Visitor numbers for the data hotel (hotel.difi.no) showing page views per dataset, and for quarter datasets, many page views that are of different formats (JSON, JSONP, XML, complete download, etc.). In addition, an approximate count for traffic (in bytes) per. dataset. The boiler for data is data about page views in AWStats. These tala are queued through a program that sums up traffic per dataset and filters out unrelevant traffic. For explanation of the various fields, including mulege values, see field definitions. OBS. Please note that statistics before 2017 are incorrect. This is a technical problem that causes us to lack traffic data for larger or smaller periods. For example, one lacks of years of data for over 100 days. Ideas for use — Create a web app that shows statistics per data set, graph for page views over time. — Summing up traffic per data settlement There may be errors in the dataset. Use the comments section if you have any questions, comments or other comments!

Context

The concept of searching and localizing vehicles from live traffic videos based on descriptive textual input has yet to be explored in the scholarly literature. Endowing Intelligent Transportation Systems (ITS) with such a capability could help solve crimes on roadways. While artificial intelligence (AI) can be a powerful tool for this data intensive application, existing state-of-the-art AI models struggle with fine-grain vehicle recognition. Typically, only reporting model performance on still input image data, often captured at high resolution and at pristine quality. These settings are not reflective of real-world operating conditions and thus, recognition accuracies typically cannot be replicated on video data. One major impediment to the advancement of fine-grain vehicle recognition models is the lack of video testbench datasets with annotated ground-truth data. Additionally, to the best of our knowledge, no metrics currently exist for evaluating the robustness and performance efficiency of a vehicle recognition model on live videos, and even less so for vehicle search and localization models. In this paper, we address these challenges by proposing V-Localize, a novel artificial intelligence framework for vehicle search and continuous localization captured from live traffic videos based on input textual descriptions. An efficient hashgraph algorithm is introduced to process input text (such as a sentence, paragraph, or report) to extract detailed target information used to query the recognition and localization model. This work further introduces two novel datasets that will help advance AI research in these challenging areas. These datasets include: a) the most diverse and large-scale Vehicle Color Recognition (VCoR) dataset with 15 colors classes -- twice as many as the number of color classes in the largest existing such dataset -- to facilitate finer-grain recognition with color information; and b) a Vehicle Recognition in Video (VRiV) dataset, which is a first of its kind video test-bench dataset for evaluating the performance of vehicle recognition models in live videos rather than still image data. The VRiV dataset will open new avenues for AI researchers to investigate innovative approaches that were previously intractable due to the lack of a traffic vehicle recognition annotated test-bench video dataset. Finally, to address the gap in the field, 5 novel metrics are introduced in this paper for adequately accessing the performance of vehicle recognition models in live videos. Ultimately, the proposed metrics could also prove intuitively effective at quantitative model evaluation in other video recognition applications. The novel metrics and VRiV test-bench dataset introduced in this paper are specifically aimed at advancing state-of-the-art research for vehicle recognition in videos. Likewise, the proposed novel vehicle search and continuous localization framework could prove assistive in cases such as of amber alerts or hit-and-run incidents. One major advantage of the proposed system is that it can be integrated into intelligent transportation system software to help aid law-enforcement.

Image Acquisition

The proposed Vehicle Recognition in Video (VRiV) dataset is the first of its kind and is aimed at developing, improving, and analyzing performance of vehicle search and recognition models on live videos. The lack of such a dataset has limited performance analysis of modern fine-grain vehicle recognition systems to only still image input data, making them less suitable for video applications. The VRiV dataset is introduced to help bridge this gap and foster research in this direction. The proposed VRiV dataset consists of up to 47 video sequences averaging about 38.5 seconds per video. The videos are recorded in a traffic setting focusing on vehicles of volunteer candidates whose ground truth make, model, year and color information are known. For security reasons and safety of participants, experiments are conducted on streets/road with low traffic density. For each video, there is a target vehicle with known ground truth information, and there are other vehicles either moving in traffic or parked on side streets, to simulate real-world traffic scenario. The goal is for the algorithm to be able to search, recognize and continuously localize just the specific target vehicle of interest for the corresponding video based on the search query. It is worth noting that the ground truth information about other vehicles in the videos are not known. The 47 videos in the testbench dataset are distributed across 7 distinct makes and 17 model designs as shown in Figure 10. The videos are also annotated to include ground truth bounding boxes for the specific target vehicles in corresponding videos. The dataset includes more than 46k annotated frames averaging about 920 frames per video. This dataset will be made available on Kaggle, and new videos will be added as they become available.

