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11 results for “Traffic Prediction”
Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks
<p>The publication titled "Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks" is supported by the STRIDE K3 project. The dataset used in the publication is uploaded here.</p>
Freeway Inductive Loop Detector Dataset for Network-wide Traffic Speed Prediction
<p>The data is collected by the inductive loop detectors deployed on freeways in Seattle area. The freeways contain I-5, I-405, I-90, and SR-520. This data set contains spatiotemporal speed information of the freeway system. At each milepost, the speed information collected from main lane loop detectors in the same direction are averaged and integrated into 5 minutes interval speed data. The raw data is provided by Washington Start Department of Transportation (WSDOT) and processed by the <a href="http://www.uwstarlab.org/">STAR Lab</a> in the University of Washington according to data quality control and data imputation procedures [1][2]. </p> <p>The data file is a pickle file that can be easily read using the read_pickle() function in the Pandas package. The data forms as a matrix and each cell of the matrix is speed value for the specific milepost and time period. The horizontal header of the data set denotes the milepost and the vertical header indicates the timestamps. For more information on the definition of milepost, please refer to this <a href="http://data.wsdot.wa.gov/traffic/">website</a>.</p> <p>This data set been used for traffic prediction tasks in several research studies [3][4]. For more detailed information about the data set, you can also refer to this <a href="https://github.com/zhiyongc/Seattle-Loop-Data">link</a>.</p> <p><strong>References</strong>:</p> <p>[1]. Henrickson, K., Zou, Y., & Wang, Y. (2015). Flexible and robust method for missing loop detector data imputation. <em>Transportation Research Record</em>, <em>2527</em>(1), 29-36.</p> <p>[2]. Wang, Y., Zhang, W., Henrickson, K., Ke, R., & Cui, Z. (2016). <em>Digital roadway interactive visualization and evaluation network applications to WSDOT operational data usage</em> (No. WA-RD 854.1). Washington (State). Dept. of Transportation.</p> <p>[3]. Cui, Z., Ke, R., & Wang, Y. (2018). Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. <em>arXiv preprint arXiv:1801.02143</em>.</p> <p>[4]. Cui, Z., Henrickson, K., Ke, R., & Wang, Y. (2018). Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. <em>arXiv preprint arXiv:1802.07007</em>.</p>
Road traffic prediction dataset.
<p><em>Public (anonymized) road traffic prediction datasets from Huawei Munich Research Center.</em></p> <p>Datasets from a variety of traffic sensors (i.e. induction loops) for traffic prediction. The data is useful for forecasting traffic patterns and adjusting stop-light control parameters, i.e. cycle length, offset and split times.</p> <p>The dataset contains recorded data from 6 crosses in the urban area for the last 56 days, in the form of flow timeseries, depicted the number of vehicles passing every 5 minutes for a whole day (i.e. 12 readings/h, 288 readings/day, 16128 readings / 56 days).</p>
Speed Prediction in Large and Dynamic Traffic Sensor Networks
<p>Aggregated traffic sensor data from Fortaleza (Brazil) in 2014.</p> <p>Please cite the following paper when using the dataset:</p> <p>R.P. Magalhaes, F. Lettich, J.A. Macedo, F.M. Nardini, R. Perego, C. Renso, R. Trani., <strong>Speed prediction in large and dynamic traffic sensor networks</strong>, Information Systems (2019) 101444, <a href="https://doi.org/10.1016/j.is.2019.101444">https://doi.org/10.1016/j.is.2019.101444</a></p> <p>You can also check details regarding the dataset in the paper.</p>
Data for internet traffic prediction using RNN
<p>Contains three .csv files which are from Wireshark captures. The device used for capturing was a MacBook Pro 2019:<br> <br> - Game streaming data captured using PS Remote Play.</p> <p>- Video streaming data captured from a movie in Prime Video.</p> <p>- File download data captured from downloading a 6GB file from Google Drive.</p>
Data from: Using citizen-collected wildlife sightings to predict traffic strike hotspots for threatened species: a case study on the southern cassowary
Assessing the causal factors underpinning the distribution and abundance of wildlife road-induced mortality can be challenging. This is particularly ubiquitous for rare or elusive species, because traffic strikes occur infrequently for these populations and information about localized abundance, distribution, and movements are generally lacking. Here we assessed if citizen-collected sightings data may serve as a low cost and efficient means of gathering long-term animal road-side presence and road crossing information, which could then be used to assess the causative factors and direct mitigation actions aimed at reducing wildlife traffic strike frequency. We explored this principle using two decades of traffic strike records and citizen-collected sightings of the southern cassowary Casuarius casuarius johnsonii. Roads have bisected the cassowaries' rainforest habitat and despite considerable investment into mitigation strategies for this species, road-induced mortality is considered one of the primary threatening processes affecting the population. Using a Bayesian approach and controlling for spatial autocorrelation with conditional autoregressive (CAR) models, we demonstrate that traffic strikes are primarily a density-dependent process in the southern cassowary. That is, traffic strike clusters occurred along stretches of road where cassowaries were most frequently sighted. There were, however, road stretches where traffic strike frequency was greater than predicted by the number of road-side sightings, illustrating when and where density-independent processes increased the mortality potential for a road-crossing cassowary. Synthesis and applications. This is the first time that citizen-collected sightings data have been used to systematically inform upon the abundance and distribution of wildlife traffic strike. The technique not only predicts where incidents are likely to occur but also helps us to understand the factors responsible for strike clustering. While not a replacement for systematic surveys, we highlight citizen-collected sightings data as a low-cost option when assessing contributing factors to vehicle-induced mortality. Accounting for density-dependent and independent processes will ensure the most effective allocation of resources when implementing wildlife traffic strike mitigation.
NGII Data Set for Black Ice Traffic Accident Prediction
<p><a href="../api/records/10863284/draft/files/NGII%20Data%20Set%20for%20Black%20Ice%20Traffic%20Accident%20Prediction.zip/content" target="_blank" rel="noopener noreferrer">Title: NGII Data Set for Black Ice Traffic Accident Prediction</a></p> <p>This dataset has been processed for scholarly purposes, utilizing data provided by the National Geographic Information Institute of Korea.</p> <p><Reference></p> <p>National Geographic Information Institute. (n.d.). National Land Information Platform. Retrieved from https://map.ngii.go.kr/ms/map/NlipMap.do</p>
Data from: Using citizen-collected wildlife sightings to predict traffic strike hotspots for threatened species: a case study on the southern cassowary
Open the record for dataset details and reuse information.
NGII Data Set for Black Ice Traffic Accident Prediction
<p>NGII Data Set for Black Ice Traffic Accident Prediction</p> <p> </p> <p> </p>
SMP coverage and traffic prediction in the municipality of Chapadinha in Maranhão
Open the record for dataset details and reuse information.
Project dataset: predicting traffic volumes and congestion in road networks equipped with ANPR cameras
<p><a href="https://github.com/ppintosilva/anpr-predict">https://github.com/ppintosilva/anpr-predict</a></p>
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