Knowledge and Data in Road Safety Management - Research at the Center for Road Safety

Knowledge and Data in Road Safety Management - Research at the Center for Road Safety by Andrew Tarko Professor of Civil Engineering Director of Center for Road Safety School of Civil Engineering Purdue University West Lafayette, Indiana, USA tarko@purdue.edu International Seminar on Road Safety Research La Universidad de los Andes November 19, 2012, Bogota, Colombia

Can Road Safety be Managed? It can if: The current safety is known The consequences of potential actions can be predicted The relevant countermeasures exist and can be identified The safety can be monitored to verify the predictions and the correctness of actions

Measuring Safety Bad news safety is difficult to measure The biggest challenge is to measure safety here and now (probability of crash and expected severity of its outcome) It is much easier to measure aggregate safety afterwards: in long periods in large areas for large groups of road user

Acquiring Knowledge Statistical analysis of observational data (causal relationship may be questioned) Experiments with postulated relevance to safety and without exposure context Good news modern surveillance, naturalistic driving (still observational)

Safety Factors Contribution (Great Britain and USA) Human factors (93%) 57% 6% 3% 27% 3% Roadway factors (34%) 2% 1% Vehicle factors (12%) Source: http://en.wikipedia.org/wiki/traffic_collision 6

What Knowledge Is Transferrable? Qualitative knowledge explaining safety-related behavior and performance General classification of which countermeasures work based on principles of physics, biomechanics, etc. Methods and processes for safety management based on mathematics, statistics, and optimization theory

(Tarko, 2009) Example - Speed Selection

Example - Speed Selection Speed selected = 130 km/h if speed limit is ignored (Elvik, 2009)

Example Transferrable Knowledge Perceived risk grows slower than actual risk Enforcing speed limits is needed Explains aggressiveness of young drivers (low risk perception) Explains ignoring unreasonably low speed limits (too high cost of complying) Consistent with observed fast driving under time pressure (high value of time)

Another Example - Power Model (Elvik, 2009) Crash Modification Factor = (Speed after/speed before) Exponent Power model exponent was believed to be universal only dependant on the severity of crash outcome

Another Example - Power Model Exponents (Elvik, 2009)

Freeway Crash Severity States comparison for freeway crashes many conditions controlled Injury Odds Ratio State Run-off-road Crash Multiple-vehicle Same-dir Crash IN 1 1 OH 0.79 1 IL 1.28 1.16 MO 1.97 1 WA 3.29 2.56 NY 2.76 4.47 OR 3.52 2.59 CO 2.15 2.27 (Villwock, Blond, and Tarko, 2008)

What Knowledge May not be Transferrable? Safety Performance Functions Crash Modification Factors Cost of crashes This knowledge is also subject to aging (Persaud, et al., 2003; Sacchi, et al., 2011; Sacchi, et al., 2012)

Two Uses of Data 1. Research: To acquire knowledge and to develop methods useful for safety management, and 2. Management: To identify safety problems and to propose most effective countermeasures

Safety Data Needs 17

Minimum Data Requirement Crash reports Basic infrastructure inventory Traffic volumes

Crash Reports Primary source of safety information Determines the quality of safety management Vary between countries and jurisdictions Underreporting Most important data are most troublesome: Crash location (Indiana: 1995 linear reference, 45-50% known; 2012 GIS coordinates, 80% known; future point and click ) Injury severity (Indiana: fatalities 30 day update, good quality; other outcomes determined by investigating police officers)

Crash Reports Crash location (Where) GIS Address Linear reference GPS receiver Point & Click Geo-code 20

Crash Data Geo-coding Digitize Geocode Crash database

Crash Data Electronic Report and Point and Click Technique Crash database

Crash Reports Injury Data K Fatal Crash Injury Severity A Incapacitating injury B Non-incap. injury C Possible injury O Property damage 23

(Tarko, et al., 2010) Injury Data Police-based vs. Hospital-based

Linked Police-hospital Data (Indiana CODES Project) Better measurement of injury level Hospital data available only for more severe cases Selection biased addressed via bivariate model of outcome and selection (Tarko and Azam, 2011)

Road Infrastructure Data Road classification Administration Functional Cross-section information GIS representation Segments Intersections Bridges Ramps Interchanges 26

Road Representation Intersections - limits Segments - splitting 27

Traffic Data Critical exposure information Annual Average Daily Traffic (AADT) Typically, available only for major roads Mitigation for local roads: use proxy exposure such as land use, proximity of arterial roads, etc. (Tarko and Azam, 2009)

CRS Road Safety Database Data Details Source Road network 440,000 state and local roads segments, 200,000 intersections Indiana Department Road data Cross-section data for state and major local roads of Transportation AADT State road segments Crash data 24 years, 8 years geo-coded, 300,000 records/year Indiana State Police Hospital data 2003-2010, 4.5 million hospital discharge records/year, between 40,000 and 63,000 annual crash-hospital links Indiana Hospital Association Death data 2003-2008, 55,000 deaths/year, death certificates data Use of seat belts 113 sites, 3 times/year, 2001-2012 New survey 190 sites twice/year 2013 onwards Indiana Criminal Justice Institute Driver data Registered motorcycle owners and all drivers born after 1978, Citations, suspensions, registration information Indiana Bureau of Motor Vehicles Census data 2000, 2010 years, demographic, socioeconomic, economic, and US Census Bureau other statistical data. Linear features such as roads, railroads, rivers, and legal boundaries (TIGER). Bridge data System 1 Bridges Bridges found on System 1 roads that include interstate highways, U.S. highways, state routes, ramps. INDOT, National Bridge Inventory Weather data US National Weather Service shape files. Weather stations in Indiana + weather historical data USNWS, National Climatic Data Center Households 2 million households as of 2010, basic socio-demographics CAS Inc. (commercial Businesses 200,000 businesses as of 2010, basic socio-demographics source)

