Transportation

The dataset consists of undirected weighted multi-graphs stored in .pkl or .net formats. These undirected graphs form instances for the multi-trip multi-depot rural postman problem. The Multi-trip multi-depot Rural Postman Problem is a variant of the Capacitated Arc Routing Problem which is to find a set of routes for vehicles having limited capacity to traverse a set of arcs from a node called depot in an undirected graph in the least possible time. The dataset consists of instances generated by modifying instances from the literature and also real-world road networks.

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Unmanned aerial vehicles (UAVs) are being used for various applications, but the associated cyber risks are also increasing. Machine learning techniques have been successfully adopted to develop intrusion detection systems (IDSs). However, none of the existing works published the cyber or physical datasets that have been used to develop the IDS, which hinders further research in this field.

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The dataset contains a collection of V2X (Vehicle-to-Everything) messages for classification, prioritization, and spam message detection. It comprises 1,000 messages with varying message types, content, priorities, and spam labels. The messages are sourced from different vehicles with specific destination vehicles or broadcast to all vehicles. They cover various message types, including traffic updates, emergency alerts, weather notifications, hazard warnings, roadwork information, and spam messages. The priority of the messages is categorized as either high, medium, or low.

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The Surface Accelerations Reference is a catalog of all longitudinal and lateral accelerations experienced by SHRP2-NDS participants. The Strategic Highway Research Program Naturalistic Driving Study (SHRP2-NDS) is the largest naturalistic driving study in the world constituting of 34.5 million miles of recorded driving data. To create the surface accelerations reference, each and every acceleration event in SHRP2-NDS was detected, summarized, and recorded creating a database of more than 1.7 billion data points.

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SWAN is a large-scale outdoor point cloud semantic segmentation, instance segmentation and object detection dataset.  The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.

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SWAN is a Large-Scale Outdoor Point Cloud semantic segmentation dataset .  The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.

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Taxi trajectories in New York City

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The dataset is available in GitHub and the link is given below:

https://github.com/sahilarora3117/capstone-19BCE1366-2023

 

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The dataset for the vehicles collusion detection in internet of vehicles is generated from internet of vehicles enviornment using VSSIM. The detail procedure of the data collection and the preprocessing procedure can be found in the publihsed paper - https://doi.org/10.1016/j.eswa.2022.119033.

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