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Ordered Probit Model of the Speed Selection Behavior: Results Based on a Korean Micro Data

  • Kyungwoo Kang
Conference paper

Abstract

Many studies on drivers’ speed selection behavior have been reported in the last decade. Most previous studies have, however, concentrated on the relationship between drivers’ speed selection and road/vehicle characteristics without considering other important factors such as personal characteristics and drivers’ perception of speed limit. This paper analyzes Korean drivers’ speed selection behavior by taking into account such factors as trip characteristics in addition to personal, vehicular, and attitudinal factors. Speed selection behavior is measured by a categorical measure over speed limit, and an ordered probit model is used to econometrically estimate the speed behavior equation. The results are as follows: i) male drivers with higher income tend to drive faster, and experienced drivers drive more higher speed than others ii) vehicles with more safety features such as ABS and Air-bag go slower than vehicles with less safety features iii) trip distance and frequency user of the road are important factors for speed selection behavior, iv) perceived speed limit on road and expectation of being caught for speeding are an important factors for driving behavior.

Keywords

Traffic Accident Speed Limit Seat Belt License Plate Safety Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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    We assume that all drivers in the model behave risk-neutral behave in order to maximize their expected utility.Google Scholar
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    Four different types of roads are selected. First, two roads in the urban area where four lanes and two lanes in each directions respectively. Another two roads in rural area where three and two lanes in each directions respectively.Google Scholar
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    Approximately, 284 observations are not useful because either the filed interview data and vehicle identification numbers are not matched or missing variables.Google Scholar
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    We first considered the possibility of collinearity when we laid out the ordered probit model, especially education level and income, age and driving experiences, horse-power and safety features of vehicles. Correlation analysis revealed that age and driving experiences has 0.5936 followed by horsepower and safety features 0.2247, and education level and monthly income 0.1472. In order to test robustness of our model, we deleted highly correlated variables. Fortunately, the results of revised models are not significant different from our original model.Google Scholar
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    Because of limited data sample for younger (18–24) drivers in our sample, we can not analyze the effects of younger driver’s speed selection behavior.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Kyungwoo Kang
    • 1
  1. 1.Hanyang UniversityAnsanKorea

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