Emerging analytic methods for transportation data analysis: Examples with highway-safety data

1. Emerging analytic methods for transportation data analysis: Examples with highway-safety data Fred Mannering University of South Florida

2. Emerging Analytic Methods • Statistical and econometric advances in the last decade plus have opened up exciting new possibilities for the analysis of data • These new methods address issues of endogeniety, self-selectivity, unobserved heterogeneity and others that allow new insights to be gained from traditional and emerging data sources

3. The Case of Highway Safety • More than 1.2 million people die annually in highway-related crashes and as many as 50 million more are injured (World Health Organization, 2013) • Highway-related crashes are projected to be the 5th leading cause of death in the world by 2030

4. Traditional Crash Data • Available mostly from police and possibly other reports • Provide basic data on the characteristics of the crash • Road conditions • Estimates of injury severity • Occupant characteristics (age, gender) • Vehicle characteristics • Crash description, primary cause, etc.

5. Emerging Data Sources • Data from driving simulators • Data from naturalistic driving • Data from automated vehicles

6. Why Analyze Traditional Crash Data? • Identify crash-prone locations • Hoping that data analysis will suggest effective countermeasures • Evaluate the effectiveness of an implemented countermeasure • Understand fundamental causes of crashes

7. Traditional Analysis Approaches: • Models of crash frequency over some specified time and space • Models of crash-injury severity (which is conditional the crash having occurred) • Some modeling approaches have combined the two (frequency and severity)

8. Crash Frequency Models: • Study crash frequency over some specified time and space • Various count-data and other methods have been used • Explanatory variables: • Traffic conditions • Roadway conditions • Weather conditions Traditional crash data

9. Crash Injury Severity Models: • Study injury severities of specific crashes • Various discrete-outcome and other methods have been used • Explanatory variables: • Traffic Conditions, Roadway conditions, Weather conditions • Specific crash data: Vehicle information, Occupant information, Crash specific characteristics Traditional crash data

10. What Methodological Barriers have Encountered? • Unobserved Heterogeneity • Endogeneity • Self-selectivity • Temporal Correlation • Spatial Correlation

11. Unobserved Heterogeneity: • Many factors influencing the frequency and severity of crashes are simply not observed • If these are correlated with observed factors, incorrect inferences could be drawn Traditional crash data

12. Example: A study finds age to be an important factor in crash frequency/severity • Problem: • Age is correlated with many underlying factors such as physical/mental health, attitudes, income, life-cycle factors, etc. • Naive methodological application: • Effects of age are a proxy for unobserved factors – the correlation may not be stable over time and inferences relating to age may be incorrect • Another example: Men and women running in a dark room Unobserved heterogeneity

13. Example:Impact of ice warning signs on frequency/severity of ice-related crashes • Analyze the frequency/severity of crashes when ice warning signs are present vs. not present • Problem: • Ice warning signs are put at locations with a high frequency and severity of ice crashes • Naive methodological application: • Effectiveness of ice-warning signs understated (may find they actually increase frequency and severity) Endogeneity

14. Risk Compensation • Advanced Safety features: • Encourage drivers to drive more aggressively to shorten travel times • Encourage distracted driving as the same level of safety can be reached with less attention Risk Compensation

15. Marginal Rate of Transformation between safety and driving intensity Risk Compensation

16. Summarizing… • If intensity is a normal good, consumption should be to the right of B • Range could be from B (consume all safety) or to C (consume all intensity) • Or even over consume intensity (for example, point E) Risk Compensation

17. Good Morning America https://www.youtube.com/watch?v=0W5Z1PKlZ5I

18. Example: Effectiveness of Side-Impact Airbags (applies to other advanced safety features) • Analyze the severity of crashes involving vehicles with and without side-impact airbags • Problem: • People owning side-impact airbags are not a random sample of the population (likely safer drivers) • Naive methodological application: • Side-impact airbag effectiveness is overstated Endogeneity: Self Selectivity

19. Example: Side Airbag Effectiveness? • Insurance Institute for Highway Safety reports: • 2004: 45% effective in reducing fatalities • 2006: 37% effective in reducing fatalities • 2008: 30% effective in reducing fatalities • 2012: 24% effective in reducing fatalities • 2014: 21% effective in reducing fatalities

