The effect of speed limits on drivers' choice of speed: A random parameters seemingly unrelated equations approach

Drivers’ choice of speed has long been known to be a critical factor in both the likelihood and severity of vehicle crashes. Given this, understanding drivers’ choice of speed and the possible effect that posted speed limits may have on this choice, is a critical element of safety research. This paper seeks to provide new insights on drivers’ speed-choice process by studying U.S. interstate highways (all of which are constructed to the same design-speed standard) under three distinct speed limits (55 mi/h, 65 mi/h and 70 mi/h). Using a survey of interstate drivers that asked respondents their normal operating speed on interstates posted with these speed limits (under light traffic conditions), a random parameters seemingly unrelated regression estimation approach is used to account for both the interrelation among the choices under the three speed limits and for the unobserved heterogeneity across respondents. The estimation results show that a wide variety of factors influence the choice of speed in the presence of speed limits, including driver age, gender, marital status, number of children, driver education level, household income, age when the driver was first licensed, and opinions about pavement quality. The findings in this paper have important implications relating to the factors that may affect speed-limit compliance, and also demonstrate the methodological potential of the random parameters seemingly unrelated regression estimation approach to address a number of safety-related problems involving a series of inter-related continuous dependent variables.

[1]  Dart,et al.  EFFECTS OF THE 88.5-KM/H (55-MPH) SPEED LIMIT AND ITS ENFORCEMENT ON TRAFFIC SPEEDS AND ACCIDENTS , 1977 .

[2]  Gudmundur F. Ulfarsson,et al.  Random parameter models of interstate crash frequencies by severity, number of vehicles involved, collision and location type. , 2013, Accident; analysis and prevention.

[3]  J. Aaker,et al.  Dimensions of Brand Personality , 1997 .

[4]  John E. Haddock,et al.  Random Parameters Seemingly Unrelated Equations Approach to the Postrehabilitation Performance of Pavements , 2012 .

[5]  Ziyou Gao,et al.  A hazard-based duration model for analyzing crossing behavior of cyclists and electric bike riders at signalized intersections. , 2015, Accident; analysis and prevention.

[6]  Panagiotis Ch. Anastasopoulos,et al.  Exploratory State-Level Empirical Assessment of Pavement Performance , 2011 .

[7]  Samuel Labi,et al.  Analyzing the Duration and Prolongation of Performance-Based Contracts through Hazard-Based Duration and Zero-Inflated Random Parameters Poisson Models , 2009 .

[8]  Peter Cummings,et al.  Freeway speed limits and traffic fatalities in Washington State. , 2002, Accident; analysis and prevention.

[9]  C. Bhat Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences , 2003 .

[10]  Fred L. Mannering,et al.  Statistical Modeling of User Perceptions of Infrastructure Condition: Application to the Case of Highway Roughness , 2006 .

[11]  Gudmundur F. Ulfarsson,et al.  Model of Relationship between Interstate Crash Occurrence and Geometrics , 2011 .

[12]  Ahmet Tortum,et al.  Accident analysis with aggregated data: the random parameters negative binomial panel count data model , 2015 .

[13]  Judson S Matthias,et al.  ANALYSIS OF THE RELATION OF ACCIDENTS AND THE 88-KM/H (55-MPH) SPEED LIMIT ON ARIZONA HIGHWAYS (ABRIDGEMENT) , 1976 .

[14]  K. Train Discrete Choice Methods with Simulation , 2003 .

[15]  Simon Washington,et al.  Hazard based models for freeway traffic incident duration. , 2013, Accident; analysis and prevention.

[16]  Pengpeng Xu,et al.  Modeling crash spatial heterogeneity: random parameter versus geographically weighting. , 2015, Accident; analysis and prevention.

[17]  F L Mannering,et al.  Male/female driver characteristics and accident risk: some new evidence. , 1993, Accident; analysis and prevention.

[18]  Fred L Mannering,et al.  An empirical assessment of fixed and random parameter logit models using crash- and non-crash-specific injury data. , 2011, Accident; analysis and prevention.

[19]  Richard Tay,et al.  A random parameters probit model of urban and rural intersection crashes. , 2015, Accident; analysis and prevention.

