fp002/train dataset hosted on Hugging Face and contributed by the HF Datasets community

TRAIN

## Overview

TRAIN is a dataset for object detection tasks - it contains A annotations for 2,280 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

All train services in the Netherlands since 2019 are stored in the train archive of Rijden de Treinen. This dataset contains all delays, cancellations and timetable changes of all trains in the Netherlands.

300 Train

## Overview

300 Train is a dataset for object detection tasks - it contains Banana 6XjG annotations for 774 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The global number of users in the 'Train Tickets' segment of the shared mobility market was forecast to continuously increase between 2025 and 2029 by in total ***** million users (+***** percent). After the ninth consecutive increasing year, the number of users is estimated to reach *** billion users and therefore a new peak in 2029. Notably, the number of users of the 'Train Tickets' segment of the shared mobility market was continuously increasing over the past years.Find other key market indicators concerning the revenue and average revenue per user (ARPU). The Statista Market Insights cover a broad range of additional markets.

Vehicle Dataset Train

## Overview

Vehicle Dataset  Train is a dataset for object detection tasks - it contains Vehicles Classes annotations for 975 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

High-speed train operation data are reliable and rich resources in data-driven research. However, the data released by railway companies are poorly organized and not comprehensive enough to be applied directly and effectively. A high-speed railway network dataset suitable for research is still lacking. To support the research in large-scale complex network, complex dynamic system and intelligent transportation, we develop a high-speed railway network dataset, containing the train operation data in different directions from October 8, 2019 to January 27, 2020, the train delay data of the railway stations, the junction stations data, and the mileage data of adjacent stations. In the dataset, weather, temperature, wind power and major holidays are considered as factors affecting train operation. Potential research values of the dataset include but are not limited to complex dynamic system pattern mining, community detection and discovery, and train delay analysis. Besides, the dataset can be used to solve various railway operation and management problems, such as passenger service network improvement, train real-time dispatching and intelligent driving assistance.

ATCS is a dataset designed to train deep learning models to volumetrically segment clouds from multi-angle satellite imagery. The dataset consists of spatiotemporally aligned patches of multi-angle polarimetry from the POLDER sensor aboard the PARASOL mission and vertical cloud profiles from the 2B-CLDCLASS product using the cloud profiling radar (CPR) aboard CloudSat.

huggingface-course/codeparrot-ds-train dataset hosted on Hugging Face and contributed by the HF Datasets community

Kitti Train

## Overview

Kitti Train is a dataset for object detection tasks - it contains Objects annotations for 7,481 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Introduction

Train Statistics: Rail transportation focuses on using vehicles that run on track, which consist of a parallel set of rails. It is one of most common form of transportation. It is the most common form of cargo transportation used by countries worldwide. As we go forward, we will learn about train statistics to understand this form of transportation. Accordingly, we will learn about the essential measures taken to ensure this has become an eco-friendly form of transportation, thus a means for low C02 emissions and carbon footprint.

Trash Dataset Train

## Overview

Trash Dataset Train is a dataset for object detection tasks - it contains Trashcans annotations for 2,578 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Weekly actual and planned coal loadings from the Surface Transportation Board's (STB) Rail Service Metrics. Railroads provide the average daily count (per week) of unit train coal traffic from major coal producing regions. They have the option of providing this metric on a train- or carload basis. For example, BNSF reports the average daily loadings for the week of coal unit trains. CSX, on the other hand, reports on a carload basis. Data are broken out by coal production region.

Norway Train

## Overview

Norway Train is a dataset for object detection tasks - it contains Roaddamage annotations for 2,914 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

CodeParrot 🦜 Dataset Cleaned (train)

Train split of CodeParrot 🦜 Dataset Cleaned.

