Rear-end collision scenarios categorized by type of human error

Hiramatsu, Machiko; Obara, Hideo

doi:10.1016/s0389-4304(00)00067-9

Cited by 5 publications

(7 citation statements)

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“…This suggests that the frequency of rear-end conflicts in the simulation increases in accordance with traffic volume on the signaled approaches. This finding is consistent with real-world situations where increased traffic volume implies shorter time gaps on average between vehicles and where shorter gaps increase the likelihood for rear-end collisions (18). Table 2 also shows the number of red light violations in each of the two simulation scenarios.…”

Section: Results Of Simulating the Ir Functionsupporting

confidence: 82%

Traffic Microsimulation Modeling to Study a Traffic Signal Incident Reduction Function

Young¹,

Archer

2009

Transportation Research Record: Journal of the Transportation R

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This paper demonstrates the use of a discrete event traffic micro-simulation model to study the safety impacts of the introduction of an incident reduction (IR) function into a vehicle-actuated traffic signal controller. The IR function is based on that used in signal controllers in Sweden. The IR function is designed to reduce the number of stop-or-go decisions by drivers when caught in the dilemma zone at the onset of the amber signal. A correctly adapted IR function was found to greatly enhance the safety performance of signalized intersections. The simulation experiment described in the paper is based on empirical data from a suburban vehicle-actuated signalized intersection. It was conducted specifically to investigate the effects of the standard IR function on three proximal safety indicators: time to collision, red light violations, and required braking rates. The simulation results demonstrate a significant improvement in the time to collision (22%), red light violations (6%), and required braking rate when the IR function was in operation and a small positive influence on traffic movement through the intersection. Importantly, this study has illustrated the potential for using traffic microsimulation modeling for traffic safety evaluation.

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Section: Results Of Simulating the Ir Functionsupporting

confidence: 82%

Traffic Microsimulation Modeling to Study a Traffic Signal Incident Reduction Function

Young¹,

Archer

2009

Transportation Research Record: Journal of the Transportation R

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“…Driving task/road scenario: Two different driving tasks/road scenarios have been selected as the most applicable for the selected research hypotheses, which are namely the free driving scenario and the following vehicle scenario. The following vehicle driving task has been selected because it has been recognized as a major road safety issue [30,36,38], mainly due to its close relation to the often catastrophic rear-end collisions, while it is important that both driving tasks, due to them being quite generic, lacking specific measures imposition, are optimum for allowing the collection of comparative data which would serve as horizontal "exposure data" for other, more sophisticated driving tasks/scenarios. 3.…”

Section: Experimental Plan Variablesmentioning

confidence: 99%

Transferability of driver speed and lateral deviation measurable performance from semi-dynamic driving simulator to real traffic conditions

Gemou

2013

Eur. Transp. Res. Rev.

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Purpose This manuscript presents the experimental plan and results of driver trials conducted with a semi-dynamic driving simulator and an equipped research vehicle in real traffic conditions. Methods 12 trainees, 12 novice (driving license less than 1.5 years; 25,000 totally run km by average) and 12 experienced drivers (driving license more than 12 years; 320,000 totally run km and 28583 km only the last year by average) participated in the trials, conducting the same driving sessions in the simulator and on-the-road, consisting of a free driving and a following vehicle scenario in the context of highways, rural and urban roads. Their maximum speed, difference of their average speed from each speed limit and their average lateral deviation in driving simulator and onthe-road were compared. A novel research framework was established for the results analysis dealing with the existence of numerical proximity between the driving simulator and the on-the-road measurements (in the sense of absolute validity), the degree of this proximity and the possibility and the type of the correction that could be applied leading to an acceptable absolute validity.Results Derived results have shown that several degrees of numerical proximity apply for different combinations of driver behaviour metrics, driver cohorts, driving tasks and road contexts, whereas the type of the driver behaviour metric seems to be the most determining factor for absolute validity existence. Conclusions Tangible findings consist of transfer algorithms that serve as the basis for redefining warning and intervention thresholds of ADAS but also driver training and assessment schemes based on driving simulators.

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“…Significant research has been conducted so far on driving simulator validity and there are various attempts to correlate the simulator results with those being extracted from trials in real traffic conditions. Relevant research started since the mid 1960's with Barrett et al (1965) and is continuing until today, with Godley et al (2001), Yu et al (2006), Kim et al (2006), Miyajima et al (2006), Hirata et al (2007), de Winter et al (2009), Riener (2010), etc. Among them, it is worth mentioning Jia et al (2011), who moved one step further, and they suggested an approach for their simulator calibration through a correlation model, relying on measurements from trials in real traffic conditions.…”

