000107380 001__ 107380
000107380 005__ 20231023123342.0
000107380 0247_ $$2doi$$a10.1016/j.trf.2020.09.011
000107380 0248_ $$2sideral$$a120444
000107380 037__ $$aART-2020-120444
000107380 041__ $$aeng
000107380 100__ $$0(orcid)0000-0002-6596-2978$$aLucas Alba, Antonio$$uUniversidad de Zaragoza
000107380 245__ $$aDistressed in the queue? Psychophysiological and behavioral evidence for two alternative car-following techniques
000107380 260__ $$c2020
000107380 5060_ $$aAccess copy available to the general public$$fUnrestricted
000107380 5203_ $$aBackground. Nature offers numerous examples of animal species exhibiting harmonious collective movement. Unfortunately, the motorized Homo sapiens sapiens is not included and pays a price for it. Too often, drivers who simply follow other drivers are caught in the worst road threat after a crash: congestions. In the past, the solution to this problem has gone hand in hand with infrastructure investment. However, approaches such as the Nagoya Paradigm propose now to see congestion as the consequence of multiple interacting particles whose disturbances are transmitted in a waveform. This view clashes with a longlasting assumption ordering traffic flows, the rational driver postulate (i.e., drivers’ alleged propensity to maintain a safe distance). Rather than a mere coincidence, the worldwide adoption of the safety-distance tenet and the worldwide presence of congestion emerge now as cause and effect. Nevertheless, nothing in the drivers’ endowment impedes the adoption of other car-following (CF) strategies. The present study questions the a priori of safety-distance, comparing two elementary CF strategies, Driving to keep Distance (DD), that still prevails worldwide, and Driving to keep Inertia (DI), a complementary CF technique that offsets traffic waves disturbances, ensuring uninterrupted traffic flows. By asking drivers to drive DD and DI, we aim to characterize both CF strategies, comparing their effects on the individual driver (how he drives, how he feels, what he pays attention to) and also on the road space occupied by a platoon of DD robot-followers. Methods. Thirty drivers (50% women) were invited to adopt DD/DI in a driving simulator following a swinging leader. The design was a repeated measures model controlling for order. The CF technique, DD or DI, was the within-subject factor. Order (DD-DI / DI-DD) was the between-subjects factor. There were four blocks of dependent measures: individual driving performance (accelerations, decelerations, crashes, distance to lead vehicle, speed and fuel consumption), emotional dimensions (measures of skin conductance and self-reports of affective states concerning valence, arousal, and dominance), and visual behavior (fixations count and average duration, dwell times, and revisits) concerning three regions of the driving scene (the Top Rear Car –TRC- or the Bottom Rear Car –BRC- of the leading vehicle and the surrounding White Space Area -WSA). The final block concerned the road space occupied by a platoon of 8 virtual DD followers. Results. Drivers easily understood and applied DD/DI as required, switching back and forth between the two. Average speeds for DD/DI were similar, but DD drivers exhibited a greater number of accelerations, decelerations, speed variability, and crashes. Conversely, DI required greater CF distance, that was dynamically adjusted, and spent less fuel. Valence was similar, but DI drivers felt less aroused and more dominant. When driving DD visual scan was centered on the leader’s BRC, whereas DI elicited more attention to WSA (i.e., adopting wider vision angles). In spite of DI requiring more CF distance, the resulting road space occupied between the leader and the 8th DD robot was greater when driving DD.
000107380 536__ $$9info:eu-repo/grantAgreement/ES/UZ-IBERCAJA/2015-B011
000107380 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000107380 590__ $$a3.261$$b2020
000107380 591__ $$aPSYCHOLOGY, APPLIED$$b39 / 83 = 0.47$$c2020$$dQ2$$eT2
000107380 591__ $$aTRANSPORTATION$$b20 / 37 = 0.541$$c2020$$dQ3$$eT2
000107380 592__ $$a1.231$$b2020
000107380 593__ $$aApplied Psychology$$c2020$$dQ1
000107380 593__ $$aTransportation$$c2020$$dQ1
000107380 593__ $$aCivil and Structural Engineering$$c2020$$dQ1
000107380 593__ $$aAutomotive Engineering$$c2020$$dQ1
000107380 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000107380 700__ $$aMelchor Galán, Óscar
000107380 700__ $$0(orcid)0000-0003-3793-669X$$aHernando Mazón, Ana$$uUniversidad de Zaragoza
000107380 700__ $$aFernández Martín, Andrés
000107380 700__ $$0(orcid)0000-0003-2160-7509$$aBlanch Micó, Maria Teresa
000107380 700__ $$0(orcid)0000-0003-3492-7544$$aLombas Fouletier, Andrés$$uUniversidad de Zaragoza
000107380 7102_ $$14009$$2730$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Psicología Básica
000107380 7102_ $$14009$$2620$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Metod.Ciencias Comportam.
000107380 773__ $$g74 (2020), 418-432$$pTransp. res., Part F Traffic psychol. behav.$$tTransportation research. Part F, Traffic psychology and behaviour$$x1369-8478
000107380 8564_ $$s557239$$uhttps://zaguan.unizar.es/record/107380/files/texto_completo.pdf$$yPostprint
000107380 8564_ $$s1604926$$uhttps://zaguan.unizar.es/record/107380/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000107380 909CO $$ooai:zaguan.unizar.es:107380$$particulos$$pdriver
000107380 951__ $$a2023-10-23-12:21:20
000107380 980__ $$aARTICLE