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作者

Bin Jiang;Jibo He;Jielin Chen;Linda Larsen

来源

URBAN FORESTRY & URBAN GREENING,Vol.58,Issue1,Article 126976

语言

英文

关键字

Driving performance;Freeway landscape;Greenness;Complexity;Mental arousal;Mental restoration

作者单位

Virtual Reality Lab of Urban Environments and Human Health, HKUrbanlabs, Faculty of Architecture, The University of Hong Kong, Hong Kong;Division of Landscape Architecture, Department of Architecture, The University of Hong Kong, Hong Kong;Department of Psychology, Tsinghua University, China;Department of Architecture, National University of Singapore, Singapore;Smart Energy Design Assistance Center, University of Illinois at Urbana-Champaign, USA;Virtual Reality Lab of Urban Environments and Human Health, HKUrbanlabs, Faculty of Architecture, The University of Hong Kong, Hong Kong;Division of Landscape Architecture, Department of Architecture, The University of Hong Kong, Hong Kong;Department of Psychology, Tsinghua University, China;Department of Architecture, National University of Singapore, Singapore;Smart Energy Design Assistance Center, University of Illinois at Urbana-Champaign, USA

摘要

Driving on freeways is a daily activity across the world. Poor driving performance on freeways can cause severe injuries and deaths. However, few studies have examined whether and to what extent different types of freeway landscapes influence driving performance. A simulated driving task was designed to measure the impacts of six types of freeway landscape on 33 participants’ driving performance. Each participant completed a driving experiment with six blocks of 90-minute driving sessions in a random sequence. During the experiment, participants’ driving performance was measured through eight parameters. A set of repeated-measure one-way ANOVA analyses show that landscapes with three-dimensional branch and foliage (shrub & tree) were generally more beneficial for driving performance than barren (concrete-paved ground) or low green landscape conditions (turf). Furthermore, a repeated-measure two-way ANOVA analysis of four conditions with vertical green foliage (two shrub and two tree conditions) showed moderate levels of greenness and complexity are optimal for driving performance.