Example of Transportation Research Part F: Traffic Psychology and Behaviour format
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Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format
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Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format Example of Transportation Research Part F: Traffic Psychology and Behaviour format
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open access Open Access

Transportation Research Part F: Traffic Psychology and Behaviour — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Applied Psychology #34 of 227 up up by 18 ranks
Civil and Structural Engineering #55 of 318 down down by 11 ranks
Automotive Engineering #18 of 95 down down by 4 ranks
Transportation #25 of 113 down down by 4 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 994 Published Papers | 5324 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 29/06/2020
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FAQ

Related Journals

open access Open Access

Springer

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

Elsevier

Quality:  
High
CiteRatio: 14.0
SJR: 3.185
SNIP: 3.547
open access Open Access

Springer

Quality:  
High
CiteRatio: 5.0
SJR: 0.741
SNIP: 1.583

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.

2.518

7% from 2018

Impact factor for Transportation Research Part F: Traffic Psychology and Behaviour from 2016 - 2019
Year Value
2019 2.518
2018 2.36
2017 1.935
2016 1.83
graph view Graph view
table view Table view

5.4

32% from 2019

CiteRatio for Transportation Research Part F: Traffic Psychology and Behaviour from 2016 - 2020
Year Value
2020 5.4
2019 4.1
2018 3.3
2017 4.0
2016 3.3
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 7% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 32% 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.

1.231

7% from 2019

SJR for Transportation Research Part F: Traffic Psychology and Behaviour from 2016 - 2020
Year Value
2020 1.231
2019 1.146
2018 0.993
2017 1.462
2016 1.111
graph view Graph view
table view Table view

1.646

1% from 2019

SNIP for Transportation Research Part F: Traffic Psychology and Behaviour from 2016 - 2020
Year Value
2020 1.646
2019 1.663
2018 1.748
2017 1.79
2016 1.464
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 7% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 1% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Transportation Research Part F: Traffic Psychology and Behaviour

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Elsevier

Transportation Research Part F: Traffic Psychology and Behaviour

Announcing... Transportation Research Part F received a 2008 Impact Factor of 1.403 which ranks it 7th in the 'Transportation' category and 23rd in 'Applied Psychology' of the Thomson Reuters Journal Citation Reports? (2009). Transportation Research Part F: Traffic Psychology ...... Read More

Engineering

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Last updated on
29 Jun 2020
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ISSN
1369-8478
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Impact Factor
High - 1.784
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
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
Numbered
[25]
i
Bibliography Example
G. E. Blonder, M. Tinkham, T. M. Klapwijk, Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion, Phys. Rev. B 25 (7) (1982) 4515–4532. URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1016/S1369-8478(00)00005-X
Car-following: a historical review
Mark Brackstone1, Mike McDonald1

Abstract:

In recent years, the topic of car-following has become of increased importance in traffic engineering and safety research. Models of this phenomenon, which describe the interaction between (typically) adjacent vehicles in the same lane, now form the cornerstone for many important areas of research including (a) simulation mod... In recent years, the topic of car-following has become of increased importance in traffic engineering and safety research. Models of this phenomenon, which describe the interaction between (typically) adjacent vehicles in the same lane, now form the cornerstone for many important areas of research including (a) simulation modelling, where the car-following model (amongst others) controls the motion of the vehicles in the network, and (b) the functional definition of advanced vehicle control and safety systems (AVCSS), which are being introduced as a driver safety aid in an effort to mimic driver behaviour but remove human error. Despite the importance of this area however, no overview of the models availability and validity exists. It is the intent of this paper therefore to briefly assess the range of options available in the choice of car-following model, and assess just how far work has proceeded in our understanding of what, at times, would appear to be a simple process. read more read less

Topics:

Human error (53%)53% related to the paper, Intelligent driver model (52%)52% related to the paper, Gipps' model (52%)52% related to the paper, Poison control (51%)51% related to the paper, System safety (51%)51% related to the paper
1,255 Citations
open accessOpen access Journal Article DOI: 10.1016/J.TRF.2015.04.014
Public opinion on automated driving: results of an international questionnaire among 5000 respondents
Miltos Kyriakidis1, Riender Happee1, J.C.F. de Winter1

Abstract:

This study investigated user acceptance, concerns, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlat... This study investigated user acceptance, concerns, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Respondents scoring higher on neuroticism were slightly less comfortable about data transmitting, whereas respondents scoring higher on agreeableness were slightly more comfortable with this. Respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data, with cross-national correlations between ρ = −0.80 and ρ = −0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders. read more read less

Topics:

