Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects

Transcription

1 TxDOT Research Project Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects Ioannis (Yianni) Tsapakis & Bahar Dadashova October 11,

2 Project Team TxDOT Darrin Jensen (PM) Darren McDaniel TRF Jacob Chau Waco America Garza Corpus Christi Brad Tiemann Tyler Jeffery Vinklarek Yoakum Rebecca Wells Atlanta Kelli Williams Odessa NonTxDOT Ismael Soto TTI Ioannis Tsapakis (PI) Karen Dixon (CoPI) Bahar Dadashova Srinivas Geedipally William Holik Jerry Le Jing Li Swapnil Samant Sushant Sharma Innovative Tools and Techniques in 2

3 Topics Research Questions General Framework Network Screening for Segments Network Screening for Intersections Crach Analysis and ViSualization (CAVS) Products Project Prioritization Tool Recommendations Innovative Tools and Techniques in 3

4 Research Questions How can TxDOT: Allocate HSIP funds in the most costeffective manner? Create a level playing field for all districts participating in the HSIP? Improve and streamline existing HSIP processes? Innovative Tools and Techniques in 4

5 General Framework Network Screening Evaluation Diagnosis Project Prioritization Countermeasure Selection Economic Appraisal Innovative Tools and Techniques in 5

6 Establish Focus Identify Network and Establish Reference Populations Select Performance Measures Apply Screening Method Screen and Evaluate Results Import three years of CRIS crash data (Excel format) into ArcGIS Import TxDOT RoadHighway Inventory Network (RHiNo) data into ArcGIS The RHiNo attribute REC is used to differentiate various segment types. Select target crashes: Fatal and incapacitating crashes Onsystem crashes Main/proper lane crashes Crashes with valid coordinates Crashes with valid highway name Crash_Severity = FATAL or INCAPACITATING INJURY On_System_Flag = Yes Road_Part = MAIN/PROPER LANE Crash_Latitude <> 0 AND Crash_Longitude <> 0 HWY <> Null Filter for onsystem main lane segments and create a feature class Criteria for determining similar segments: Functional classification: two adjacent segments belong to the same roadway functional class Highway name: two adjacent segments have the same highway name Number of lanes: two adjacent segments have the same number of lanes ADT: the difference in ADT values between two adjacent segments is less than or equal to a certain percent, which varies by the magnitude of the ADT Median width: the difference between two adjacent segments is less than or equal to 0.5 ft. Inside shoulder width: the difference between two adjacent segments is less than or equal to 0.5 ft. Outside shoulder width: the difference between two adjacent segments is less than or equal to 0.5 ft. Lane width: the difference between two adjacent segments is less than or equal to 0.5 ft. Inside/outside shoulder use: both adjacent segments allow curb parking (either diagonal or parallel parking) on inside/outside shoulder or both do not allow shoulder parking Add a lanewidth attribute to the feature class Lane width is calculated as SUR_W (surface width) divided by NUM_LANES (number of lanes). Functional class R1 R2 R3 R4 R5 R6 R7 U1 U2 U3 U4 U5 U6 U7 Delete fields not needed Geographic coordinate system: GCS_WGS_1984 Calculate the lane width Dissolve main lane segments based on selected attributes The attributes include district, county, highway name, functional class, AADT, number of lanes, lane width, shoulder width and use (both inside and outside), and median width. Display selected crashes on ArcMap Use the functional classification from the TxDOT Roadway Safety Design Workbook to reclassify RHiNo segments in order to apply SPFs ±20% ±30% ±40% ±50% ±50% Export displayed crashes as feature class Project dissolved feature class Project crash feature class Projection coordinate system: NAD_1983_2011_Texas_Centric_Mapp ing_system_lambert Inside Outside Highway Number of Median ADT shoulder shoulder Lane width name lanes width width width ±30% ±0.5 ft. ±0.5 ft. ±40% ±0.5 ft. ±0.5 ft. ±40% ±0.5 ft. ±0.5 ft. ±50% ±50% ±20% ±0.5 ft. ±0.5 ft. ±0.5 ft. ±0.5 ft. Create segment groups based on HPMS functional classification Projection coordinate system: NAD_1983_2011_Texas_Centric_ Mapping_System_Lambert Dissolve segments in each group based on selected attributes Inside shoulder use Outside shoulder use Merge adjacent segments with similar characteristics for each group Find adjacent segments for each segment Identify similar adjacent segments Update attribute values for identified similar adjacent segments Merge similar adjacent segments Use the ArcGIS tool Dissolve to merge segments For the attributes functional class, highway name, and number of lanes retain the original values For the attributes ADT, median width, inside shoulder width, outside shoulder width, and lane width update attribute values for both segments with a lengthweighted average value For the attributes inside shoulder use and outside shoulder use indicate whether diagonal or parallel parking is available Combine all groups of segments into one feature class Sort segments based on functional classification and highway name Assign new ID to each segment Find two nearest segments for each crash Use ArcGIS tool Generate Near Table Disaggregate the feature class of all segments into separate feature classes based on functional classification Map crashes on segments Both segments highway names do not match with the highway name of the crash Only one segment s highway name matches with the highway name of the crash Project crash to corresponding