Section: Executive Summarymentioning

confidence: 99%

“…Έρεη πξαγκαηνπνηεζεί πιεζψξα εξεπλψλ ζρεηηθά κε ηελ αμηνπηζηία ησλ πξνζνκνησηψλ νδήγεζεο θαη είλαη πνηθίιεο νη πξνζπάζεηεο ζπζρέηηζεο ησλ απνηειεζκάησλ απφ πξνζνκνησηή νδήγεζεο κε απηά απφ δνθηκέο ζε πξαγκαηηθέο ζπλζήθεο. Ζ ζρεηηθή έξεπλα μεθίλεζε απφ ηα κέζα ηεο δεθαεηίαο ηνπ 1960 κε ηνπο Barrett et al (1965) θαη εθηείλεηαη κέρξη ηηο κέξεο καο κε ηνπο Godley et al (2001), Yu et al (2006), Kim et al (2006), Miyajima et al (2006), Hirata et al (2007), de Winter et al (2009), Riener (2010), θ.α. Αλάκεζά ηνπο, μερσξίδνπλ νη Jia et al (2011), πνπ πξνρψξεζαλ έλα βήκα παξαπέξα, θαη πξφηεηλαλ κία πξνζέγγηζε ξχζκηζεο/βαζκνλφκεζεο ηνπ πξνζνκνησηή ηνπο κέζσ ελφο κνληέινπ ζπζρέηηζεο, κε βάζε ηα απνηειέζκαηα απφ δνθηκέο ζε πξαγκαηηθέο ζπλζήθεο.…”

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“…Ζ κελ ειεχζεξε νδήγεζε γηαηί αθνξά ηε ζπλνιηθή ζπκπεξηθνξά ηνπ νδεγνχ, θαη ε αθνινχζεζε νρήκαηνο εμαηηίαο ηνπ ξφινπ πνπ παίδεη ε αληίιεςε ηεο ηαρχηεηαο θαη ηεο επηηάρπλζεο απφ κέξνπο ηνπ νδεγνχ («Με πφζε αθξίβεηα κπνξεί ν νδεγφο λα θαζνξίζεη κία αζθαιή απφζηαζε απφ ην πξνπνξεπφκελν φρεκα;»). πγθεθξηκέλα, ε αθνινχζεζε νρήκαηνο έρεη αλαγλσξηζηεί σο έλα απφ ηα θπξηφηεξα πξνβιήκαηα νδηθήο αζθάιεηαο (Carsten et al 1989, Evans 1991, Hiramatsu & Obara 2000, αθνχ αλεπηηπρείο αθνινπζήζεηο νρήκαηνο κπνξεί λα νδεγήζνπλ ζε νπίζζηεο ζπγθξνχζεηο πνπ απνηεινχλ ην 13% ησλ αηπρεκάησλ ζηελ Δπξψπε (Fiorani et al, 2005).…”

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Μοντελοποίηση Συμπεριφοράς Οδηγού Σε Προσομοιωτή Οδήγησης Και Συσχέτιση Με Πραγματικές Συνθήκες Οδήγησης

Gemou¹,

Γκέμου²

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such, unable to contribute decisively in road safety as well as in the cost-effectiveness of the research community investments. Furthermore, currently, it is necessary to validate a simulator for a number of research hypotheses, prior to its use, and, afterwards, verify the outcomes emerging from it in real traffic conditions. Thus, instead of being able to exploit the multiple benefits from the-even conditional-use of the simulator (in place of real traffic conditions), the research scope is being transposed to the study of its validity, which, quite often, leads to ambiguous results (and does not allow the direct use of the simulator). This is mainly due to the fact that there is no functional way yet proposed, to calculate the basic analogies of the driving behaviour between the driving simulator and the real traffic conditions (Kendall & Jones, 1999;McGehee et al., 2002). Even the correlation model of Jia et al. ( 2011) (which is utilising the metrics distribution), is useful for the overall comparison of the drivers' performance and, as such, for the evaluation of the overall effects, but not for the prediction of the behaviour of each individual (and new in the sample) driver, since it cannot be known in advance, in which point of the distribution s/he is placed in (i.e. not useful in the driving skills assessment field).According to several researchers, such as Riener ( 2010), in order to be able to use the driving simulator in place of a vehicle in real traffic conditions, it should be ensured that the emerging results will be transferrable to reality. The first proposed solution is the use of high fidelity simulators, which are usually expensive and which still do not necessarily guarantee validity of results (Kappe & Emmerik 2005, Vlakveld 2005). The second proposed solution is the use of a corrective conversion matrix/model, that will provide for each simulator of any level of fidelity the corrective factor for each variable, which, after being applied in the respective simulator results, will approximate as much as possible the corresponding result in the real traffic. The later proposal summarises the objective of the current dissertation, which aims to investigate the existence of an a priori correlation of driving performance between (each specific) driving simulator and real traffic conditions, regardless of the particular research hypotheses, and, in case this exists, the identification of an algorithm for transformation of simulator data to real traffic ones.

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Rear-end collision scenarios categorized by type of human error

Cited by 5 publications

References 0 publications

Traffic Microsimulation Modeling to Study a Traffic Signal Incident Reduction Function

Traffic Microsimulation Modeling to Study a Traffic Signal Incident Reduction Function

Transferability of driver speed and lateral deviation measurable performance from semi-dynamic driving simulator to real traffic conditions

Μοντελοποίηση Συμπεριφοράς Οδηγού Σε Προσομοιωτή Οδήγησης Και Συσχέτιση Με Πραγματικές Συνθήκες Οδήγησης

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