Measures of speeding from a GPS-based travel behavior survey

Abstract Objective: Lacking information about actual driving speed on most roads in the Minneapolis–St. Paul region, we determine car speeds using observations from a Global Positioning System (GPS)-based travel survey. Speed of travel determines the likelihood and consequences of collisions. We identify the road segments where speeding occurs. This article then analyzes the relationship between link length, traveler characteristics, and speeding using GPS data collected from 152 individuals over a 7-day period as part of the Minneapolis–St. Paul Travel Behavior Inventory. Methods: To investigate the relationship, we employed an algorithm and process to accurately match the GPS data with geographic information system (GIS) databases. Comparing actual travel speed from GPS data with posted speed limits, we measure where and when speeding occurs and by whom. We posit that link length and demographics shape the decision to speed. Results: Speeding is widespread under both high speed limits (e.g., 60 mph [97 km/h]) and low speed limits (less than 25 mph [40 km/h]); in contrast, speeding is less common at 30–35 mph (48–56 km/h). The results suggest that driving patterns depend on the road type. We also find that when there are many intersections, the average link speed (and speeding) drops. Long links are conducive to speeding. Younger drivers and more educated drivers also speed more, and speeding occurs more often in the evening. Conclusions: Road design and link length (or its converse, frequency of intersections) affect the likelihood of speeding. Use of increasingly available GPS data allows more systematic empirical analysis of designs and topologies that are conducive to road safety.

[1]  D Shinar,et al.  Self-reports of safe driving behaviors in relationship to sex, age, education and income in the US adult driving population. , 2001, Accident; analysis and prevention.

[2]  Norman W Garrick,et al.  Does street network design affect traffic safety? , 2011, Accident; analysis and prevention.

[3]  David M Levinson,et al.  The impact of gasoline price changes on traffic safety: a time geography explanation , 2013 .

[4]  Jessica Schoner,et al.  Differences between Walking and Bicycling over Time: Implications for Performance Management , 2015 .

[5]  Katarzyna Sila-Nowicka.  Analysis of Actual Versus Permitted Driving Speed: a Case Study from Glasgow, Scotland , 2018 .

[6]  David M Levinson,et al.  Intra-household bargaining for school trip accompaniment of children: A group decision approach , 2016 .

[7]  Hideo Yamanaka,et al.  Empirical analysis of gross vehicle weight and free flow speed and consideration on its relation with differential speed limit. , 2011, Accident; analysis and prevention.

[8]  B Baruya.  SPEED-ACCIDENT RELATIONSHIPS ON EUROPEAN ROADS , 1998 .

[9]  Scott V Masten,et al.  Crash and traffic violation rates before and after licensure for novice California drivers subject to different driver licensing requirements. , 2014, Journal of safety research.

[10]  Adrian B Ellison,et al.  Personality, risk aversion and speeding: an empirical investigation. , 2010, Accident; analysis and prevention.

[11]  D. Levinson,et al.  Towards a Metropolitan Fundamental Diagram Using Travel Survey Data , 2016, PloS one.

[12]  Toshihiro Yokoo.  The effect of road network structure on speeding using GPS data , 2016 .

[13]  Chengyang Zhang,et al.  Map-matching for low-sampling-rate GPS trajectories , 2009, GIS.

[14]  Esko Keskinen,et al.  Has the difference in accident patterns between male and female drivers changed between 1984 and 2000? , 2004, Accident; analysis and prevention.

[15]  Geoff Boeing,et al.  OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks , 2016, Comput. Environ. Urban Syst..

[16]  Oliver Carsten,et al.  What benefit does Intelligent Speed Adaptation deliver: a close examination of its effect on vehicle speeds. , 2012, Accident; analysis and prevention.

[17]  M Meadows,et al.  CHARACTERISTICS OF SPEEDING, VIOLATING AND THRILL-SEEKING DRIVERS , 2001 .

[18]  Laure Charleux,et al.  Deriving Mobility Archetypes from Household Travel Survey Data , 2018 .

[19]  P. Delhomme,et al.  Young drivers’ sensation seeking, subjective norms, and perceived behavioral control and their roles in predicting speeding intention: How risk-taking motivations evolve with gender and driving experience , 2011 .

[20]  George Kanellaidis,et al.  Factors affecting drivers' choice of speed on roadway curves , 1995 .

[21]  John Golias,et al.  A survey of drivers' attitudes toward speed limit violations , 1995 .

[22]  Wenyun Tang,et al.  Deviation between Actual and Shortest Travel Time Paths for Commuters , 2018, Journal of Transportation Engineering, Part A: Systems.

[23]  Ashim Kumar Debnath,et al.  A Tobit model for analyzing speed limit compliance in work zones , 2014 .

[24]  David M Levinson,et al.  Is Bikesharing Contagious?: Modeling Its Effects on System Membership and General Population Cycling , 2016 .

[25]  M. Aoki.  To University of Minnesota , 2018 .

[26]  Andrew Guthrie,et al.  Weakening Obstacles to Transit Use: Changes in Relationships with Child Rearing and Automobile Access from 2000 to 2010 , 2016 .