segment Both segments highway names match with the highway name of the crash Use the ArcGIS tool Locate Features Along Routes Select the segment that is closer to the crash Extract DFO for projected crash from RHiNo Review availability of other data and functions at TxDOT Apply available data and functions For example, SPFs calibrated for Texas roads Select performance measures Generate a feature class of points along each segment at 0.1 mile interval Use multiple performance measures to improve the level of confidence in the results. Performance measures that currently can be used at TxDOT are: Average crash frequency Crash rate Critical rate Excess predicted average crash frequency using method of moments Excess expected average crash frequency using SPFs Probability of specific crash types exceeding threshold proportion Excess proportions of specific crash types Apply the ArcGIS tool Generate Points Along Lines Window size is 0.3 miles Window moves at 0.1 mile increment Assign number to each generated point Numbering starts at 1 for each segment. Both start and end points are numbered. Assign window group number(s) to each generated point For segments <= 0.3 miles, only end points are labeled as Window Group 1 For segments > 0.3 but <= 0.6 miles, multiple points are labeled as Window Group 1, or Window Group 2 depending on the location of point Apply sliding window method to roadway segments of a specific functional class For segments > 0.6 miles, multiple points are labeled as Window Group 1, or Window Group 2, or Window Group 3 depending on the location of point Split the point feature class into three feature classes by window group Window ID = Segment ID + _ + Window Group Number + _ + FID Sites that repeatedly appear at the higher end of the list could become the focus of more detailed site investigations Sites that repeatedly appear at the low end of the list could be ruled out for needing further investigation Differences in the rankings due to various performance measures will become most evident at sites that are ranked in the middle of the list Apply the ArcGIS tool Split Line at Point Split segments at points from each window group respectively Assign window ID to newly created windows (subsegments) Calculate performance measures for each window Rank windows based on one or multiple performance measures Network Screening Diagnosis Select Countermeasures Economic Appraisal Prioritize Projects Safety Effectiveness Evaluation Establish Focus Identify Contributing Factors Review CAVS Data Quantify Crash Reduction Identify Economically Justified Countermeasures Evaluation Types Crashes occurred on onsystem mainlanes Consider human, vehicle, and roadway contributing factors before, during, Review crash locations using GIS tools and after the crash. Possible contributing factors associated with different Calculate monetary benefits: Identify one or more candidate countermeasures for possible Safety effectiveness evaluation may include: Reduce number and severity of fatal and incapacitating injury crashes Descriptive statistics of crash conditions (e.g., counts of crashes by type, manners of collision and types of crashes on roadway segments include, o Estimate change in crashes by severity implementation at each site (the countermeasures must be economically o Evaluating a single project at a specific site to determine the severity, roadway or environmental conditions) but are not limited to: o Convert change in crash frequency to annual monetary value justified based on economic appraisal results) effectiveness of the project o Vehicle rollover o Convert monetary value to a present value Return to the step of Select Countermeasures if considered o Evaluating a group of similar projects to determine the effectiveness of roadside design Calculate costs: countermeasures are not economically justified those projects inadequate shoulder width o Calculate construction and other implementation costs o Evaluating a group of similar projects to quantify a CMF for a o Convert costs to present value countermeasure Assess Supporting Documentation excessive speed o Assessing the overall effectiveness of specific types of projects or Economic evaluation methods for individual sites: Identify Network and Establish Reference Populations Obtain and review documented information that provides addition pavement design o Net present value method countermeasures in comparison to their costs perspective to the CAVS data review. This information may include: o Fixed object o Benefitcost ratio (BCR) method Onsystem main lane segments Prioritize Projects o Current traffic volumes for all travel modes obstruction in or near roadway o Costeffectiveness analysis (effectiveness is measured by the Group roadway segments by HPMS functional class inadequate lighting difference between predicted crash frequency and observed crash Prioritization methods include: o Asbuilt construction plans inadequate pavement markings frequency) o Incremental benefitcost analysis ranking o Relevant design criteria and pertinent guidelines o Ranking by economic effectiveness measures o Inventory of field conditions inadequate signs, delineators, guardrail o Optimization methods (including basic optimization methods and multiobjective Safety Evaluation Methods o Relevant photo or video logs o Nighttime resource allocation method) Evaluation methods include: o Maintenance logs poor visibility or lighting o Observational before/after evaluation studies Select Performance Measures o Recent traffic operations or