20. Ignoring self-selectivity will almost always overstate the effectiveness of new safety features due to self-selectivity • May mask important factors relating to possible risk compensation, etc. • Statistical corrections must be used • Another Example: Smoking during pregnancy Endogeneity: Self Selectivity

21. Example: Effectiveness of Motorcycle Safety Courses • Analyze the frequency and severity of crashes involving riders with and without course experience • Problem: • People taking the course are not a random sample of the population (likely less skilled) • Naive methodological application: • Effectiveness of the course understated (course participants may have higher crash rates) Endogeneity: Self Selectivity

22. Underlying issue: • There is unobserved heterogeneity about drivers that can manifest itself as a self-selectivity problem • This can mask causality and lead to erroneous inferences and policies Endogeneity: Self Selectivity

23. Temporal and Spatial Correlation • Crashes in close spatial proximity will share correlation due to unobserved factors associated with space (unobserved visual distractions, sight obstructions, etc.) • Crashes in occurring near the same or similar times will share correlation due to unobserved factors associated with time (precise weather conditions, similar sun angle, etc.) • Spatial econometrics Traditional crash data

24. Omitted Variables • Many crash frequency models use few explanatory variables (some only use traffic) • This creates a massive bias in parameter estimates that most certainly will lead to incorrect and temporally unstable inferences Traditional crash data

25. Building on Old Research • Highway Safety Manual (HSM) in the U.S. is an important practice-oriented document • However, it is several methodological generations behind the cutting-edge econometrics in the field • Problem: Some researchers view the HSM as the cutting edge and they base their work on terribly outdated methods and thinking Traditional crash data

26. DATA

27. Traditional Data Methodological Opportunities

28. Emerging Data Sources

29. New Data • Naturalistic Driving Data – extensively instrumented conventionally operated vehicles • Simulator Data – massive amounts of data collected from driving simulators • Automated Vehicle Data – including automated vehicle performance and response of drivers of conventional vehicles

30. New Data • Unobserved heterogeneity • Endogeneity • Self-selectivity (route choices, etc.) • Temporal correlations • Spatial correlations • Vehicle-to-vehicle correlations • Realism (for naturalistic driving and simulator data, how does the experiment affect behavior) Naturalistic Driving, Simulator, Automated Vehicle Data

31. Automated Vehicle Data: • Complex and heterogeneous responses of conventional vehicle drivers to automated-vehicles • Understanding driver responses will be critical to proper design of automated vehicle systems Naturalistic Driving, Simulator, Automated Vehicle Data

32. Current Methodological Frontier • Random parameter/finite-mixture models • Multi-state models (Markov switching) • Simultaneous equation models including multivariate models • Heckman-type selectivity correction techniques • Others

33. Some Recent Papers • An exploration of the offset hypothesis using disaggregate data: The case of airbags and antilock brakes. Journal of Risk and Uncertainty • Basis for GMA 2006 video • Addresses self-selectivity (safe drivers buy safe vehicles) • Addresses changing behavior over time due to risk compensation

34. Some Recent Papers (cont.) • The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach. Transportation Research Part B (2013) • Effectiveness of guardian supervision is highly variable and influenced by many unknown factors • Studied by considering latent-class heterogeneity and heterogeneity within classes

35. Some Recent Papers (cont.) • The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity. Transportation Research Part B (2014) • Accounting for cross-sectional and time-varying heterogeneity can be difficult • Markov switching between two or more safety states can be used to address time-varying heterogeneity while traditional random parameters can address cross-sectional heterogeneity

36. Multi-state: Driver adaptation to adverse weather

37. Some Recent Papers (cont.) • Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods in Accident Research 11, 1-16 (2016). • Outlines the need to address unobserved heterogeneity and alternative heterogeneity models

38. Some Recent Papers (cont.) • The effect of passengers on driver-injury severities: A random parameters heterogeneity-in-means approach. Under Review (2017) • Using heterogeneity in means as an extension of fixed-mean random parameters models

39. Summary • In the past, comparatively “static” data quality and quantity has enabled sophisticated methodological applications to extract much of the available information • A new data-rich era is beginning • With few exceptions, sophisticated methodologies have not been widely used in analyzing these data

40. Methodological applications are needed that address underlying data issues (unobserved heterogeneity, etc.) • The methodological frontier needs to expand to include sophisticated new statistical and econometric methods Summary (cont.)