Transportation

This repository shares some simultaneous localization and mapping (SLAM) datasets and detection datasets for railroad application.  In the first stage, we decide to realease some LiDAR, visual, IMU sequence for freight-traffic railways. The ground truth cannot be provided due to safety regulations. Please infer https://github.com/YushengWHU/Railroad-dataset  for detailed information.

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The number of private vehicles is still increasing from year to year. In order to limit environmental damage, a proper way of dealing with this trend is the introduction of intelligent automotive infrastructure. Besides traffic management solutions, smart parking guidance systems are important for reducing unnecessary traffic. For this, a key prerequisite are sensor networks that provide information about the occupancy state of every single parking spot in the parking infrastructure of high traffic targets e.g. nearby an airport or shopping mall.

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This dataset contains tourist trajectories within  Bangkok, which are extracted from taxi GPS data during the Songkran Festival period (April 11–17, 2019). 

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This dataset contains the vehicular traces from a location in Jeju-si, South Korea. The dataset contains 8,495,739 traces of vehicles. It comprises of major areas/junctions of which one is the intersection from where the Jeju International Airport and Jeju Seaport traffic passes on daily. Jeju International Airport is one of the busiest airpots in the world. Four types of vehicles were considered in the simulation of dataset, i.e., buses, trucks, passenger-cars, taxies. Each trace contains:

  1. time (timestep in seconds)

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This dataset contains segmented taxi trajectories with passengers within Bangkok, which are extracted from taxi GPS data during the Songkran Festival period (April 11–17, 2019), published by the ITIC Foundation (https://org.iticfoundation.org/). 

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This repository contains the problem instances for the paper "Communication-aware Drone Delivery Problem".

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A commonly used definition of spatial disorientation (SD) in aviation is "an erroneous sense of one’s position and motion relative to the plane of the earth’s surface". There exists a wide range of SD use-cases dictated by situational factors, therefore SD has been predominantly studied using reduced motion detection experimental contexts in isolation. The study of SD by use-case makes it difficult to understand general SD occurrence and thus provide viable solutions. To investigate SD in a generalized manner, a two-part Human Activity Recognition (HAR) study was performed.

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A reasonable approach to cope with increasing car traffic is the application of large-scale car traffic management solutions. Dense and widely applied car traffic monitoring is an important key prerequisite for this.

Established solutions like e.g. induction loops, video-camera-based systems, or radar, do not suit all the needs with regard to installation effort, privacy, and cost efficiency.

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To ensure the usability and reliability of the collected data, one Hikvision monitoring camera (iDS-TCV900-AE/25) is deployed at the entrance of Taijia Expressway in Shanxi province in China for image capturing. This camera is installed on the roadside pole with a height of 5.8 meters and uses the infrared flash as the supplementary lighting. The captured images cover two lanes of the expressway, with the resolution being 4096*2160. All images are captured during the period of November 2019 to April 2020.

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Simulation results from EVI-OnDemand estimating need for infrastructure to support electric ride-hailing vehicles in the US.

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