Example of IEEE Transactions on Intelligent Transportation Systems format
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Example of IEEE Transactions on Intelligent Transportation Systems format Example of IEEE Transactions on Intelligent Transportation Systems format Example of IEEE Transactions on Intelligent Transportation Systems format Example of IEEE Transactions on Intelligent Transportation Systems format
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Example of IEEE Transactions on Intelligent Transportation Systems format Example of IEEE Transactions on Intelligent Transportation Systems format Example of IEEE Transactions on Intelligent Transportation Systems format Example of IEEE Transactions on Intelligent Transportation Systems format
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IEEE Transactions on Intelligent Transportation Systems — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Mechanical Engineering #15 of 596 up up by 2 ranks
Automotive Engineering #3 of 95 down down by 2 ranks
Computer Science Applications #23 of 693 up up by 7 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1443 Published Papers | 19522 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 27/06/2020
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Related Journals

open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 8.7
SJR: 0.831
SNIP: 2.366
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Taylor and Francis

Quality:  
High
CiteRatio: 6.4
SJR: 0.884
SNIP: 1.244
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Taylor and Francis

Quality:  
High
CiteRatio: 6.8
SJR: 1.321
SNIP: 1.764

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

6.319

10% from 2018

Impact factor for IEEE Transactions on Intelligent Transportation Systems from 2016 - 2019
Year Value
2019 6.319
2018 5.744
2017 4.051
2016 3.724
graph view Graph view
table view Table view

13.5

6% from 2019

CiteRatio for IEEE Transactions on Intelligent Transportation Systems from 2016 - 2020
Year Value
2020 13.5
2019 12.7
2018 10.8
2017 8.3
2016 7.3
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 10% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 6% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.591

16% from 2019

SJR for IEEE Transactions on Intelligent Transportation Systems from 2016 - 2020
Year Value
2020 1.591
2019 1.897
2018 1.412
2017 1.175
2016 0.984
graph view Graph view
table view Table view

3.204

11% from 2019

SNIP for IEEE Transactions on Intelligent Transportation Systems from 2016 - 2020
Year Value
2020 3.204
2019 3.605
2018 3.632
2017 2.788
2016 2.734
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 16% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 11% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

IEEE Transactions on Intelligent Transportation Systems

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IEEE

IEEE Transactions on Intelligent Transportation Systems

The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologi...... Read More

Engineering

i
Last updated on
27 Jun 2020
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ISSN
1524-9050
i
Impact Factor
Very High - 3.789
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
IEEEtran
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Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

Journal Article DOI: 10.1109/TITS.2014.2345663
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li1, Fei-Yue Wang

Abstract:

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic predict... Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance. read more read less

Topics:

Traffic flow (65%)65% related to the paper, Traffic generation model (63%)63% related to the paper, Intelligent transportation system (53%)53% related to the paper, Autoencoder (53%)53% related to the paper
2,306 Citations
open accessOpen access Journal Article DOI: 10.1109/TITS.2005.848368
Detecting stress during real-world driving tasks using physiological sensors
Jennifer Healey1, Rosalind W. Picard2

Abstract:

This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston ... This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97% across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers. read more read less
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1,777 Citations
open accessOpen access Journal Article DOI: 10.1109/TITS.2006.884615
The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics
B. van Arem, C.J.G. van Driel, R. Visser1

Abstract:

Cooperative adaptive cruise control (CACC) is an extension of ACC. In addition to measuring the distance to a predecessor, a vehicle can also exchange information with a predecessor by wireless communication. This enables a vehicle to follow its predecessor at a closer distance under tighter control. This paper focuses on the... Cooperative adaptive cruise control (CACC) is an extension of ACC. In addition to measuring the distance to a predecessor, a vehicle can also exchange information with a predecessor by wireless communication. This enables a vehicle to follow its predecessor at a closer distance under tighter control. This paper focuses on the impact of CACC on traffic-flow characteristics. It uses the traffic-flow simulation model MIXIC that was specially designed to study the impact of intelligent vehicles on traffic flow. The authors study the impacts of CACC for a highway-merging scenario from four to three lanes. The results show an improvement of traffic-flow stability and a slight increase in traffic-flow efficiency compared with the merging scenario without equipped vehicles read more read less

Topics:

Cooperative Adaptive Cruise Control (70%)70% related to the paper, Traffic flow (52%)52% related to the paper, Adaptive control (51%)51% related to the paper, Intelligent transportation system (50%)50% related to the paper, Traffic simulation (50%)50% related to the paper
View PDF
1,347 Citations
Journal Article DOI: 10.1109/TITS.2011.2158001
Data-Driven Intelligent Transportation Systems: A Survey
Junping Zhang1, Fei-Yue Wang, Kunfeng Wang, Wei-Hua Lin2, Xin Xu3, Cheng Chen

Abstract:

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sour... For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented. read more read less

Topics:

Advanced Traffic Management System (66%)66% related to the paper, Intelligent transportation system (62%)62% related to the paper
1,336 Citations
open accessOpen access Journal Article DOI: 10.1109/TITS.2019.2935152
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
Ling Zhao1, Yujiao Song1, Chao Zhang2, Yu Liu3, Pu Wang1, Tao Lin4, Min Deng1, Haifeng Li1

Abstract:

Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road netwo... Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCN . read more read less

Topics:

Graph (abstract data type) (52%)52% related to the paper
View PDF
1,188 Citations
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IEEE Transactions on Intelligent Transportation Systems format uses IEEEtran citation style.

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Frequently asked questions

1. Can I write IEEE Transactions on Intelligent Transportation Systems in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the IEEE Transactions on Intelligent Transportation Systems guidelines and auto format it.

2. Do you follow the IEEE Transactions on Intelligent Transportation Systems guidelines?

Yes, the template is compliant with the IEEE Transactions on Intelligent Transportation Systems guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in IEEE Transactions on Intelligent Transportation Systems?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the IEEE Transactions on Intelligent Transportation Systems citation style.

4. Can I use the IEEE Transactions on Intelligent Transportation Systems templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for IEEE Transactions on Intelligent Transportation Systems.

5. Can I use a manuscript in IEEE Transactions on Intelligent Transportation Systems that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper IEEE Transactions on Intelligent Transportation Systems that you can download at the end.

6. How long does it usually take you to format my papers in IEEE Transactions on Intelligent Transportation Systems?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in IEEE Transactions on Intelligent Transportation Systems.

7. Where can I find the template for the IEEE Transactions on Intelligent Transportation Systems?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IEEE Transactions on Intelligent Transportation Systems's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the IEEE Transactions on Intelligent Transportation Systems's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. IEEE Transactions on Intelligent Transportation Systems an online tool or is there a desktop version?

SciSpace's IEEE Transactions on Intelligent Transportation Systems is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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11. What is the output that I would get after using IEEE Transactions on Intelligent Transportation Systems?

After writing your paper autoformatting in IEEE Transactions on Intelligent Transportation Systems, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE Transactions on Intelligent Transportation Systems's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for IEEE Transactions on Intelligent Transportation Systems?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IEEE Transactions on Intelligent Transportation Systems. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In IEEE Transactions on Intelligent Transportation Systems?

The 5 most common citation types in order of usage for IEEE Transactions on Intelligent Transportation Systems are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the IEEE Transactions on Intelligent Transportation Systems?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IEEE Transactions on Intelligent Transportation Systems's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download IEEE Transactions on Intelligent Transportation Systems in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in IEEE Transactions on Intelligent Transportation Systems Endnote style according to Elsevier guidelines.

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