Examining Heterogeneity of Driver Behavior with Temporal and Spatial Factors

Temporal and spatial characteristics of the road environment are known to influence driver behavior and, consequently, the risk of a crash that causes injury or fatality. Nonetheless, much of the understanding of the risks of injury and fatality associated with driving relies heavily on police crash records. These records capture the most serious of crashes but underreport other events. Studies that rely on these data sources typically ignore the temporal and spatial factors. Advances in technology have enabled more detailed study of driving on a day-to-day basis and, therefore, provided the opportunity to examine driver behavior for the same driver across time and space. However, this ability has its own challenges. These challenges include extensive intra- and interdriver heterogeneity, which is not apparent when traditional data collection methods are used. A framework and a methodology for isolating the influence of drivers' inherent characteristics on driver behavior are presented. This study was done by constructing temporal and spatial identifiers that controlled for the influence of the road environment. Results include analyses conducted by using empirical driving information collected from 106 vehicles in Sydney, Australia, to examine the effectiveness of this approach. The results indicated that in 80% of road environments there was less intradriver variability in speeding behavior than interdriver variability when temporal and spatial characteristics were accounted for. Clustering and regression analyses for the most frequently observed road environments are also presented. Driver personality characteristics are significant for evening trips home on residential roads, and acceleration profiles are significant in evening trips on roads with 50- and 60-km/h speed limits.

[1]  Thomas A. Dingus,et al.  The 100-Car Naturalistic Driving Study Phase II – Results of the 100-Car Field Experiment , 2006 .

[2]  Charles Musselwhite,et al.  Attitudes towards vehicle driving behaviour: categorising and contextualising risk. , 2006, Accident; analysis and prevention.

[3]  Charles Goldenbeld,et al.  The credibility of speed limits on 80 km/h rural roads: The effects of road and person(ality) characteristics. , 2007, Accident; analysis and prevention.

[4]  Jennifer Ogle,et al.  Quantitative assessment of driver speeding behavior using instrumented vehicles , 2005 .

[5]  Chetan Dave,et al.  Eliciting risk preferences: When is simple better? , 2008 .

[6]  M. Bierlaire,et al.  Exploring the potentials of automatically collected GPS data for travel behaviour analysis , 2002 .

[7]  Kim S. Sankey,et al.  Relationships between young drivers' personality characteristics, risk perceptions, and driving behaviour. , 2008, Accident; analysis and prevention.

[8]  Frank Lai,et al.  Driving simulators for robust comparisons: a case study evaluating road safety engineering treatments. , 2010, Accident; analysis and prevention.

[9]  Karin Brundell-Freij,et al.  Influence of street characteristics, driver category and car performance on urban driving patterns , 2005 .

[10]  E. Petridou,et al.  Human factors in the causation of road traffic crashes , 2004, European Journal of Epidemiology.

[11]  Dot Hs,et al.  The 100 Car Naturalistic Driving Study , 2002 .

[12]  R. Fuller,et al.  Iconography : Task difficulty and risk in the determination of driver behaviour , 2007 .

[13]  David Shinar,et al.  The tendency of drivers to pass other vehicles , 2005 .

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

[15]  Barry C. Watson,et al.  The impact of police speed enforcement practices on self-reported speeding: an exploration of the effects of visibility and mobility , 2009 .

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

[17]  M. Anthony Machin,et al.  Understanding the unique contribution of aversion to risk taking in predicting drivers' self-reported speeding , 2010 .

[18]  Simon Fifer,et al.  Analysis of a financial incentive to encourage safer driving practices , 2011 .

[19]  Samuel G Charlton,et al.  Explicit and implicit processes in behavioural adaptation to road width. , 2006, Accident; analysis and prevention.

[20]  Eva Ericsson,et al.  Variability in urban driving patterns , 2000 .

[21]  D. Hensher The valuation of commuter travel time savings for car drivers: evaluating alternative model specifications , 2001 .

[22]  Richard Tay,et al.  Managing speed at school and playground zones. , 2011, Accident; analysis and prevention.

[23]  Anna Maria Giannini,et al.  Young novice driver subtypes: relationship to driving violations, errors and lapses. , 2010, Accident; analysis and prevention.

[24]  Lisa N. Wundersitz,et al.  Identifying and improving exposure measures , 2008 .

[25]  Wassim G. Najm,et al.  ANALYSIS OF FATAL CRASHES DUE TO SIGNAL AND STOP SIGN VIOLATIONS , 2004 .

[26]  Thomas F. Golob,et al.  Structural Equation Modeling For Travel Behavior Research , 2001 .

[27]  Robert W Whissell,et al.  The Speeding Attitude Scale and the Role of Sensation Seeking in Profiling Young Drivers at Risk , 2003, Risk analysis : an official publication of the Society for Risk Analysis.

[28]  Peter R. Stopher,et al.  Evaluating voluntary travel behaviour change: Suggested guidelines and case studies , 2009 .

[29]  Sheryl B. Ball,et al.  Risk aversion and physical prowess: Prediction, choice and bias , 2010 .

[30]  Stephen Greaves,et al.  Capturing speeding behaviour in school zones using GPS technology , 2013 .

[31]  Stephen Greaves,et al.  Development of a Global Positioning System Web-Based Prompted Recall Solution for Longitudinal Travel Surveys , 2010 .

[32]  Randall Guensler,et al.  Relationships between Crash Involvement and Temporal-Spatial Driving Behavior Activity Patterns , 2007 .