incorporating safety into transportation planning for small and medium-sized communities teng wang...

Incorporating Safety into Transportation Planning for Small and Medium-Sized

Communities

Teng Wang

04/21/23www.InTrans.iastate.edu 1

Program of Study Committee: Dr. Nadia GkritzaDr. Reg Souleyrette

Dr. Alicia Carriquiry


Thesis Outline

`


Introduction

Review of MPOs State-of-the Practice

PLANSAFE usRAP–style Risk Mapping

Conclusion, Recommendation & Limitation

Data Collection & Descriptive Analysis

Empirical Bayes


Introduction


Motivation & Background Summary• Safety facts:

o over 40,000 fatalities/yr in the US (2002-2007).o 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?


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


Review of MPOs State-of-the Practice


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)


Ames Area MPOAAMPO 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


Summary

MPOs' TIP and LRTPCriteria AAMPO JCCOG CAMPO WVTC LCVMPO BENDMPO

Mention Safety Planning X X X X XTool or Methodology of Safety Planning

Safety Performance Listed in Goals/Objectives X X X X X X

Consider all Modes of Transportation X X X X XCandidate Sites to be improved X X X X X X

GIS-based Crash Map X X X


Data Collection & Descriptive Analysis


Crash DataGeocoded crash points: severity, users, and collision type.


Fatal and Injury Crashes, 2002 to 2008

14

Crash Statistics, City of Ames, 2002-2008

Year Total Crashes Fatalities Major Injuries Minor/Possible Injuries

2002 1000 0 21 292

2003 1079 2 20 291

2004 1114 1 11 310

2005 1035 2 13 237

2006 963 4 19 296

2007 1077 3 23 329

2008 1248 0 17 343

Total 7516 12 124 2098

Year 2002 2003 2004 2005 2006 2007 2008 Total

Ames 1000 1079 1114 1035 963 1077 1248 7516

Iowa 59666 59440 59192 58644 54815 60112 61194 413063

Percentage % 1.68 1.82 1.88 1.76 1.76 1.79 2.04 1.82

Source: Iowa DOT statewide geocoded crash database


Road Network Data

Geocoded road network data: functional class, AADT and segment length.


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

Two-lane Local 790 167.4 0.212 683 41.7 1691 242 1.44 5.79 2 21Two-lane Collector 66 35.8 0.542 3217 42 631 90 2.52 2.15 2 5Two-lane Arterial 41 17.2 0.420 7189 45.1 607 87 5.04 1.92 0 9

Four-lane Undivided 55 18.1 0.329 9557 63.1 2236 319 17.65 5.06 6 28Four-lane Divided 44 12.7 0.289 10064 46.7 1508 215 16.96 4.61 0 25

Freeway 3 12.5 4.167 19080 87.1 569 81 6.50 0.93 2 19Ramp 33 8.5 0.258 2908 0.9 168 24 2.82 26.67 0 3Total 1032 272.2 0.264 2102 326.6 7410 1059 3.89 3.24 12 110

Road TypeFatal

CrashesTotal

FrequencyAnnual

Frequency Annual Density

Annual Rate per M VMT

Total Crashes Sections

Major Injury

Crashes

Annual VMT

(Million)

Average AADT

(vel/day)

Average Length

(mi)

Road Miles


Socio-DemographicsPopulation, housing, children, working adults, population density, etc.


PLANSAFE


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


Developing PlanSafe SPFs:

1. perform geospatial analysis to assign crashes (points) to the road network (lines) and then assign road network to TAZs (polygons)

2. aggregate crash and road network data to the TAZ-level

3. aggregate census data from block or block group level to the TAZ-level

4. build log linear regression crash frequency models based on the data collected

Note: 80 TAZs for the Ames MPO


•Total Crashes (KABCO)•Fatal and Incapacitating Injury Crashes (KA)•Fatal and Injury Crashes (KAB)•Pedestrian Crashes•Bicycle Crashes•Property Damage Only Crashes (O)

PLANSAFE crash frequency models


Due to the small sample size, only two crash frequency models could be calibrated:

• Total Crashes

• Minor Injury Crashes


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


Total Crash Frequency Model


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.


Minor Injury Crash Frequency Model


PLANSAFE Software Analysis

User Interface and Analysis Steps:

1. Select Analysis Area and Units

2. Prepare Current Baseline Data

3. Select Target Area

4. Prepare Future Baseline Data

5. Predict Baseline Safety

6. Evaluate Safety Projects


PLANSAFE Software Analysis

Safety project evaluation results report


PLANSAFE Key Findings

• models are system-level

• Requires road network, crash and socio-demographic data

• Software is user friendly


Empirical Bayes


Why Empirical Bayes (EB)?• For MPOs not currently quantifying safety• For MPOs using traditional frequency, rate candidate list

o to correct for the “regression-to-mean” bias if only “high crash” locations are being consideredo to increase the precision of estimation of future crash frequency in the absense of change (for improved B/A analysis)

Required:

1. for a road segment or intersection in question: historical crash count/frequency.

