Performance Analysis of Model for Road Surface Temperature Change Pattern by Road Section

Kim, Jin Guk; Yang, Choong Heon; Ryu, Hyeon Ju; Moon, Jae Pil

doi:10.7855/ijhe.2018.20.5.121

Cited by 2 publications

(1 citation statement)

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“…Issues related to driver responses to real-time road freezing risk information have not yet been studied in detail. In previous research, a model for estimating road surface temperature changes according to weather conditions was developed based on a machine learning technique [19][20][21][22]. In South Korea, a road surface freezing risk information service is planned based on the results of this model, which will provide real-time messages such as "Caution Road Surface Freezing Ahead" to drivers.…”

Section: Introductionmentioning

confidence: 99%

Driver Preference and Level of Trust Related to Real-Time Road Freezing Risk Information

Khedher

Yang

Kim

et al. 2020

Journal of Advanced Transportation

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Bad weather conditions can affect normal driving by substantially hindering visibility. Among all adverse weather conditions, road freezing is probably the most dangerous to drivers because slippery roads reduce surface friction and can lead to loss of vehicle control. This paper evaluates driver preference of receiving real-time road freezing risk information and explores the factors that would most influence drivers’ trust in a future road freezing information service. A survey was conducted in the metropolitan areas of South Korea during January and February 2019. The survey was completed by 231 driver’s license holders of 18 years or older, and the results were used for statistical analysis. According to the survey results, the variable message sign (VMS) is a very important system from the perspective of public benefit. Car-navigation systems are preferred for age categories of 21∼30 and over 50. In addition, ordinal regression was used to analyze the causal relationship between the level of trust regarding road freezing risk information and its controlling factors. The ordered log odds of drivers with previous accident experience due to slippery roads exhibit a higher level of trust in road freezing risk information because the coefficient is positive. Moreover, drivers with a constant commute time show a lower level of trust in road freezing risk information. These findings provide a foundation for planning the scope of future road freezing risk information service, as well as the specific service targets and type of information, especially during the winter season.

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Section: Introductionmentioning

confidence: 99%