiration

The US-Accidents dataset can be used for numerous applications, such as real-time car accident prediction, studying car accident hotspot locations, casualty analysis, extracting cause and effect rules to predict car accidents, and studying the impact of precipitation or other environmental stimuli on accident occurrence. The most recent release of the dataset can also be useful for studying the impact of COVID-19 on traffic behavior and accidents.

Sampled Data (New!)

For those requiring a smaller, more manageable dataset, a sampled version is available which includes 500,000 accidents. This sample is extracted from the original dataset for easier handling and analysis.

Other Details

Please note that the dataset may be missing data for certain days, which could be due to network connectivity issues during data collection. Regrettably, the dataset will no longer be updated, and this version should be considered the latest.

Usage Policy and Legal Disclaimer

This dataset is being distributed solely for research purposes under the Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By downloading the dataset, you agree to use it only for non-commercial, research, or academic applications. If you use this dataset, it is necessary to cite the papers mentioned above.

Inquiries or need help?

For any inquiries or assistance, please contact Sobhan Moosavi at sobhan.mehr84@gmail.com

Background and Dataset Creation:

Patches derived from whole images of oilseed rape (Brassica napus) plants from the 'Collection of side view and top view RGB images of Brassica napus from a large scale, high throughput experiment' dataset and their associated annotations, which were used to train, validate and test patch classification models as described in the following paper:

Corcoran, E., Hosseini, K., Siles, L., Kurup, S., and Ahnert, S. 2024. 'Automated dynamic phenotyping of whole oilseed rape (Brassica napus) plants from images collected under controlled conditions', Frontiers in Plant Science (under review).

Patches were created and annotated using the MapReader pipeline. Please see:

Kasra Hosseini, Daniel C. S. Wilson, Kaspar Beelen, and Katherine McDonough. 2022. MapReader: a computer vision pipeline for the semantic exploration of maps at scale. In Proceedings of the 6th ACM SIGSPATIAL International Workshop on Geospatial Humanities (GeoHumanities '22). Association for Computing Machinery, New York, NY, USA, 8–19. https://doi.org/10.1145/3557919.3565812

Kasra Hosseini, Rosie Wood, Andy Smith, Katie McDonough, Daniel C.S. Wilson, Christina Last, Kalle Westerling, and Evangeline Mae Corcoran. “Living-with-machines/mapreader: End of Lwm”. Zenodo, July 27, 2023. https://doi.org/10.5281/zenodo.8189653.

File structure:

Annotations

The 'annotations_six_label_sv_5.zip' folder contains annotations for the entire patch dataset in .csv format, these files have two columns 'image_id', 'label' in which:

'image_id' = the path to each image patch

'label' = the label assigned to each patch by the annotator indicated which part of the plant the patch primarily contained, or if it was part of the background. Labels: '0' = non-plant background, '1' = open flower, '2' = flower bud, '3' = leaf, '4' = greed pod containing seed, '5' = branch.

Patches

The 'b_napus_patch_data.zip' folder contains all patches in csv format. Each file is named in a consistent format e.g. "patch-1580-330-1590-340-#2018-07-06_00_VIS_sv_000-0-0-0.png#.PNG" where '1580-330-1590-340' are the x and y coordinates of the patch boundary and '#2018-07-06_00_VIS_sv_000-0-0-0.png#' indicates the image in the 'Collection of side view and top view RGB images of Brassica napus from a large scale, high throughput experiment' from which the patch was derived.

This is a subset of dataset https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents with some columns dropped making it easier for practicing EDA
Acknowledgements If you use this dataset, please kindly cite the following papers:

Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.

Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. "Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights." In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.

Content This dataset was collected in real-time using multiple Traffic APIs. It contains accident data collected from February 2016 to March 2023 for the Contiguous United States. For more details about this dataset, please visit [here].

Inspiration The US-Accidents dataset can be used for numerous applications, such as real-time car accident prediction, studying car accident hotspot locations, casualty analysis, extracting cause and effect rules to predict car accidents, and studying the impact of precipitation or other environmental stimuli on accident occurrence. The most recent release of the dataset can also be useful for studying the impact of COVID-19 on traffic behavior and accidents.

Sampled Data (New!) For those requiring a smaller, more manageable dataset, a sampled version is available which includes 500,000 accidents. This sample is extracted from the original dataset for easier handling and analysis.

Other Details Please note that the dataset may be missing data for certain days, which could be due to network connectivity issues during data collection. Regrettably, the dataset will no longer be updated, and this version should be considered the latest.

Usage Policy and Legal Disclaimer This dataset is being distributed solely for research purposes under the Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By downloading the dataset, you agree to use it only for non-commercial, research, or academic applications. If you use this dataset, it is necessary to cite the papers mentioned above.

Inquiries or need help? For any inquiries or assistance, please contact Sobhan Moosavi at sobhan.mehr84@gmail.com

Description

This is a countrywide dataset of road construction and closure events, which covers 49 states of the US. Construction events in this dataset could be any roadwork, ranging from fixing pavements to substantial projects that could take months to finish. The data is collected from Jan 2016 to Dec 2021, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by a variety of entities, such as the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road-networks. Currently, there are about 6.2 million construction and closure records in this dataset.

Acknowledgements

Please cite the following paper if you use this dataset: - Karimi Monsefi, Amin, Sobhan Moosavi, and Rajiv Ramnath. “Will there be a construction? Predicting road constructions based on heterogeneous spatiotemporal data.”, In Proceedings of the 30th ACM SIGSPATIAL 2022.

Inspiration

This dataset can be used for numerous applications such as short- and long-term road construction prediction, road closure prediction, studying road constructions' life cycle, deriving insights to guide city planners to smartly choose constructions sites with least impact on traffic flow, and also studying the impact of precipitation or other environmental stimuli on need for road work.

Usage Policy and Legal Disclaimer

This dataset is being distributed only for Research purposes, under Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By clicking on download button(s) below, you are agreeing to use this data only for non-commercial, research, or academic applications. You may need to cite the above papers if you use this dataset.

Inquiries or need help?

For any inquiries, contact me at moosavi.3@osu.edu

This dataset contains the GPS trajectories of 10,357 taxis during the period of Feb. 2 to Feb. 8, 2008 within Beijing. The total number of points in this dataset is about 15 million and the total distance of the trajectories reaches to 9 million kilometers.

Features

Each line of a file has the following fields, separated by comma: taxi id, date time, longitude, latitude

Citation

Please cite the following papers when using the dataset: [1] Jing Yuan, Yu Zheng, Xing Xie, and Guangzhong Sun. Driving with knowledge from the physical world. In The 17th ACM SIGKDD international conference on Knowledge Discovery and Data mining, KDD ’11, New York, NY, USA, 2011. ACM. [2] Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, and Yan Huang. Tdrive: driving directions based on taxi trajectories. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS ’10, pages 99–108, New York, NY, USA, 2010. ACM.