Public Transport Timetable Image Dataset
#image classification #image recognition #pattern recognition #image recognition #transport data analysis #machine learning
- 500 records
- 1.3G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
Currently, efficient management and optimization of transport systems are crucial for the urbanization process. However, many public transport timetable data are fragmented and non-uniform in format, creating challenges for data processing. Existing solutions often rely on manual data entry or data sources in specific formats, leading to inefficiency and greater difficulty in data processing. This dataset aims to address technical issues and business needs in transport information system integration and intelligent analysis with standardized timetable image data. Data collection utilizes high-resolution cameras capturing images at major transport hubs and bus stops in multiple cities, ensuring a wide coverage of timetable types. Quality control includes a three-level, multi-round annotation process to ensure accuracy and consistency in labels, verified by a team of professional transport data analysts. Data preprocessing involves image cropping, denoising, and format conversion to ensure data storage in high-quality JPG format, organized and categorized by city, date, and timetable type.
Dataset Insights
Sample Examples
45701ef9**.jpg|3024*4032|1.06 MB
60820f96**.jpg|3060*4080|1.66 MB
e2a5c3a9**.jpg|3060*4080|1.74 MB
749d6b43**.jpg|3024*4032|1.69 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| vehicle_type | string | The type of vehicle shown in the image, such as bus, subway, train, etc. |
| route_number | string | The public transport route number corresponding to the schedule shown in the image. |
| station_name | string | The name of the station or stop shown in the image. |
| departure_time | string | The departure time of the vehicle as shown in the schedule. |
| arrival_time | string | The arrival time of the vehicle as shown in the schedule. |
| platform_number | string | The platform number shown in the schedule or station information. |
| service_days | string | The days or dates on which the vehicle runs as indicated in the schedule. |
| language | string | The language used in the schedule text shown in the image. |
| text_orientation | string | The orientation of the text in the image, such as horizontal or vertical. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- Which research fields is this dataset suitable for?
- The Public Transport Timetable Image Dataset is suitable for scientific research, particularly in the fields related to computer vision and pattern recognition.
- Why is the Public Transport Timetable Image Dataset needed for scientific research?
- Because the dataset provides rich visual information that can be used for image processing research, transport system optimization, and automated text recognition technologies.
- Does the Public Transport Timetable Image Dataset include images from real-world scenarios?
- Yes, the images in the dataset are sourced from actual public transportation environments, providing real-world scenario pictures.
- What are the potential applications of using the Public Transport Timetable Image Dataset?
- Potential applications include testing and enhancing visual recognition algorithms, developing intelligent transport systems, and researching automated ticketing information processing systems.
- What machine learning tasks are the images in this dataset suitable for?
- The images in this dataset are suitable for classification, object detection, and Optical Character Recognition (OCR) machine learning tasks.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Public Transport Timetable Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/3596b8f900b0bd2c6cc397b774202e22?dataset_scene_cate_type=9},
urldate={2026-02-04},
keywords={public transport, timetable dataset, image recognition dataset, transport data analysis},
version={1.0}
}Using this in research? Please cite us.
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