  Dataset structure

DatasetDict({ train: Dataset({ features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'], num_rows: 5300000 }) })

In 2023, the United States registered 995 rail fatalities and around 6,700 non-fatal injuries on the railroads. Injury and fatality numbers had fallen during the COVID-19 pandemic in 2020 and 2021, but 2022 saw a resurgence of accidents above pre-pandemic levels. Positive Train Control (PTC) Ever since the enactment of the Rail Safety Improvement Act of 2008 (RSIA), the U.S. rail industry has been working on implementing the Positive Train Control (PTC) technology on over 60,000 miles of the rail network. The original deadline of December 31, 2015 was extended to 2018 after several railroads have stated they are unable to meet the deadline due to technical and legal complications. At the end of 2020, PTC had been implemented on railway routes that were required to do so. The Positive Train Control consists of a bundle of state-of-the-art technologies is meant to avoid accidents caused by human error, track or equipment malfunction, faulty movement of trains through a grade crossing, breaches on railroad tracks and other types of train operator errors. More precisely, PTC aims to prevent train-on-train collisions, deflections caused by excessive speed or other derailments on tracks where maintenance operations are underway. The technology uses a number of factors like weight and length of the train, track composition, train speed and train authorization in order to calculate the adequate stopping distance for a train. The PTC system includes an onboard locomotive system that monitors the position and speed of the train and triggers braking in case of speeding or unapproved train movement; a wayside system to check track signals, switches and track circuits for movement authorization; and a back office server that stores all relevant rail traffic information. Until 2015, 6.4 billion U.S. dollars have been spent for the PTC deployment on over more than 2,300 of track miles and more than 4,500 locomotives. By 2020, the total costs had risen to more than 10 billion U.S. dollars.

This spatial dataset includes the train lines generated from the shape.txt file in the Sydney Trains GTFS static bundle. Spatial route datasets included in the following formats and will be updated on a monthly basis: csv geojson

Dataset

This dataset was created by haneenhossam

Contents

In 2023, China's national rail services transported more than *********** passengers, representing a significant rise of ***** percent from the previous year. The sharp decline in passenger numbers between 2020 and 2022 was largely caused by the prolonged control measures and subsequent lockdowns in response to the COVID-19 pandemic. The rail system in China As of 2023, the total operating length of the rail network in China surpassed ******* kilometers, with the provinces of Henan, Jiangsu, and Guangdong, having the highest rail passenger transport capacities. Shanxi and Inner Mongolia were the two regions with the highest freight volume. Due to the varied distribution of resources throughout the country, the rail system in China plays a crucial role in re-distribution. High speed rail (HSR) Being one of the largest countries on earth in terms of area and population, the need for transporting goods and people efficiently on a large scale pushed China to invest in the development of high-speed rail over the last decade, which has led to one of the most developed high-speed rail systems in the world. In 2024, the total length of the HSR network in China reached ****** kilometers, and the number would further grow to ****** kilometers in 2035. Four of the top eight fastest trains in the world are operating in China, and the CRRC Qingdao Sifang 2021 Maglev had a record speed of *** kilometers per hour. The total volume of passengers transported by high-speed railways in China climbed up to a peak of ************ in 2019 before declining sharply in 2020 due to the COVID-19 outbreak.    

The model train market, encompassing diverse applications like collector's items, children's toys, and even business research (potentially for logistical simulations), is experiencing steady growth. While the exact market size for 2025 isn't specified, considering a plausible CAGR of 5% (a conservative estimate given the niche nature of the market and potential for collector value appreciation), and assuming a 2019 market size of $500 million, the 2025 market size could be estimated at approximately $640 million. This growth is driven by several factors: increasing disposable incomes in developing nations leading to higher spending on hobbies, the enduring appeal of model trains as a collectible and nostalgic item, and the innovative use of model train systems in educational settings and business simulations. Furthermore, the market segments of DC and AC current systems cater to different technical preferences, driving further specialization within the market. Key restraining factors include the high entry cost for hobbyists, competition from digital entertainment, and the overall cost of maintaining and expanding elaborate model railway systems. The growth in e-commerce and targeted marketing towards specific collector communities, however, are likely to offset some of these restraints. The geographical distribution of the market reveals varying levels of market penetration. North America and Europe likely hold the largest market shares due to established collector bases and higher disposable incomes. Asia-Pacific, particularly China and Japan, presents a significant growth opportunity due to rising middle-class populations and an increasing appreciation for intricate hobby items. Manufacturers like Märklin, a renowned player, hold significant market share, and the presence of diverse companies catering to different price points and train types (e.g., from toy-oriented manufacturers like Disney to high-end collectors' brands like Exoto) suggests a dynamic and segmented market landscape. The forecast period from 2025 to 2033 is expected to see continued growth, though the pace may fluctuate based on economic conditions and shifting consumer preferences. Strategic acquisitions, new product innovations (e.g., incorporating smart technology), and a focus on environmentally friendly materials will likely shape the future of this market.