Springer Nature

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.

PublisherSpringer Nature
Reference styleSuperscript 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 inTraffic Prediction and Management Techniques Transportation Planning and Optimization Human Mobility and Location-Based Analysis Urban Transport and Accessibility Advanced Neural Network Applications
ISSN2523-3556
h-index18
i10-index32
Total citations966
Article processing charge$2,890
Top institutions publishing hereIowa State University
Journal websitelink.springer.com
You getA submission-ready PDF and the editable LaTeX source — ready to submit.

Papers published in Journal of Big Data Analytics in Transportation per year

12
2019
20
2020
17
2021
11
2022

Citation impact of Journal of Big Data Analytics in Transportation by publication year

219
2019
400
2020
236
2021
99
2022

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

Correlating Hard-Braking Activity with Crash Occurrences on Interstate Construction Projects in Indiana

Jairaj Desai, Howell Li, Jijo K. Mathew et al. · 16 Nov 2020

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…

A Proactive Approach to Evaluating Intersection Safety Using Hard-Braking Data

Margaret Hunter, Enrique D. Saldivar-Carranza, Jairaj Desai et al. · 3 Apr 2021

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…

Short-Term Prediction of Demand for Ride-Hailing Services: A Deep Learning Approach

Long Chen, Piyushimita Thakuriah, Konstantinos Ampountolas · 21 Apr 2021

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.…

Journal of Big Data Analytics in Transportation template — frequently asked questions

How do I write a paper in the Journal of Big Data Analytics in Transportation format?
In DocuGuru you write your manuscript in a normal editor — no LaTeX setup required — and select the Journal of Big Data Analytics in Transportation template. When you export, DocuGuru compiles the paper into the official Springer Nature format and hands you a submission-ready PDF along with the editable LaTeX source.
What reference style does Journal of Big Data Analytics in Transportation use?
Journal of Big Data Analytics in Transportation uses Superscript numbered (Nature) references, shown as superscript numerals in the text. DocuGuru formats every in-text citation and the reference list in this exact style automatically. A reference appears like this: 1. Smith, A., Jones, B. & Lee, C. A representative article title. Journal of Big Data Analytics in Transportation 12, 45–58 (2023).
Do I need to know LaTeX to submit to Journal of Big Data Analytics in Transportation?
No. DocuGuru generates the sn-jnl LaTeX class and compiles the PDF for you in the background, so you get a Springer Nature-ready Journal of Big Data Analytics in Transportation document without writing any LaTeX. If you do want it, the LaTeX source is included in the export.
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Yes. Paste or upload your current manuscript — Word, LaTeX, Markdown, or plain text — and DocuGuru reflows it into the Journal of Big Data Analytics in Transportation format with correct headings, figures, tables, and superscript citations.
Who publishes Journal of Big Data Analytics in Transportation?
Journal of Big Data Analytics in Transportation is a multidisciplinary journal published by Springer Nature. DocuGuru's Journal of Big Data Analytics in Transportation template matches Springer Nature's official submission format.
Can I export a submission-ready Journal of Big Data Analytics in Transportation PDF?
Yes — DocuGuru produces a PDF built with the official Journal of Big Data Analytics in Transportation template (the sn-jnl class) that is ready to submit to Springer Nature, together with the matching LaTeX source files.
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