Fred Mannering Charles Pankow Professor of Civil Engineering Purdue University Emerging analytic methods for transportation data analysis: Examples with highway-safety data

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 Emerging Analytic Methods

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 5 th leading cause of death in the world by 2030 The Case of Highway Safety

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. Traditional Crash Data

Data from driving simulators Data from naturalistic driving Data from automated vehicles Emerging Data Sources

Identify crash-prone locations Hoping that data analysis will suggest effective countermeasures Evaluate the effectiveness of an implemented countermeasure Why Analyze Traditional Crash Data?

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) Traditional Analysis Approaches:

Traditional crash data 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 Crash Frequency Models:

Traditional crash data 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 Crash Injury Severity Models:

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

Traditional crash data 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 Unobserved Heterogeneity:

Unobserved heterogeneity Problem: Problem: Age is correlated with many underlying factors such as physical/mental health, attitudes, income, life-cycle factors, etc. Naive methodological application: 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 Example: A study finds age to be an important factor in crash frequency/severity

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

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

Marginal Rate of Transformation between safety and driving intensity

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

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

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

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

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

Endogeneity: Self Selectivity 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 Underlying issue:

Traditional crash data 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 Temporal and Spatial Correlation

Traditional crash data 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 Omitted Variables

Traditional crash data 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 Building on Old Research

DATA

Methodological Opportunities Traditional Data

Emerging Data Sources

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 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) New Data Naturalistic Driving, Simulator, 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 Automated Vehicle Data: Naturalistic Driving, Simulator, Automated Vehicle Data

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

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 Some Recent Papers

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 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 Some Recent Papers (cont.)

Multi-state: Driver adaptation to adverse weather

Implementing technology to improve public highway performance: A leapfrog technology from the private sector is going to be necessary. Economics of Transportation (2014) Outlines the economic benefits and implementation barriers to new transportation technologies including automated vehicles Some Recent Papers (cont.)

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 Summary

Summary (cont.) 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