Poison control (50%)50% related to the paper
View PDF
771 Citations
Journal Article DOI: 10.1016/J.TRF.2005.04.012
Effects of visual and cognitive load in real and simulated motorway driving
Johan Engström1, Emma Johansson1, Joakim Östlund2

Abstract:

As part of the HASTE European Project, effects of visual and cognitive demand on driving performance and driver state were systematically investigated by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS). The present paper reports results from simulated and real motorway driving. Data were collected i... As part of the HASTE European Project, effects of visual and cognitive demand on driving performance and driver state were systematically investigated by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS). The present paper reports results from simulated and real motorway driving. Data were collected in a fixed base simulator, a moving base simulator and an instrumented vehicle driven in real traffic. The data collected included speed, lane keeping performance, steering wheel movements, eye movements, physiological signals and self-reported driving performance. The results show that the effects of visual and cognitive load affect driving performance in qualitatively different ways. Visual demand led to reduced speed and increased lane keeping variation. By contrast, cognitive load did not affect speed and resulted in reduced lane keeping variation. Moreover, the cognitive load resulted in increased gaze concentration towards the road centre. Both S-IVIS had an effect on physiological signals and the drivers’ assessment of their own driving performance. The study also investigated differences between the three experimental settings (static simulator, moving base simulator and field). The results are discussed with respect to the development of a generic safety test regime for In-vehicle Information Systems. read more read less

Topics:

European union (51%)51% related to the paper, Cognitive load (51%)51% related to the paper, Poison control (51%)51% related to the paper
756 Citations
open accessOpen access Journal Article DOI: 10.1016/J.TRF.2014.06.016
Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence
Joost C. F. de Winter1, Riender Happee1, Marieke Martens2, Neville A. Stanton3

Abstract:

Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers' workload and situat... Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers' workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway. read more read less

Topics:

Driving simulator (60%)60% related to the paper, Poison control (52%)52% related to the paper, Workload (51%)51% related to the paper
View PDF
544 Citations
open accessOpen access Journal Article DOI: 10.1016/J.TRF.2014.09.005
Transition to manual: driver behaviour when resuming control from a highly automated vehicle
Natasha Merat1, A. Hamish Jamson1, Frank Lai1, M. C. Daly1, Oliver Carsten1

Abstract:

A driving simulator study was designed to investigate drivers’ ability to resume control from a highly automated vehicle in two conditions: (i) when automation was switched off and manual control was required at a system-based, regular interval and (ii) when transition to manual was based on the length of time drivers were lo... A driving simulator study was designed to investigate drivers’ ability to resume control from a highly automated vehicle in two conditions: (i) when automation was switched off and manual control was required at a system-based, regular interval and (ii) when transition to manual was based on the length of time drivers were looking away from the road ahead. In addition to studying the time it took drivers to successfully resume control from the automated system, eye tracking data were used to observe visual attention to the surrounding environment and the pattern of drivers’ eye fixations as manual control was resumed in the two conditions. Results showed that drivers’ pattern of eye movement fixations remained variable for some time after automation was switched off, if disengagement was actually based on drivers’ distractions away from the road ahead. When disengagement was more predictable and system-based, drivers’ attention towards the road centre was higher and more stable. Following a lag of around 10 s, drivers’ lateral control of driving and steering corrections (as measured by SDLP and high frequency component of steering, respectively) were more stable when transition to manual control was predictable and based on a fixed time. Whether automation transition to manual was based on a fixed or variable interval, it took drivers around 35–40 s to stabilise their lateral control of the vehicle. The results of this study indicate that if drivers are out of the loop due to control of the vehicle in a limited self-driving situation (Level 3 automation), their ability to regain control of the vehicle is better if they are expecting automation to be switched off. As regular disengagement of automation is not a particularly practical method for keeping drivers in the loop, future research should consider how to best inform drivers of their obligation to resume control of driving from an automated system. read more read less

Topics:

Poison control (53%)53% related to the paper, Driving simulator (53%)53% related to the paper
528 Citations
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Frequently asked questions

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Yes, the template is compliant with the Transportation Research Part F: Traffic Psychology and Behaviour 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 Transportation Research Part F: Traffic Psychology and Behaviour?

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5. Can I use a manuscript in Transportation Research Part F: Traffic Psychology and Behaviour 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 Transportation Research Part F: Traffic Psychology and Behaviour that you can download at the end.

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13. What is Sherpa RoMEO Archiving Policy for Transportation Research Part F: Traffic Psychology and Behaviour?

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 F: Traffic Psychology and Behaviour. 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 F: Traffic Psychology and Behaviour?

The 5 most common citation types in order of usage for Transportation Research Part F: Traffic Psychology and Behaviour are:.

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

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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 F: Traffic Psychology and Behaviour Endnote style according to Elsevier guidelines.

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