HSM: Another Tool for Safety Management in Wyoming Excellence in Transportation 1 Acronym Soup • WYDOT – Wyoming Department of Transportation • HSM – Highway Safety Manual • ITE – Institute of Traffic Engineers • SMS – Safety Management System • CARE – Critical Analysis Reporting Environment • CMF – Crash Modification Factor (AMF in HSM) • B/C – Benefit-Cost Ratio • NPV – Net Present Value • SPF – Safety Performance Function • RTM – Regression-to-the-mean • ARF – Accident Reduction Factor (CRF in HSM) • AADT – Annual Average Daily Traffic 2 Early Stages • Started before the Highway Safety Manual was finally approved or published • Highway Safety, Traffic, Road Design, and District Operations people did attend a oneday introductory training at an ITE meeting in May, 2010 • Plan is to incorporate the quantitative processes into WYDOT’s Safety Management System 3 The WYDOT SMS Process Pavement Project Recommendations Effectiveness Studies Theme Studies Feedback Loop District Inputs Bridge Project Recommendations Safety Issue Identification / quantification Safety Project Definition Safety Project Prioritization Project Planning Project Deployment CARE Intersection Magic Safety Analyst Etc. Network Safety Index • For all Safety work • Done in advance Includes all Safety Projects STIP, etc. Excellence in Transportation 4 Highway Safety Manual • Crash modification factors • Project planning input • Process to calibrate for state Excellence in Transportation 5 Where the HSM fits in to Project Definition Safety Project Definition step Select appropriate remedy(ies) Identify expected improvements (CMF) Calculate lifecycle benefits (NPV) Methods for estimating safety effectiveness (of a remedy / set of remedies) Needs for each Part C – Predictive Method (with calibrated SPF, HSM CMFs) Appropriate SPF, calibration, multiple data sets, and CMF (limited) Un-calibrated SPF and CMFs (HSM, Part D, others) Appropriate SPF, multiple data sets, and CMF Observed crash frequency and CMFs (HSM, Part D, others) Historical crash data and CMF, doesn’t address RTM In decreasing order of reliability; from HSM training (4.56) Also, calculate lifecycle cost, project B/C ratio Excellence in Transportation 6 Which Method to use? • Use HSM Part C – Predictive Method – Pros: Most reliable (according to HSM) – Cons: Needs lots of data sets (WYDOT does not have several); needs calibration (extensive process) • Use un-calibrated SPF – Pros: Still reliable (according to HSM) – Cons: Needs lots of data sets (WYDOT does not have several); • Use observed (historic crashes) – Pros: Needs least amount of additional data; familiar to people (similar to previous use of ARF) – Cons: Least reliable (according to HSM) Excellence in Transportation 7 Applying CMF – Using observed crash frequency Specific Site being analyzed Observed crashes Select remedies Identify CMF* Expected change in crash counts Expected benefits of remedy(ies) * Generally studies refer to Crash / Accident Reduction Factors which translate to CMF Excellence in Transportation 8 Calibrate each SPF Applying CMF – HSM Part C Predictive Method Determine C for each SPF Select appropriate SPF Determine SPF for site (based on facility) Determine volume for site Adjust SPF (based on site) Expected average crash frequency for the site Identify site characteristics (delta from baseline) Up to 18 parameters Select Remedy(ies) Engineering judgement Find corresponding CMF for the remedy(ies) Apply CMF From HSM lists (Part C, Part D) Difference = expected benefits of remedy(ies) Excellence in Transportation 9 HSM Part C – Expected average crash frequency (example; 2-lane rural highway segment) Expected change in average crash frequency for specific site given selected remedies = AADT x Length + (365*10-6) x e-0.312 X CMF(lw) X CMF(swt) X CMF(hc) X CMF(se) X CMF(gr) X CMF(dd) X CMF(crs) X CMF(pl) X CMF(2lt) X CMF(rd) X CMF(l) X CMF(ase) X 1 - ( CMF1 X CMF2 ) Excellence in Transportation 10 HSM Core Data Needs for Segments Factor Unit WYDOT Status Length of segment miles OK AADT veh/day OK1 Lane width ft OK Shoulder width ft OK Shoulder type paved, gravel, etc. OK Length of horizontal curve mi OK Radius of curvature ft OK Spiral transition present y/n OK Superelevation variance ft/ft Not yet Grade % OK Driveway density driveways/mile Working2 Centerline rumble strips present y/n Assume no Passing lanes present y/n maps3?? Two-way left turn lanes present y/n maps3?? Roadside rating hazard 1-7 scale maps4?? Segment lighting present y/n Assume no Auto speed enforcement present y/n Assume no Calibration factor NOTES: 1.However, need projected future traffic volumes 2.Data identified as coming from Pathways log. 3.Roadway width will catch many of these, but not all. Should we modify number of lanes table for this? 4.DCE’s agreed to gather this data in FY 2011, specifics to be determined? TBD Excellence in Transportation 11 Observations & Conclusions • HSM Predictive method requires too much effort at this point – To obtain missing data sets, to perform SPF calibration • HSM “Observed Crash Frequency” method can readily be used in WYDOT’s SMS approach – Easier to combine multiple remedies – Decision needed regarding “expected” crash rates • Not all WYDOT remedies are covered by HSM Part C CMFs – Can also use Part D CMFs but with caveats • Need additional data – Expected future traffic volumes (or at least growth rate) • In order to calculate lifecycle crash reduction – Roadside data • In order to support some of the CMFs (e.g. way left turn lanes, passing lanes, though partially available in the # lanes table) Excellence in Transportation 12 Going Forward • Who should attend HSM training? • Finalize the starting remedies to consider for now • Identify appropriate CMF values to use for each remedy – Work with District Traffic Engineers – Document logic for selection of values • DiExSys is continuing research (Highway Safety) – Terrain (flat/rolling/mountainous) might be a sufficient surrogate for roadside hazard rating • Address need for future traffic volume data • Other possibilities – Prototype HSM calculation of expected average crash frequency for different segments using available data sets – Determine need/ability to get missing data sets Excellence in Transportation 13