Example of IET Intelligent Transport Systems format
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Example of IET Intelligent Transport Systems format Example of IET Intelligent Transport Systems format Example of IET Intelligent Transport Systems format Example of IET Intelligent Transport Systems format Example of IET Intelligent Transport Systems format Example of IET Intelligent Transport Systems format Example of IET Intelligent Transport Systems format Example of IET Intelligent Transport Systems format Example of IET Intelligent Transport Systems format Example of IET Intelligent Transport Systems format
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open access Open Access
recommended Recommended

IET Intelligent Transport Systems — Template for authors

Publisher: IET Publications
Categories Rank Trend in last 3 yrs
Law #29 of 722 up up by 28 ranks
Environmental Science (all) #44 of 220 up up by 14 ranks
Mechanical Engineering #132 of 596 up up by 16 ranks
Transportation #35 of 113 down down by 4 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 672 Published Papers | 3094 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 30/06/2020
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Related Journals

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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.

2.48

21% from 2018

Impact factor for IET Intelligent Transport Systems from 2016 - 2019
Year Value
2019 2.48
2018 2.05
2017 1.387
2016 1.194
graph view Graph view
table view Table view

4.6

21% from 2019

CiteRatio for IET Intelligent Transport Systems from 2016 - 2020
Year Value
2020 4.6
2019 3.8
2018 3.3
2017 2.9
2016 2.3
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 21% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 21% 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.

0.579

8% from 2019

SJR for IET Intelligent Transport Systems from 2016 - 2020
Year Value
2020 0.579
2019 0.627
2018 0.509
2017 0.436
2016 0.452
graph view Graph view
table view Table view

1.381

6% from 2019

SNIP for IET Intelligent Transport Systems from 2016 - 2020
Year Value
2020 1.381
2019 1.468
2018 1.509
2017 1.108
2016 1.079
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 8% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

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

IET Intelligent Transport Systems

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IET Publications

IET Intelligent Transport Systems

IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Information collection and processing; Data Integration and analytics; In-vehicle ...... Read More

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Last updated on
30 Jun 2020
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ISSN
1751-956X
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Impact Factor
High - 1.194
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Acceptance Rate
Not provided
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Frequency
2 issues per year
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Open Access
Yes
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder, G.E., Tinkham, M., Klapwijk, T.M.: ‘Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion’, Phys Rev B, 1982, 25, (7), pp. 4515–4532. Available from: 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1049/IET-ITS.2016.0208
LSTM network: a deep learning approach for short-term traffic forecast
Zheng Zhao1, Weihai Chen1, Xingming Wu1, Peter C. Y. Chen, Jingmeng Liu1

Abstract:

Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effectiv... Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance. read more read less

Topics:

Traffic generation model (61%)61% related to the paper, Road traffic control (54%)54% related to the paper, Intelligent transportation system (52%)52% related to the paper
View PDF
1,204 Citations
open accessOpen access Journal Article DOI: 10.1049/IET-ITS.2009.0070
Reinforcement learning-based multi-agent system for network traffic signal control
I. Arel1, C. Liu1, T. Urbanik1, Airton G Kohls1

Abstract:

A challenging application of artificial intelligence systems involves the scheduling of traffic signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi-agent system and reinforcement learning (RL) framework to obtain an efficient traffic signal control policy. The latter is aimed at mini... A challenging application of artificial intelligence systems involves the scheduling of traffic signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi-agent system and reinforcement learning (RL) framework to obtain an efficient traffic signal control policy. The latter is aimed at minimising the average delay, congestion and likelihood of intersection cross-blocking. A five-intersection traffic network has been studied in which each intersection is governed by an autonomous intelligent agent. Two types of agents, a central agent and an outbound agent, were employed. The outbound agents schedule traffic signals by following the longest-queue-first (LQF) algorithm, which has been proved to guarantee stability and fairness, and collaborate with the central agent by providing it local traffic statistics. The central agent learns a value function driven by its local and neighbours' traffic conditions. The novel methodology proposed here utilises the Q-Learning algorithm with a feedforward neural network for value function approximation. Experimental results clearly demonstrate the advantages of multi-agent RL-based control over LQF governed isolated single-intersection control, thus paving the way for efficient distributed traffic signal control in complex settings. read more read less

Topics:

Network traffic control (62%)62% related to the paper, Intelligent agent (58%)58% related to the paper, Multi-agent system (58%)58% related to the paper, Reinforcement learning (56%)56% related to the paper, Intelligent transportation system (53%)53% related to the paper
463 Citations
Journal Article DOI: 10.1049/IET-ITS:20060020
Deriving origin destination data from a mobile phone network
Noelia Caceres1, Johan Wideberg1, Francisco G. Benitez1

Abstract:

Acquiring high-quality origin-destination (OD) information for traffic in a geographic area is both time consuming and expensive while using conventional methods such as household surveys or roadside monitoring. These methods generally present only a snapshot of traffic situation at a certain point in time, and they are updat... Acquiring high-quality origin-destination (OD) information for traffic in a geographic area is both time consuming and expensive while using conventional methods such as household surveys or roadside monitoring. These methods generally present only a snapshot of traffic situation at a certain point in time, and they are updated in time intervals of up to several years. A technique was developed that makes use of the global system for mobile communications (GSM) mobile phone network. Instead of monitoring the flow of vehicles in a transportation network, the flow of mobile phones in a cell-phone network is measured and correlated to traffic flow. This methodology is based on the fact that a mobile phone moving on a specific route always tends to change the base station nearly at the same position. For a first pilot study, a GSM network simulator has been designed, where network data can be simulated, which is then extracted from the phone network, correlated, processed mathematically and converted into an OD matrix. Primary results show that the method has great potential, and the results inferred are much more cost-effective than those generated with traditional techniques. This is due to the fact that no change has to be made in the GSM network, because the information that is needed can readily be extracted from the base station database, that is the entire infrastructure needed is already in place read more read less

Topics:

GSM services (70%)70% related to the paper, Mobile station (69%)69% related to the paper, Timing advance (67%)67% related to the paper, Mobile phone signal (66%)66% related to the paper, Floating car data (64%)64% related to the paper
View PDF
292 Citations
open accessOpen access Journal Article DOI: 10.1049/IET-ITS.2017.0153
Traffic light control using deep policy-gradient and value-function-based reinforcement learning
Seyed Sajad Mousavi1, Michael Schukat1, Enda Howley1

Abstract:

Recent advances in combining deep neural network architectures with reinforcement learning (RL) techniques have shown promising potential results in solving complex control problems with high-dimensional state and action spaces. Inspired by these successes, in this study, the authors built two kinds of RL algorithms: deep pol... Recent advances in combining deep neural network architectures with reinforcement learning (RL) techniques have shown promising potential results in solving complex control problems with high-dimensional state and action spaces. Inspired by these successes, in this study, the authors built two kinds of RL algorithms: deep policy-gradient (PG) and value-function-based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The PG-based agent maps its observation directly to the control signal; however, the value-function-based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Their methods show promising results in a traffic network simulated in the simulation of urban mobility traffic simulator, without suffering from instability issues during the training process. read more read less

Topics:

Road traffic control (59%)59% related to the paper, Reinforcement learning (58%)58% related to the paper, Adaptive control (56%)56% related to the paper, Artificial neural network (53%)53% related to the paper, Optimal control (53%)53% related to the paper
View PDF
263 Citations
Journal Article DOI: 10.1049/IET-ITS.2012.0032
Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel
Sang-Joong Jung1, Heung-Sub Shin1, Wan-Young Chung1

Abstract:

Real time driver health condition monitoring system with drowsiness alertness was proposed. A new embedded electrocardiogram (ECG) sensor with electrically conductive fabric electrodes on the steering wheel of a car was designed to monitor the driver's health condition. The ECG signals were measured at a sampling rate of 100 ... Real time driver health condition monitoring system with drowsiness alertness was proposed. A new embedded electrocardiogram (ECG) sensor with electrically conductive fabric electrodes on the steering wheel of a car was designed to monitor the driver's health condition. The ECG signals were measured at a sampling rate of 100 Hz from the driver's palms as they stay on a pair of conductive fabric electrodes located on the steering wheel. Practical tests were conducted using an embedded ECG sensor with a wireless sensor node, and their performance was assessed under non-stop 2 h driving test. The ECG signals were measured and transmitted wirelessly to a base station connected to a server PC in personal area network environment. The driver's health condition such as the normal, fatigued and drowsy states was analysed by evaluating the heart rate variability in the time and frequency domains. read more read less

Topics:

Steering wheel (55%)55% related to the paper
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206 Citations
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Frequently asked questions

1. Can I write IET Intelligent Transport 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 IET Intelligent Transport Systems guidelines and auto format it.

2. Do you follow the IET Intelligent Transport Systems guidelines?

Yes, the template is compliant with the IET Intelligent Transport 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 IET Intelligent Transport 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 IET Intelligent Transport Systems citation style.

4. Can I use the IET Intelligent Transport 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 IET Intelligent Transport Systems.

5. Can I use a manuscript in IET Intelligent Transport 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 IET Intelligent Transport Systems that you can download at the end.

6. How long does it usually take you to format my papers in IET Intelligent Transport 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 IET Intelligent Transport Systems.

7. Where can I find the template for the IET Intelligent Transport 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 IET Intelligent Transport 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 IET Intelligent Transport 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. IET Intelligent Transport Systems an online tool or is there a desktop version?

SciSpace's IET Intelligent Transport 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.

10. I cannot find my template in your gallery. Can you create it for me like IET Intelligent Transport Systems?

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After writing your paper autoformatting in IET Intelligent Transport Systems, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IET Intelligent Transport 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 IET Intelligent Transport 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 IET Intelligent Transport 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 IET Intelligent Transport Systems?

The 5 most common citation types in order of usage for IET Intelligent Transport 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 IET Intelligent Transport 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 IET Intelligent Transport Systems's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download IET Intelligent Transport 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 IET Intelligent Transport Systems Endnote style according to Elsevier guidelines.

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