Content

There is one main zip file available for download. The zip file contains 94 files. 1) 47 video files 2) 47 ground-truth annotated files which identifies locations where the vehicle of interest is in the frame. Each video file is labelled with the corresponding vehicle brand name, model, year, and color information.

Terms and Conditions

• Videos provided in this dataset are freely available for research and education purposes only. Please be sure to properly credit the authors by citing the article below.
• Be sure to upvote this dataset if you find it useful by scrolling up and clicking the ^ sign at the top-right corner of the cover image of this page.
• Be sure to blur out all plate numbers before publishing any of the contents available in this dataset.

Acknowledgements

Any publication using this database must reference to the following journal manuscript:

• Panetta, Karen, Landry Kezebou, Victor Oludare, James Intriligator, and Sos Agaian. 2021. "Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos" AI 2, no. 4: 684-704. https://doi.org/10.3390/ai2040041
• open access : https://www.mdpi.com/2673-2688/2/4/41

Note: if the link is broken, please use http instead of https.

In Chrome, use the steps recommended in the following website to view the webpage if it appears to be broken https://www.technipages.com/chrome-enabledisable-not-secure-warning

VCoR dataset: https://www.kaggle.com/landrykezebou/vcor-vehicle-color-recognition-dataset VRiV dataset: https://www.kaggle.com/landrykezebou/vriv-vehicle-recognition-in-videos-dataset

For any enquires regarding the VCoR dataset, contact: landrykezebou@gmail.com

Traffic and road condition information captured in the QLDTraffic system is available for use by external developers via GeoJSON feeds.

These feeds cover Hazards, Crashes, Congestion, Flooding, Roadworks and Special Events and Web Cameras details.

The information provided in these feeds is on an 'as is' basis. Additional details about the available information on the QLDTraffic site can be viewed on our disclaimer page .

Transport and Main Roads (TMR) is constantly striving to ensure that they provide accurate, reliable and timely traffic and road condition information.

Given this, the previously available API has undergone changes in August 2016 with the creation of the QLDTraffic public website.

Details on accessing QLDTraffic’s traffic and road condition information via GeoJSON can be found in the QLDTraffic website application programming interface (API) specification.

This API has been developed to allow improved integration of QLDTraffic traffic and road condition information with external systems.

Please be aware that this specification may be subject to change.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - KNIME Lab Manual (1st ed.). CRC Press. https://doi.org/10.1201/9781003304357 Update Log the dataset and tool for ABM Crime Simulation were updated on August 3, 2023, the toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv R_ArcGIS_Tools.tbx (PCAandFA, SpatialRegressionModel) 9 Louisiana Louisiana.gdb MLR Tools Pro.tbx R_ArcGIS_Tools.tbx (RegionalizationR) 10 SimuCity SimuCity.gdb Garin-Lowry.tbx 11A Columbus Columbus.gdb R_ArcGIS_Tools.tbx (WasteCommuteR) 11B Xiantao XT.gdb R_ArcGIS_Tools.tbx (MiniMaxR, MAEP) 12A Chicago ZoneEffect.gdb 12B BRMSA BRMSAmc.gdb MCSimulation.tbx 13 ABMSIM Data ...

As of April 2024, Facebook had an addressable ad audience reach 131.1 percent in Libya, followed by the United Arab Emirates with 120.5 percent and Mongolia with 116 percent. Additionally, the Philippines and Qatar had addressable ad audiences of 114.5 percent and 111.7 percent.