Example of Transportation Research Part C: Emerging Technologies format
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Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format
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Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format Example of Transportation Research Part C: Emerging Technologies format
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open access Open Access
recommended Recommended

Transportation Research Part C: Emerging Technologies — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Civil and Structural Engineering #3 of 318 down down by 1 rank
Automotive Engineering #2 of 95 -
Computer Science Applications #20 of 693 up up by 11 ranks
Transportation #4 of 113 down down by 1 rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1143 Published Papers | 16037 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 08/06/2020
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Related Journals

open access Open Access

Springer

Quality:  
High
CiteRatio: 3.3
SJR: 0.522
SNIP: 1.289
open access Open Access

Elsevier

Quality:  
High
CiteRatio: 5.4
SJR: 1.231
SNIP: 1.646
open access Open Access
recommended Recommended

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.

6.077

5% from 2018

Impact factor for Transportation Research Part C: Emerging Technologies from 2016 - 2019
Year Value
2019 6.077
2018 5.775
2017 3.968
2016 3.805
graph view Graph view
table view Table view

14.0

15% from 2019

CiteRatio for Transportation Research Part C: Emerging Technologies from 2016 - 2020
Year Value
2020 14.0
2019 12.2
2018 9.6
2017 8.3
2016 7.4
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 5% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 15% 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.

3.185

5% from 2019

SJR for Transportation Research Part C: Emerging Technologies from 2016 - 2020
Year Value
2020 3.185
2019 3.342
2018 2.611
2017 2.293
2016 1.998
graph view Graph view
table view Table view

3.547

2% from 2019

SNIP for Transportation Research Part C: Emerging Technologies from 2016 - 2020
Year Value
2020 3.547
2019 3.477
2018 3.179
2017 3.11
2016 2.67
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 5% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Transportation Research Part C: Emerging Technologies

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Elsevier

Transportation Research Part C: Emerging Technologies

The focus of Transportation Research: Part C is high-quality, scholarly research that addresses development, applications, and implications, in the field of transportation, of emerging technologies from such fields as operations research, computer science, electronics, control...... Read More

Engineering

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Last updated on
08 Jun 2020
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ISSN
0968-090X
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Impact Factor
Very High - 3.662
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Open Access
No
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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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Bibliography Name
elsarticle-num
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Citation Type
Author Year
(Blonder et al., 1982)
i
Bibliography Example
Blonder, G. E., Tinkham, M., Klapwijk, T. M., 1982. Transition from metallic to tunneling regimes in su-perconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 25 (7), 4515–4532. URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1016/J.TRC.2015.03.014
Long short-term memory neural network for traffic speed prediction using remote microwave sensor data
Xiaolei Ma1, Zhimin Tao1, Yinhai Wang2, Haiyang Yu1, Yunpeng Wang1

Abstract:

Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay... Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability. read more read less

Topics:

Artificial neural network (54%)54% related to the paper
1,521 Citations
Journal Article DOI: 10.1016/J.TRC.2013.12.001
The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios
Daniel J. Fagnant1, Kara M. Kockelman1

Abstract:

Carsharing programs that operate as short-term vehicle rentals (often for one-way trips before ending the rental) like Car2Go and ZipCar have quickly expanded, with the number of US users doubling every 1–2 years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service... Carsharing programs that operate as short-term vehicle rentals (often for one-way trips before ending the rental) like Car2Go and ZipCar have quickly expanded, with the number of US users doubling every 1–2 years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service used on demand. The advent of autonomous or fully self-driving vehicles will address many current carsharing barriers, including users’ travel to access available vehicles. This work describes the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use. The model operates by generating trips throughout a grid-based urban area, with each trip assigned an origin, destination and departure time, to mimic realistic travel profiles. A preliminary model run estimates the SAV fleet size required to reasonably service all trips, also using a variety of vehicle relocation strategies that seek to minimize future traveler wait times. Next, the model is run over one-hundred days, with driverless vehicles ferrying travelers from one destination to the next. During each 5-min interval, some unused SAVs relocate, attempting to shorten wait times for next-period travelers. Case studies vary trip generation rates, trip distribution patterns, network congestion levels, service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed. read more read less

