Does vehicle colour influence crash risk?

Newstead, Stuart; D'Elia, Angelo Dominic

doi:10.1016/j.ssci.2010.05.001

Cited by 20 publications

(10 citation statements)

References 6 publications

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“…This effect was in part due to the frustrating situation where the participant's car was blocked by a vehicle after a traffic light turned green, as well as the stereotype that (French) people have about red car owners, who are considered 'show-offs'. Guéguen's results could in part explain why red cars have been recently associated with high crash risk (Newstead & D'Elia, 2010). Previous studies, however, have observed the opposite trend-that is, lower risk of crash for light-colored vehicles (such as red) compared with darker colored vehicles (Furness et al, 2003;Lardelli-Claret et al, 2002).…”

Section: Seeing Red On the Roadmentioning

confidence: 89%

Seeing Red on the Road

et al. 2015

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Human and animal research has found that red perception is associated with specific behavioral reactions, generally characterized by intense responses. Here, we explored whether red cars are perceived as more dangerous than other colored cars. One hundred Spanish drivers examined several road scenarios which involved hazardous cars with different colors: red, green, yellow, black, gray, and white. Driver's behavior (response time and probability of braking) and the perceived level of risk for each scenario were analyzed. Although car color affected participants' response times, contrary to expectations, red cars did not elicit faster responses or higher perceived levels of risk.

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Section: Seeing Red On the Roadmentioning

confidence: 89%

Seeing Red on the Road

et al. 2015

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“…Wang et al [76] clustered clients using language variables. Newstead and D'Elia [77] showed that grouping customers based on the color of their vehicles is very important in the field of insurance. Using the clustering method, Zeithaml et al [68] introduced the concept of customer pyramid through which they are divided according to customer profitability.…”

Section: Methodsmentioning

confidence: 99%

Financial Inclusion Paradigm Shift in the Postpandemic Period. Digital-Divide and Gender Gap

Vasile

Panait

Apostu

2021

IJERPH

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Financial inclusion is strongly differentiated by age groups and countries and the pandemic has highlighted the increased gaps and inequalities but also the weaknesses of the system, in terms of flexibility, access and facilities of the customer-bank relationship and also from the perspective of the financial education of young generations and vulnerable people, active in the labor market. Based on the available data provided by the Global Findex database, and some findings after more than one year of COVID-19 crisis we outlined the main aspects of financial digitization, by categories of people and countries. At the same time, we identified the challenges and problems during the pandemic that significantly adjusted the consumption pattern of citizens and increased the need for on-line access for financial transactions. Starting from the analysis of the inequality of access to financial instruments in the last years, from the informational asymmetry in financial education and the challenges of the pandemic period, we underlined the main coordinates of changing the model of sustainable financial inclusion—based on five pillars—access, education, support tools, CSR and resilience. The research results highlight the need for convergence in providing opportunities to consider financial inclusion as a public good and an active tool to increase consumers’ satisfaction and the quality of life of individuals.

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“…A study showed that no single color was significantly safer or riskier than white [19]. However, some studies have indicated that a vehicle's color influences its crash risk [20][21][22][23][24][25]. Using probability theory and the results of a survey, Solomon and King determined that lime-yellowcolored fire engines were significantly safer than red ones.…”

Section: Introductionmentioning

confidence: 99%

“…e survey indicated that the accident rate of red vehicles (15.3 accidents per 100,000 runs) is double of that of limeyellow vehicles (31.9 accidents per 100,000 runs). An analysis of Australian accident data used induced exposure methods to show a close relationship between a vehicle's color and the crash risk [24]. In general, it can be concluded that compared with white vehicles, several colors are closely linked with higher crash rates.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Research on Driver Visibility of Black-and-White Striped Vehicles

Xie

Zhu

et al. 2020

Journal of Advanced Transportation

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The vehicle color is considered to be a significant factor affecting driver visibility. The primary objective of this study is therefore to determine the impact of black-and-white striped vehicles (BWVs) on driver visibility through simulation-based experiments. In these experiments, subjects were asked to perform front and rear target identification tasks under daylight and twilight conditions. Then, a 2 (lighting conditions) × 2 (vehicle size) × 5 (vehicle color) analysis of variance was conducted for each task. Under the front identification scenario, the main factors affecting visibility were found to be lighting conditions, vehicle size, vehicle color, and the interactions between these factors. Under the rear identification scenario, lighting conditions and vehicle color were found to be the main factors. The results of this study demonstrate that driver visibility of BWVs is poorer than that of other colors of vehicles and that BWV visibility is susceptible to lighting conditions.

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Does vehicle colour influence crash risk?

Cited by 20 publications

References 6 publications

Seeing Red on the Road

Seeing Red on the Road

Financial Inclusion Paradigm Shift in the Postpandemic Period. Digital-Divide and Gender Gap

Simulation-Based Research on Driver Visibility of Black-and-White Striped Vehicles

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