transportation studies poor sign visibility o Observational before/after evaluation studies using SPFs the inadequate channelization or delineation Empirical Bayes Method Given the data that are currently available at TxDOT, consider the following o Land use mapping and traffic access control characteristics excessive speed Evaluate NonMonetary Factors o Observational before/after evaluation study using the comparisongroup performance measures: o Historic patterns of adverse weather method o Average crash frequency o Known land use plans for the area o Wet pavement Nonmonetary considerations include: o Observational before/after evaluation studies to evaluate shifts in o Crash rate o Records of public comments on transportation issues pavement design o Public demand collision crash type proportions inadequate pavement markings o Public perception and acceptance of safety improvement projects o Observational crosssectional studies o Critical rate o Roadway improvement plans in the site vicinity inadequate maintenance o Meeting established and communityendorsed policies to improve o Experimental before/after evaluation studies o Excess predicted average crash frequency using method of moments o Anecdotal information about travel through the site mobility or accessibility along a corridor o Excess expected average crash frequency using SPFs o Oppositedirection sideswipe or headon o Air quality, noise, and other environmental considerations o Probability of specific crash types exceeding threshold proportion inadequate roadway geometry o Road user needs inadequate shoulders o Providing a context sensitive solution that is consistent with a o Excess proportions of specific crash types Assess Field Conditions excessive speed community s vision and environment Validate safety concerns identified from the review of crash data and o Runofftheroad relevant documentation inadequate lane width Travel through the site from all possible directions and modes slippery pavement Consider the following factors: inadequate median width Select Screening Method o Roadway and roadside characteristics o Bridges o Traffic conditions alignment Sliding window method (preferred) o Traveler behavior narrow roadway Simple ranking method (simple, but not as reliable as sliding window o Roadway consistency visibility method) o Land uses o Weather conditions o Evidence of problems (e.g., broken glass, skid marks, damaged signs) Select Potential Countermeasures Review CAVS data and identify possible contributing factors Screen and Evaluate Results Identify countermeasures that may address the contributing factors Calculate performance measure(s) for each site Identify Safety Concerns Conduct costbenefit analysis, if possible, to select preferred treatments Create table and map that show the results of network screening Compile information to identify any specific crash patterns that could be Rank sites based on performance measure(s) addressed by one or multiple countermeasures Level 2 Diagram Network Screening A B Network Screening for OnSystem MainLane Segments C D E F G Crash Analysis and ViSualization (CAVS) CRIS Database Extract crash data from CRIS for the last three years Import data table in ArcGIS Crate feature class for each selected WC or combination of WCs Filter for: Fatal (K) and incapacitating (A) injury crashes Onsystem crashes Mainlane crashes Filter for specific crash types Import RHiNo in ArcGIS Create unique symbol for each feature class Apply data quality control criteria Map crashes on RHiNo using geographical coordinates Select crashes with valid: Highway name Geographical coordinates (latitude and longitude) Geographic coordinate system: GCS_WGS_1984 Convert each feature class into a kml layer Extract preventable crash criteria for each work code (WC) Create shapefile containing mapped crashes KML layers Source: TxDOT HSIP Work Code Manual Determine applicable WCs for each crash location Filter crash data by single WC or combination of WCs More kml layers need to be created. All kml layers have been created. Project Prioritization B(i) = SII(i) x C(i) i = 1, 2,, n SII(i) = Safety improvement index of project i C(i)= Construction cost of project i Sort projects in Calculate benefits Enter project data Filter for projects ascending order B(i) of each in Excel with SII>1 by construction project i cost For example: Start with the first two Same project IBCR = (B(2)B(1)) / (C(2)C(1)) projects in the list costs Compute Calculate the Calculate the incremental difference in costs difference in IBCR<1 benefit cost ratio between two benefits between (IBCR) between projects two projects two projects IBCR>1 Consider project with higher cost and compare it with next More projects in Repeat calculations for project in the list the initial list to the remaining unranked be compared projects in the initial list Consider project with Include it with All projects have lower cost and other projects been ranked compare it with next determined to be and removed project in the list the best economic from the initial list investment End of the list Consider project with (all projects have Rank the project based on the order in higher benefits and been compared) which it was included with the other compare it with next projects that were previously determined project in the list Consider the to be the best economic investment. Remove the project selected in project selected in the last pairing as the last pairing the best economic from the initial list investment Innovative Tools and Techniques in 6

