Dr. Simon Washington, Professor Department of Civil & Environmental Engineering, Ira A. Fulton School of Engineering Arizona State University Transportation Safety Planning Working Group “Analysis Tools” March 27-28, 2006

FULTON s c h o o l o f e n g i n e e r i n g Acknowledgements n The majority of the research describe here was paid for by NCHRP (8-44). n Participants in 8-44 included: Dr. Michael Meyer Dr. Eric Dumbaugh Ms. Ida van Schalkwyk Mr. Matthew Zoll Ms. Sudeshna Mitra Ms. Ashley Chang

FULTON s c h o o l o f e n g i n e e r i n g Presentation Overview n Background: Planning-level Safety Forecasting (PLANSAF) n Justification for PLANSAF models n General Modeling Approach n PLANSAF Examples n NCHRP Objectives n Research Tasks

FULTON s c h o o l o f e n g i n e e r i n g Background: Need for PLANSAF Models n Setting safety targets –Establish reasonable targets for fatal, injury, pedestrian, etc. n Predict safety impacts of large-scale projects –Safety impacts of future population, schools, transportation infrastructure n Compare and contrast growth scenarios –Infill vs. sprawl, interstate vs. expressways, etc. n Examine safety impact of region-wide policies/programs –Implementing region-wide photo-enforcement for red light running, etc. n Support PROACTIVE safety planning

FULTON s c h o o l o f e n g i n e e r i n g Background: Planning-level Safety Forecasting n NCHRP 8-44 completed fall 2005 n It resulted in a Manual for MPOs and DOTs on how to incorporate safety into long- range transportation planning n It also identified software and analysis tools available………. n And significant GAPS in software/tools…….

FULTON s c h o o l o f e n g i n e e r i n g Background: Transportation Planning Process

FULTON s c h o o l o f e n g i n e e r i n g Background: Macroscopic vs. microscopic safety models n PLANSAF models differ from microscopic models in that: –They should not be used to guide selection of microscopic safety investments –Input data are aggregate and not site specific (TAZ is smallest unit of analysis) –Focus is prediction NOT explanation –They should be used to inform corridor or region- wide alternatives comparisons

FULTON s c h o o l o f e n g i n e e r i n g Justification for PLANSAF (TAZ level) models n Crashes are largely random events… –90%+ human error: distractions, speeding, following too closely n Aggregate safety differences substantiated…. –Young and elderly drivers; minorities/males and safety restraints; intersections vs. segments; high vs. low speeds; urban vs. rural; facility design levels; etc. n Models for prediction have fewer restrictions than models for explanation….. –Inference, or effects of isolated variables (estimated coefficients) not too important, multicollinearity tolerated; goodness of fit and predictive ability most important

FULTON s c h o o l o f e n g i n e e r i n g PLANSAFE Core Methodology 1.Model Calibration: Using local/regional data, calibrate safety forecasting models to predict baseline conditions 2.Define analysis area and supporting data: Define investment/growth scenarios: corridor, sub-regional, regional 3.Run future baseline forecast: Forecast future safety for growth scenario 4.Select safety investment alternatives: Which safety investments will be made? 5.Provide output for decision-makers: Will include estimated effects and uncertainty

FULTON s c h o o l o f e n g i n e e r i n g Variables in the models (1)…….. VARIABLEDESCRIPTION (all units are calculated per TAZ) Total Accident Frequency Model POP_PACPopulation density (population estimates from U.S. Census SF1) in persons per acre POP16_64Total population of ages 16 to 64 (from U.S. Census SF1) TOT_MILETotal mileage of all functional classes of roads Property Damage Only Accident Frequency Model PH_URBNumber of urban housing units (U.S. Census SF1) as portion of all housing units POP_PACPopulation density (population estimates from U.S. Census SF1) in persons per acre VMTVehicle miles traveled (it is estimated using road section lengths and section traffic counts) Fatal Accident Frequency Model INT_PMINumber of intersections per mile (using total mileage in the TAZ) PNF_0111Total mileage of urban and rural interstates as a portion of the total mileage (federal functional classifications 01 and 11) PNF_0512Total mileage of other freeways and expressways (i.e., not interstate and also not principal arterials) as a portion of the total mileage POP00_15Total population of ages 0 to 15 (from U.S. Census SF1) PPOPMINTotal number of minorities (from U.S. Census SF1) as a portion of the total population. Incapacitating and Fatal Accident Frequency Model INT_PMINumber of intersections per mile (using total mileage in the TAZ) PNF_0111Total mileage of urban and rural interstates as a portion of the total mileage (federal functional classes 01 and 11) PNF_0512Total mileage of other freeways and expressways (i.e., not interstate and also not principal arterials) as a portion of the total mileage POP00_15Total population of ages 0 to 15 (from U.S. Census SF1)