Safety Database Renewal Acquiring New Data Reformatting Source Data Converting Data Linking Data Source Data 1 Source Data 2 Reformatted Source Data 1 Reformatted Source Data 2 Processed Data A Linked Database Source Data n Reformatted Source Data n Processed Data X

Approaches to Safety Management Normative Subjective Objective Comply to Standards Follow Expert Judgment Analyze Safety Data 32

Data-driven Management Cycle Data Analysis Action 33

Indiana Safety Management Strategic Highway Safety Plan (INDOT, ICJI) Hazard Elimination Program Road screening for hazards Site investigation of high-crash roads Economic evaluation and selection of safety interventions Targeted Safety Programs Road screening for certain deficiencies Implementation of safety interventions 34

SYSTEM-LEVEL ANALYSIS

Safety Needs Identification Program - SNIP A comprehensive safety evaluation tool to address statewide safety investment needs Identify safety needs based on an excessive number of crashes in user-defined categories Develop a method of screening the road network for road deficiencies 36

Roads with Speeding Problem Lafayette Area

SNIP2 (1) Improved interface (2) Improved features (3) Matching identified safety needs with safety programs within the budget

SNIP2

SNIP2

SNIP2

SNIP2

ANALYZING INDIVIDUAL ROADS

Road Hazard Analysis Tool RoadHAT The RoadHAT is a computer implementation of the Guidelines for Highway Safety Improvements in Indiana (Tarko and Kanodia, 2004) for analyzing high-crash roads. 44

RoadHAT2 Forms 45

RoadHAT2 The user can defined its own: Types of roads Safety Performance Functions Average crash costs in user-defined categories

COUNTERMEASURES

Transferrable General Safety Measures Managing exposure to risk through transport and land-use policies Designing roads for road injury prevention Providing visible, crash-protective, smart vehicles Securing compliance with safety rules and promoting safe behavior Delivering post-crash care 48

Do All Countermeasures Work Everywhere? According to various USA studies: Widening paved shoulders from 3 ft to 8 ft reduces crashes by 12 percent

Importance of Local Conditions USA

Transferrable Countermeasures? CMF 30 10 =2.2/4.0 = 0.55 CMF Rear-End = 0.06 CMF Right-Angle = 0.32 (Brown and Tarko, 1999) (Li and Tarko, 2011)

(Tarko, 2012) Safety Measurement Revisited Exceedance as a Crash Surrogate

Safety Measurement Revisited LIDAR a new measurement technique

CLOSURE A healthy balance of transferrable knowledge and own components should be applied to safety management Good data support research (developing and updating safety models) and effective management Developing of a good database takes time and should begin as soon as possible Data collection and sharing was a starting point of communication between Indiana organizations There is still a long road to good safety management even in countries who started earlier than others Safety measurement methodology still requires improvement

Selected Sources Villwock, N. M., N. P. Blond, and A. P. Tarko. Risk Assessment of Various Median Treatments of Rural Interstates. Publication FHWA/IN/JTRP-2006/29. Joint Transportation Research Program, Indiana Department of Transportation and Purdue University, West Lafayette, Indiana, 2008. Elvik, R. The Power Model of the relationship between speed and road safety. Institute of Transport Economics, Norwegian Centre of Transport Research, TOI report 1034/2009, 2009. Tarko, A.P. Modeling drivers speed selection as a trade-off behavior, Accident Analysis & Prevention, Volume 41, Issue 3, May 2009, Pages 608-616. Persaud, B., D. Lord, and J. Palmisano. Calibration and Transferability of Accident Prediction Models for Urban Intersections, Transportation Research Record 1784, 2003, pages 57-64. Sacchi, E., M. Bassani, B. Persaud. Comparison of Safety Performance Models for Urban Roundabouts in Italy and Other Countries. Transportation Research Record: Journal of the Transportation Research Board, No. 2265, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 253 259. Sacchi, E. and M. Bassani. and B. Persaud. Assessing International Transferability of Highway Safety Manual Crash Prediction Algorithm and Its Components. Transportation Research Record: Journal of the Transportation Research Board, No. 2279, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 90 98. Tarko, A.P. and A. Issariyanukula, H. Bar-Gera, Model-Based Application of Abbreviated Injury Scale to Police-Reported Crash Injuries. Transportation Research Record: Journal of the Transportation Research Board, No. 2148, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 59 68. Tarko, A. and Md.S. Azam. Pedestrian injury analysis with consideration of the selectivity bias in linked police-hospital data. Accident Analysis and Prevention 43 (2011), pages 1689 1695. Tarko, A, and M. Kanodia. Effective and Fair Identification of Hazardous Locations. Transportation Research Record: Journal of the Transportation Research Board, No. 1897, TRB, National Research Council, Washington, D.C., 2004, pp. 64 70. Li, W. And A. Tarko. Effect of Arterial Signal Coordination on Safety. Transportation Research Record: Journal of the Transportation Research Board, No. 2237, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 51 59. Tarko, A. Use of crash surrogates and exceedance statistics to estimate road safety. Accident Analysis and Prevention 45, 2012, pages 230 240. Tarko, A. and S. Azam. Safety Screening of Road Networks with Limited Exposure Data. Transportation Research Record: Journal of the Transportation Research Board, No. 2102, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 18 26. Brown, H. and A. Tarko. Effects of Access Control on Safety on Urban Arterial Streets. Transportation Research Record: Journal of the Transportation Research Board, No. 1665, Transportation Research Board of the National Academies, Washington, D.C., 1999, pp. 68 74.

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