Journal of Big Data Analytics in Transportation Template
Write in a clean editor, then format for Journal of Big Data Analytics in Transportation in one click — DocuGuru applies the official Springer Nature template with superscript references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the Journal of Big Data Analytics in Transportation format
Journal of Big Data Analytics in Transportation is a peer-reviewed journal published by Springer Nature, covering Traffic Prediction and Management Techniques, Transportation Planning and Optimization, Human Mobility and Location-Based Analysis.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Journal of Big Data Analytics in Transportation 12, 45–58 (2023).
Formats any DOI in Journal of Big Data Analytics in Transportation style. No sign-up. |
| Publishes research in | Traffic Prediction and Management Techniques Transportation Planning and Optimization Human Mobility and Location-Based Analysis Urban Transport and Accessibility Advanced Neural Network Applications |
| ISSN | 2523-3556 |
| h-index | 18 |
| i10-index | 32 |
| Total citations | 966 |
| Article processing charge | $2,890 |
| Top institutions publishing here | Iowa State University |
| Journal website | link.springer.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Journal of Big Data Analytics in Transportation per year
Citation impact of Journal of Big Data Analytics in Transportation by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in Journal of Big Data Analytics in Transportation
Abstract The Federal Highway Administration (FHWA) reported between 2016 and 2017, fatal crashes in work zones increased by 3%, while fatal crashes outside of work zones decreased by 1.5%. The FHWA also reported that work zones account for approximately 10% of the nation’s overall congestion and 24% of unexpected interstate delays. This paper reports on…
Abstract Typical safety improvements at signalized intersections are identified and prioritized using crash data over 3–5 years. Enhanced probe data that provides date, time, heading, and location of hard-braking events has recently become available to agencies. In a typical month, over six million hard-braking events are logged in the state of Indiana. This study compared…
Abstract As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve traffic congestion, and enhance the passenger experience. This paper proposes UberNet , a deep learning convolutional neural network for short-time prediction of demand for ride-hailing services.…