[20]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[21]  Fred L. Mannering,et al.  MODELING THE ENDOGENEITY OF LANE-MEAN SPEEDS AND LANE-SPEED DEVIATIONS: A STRUCTURAL EQUATIONS APPROACH , 1998 .

[22]  Fred L Mannering,et al.  A note on modeling vehicle accident frequencies with random-parameters count models. , 2009, Accident; analysis and prevention.

[23]  John M. Rose,et al.  Specification issues in a generalised random parameters attribute nonattendance model , 2013 .

[24]  Tarek Sayed,et al.  A full Bayes multivariate intervention model with random parameters among matched pairs for before-after safety evaluation. , 2011, Accident; analysis and prevention.

[25]  Gudmundur F. Ulfarsson,et al.  A heterogeneity-in-means count model for evaluating the effects of interchange type on heterogeneous influences of interstate geometrics on crash frequencies , 2014 .

[26]  Robert McNown,et al.  A Cost-Benefit Analysis of the 55 MPH Speed Limit , 1984 .

[27]  Aman Ullah,et al.  Estimation of Seemingly Unrelated Regressions with Random Coefficients , 1974 .

[28]  Fred L. Mannering,et al.  An empirical analysis of driver perceptions of the relationship between speed limits and safety , 2009 .

[29]  Dominique Lord,et al.  The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. , 2011, Accident; analysis and prevention.

[30]  J. Halton On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .

[31]  Fred L. Mannering,et al.  The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .

[32]  Fred L Mannering,et al.  Highway accident severities and the mixed logit model: an exploratory empirical analysis. , 2008, Accident; analysis and prevention.

[33]  Fred L. Mannering,et al.  Principles of Highway Engineering and Traffic Analysis , 1990 .

[34]  Dominique Lord,et al.  Comparing Three Commonly Used Crash Severity Models on Sample Size Requirements: Multinomial Logit, Ordered Probit, and Mixed Logit Models , 2014 .

[35]  Samuel Labi,et al.  Empirical Assessment of the Likelihood and Duration of Highway Project Time Delays , 2012 .

[36]  Jack D Jernigan,et al.  THE IMPACT OF THE 65 MPH SPEED LIMIT ON VIRGINIA'S RURAL INTERSTATE HIGHWAYS THROUGH 1989. STATUS REPORT , 1990 .

[37]  John E. Haddock,et al.  Household Automobile and Motorcycle Ownership Analyzed with Random Parameters Bivariate Ordered Probit Model , 2012 .

[38]  Md. Tazul Islam,et al.  Effects of spatial correlation in random parameters collision count-data models , 2015 .

[39]  Fred L. Mannering,et al.  Effects of Interstate Speed Limits on Driving Speeds: Some New Evidence , 2007 .

[40]  John E. Haddock,et al.  Analysis of Urban Travel Times: Hazard-Based Approach to Random Parameters , 2012 .

[41]  Hjp Harry Timmermans,et al.  Accounting for heterogeneity in travel episode satisfaction using a random parameters panel effects regression model , 2014 .

[42]  Brendan J. Russo,et al.  Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model , 2014 .

[43]  A. Zellner An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias , 1962 .

[44]  Fred L Mannering,et al.  A study of factors affecting highway accident rates using the random-parameters tobit model. , 2012, Accident; analysis and prevention.

[45]  Chandra R. Bhat,et al.  Analytic methods in accident research: Methodological frontier and future directions , 2014 .

[46]  Fred L. Mannering,et al.  Analysis of Pavement Overlay and Replacement Performance Using Random Parameters Hazard-Based Duration Models , 2015 .

[47]  Saeed Maghsoodloo,et al.  THE SAFETY IMPACT OF THE 65 MPH SPEED LIMIT: A CASE STUDY USING ALABAMA ACCIDENT RECORDS. FINAL GRANT REPORT , 1989 .

[48]  Konstantina Gkritza,et al.  Cost Savings Analysis of Performance-Based Contracts for Highway Maintenance Operations , 2010 .

[49]  Fred Mannering,et al.  An exploration of the offset hypothesis using disaggregate data: The case of airbags and antilock brakes , 2006 .

[50]  Hjp Harry Timmermans,et al.  Binomial Random Parameters Logistic Regression Model of Housing Satisfaction , 2014 .