Topics:

Trip generation (59%)59% related to the paper, Trip distribution (59%)59% related to the paper, Travel behavior (55%)55% related to the paper
View PDF
938 Citations
Journal Article DOI: 10.1016/J.TRC.2014.01.005
Short-term traffic forecasting: Where we are and where we’re going
Eleni I. Vlahogianni1, Matthew G. Karlaftis1, John Golias1

Abstract:

Since the early 1980s, short-term traffic forecasting has been an integral part of most Intelligent Transportation Systems (ITS) research and applications; most effort has gone into developing methodologies that can be used to model traffic characteristics and produce anticipated traffic conditions. Existing literature is vol... Since the early 1980s, short-term traffic forecasting has been an integral part of most Intelligent Transportation Systems (ITS) research and applications; most effort has gone into developing methodologies that can be used to model traffic characteristics and produce anticipated traffic conditions. Existing literature is voluminous, and has largely used single point data from motorways and has employed univariate mathematical models to predict traffic volumes or travel times. Recent developments in technology and the widespread use of powerful computers and mathematical models allow researchers an unprecedented opportunity to expand horizons and direct work in 10 challenging, yet relatively under researched, directions. It is these existing challenges that we review in this paper and offer suggestions for future work. read more read less

Topics:

Intelligent transportation system (51%)51% related to the paper
927 Citations
Journal Article DOI: 10.1016/S0968-090X(02)00009-8
Comparison of parametric and nonparametric models for traffic flow forecasting
Brian L. Smith1, Billy M. Williams2, R Keith Oswald1

Abstract:

Single point short-term traffic flow forecasting will play a key role in supporting demand forecasts needed by operational network models. Seasonal autoregressive integrated moving average (ARIMA), a classic parametric modeling approach to time series, and nonparametric regression models have been proposed as well suited for ... Single point short-term traffic flow forecasting will play a key role in supporting demand forecasts needed by operational network models. Seasonal autoregressive integrated moving average (ARIMA), a classic parametric modeling approach to time series, and nonparametric regression models have been proposed as well suited for application to single point short-term traffic flow forecasting. Past research has shown seasonal ARIMA models to deliver results that are statistically superior to basic implementations of nonparametric regression. However, the advantages associated with a data-driven nonparametric forecasting approach motivate further investigation of refined nonparametric forecasting methods. Following this motivation, this research effort seeks to examine the theoretical foundation of nonparametric regression and to answer the question of whether nonparametric regression based on heuristically improved forecast generation methods approach the single interval traffic flow prediction performance of seasonal ARIMA models. read more read less

Topics:

Nonparametric regression (66%)66% related to the paper, Semiparametric regression (64%)64% related to the paper, Nonparametric statistics (58%)58% related to the paper, Autoregressive integrated moving average (58%)58% related to the paper, Regression analysis (53%)53% related to the paper
926 Citations
Journal Article DOI: 10.1016/J.TRC.2016.07.007
Influence of connected and autonomous vehicles on traffic flow stability and throughput
Alireza Talebpour1, Hani S. Mahmassani2

Abstract:

The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, ... The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on-board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology-appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model’s assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios. read more read less

Topics:

Traffic flow (53%)53% related to the paper
893 Citations
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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 Transportation Research Part C: Emerging Technologies citation style.

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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 Transportation Research Part C: Emerging Technologies that you can download at the end.

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After writing your paper autoformatting in Transportation Research Part C: Emerging Technologies, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Transportation Research Part C: Emerging Technologies'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 Transportation Research Part C: Emerging Technologies?

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 Transportation Research Part C: Emerging Technologies. 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 Transportation Research Part C: Emerging Technologies?

The 5 most common citation types in order of usage for Transportation Research Part C: Emerging Technologies 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 Transportation Research Part C: Emerging Technologies?

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16. Can I download Transportation Research Part C: Emerging Technologies 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 Transportation Research Part C: Emerging Technologies Endnote style according to Elsevier guidelines.

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