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Incorporating Safety into Transportation Planning for Small and Medium-Sized Communities

This thesis outlines the incorporation of safety into transportation planning in small and medium-sized communities, including data collection and analysis, risk mapping, and recommendations for improvement.

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Incorporating Safety into Transportation Planning for Small and Medium-Sized Communities

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  1. Incorporating Safety into Transportation Planning for Small and Medium-Sized Communities Teng Wang Program of Study Committee: Dr. Nadia Gkritza Dr. Reg Souleyrette Dr. Alicia Carriquiry www.InTrans.iastate.edu

  2. Thesis Outline www.InTrans.iastate.edu

  3. ` Introduction Review of MPOs State-of-the Practice Data Collection & Descriptive Analysis PLANSAFE Empirical Bayes usRAP–style Risk Mapping Conclusion, Recommendation & Limitation www.InTrans.iastate.edu

  4. Introduction www.InTrans.iastate.edu

  5. Motivation & Background Summary • Safety facts: • over 40,000 fatalities/yr in the US (2002-2007). • about 1,000 crashes per year in Ames (2002-2008). • Historically, safety was not explicitly considered in the transportation planning process • SAFETEA-LU emphasizes safety performance measures & transparency. • Desire to move from • “reactive” to “proactive” safety management • project-level to systems-level • Lack of resources, staff and experience is a challenge • to make and monitor progress towards transportation safety goals, need tools • new tools and methods are available … do they “fit” the small urban planning model? www.InTrans.iastate.edu

  6. Research Objective and Tasks • Literature Review • Data Collection and Descriptive Data Analysis • Calibrate safety systems-level predictive PLANSAFE models • Develop Empirical Bayes Models for identification of high crash locations • usRAP application • Conclusions, Recommendations and Limitations www.InTrans.iastate.edu

  7. Review of MPOs State-of-the Practice www.InTrans.iastate.edu

  8. Ames Area MPO • Population: 50,731 (Census, 2000) • 56,510 (Iowa Data Center, 2008) • Area: 21.6 square miles (Census, 2000) • MPO: 36 square miles, designated in year 2003 • Iowa State University : 28,682 students (as of Fall 2010) www.InTrans.iastate.edu

  9. Ames Area MPO • AAMPO Final 2035 Long Range Transportation Plan • Chapter 2.2 Goals and Objectives • Incorporate strategies to promote safety and security across the entire network. • Chapter 10 Safety and Security • descriptive crash data analysis such as the crash counts by severities • GIS crash density map • safety candidate locations • roundabouts and access management www.InTrans.iastate.edu

  10. Summary www.InTrans.iastate.edu

  11. Data Collection & Descriptive Analysis www.InTrans.iastate.edu

  12. Crash Data Geocoded crash points: severity, users, and collision type. www.InTrans.iastate.edu

  13. Fatal and Injury Crashes, 2002 to 2008 www.InTrans.iastate.edu

  14. Crash Statistics, City of Ames, 2002-2008 Source: Iowa DOT statewide geocoded crash database

  15. Road Network Data Geocoded road network data: functional class, AADT and segment length. www.InTrans.iastate.edu

  16. Risk Mapping Data Summary (Ames Metropolitan Area 2002-2008) : Note: Only non-zero AADT road segments are used in the usRAP style risk mapping analysis www.InTrans.iastate.edu

  17. Socio-Demographics Population, housing, children, working adults, population density, etc. www.InTrans.iastate.edu

  18. PLANSAFE www.InTrans.iastate.edu

  19. PLANSAFE: • For forecasting and estimating safety due to changes in socio-demographics, traffic demand, road network and planning-level countermeasures. • NCHRP 08-44: Incorporating Safety into Long-Range Transportation Planning • NCHRP 08-44(02): Transportation Safety Planning: Forecasting the Safety Impacts of Socio-Demographic Changes and Safety Investments www.InTrans.iastate.edu

  20. Developing PlanSafe SPFs: • perform geospatial analysis to assign crashes (points) to the road network (lines) and then assign road network to TAZs (polygons) • aggregate crash and road network data to the TAZ-level • aggregate census data from block or block group level to the TAZ-level • build log linear regression crash frequency models based on the data collected • Note: 80 TAZs for the Ames MPO www.InTrans.iastate.edu

  21. PLANSAFE crash frequency models • Total Crashes (KABCO) • Fatal and Incapacitating Injury Crashes (KA) • Fatal and Injury Crashes (KAB) • Pedestrian Crashes • Bicycle Crashes • Property Damage Only Crashes (O) www.InTrans.iastate.edu 12/20/2019 21

  22. Due to the small sample size, only two crash frequency models could be calibrated: • Total Crashes • Minor Injury Crashes www.InTrans.iastate.edu 12/20/2019 22

  23. Total Crash Frequency Model EQ1: Total crash frequency (per TAZ) = exp(3.18 - 0.0276(POP_PAC) + 0.581(PNF_0214) + 0.000375(POP16_64) -0.0000195(HH_INC)) – 1 As PNF_0214 increases, total crash frequency increases As HH_INC increases, total crash frequency decreases www.InTrans.iastate.edu 12/20/2019 23

  24. Total Crash Frequency Model www.InTrans.iastate.edu 12/20/2019 24

  25. Minor Injury Crash Frequency Model EQ 2: Minor injury crash frequency = exp(0.757 - 0.0637(TOT_MILE) + 0.000560(HU) + 0.465(PNF_0214) + 0.00198(POP16_64) + 0.0139(INT) – 0.00000535(HH_INC) – 0.00185 (POPTOT) +0.000158(ACRE)) – 1 As TOT_MILE increases, minor injury crash frequency decreases. As HU increases, minor injury crash frequency increases. As HH_INC increases, minor injury crash frequency decreases. www.InTrans.iastate.edu 12/20/2019 25