FULTON s c h o o l o f e n g i n e e r i n g Variables in the models (2) Nighttime Accident Frequency Model MI_PACRETotal mileage of the TAZ per acre of the TAZ PNF_0111Total mileage of urban and rural interstates as a portion of the total mileage in the TAZ (federal functional classes 1 and 11) PNF_0214Total mileage of urban and rural principal arterials as a portion of the total mileage in the TAZ (federal functional classes 2 and 14) PNF_0512Total mileage of other freeways and expressways (i.e., not interstate and also not principal arterials) as a portion of the total mileage PPOPMINTotal number of minorities (from U.S. Census SF1) as a portion of the total population. WORKERSTotal number of workers 16 years and older (from U.S. Census SF3) Accidents Involving Pedestrians Frequency Model HH_INCMedian household income in 1999 (P from U.S. Census SF3) POP_PACPopulation density (population estimates from U.S. Census SF1) in persons per acre POPTOTTotal population (P from U.S. Census SF1) PWTPRVProportion of workers 16 years and older that use a car, truck, or a van as a means of transportation to work (from U.S. Census SF3) Injury Accident Frequency Model HU_PACRENumber of housing units per acre: (H from U.S. Census SF1)/Acres PPOPURBUrban population (P from U.S. Census SF1) as a portion of the total population. VMTVehicle miles traveled (it is estimated using road section lengths and section traffic counts) Accidents Involving Bicycles Frequency Model HUNumber of housing units (from U.S. Census SF1) TOT_MILETotal mileage of all functional classes of roads VMTVehicle miles traveled (it is estimated using road section lengths and section traffic counts) WORK_PACTotal number of workers 16 years and over (from U.S. Census SF3) per acre

FULTON s c h o o l o f e n g i n e e r i n g Predictions from PLANSAF (1)

FULTON s c h o o l o f e n g i n e e r i n g Predictions from PLANSAF (2)

FULTON s c h o o l o f e n g i n e e r i n g Predictions from PLANSAF (3)

FULTON s c h o o l o f e n g i n e e r i n g Predictions from PLANSAF (4)

FULTON s c h o o l o f e n g i n e e r i n g Simple Example: 10 TAZ forecast of Incapacitating & Fatal Injuries A corridor improvement is being considered that will bring about new residential and commercial development to 10 TAZs, as well as increased population and resultant traffic volumes. A host of new intersections will be added because of the project, as well as new road mileage. Interest focuses on what changes to safety are anticipated as result of this project.

FULTON s c h o o l o f e n g i n e e r i n g Baseline and Future Data for 10 TAZs TAZ NUMBERINT_DensityUrban/rural interstates proportion Other freeways and expressways proportion Total 0 to 15 Pop Base Year Data for Existing Conditions Data for Future Conditions at Implementation of Planned Project

FULTON s c h o o l o f e n g i n e e r i n g Baseline Data for Status Quo TAZObserved CrashesPredicted CrashesBCF Totals unbiased BCF average BCF std.dev. BCF CV BCF 0.179

FULTON s c h o o l o f e n g i n e e r i n g Predicted Project Scenario Safety TAZPredicted Project Scenario Crash Frequency BCFAdjusted Project Scenario Crash Frequency Total87.31

FULTON s c h o o l o f e n g i n e e r i n g Safety Forecast Results: As a result of the proposed project there is an anticipated increase in serious incapacitating injuries and fatalities from 68 to 87, or 19 additional crashes (new population, new roads, etc.) If a 20% reduction in these crash types was desired (a Plan Target), then 87(.80) = 69 crashes is the future safety target. Safety investments would need to identified to reduce crashes from 87 to 69 (a reduction of 18 crashes) NOTE: Overall crashes have increased (from 68 to 69) even though safety improvements are made!

FULTON s c h o o l o f e n g i n e e r i n g NCHRP Objectives n To develop a robust, defensible, and accurate analytical set of algorithms to forecast the safety impacts of engineering and behavioral countermeasure investments at the planning-level n To develop user-friendly software, compatible to the extent possible with planning-level data inputs, to incorporate the analytical procedures for forecasting safety n To develop guidance materials to accompany the analytical procedures and software

FULTON s c h o o l o f e n g i n e e r i n g NCHRP Transportation Safety Planning: Forecasting the Safety Impacts of Socio-Demographic Changes and Safety Countermeasures Will continue/expand work started during NCHRP 8-44 Start: Spring 06 End: Fall 08

FULTON s c h o o l o f e n g i n e e r i n g NCHRP Information Team MemberRoleTechnical Contributions Simon Washington Professor Civil & Environmental Engineering Principal Investigator, Administrator, Manager Statistical Model Development, Countermeasure Evaluation (Behavioral and Engineering), software development and planning scenarios analysis, model components integration Subhrajit Guhathakurta Associate Professor Planning Investigator: Planning Process and Software Development Planning scenarios analysis, software development, graphical user- interface development/testing Edward Saddalla Professor Psychology Investigator: Behavioral Countermeasure Evaluation, Risk Behavioral (soft-side) countermeasure and program evaluation, components integration Ida van Schalkwyk Research Professional Civil & Environmental Engineering Investigator: Engineering Countermeasure Evaluation: Statistical Model Development Modeling at TAZ level, socio-demographic modeling, engineering countermeasure evaluation, components integration Ph.D. Students (2) TBD Research SupportSoftware development (analytics and graphical user-interface), statistical modeling, general research support.

Questions & Comments