7 Network Screening for Segments A 0.2 miles 0.1 miles AB = 1.0 mile B Innovative Tools and Techniques in 7

8 Performance Measures HSM Performance Measure 1. Crash Frequency 2. Crash Rate 3. Equivalent Property Damage Only (EPDO) Average Crash Frequency 4. Relative Severity Index (RSI) 5. Critical Rate 6. Excess Predicted Average Crash Frequency Using Method of Moments 7. Level of Service of Safety (LOSS) 8. Excess Predicted Average Crash Frequency Using Safety Performance Functions (SPFs) 9. Probability of Specific Crash Types Exceeding Threshold Proportion 10. Excess Proportion of Specific Crash Types 11. Expected Average Crash Frequency with Empirical Bayes (EB) Adjustment 12. EPDO Average Crash Frequency with EB Adjustment 13. Excess Expected Average Crash Frequency with EB Adjustment Innovative Tools and Techniques in 8

9 Network Screening ArcMap Toolbox Innovative Tools and Techniques in 9

10 Network Screening ArcMap Toolbox Innovative Tools and Techniques in 10

11 Network Screening ArcMap Toolbox Innovative Tools and Techniques in 11

12 Network Screening Results Provide to all Districts in 2018 & 2019 HSIP Innovative Tools and Techniques in 12

13 Intersection Data Collection Northern San Antonio Innovative Tools and Techniques in 13

14 Intersection Data Intersection Data CRIS RHiNo Manually Collected Crash Numbers Crash Severity Crash Location ADT Lane Width Shoulder Width Traffic Control & # Legs # Through, Left and Right Turn Lanes Rightturn Channelization Innovative Tools and Techniques in 14

15 Evaluation: KABC Crashes *Bold numbers are the intersection IDs Innovative Tools and Techniques in 15

16 Summary: Performance Measures Performance Measure Accounts for RTM Bias Accounts for Traffic Volume Accounts for Data Variance Uses Roadway Design Elements Establishes Threshold for Similar Sites Applicability Requires Reference Population based Computation Developed for Specific Crash Type Crash Frequency No No No No No Simple No No but can be applied Crash Rate No Yes, but can No No No Simple No No but can be applied be biased towards low traffic volume Critical Rate No Yes Yes No Yes Moderate Yes No but can be applied Excess PACF Using Method of Moments Excess PACF Using Safety Performance Functions (SPFs) Probability of Specific Crash Types Exceeding Threshold Proportion No Yes Yes No Uses average crash frequency per reference population No Yes No Yes Uses predicted average crash frequency Not Affected Difficult Yes No but can be applied Difficult (if the variables used for CMFs are missing) No but requires CMFs which are RP based No Yes No Yes Difficult Yes Yes, but could be biased towards the sites with unusually high crash frequency Yes Innovative Tools and Techniques in 16

17 Network Screening Results Loop 410 Very high risk (Adjusted Weighted Ranking <= 10%) Very low risk (Adjusted Weighted Ranking >= 90%) Innovative Tools and Techniques in 17

18 CAVS Innovative Tools and Techniques in 18

19 CAVS Implementation Project

20 CAVS Implementation Project

21 Master Plans Implementation Project

22 Project Prioritization IBCR Innovative Tools and Techniques in 22

23 Benefits # projects submitted to HSIP by 57% Time to identify projects by 1550% Project prioritization method complements SII Innovative Tools and Techniques in 23

24 Recommendations Implement network screening & CAVS (56912) Evaluate projects and countermeasures (06961) Develop intersection inventory Develop new SPFs Innovative Tools and Techniques in 24

25 Thank You!!! Ioannis Tsapakis (210) Karen Dixon (979) Innovative Tools and Techniques in 25