  26. Minor Injury Crash Frequency Model www.InTrans.iastate.edu 12/20/2019 26

  27. PLANSAFE Software Analysis User Interface and Analysis Steps: Select Analysis Area and Units Prepare Current Baseline Data Select Target Area Prepare Future Baseline Data Predict Baseline Safety Evaluate Safety Projects www.InTrans.iastate.edu 12/20/2019 27

  28. PLANSAFE Software Analysis Safety project evaluation results report www.InTrans.iastate.edu 12/20/2019 28

  29. PLANSAFE Key Findings • models are system-level • Requires road network, crash and socio-demographic data • Software is user friendly www.InTrans.iastate.edu

  30. Empirical Bayes www.InTrans.iastate.edu

  31. Why Empirical Bayes (EB)? • For MPOs not currently quantifying safety • For MPOs using traditional frequency, rate candidate list • to correct for the “regression-to-mean” bias if only “high crash” locations are being considered • to increase the precision of estimation of future crash frequency in the absense of change (for improved B/A analysis) • Required: • for a road segment or intersection in question: historical crash count/frequency. • Data from similar sites to develop or calibrate an appropriate SPF. • Regression parameters (in order to weight between observation and mode predictions) www.InTrans.iastate.edu

  32. EB Estimate of the Expected Crashes for an entity = Weight * Crashes expected on similar entities + (1 – Weight) * Count of crashes on this entity, where 0 ≤ Weight ≤ 1 Where W = weight applied to model estimate μ = mean number of crashes/year from model φ = overdispersion parameter Y = the number of years during which the crash count was taken www.InTrans.iastate.edu

  33. SPF: general expression for the Negative Binomial regression model where EXP (εi) is a Gamma distribution with mean 1 and variance α. The Negative Binomial regression model has an additional overdispersion parameter Phi (φ) Model Specification Where μ = number of crashes/year from model L = Length of the road segment in mile e = base of natural logarithms AADT = Annual Average Daily Traffic of the road segment α = Intercept β = parameter for AADT Note: for intersections, L is taken as 1 and AADT = DEV www.InTrans.iastate.edu

  34. Average crashes for each type of road to build SPFs www.InTrans.iastate.edu

  35. Summary statistics www.InTrans.iastate.edu

  36. Negative binomial estimated equations by road type S www.InTrans.iastate.edu

  37. Overdispersion parameter Phi (φ) for all each SPFs calibrated on different years of crashes These Phi values in bold are the largest Phi values among SPFs in each type of road (Note: for 2 lane arterial (2LArterial), the largest Phi value is from the SPF08, but the variables in the SPF08 model are not significant, so I used the second largest Phi value from SPF02-08 instead). www.InTrans.iastate.edu

  38. EB Analysis Results EB estimates are calculated by using the largest “Phi value” SPFs www.InTrans.iastate.edu

  39. EB Analysis Results EB estimates are calculated by using the most comprehensive crash data from 02-08 to build SPFs www.InTrans.iastate.edu

  40. EB Analysis Key Findings • the EB method is better than the average crash method for predicting crashes, as indicated by the smaller RMSEs. • generally, RMSEs become smaller when more years of crash data are used • However, this trend only holds for up to 5 years. • Crash data over 5 years do not accurately represent the current safety situation for the site • EB more accurate than the average crash method. • Crash history over 4 years, EB is not preferred. www.InTrans.iastate.edu

  41. usRAP-style Risk Mapping www.InTrans.iastate.edu

  42. usRAP Risk Mapping and RPS protocols were investigated for applicability • Risk Mapping included four maps: • crash density • crash rate • crash rate ratio • potential crash savings • usRAP has never been used on urban local roads www.InTrans.iastate.edu 12/20/2019 42

  43. usRAP-style crash density risk map • - crashes per mile • - Useful for road authorities to identify high crash segments • High: top 5% crash density of all segments by total mileage, • medium-high: 5%-15%, • medium: 15%-35%, • low-medium: 35%-60% • Low: 60%-100%. www.InTrans.iastate.edu 12/20/2019 43

  44. usRAP-style risk crash rate map • Crashes per 100M vehicle mile traveled • Useful to motorists who want to reduce risk www.InTrans.iastate.edu 12/20/2019 44

  45. usRAP-style risk crash rate ratio map • Rate compared to the “average” road in that class • useful to identify poorly performing roads www.InTrans.iastate.edu 12/20/2019 45

  46. usRAP-style risk Potential Crash Savings map • the number of total crashes saved per mile in seven years if crash rate were reduced to the average crash rate for similar roads • most useful map www.InTrans.iastate.edu 12/20/2019 46

  47. usRAP-style Risk Mapping Key Findings • Clear and easy to understand • can be used to identify road segments that may have the highest potential for improvement (engineering or enforcement) www.InTrans.iastate.edu

  48. Conclusions & Limitations • Conclusions: • All three tools may be readily applied • Methods provide quantitative, proactive tools • PLANSAFE and usRAP can be used at the system-level • EB is quantitative – may require more data for reliable models • usRAP-style risk mapping provides visual explanation of safety issues • Limitations: • All three tools require large amounts of detailed data (for Ames, risk maps can be produced with available data – EB and PlanSafe models may require more comparable data to improve models) www.InTrans.iastate.edu

  49. Recommendations: • follow up work with • usRAP RPS • test of policy sensitivity of PlanSafe • presentation of results to MPO staff, decision makers and public for feedback www.InTrans.iastate.edu

  50. Questions? www.InTrans.iastate.edu

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