2. Data from similar sites to develop or calibrate an appropriate SPF.

3. Regression parameters (in order to weight between observation and mode predictions)


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 estimateμ = mean number of crashes/year from modelφ = overdispersion parameterY = the number of years during which the crash count was taken


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 modelL = Length of the road segment in milee = base of natural logarithmsAADT = Annual Average Daily Traffic of the road segmentα = Interceptβ = parameter for AADT

Note: for intersections, L is taken as 1 and AADT = DEV


Average crashes for each type of road to build SPFs

No. of Ave. Crashes 2LArterial 2LCollect 2LLCOAL 4LD 4LU Freeway RAMP Total

SPF08 78 124 301 233 360 104 30 1230

SPF07-08 86 103 234 216 331 92 28 1090

SPF06-08 81 87 230 200 316 87 27 1028

SPF05-08 79 82 200 198 311 79 25 974

SPF04-08 82 80 214 203 315 78 24 996

SPF03-08 80 76 185 207 310 76 20 954

SPF02-08 76 75 188 204 307 76 20 946

# of obs. 41 66 790 44 55 3 33

Total Length 17.22 35.76 167.35 12.7 18.13 12.46 8.52

% of Length 6.33 13.14 61.49 4.67 6.66 4.58 3.13


Summary statistics

Model(SPFs)

Crash

Mean(Variance)

Crash

Max./Min.

AADT Mean(Std

Dev.)

AADT

Max./Min.

# of

Obs.

2LArterial(SPF02-08) 1.9(4.9) 12/0 7200(3100) 15,100/1500 41

2LCollect(SPF08) 1.9(10.7) 22/0 3200(2300) 8700/50 66

2LLCOAL(SPF07-

08) 0.30(1.1) 12/0 680(1030) 15,600/6 790

4LD(SPF05-08) 4.5(57.4) 39/0 10,000(4500) 22,700/390 44

4LU(SPF07-08) 6.0(90.5) 47/0 10,000(4800) 24,200/1100 55

S


Negative binomial estimated equations by road type

ROADTYPE SPFs

2LArterial(SPF02-08) Crashes/year = LENGTH*e-8.36*AADT1.12

2LCollec(SPF08) crashes/year = LENGTH*e-6.20*AADT0.967

2LLOCAL(SPF07-08) crashes/year = LENGTH*e-5.40*AADT0.884

4LD(SPF05-08) crashes/year = LENGTH*e-6.67*AADT1.04

4LU(SPF07-08) crashes/year = LENGTH*e-6.45*AADT1.01


Overdispersion parameter Phi (φ) for all each SPFs calibrated on different years of crashes

Phi (φ) 2LArterial 2LCollect 2LLCOAL 4LD 4LU Freeway RAMP

SPF08 0.18 0.62 2.1 0.96 0.59 N/A N/A

SPF07-08 0.0079 0.59 3.08 1.18 0.70 N/A N/A

SPF06-08 0.0013 0.49 1.15 1.10 0.43 N/A N/A

SPF05-08 0.00020 0.53 1.49 1.23 0.39 N/A N/A

SPF04-08 0.018 0.38 0.65 1.20 0.35 N/A N/A

SPF03-08 0.021 0.48 1.12 1.23 0.35 N/A N/A

SPF02-08 0.12 0.53 0.57 0.89 0.36 N/A N/A

# of obs. 41 66 790 44 55 3 33

Total Length 17.2 35.8 167 12.7 18.1 12.5 8.5

% of Length 6.3 13.1 61.5 4.7 6.7 4.6 3.1These 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).


EB Analysis ResultsEB estimates are calculated by using the largest “Phi value” SPFs


EB Analysis ResultsEB estimates are calculated by using the most comprehensive crash data from 02-08 to build SPFs


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.


usRAP-style Risk Mapping


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


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%.


usRAP-style risk crash rate map-Crashes per 100M vehicle mile traveled- Useful to motorists who want to reduce risk


usRAP-style risk crash rate ratio map-Rate compared to the “average” road in that class- useful to identify poorly performing roads


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


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)


Conclusions & LimitationsConclusions:

• 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)


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


Questions?


Other MPOs

MPO Database from FHWA: • area less than 1,000 sq miles and population up to 140,000• similar area, population with Ames

Five MPOs:• Johnson County COG (JCCOG) Major City: Iowa City, IA. Area: 89 Sq. Miles Populations: 88980

• Corvallis Area MPO (CAMPO) Major City: Corvallis, OR. Area: 38 Sq. Miles Population: 59277

• Wenatchee Valley Transportation Council (WVTC) Major City: Wenatchee, WA. Area: 41 Sq. Miles Population: 56627

• Lewis-Clark Valley MPO (LCVMPO) Major City: Asotin, WA, ID. Area: 43 Sq. Miles Population: 50856.

• Bend MPO Major City: Bend, OR. Area: 46 Sq. Miles Population: 59027.

incorporating safety into transportation planning for small and medium-sized communities teng wang...

Documents

transportation safety

crash counts

crash statistics

descriptive data analysis

improved xxxxxxgisbased

iowa data center

iowa state university

ames area mpo population