117983 20240319081013.0 doi 10.1016/j.trf.2022.06.003 sideral 129125 ART-2022-129125 eng Tenenboim, Einat Car following with an inertia-oriented driving technique: A driving simulator experiment 2022 Access copy available to the general public Unrestricted For many decades, car-following (CF) and congestion models have assumed a basic invariance: drivers’ default driving strategy is to keep the safety distance. The present study questions that Driving to keep Distance (DD) is a traffic invariance and, therefore, that the difference between the time required to accelerate versus decelerate must necessarily determine the observed patterns of traffic oscillations. Previous studies have shown that drivers can adopt alternative CF strategies like Driving to keep Inertia (DI) by following basic instructions. The present work aims to test the effectiveness of a DI course that integrates 4 tutorials and 4 practice sessions in a standard PC computer designed to learn more adaptive driving behaviors in dense traffic. Methods. Sixty-eight drivers were invited to follow a leading car that varied its speed on a driving simulator, then they took a DI course on a PC computer, and finally they followed a fluctuating leader again on the driving simulator. The study adopted a pretest-intervention-posttest design with a control group. The experimental group took the full DI course (tutorials and then simulator practice). The control group had access to the DI simulator but not to the tutorials. Results. All participating drivers adopted DD as the default CF mode on the pre-test, yielding very similar results. But after taking the full DI course, the experimental group showed significantly less accelerations, decelerations, and speed variability than the control group, and required greater CF distance, that was dynamically adjusted, spending less fuel in the post-test. A group of 8 virtual cars adopting DD required less space on the road to follow the drivers that took the DI course. info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 4.1 2022 TRANSPORTATION 18 / 37 = 0.486 2022 Q2 T2 PSYCHOLOGY, APPLIED 26 / 83 = 0.313 2022 Q2 T1 1.352 2022 Applied Psychology 2022 Q1 Transportation 2022 Q1 Civil and Structural Engineering 2022 Q1 Automotive Engineering 2022 Q1 8.4 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Lucas-Alba, Antonio Universidad de Zaragoza (orcid)0000-0002-6596-2978 Melchor, Óscar M. Toledo, Tomer Bekhor, Shlomo 4009 730 Universidad de Zaragoza Dpto. Psicología y Sociología Área Psicología Básica 89 (2022), 72-83 Transp. res., Part F Traffic psychol. behav. Transportation research. Part F, Traffic psychology and behaviour 1369-8478 2995951 http://zaguan.unizar.es/record/117983/files/texto_completo.pdf Versión publicada 1911173 http://zaguan.unizar.es/record/117983/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:117983 articulos driver 2024-03-18-15:23:59 ARTICLE