marshall - cnu street network presentation

Street Networks, Road Safety & Sustainability

Wesley Marshall, P.E.Norman W. Garrick, PhD

Center for Transportation & Urban Planning

University of Connecticut

Sustainable Transportation Networks Congress for the New Urbanism XVIIJune 13, 2009Denver, Colorado

Road Safety…

in the United States

Fatalities per million VMT

20

1925

15

10

01935 1945 1955 1965 1975 1985 1995 2005

5

Fatalities: 8VMT: 2 million miles

Fatalities per million VMT = 4

Fatalities: 8VMT: 1 million miles

Fatalities per million VMT = 8

Population: 50,000Fatalities per 100k pop. = 16

Population: 100,000

Population per 100k pop. = 8


20

3,000

1925

VMT

(billions

)

15

10

0

2,000

1,000

0

Population

1935 1945 1955 1965 1975 1985 1995 2005

24 x

2.5 x5

0

Average VMT (per capita per day)

60,000

40,000

20,000

0

Total No. of Fatalities

Population

VMT

(billions

)


20

1925

15

10

01935 1945 1955 1965 1975 1985 1995 2005

5

30

25

Fatalities per 100,000 population

Road Safety in the U.S.

Road Fatalities per 100,000 Population by CountryUnited States

(Source: Organisation for Economic Co-operation and Development, OECD)

(Source: Organisation for Economic Co-operation and Development, OECD)

International Road Safety

Why is the U.S. falling behind the rest of the world when it

comes to safety in the transportation system?

When it comes to trying to make our roads safer…

The focus tends to be on finding the most

problematic locations and fixing them

http://imgs.sfgate.com/c/pictures/2007/01/03/ba_octavia28_009_rad.jpg

www.streetsblog.org

Road Safety

24 medium-sized California cities

Cities selected to represent a range of traffic safety levels

Geographically diverse with locally

generated traffic

California City Study

CALIFORNIA CITY COMPARISON

SaferCities

Less SafeCities

Population(average) 65,71959,845

Fatalities per100,000 pop.

(per year)

3.39.8

Road Fatalities(total over 11 years)

257771

Non-HW Fatalities per 100,000 pop.

(per year)

2.58.6

Non-HighwayRoad Fatalities

(total over 11 years)

200676

Relative Risk = 3.4

per city per year 1.95.8

Relative Risk = 3.0

per city per year 1.55.1

Why are these places so different in terms of

safety outcomes?

CALIFORNIA CITY COMPARISON

Mode Share

Driving 84.1%95.8%

PopulationDensity 5,736 per sq. mi.2,673 per sq. mi.

Avg. Year ofIncorporation 18951932

Walking 5.4%1.7%

Biking 4.1%0.7%

Transit 6.6%1.7%

SaferCities

Less SafeCities

Davis

1940195019601970198019902000

Measuring Street Networks

How Do We Characterize Street Networks?

Connected

GriddedDense

Hierarchical

PatternsBlock Size

Link to Node Ratio

Road Density Intersection Density

There are 3 fundamental items of interest in characterizing a street network…

Street ConnectivityStreet Network DensityStreet Patterns

Characterizing Street Networks

i.ii.iii.

HIGH CONNECTIVITY ≠ A DENSE NETWORK

Intersection Density

144 144 144

Link to Node Ratio

1.61 1.13 1.16

HIGH CONNECTIVITY ≠ A DENSE NETWORK

Simplifying Street Patterns

Citywide Macroscopic NetworkN

eig

hb

orh

ood M

icro

Netw

ork

LinearTree

GridTributary Radial

Gri

dTre

e

Adapted from Stephen Marshall, Streets & Patterns

Carlsbad, California

LinearTree

GridTributary Radial

Gri

dTre

e

NETWORK COMPARISON

Avg. Year of Development 1965 1974 19661966

Avg. Year of Development 1950 Pre 1940 Pre 1940

Crash Data

Results

SaferCities

Less SafeCities

Intersection Density 106 per sq. mi. 63 per sq. mi.

Dead EndDensity

32 per sq. mi. 23 per sq mi.

% Dead Ends 23.2% 26.7%

Macro Node Density 7.5 per sq. mi. 4.9 per sq. mi.

% Major Nodes 7.1% 7.8%

Link to Node Ratio 1.34 1.29

Connected Node Ratio 0.76 0.73

STREET NETWORK COMPARISON

Connectivity Measures

-40.6%

-28.1%

-34.7%

-3.7%

-4.0%

Difference

SaferCities

Less SafeCities

Fatal Crashesper 100,000 pop. 3.3 per year 9.8 per year

Severe Crashesper 100,000 pop. 16.4 per year 18.4 per year

Severity Risk(% Fatal or Severe)

1.9% 3.2%

Macro Road Fatal or Severe per 100k pop. 16.4 per year 17.4 per year

Severity Risk(% Fatal or Severe) 1.9% 3.2%

Micro Road Fatal orSevere per 100k pop. 2.7 per year 4.6 per year

Severity Risk(% Fatal or Severe) 1.5% 3.1%

ROAD SAFETY COMPARISON

197.0%

12.2%

6.1%

70.4%

58.8%

Difference

68.4%

68.4%

SAFER CITIES – NETWORK DENSITY

NetworkDensity

Comparison

< 81

Risk of Injury

(non-highway)

41.0%

81-144

38.5%

144-225

39.1%

225+

37.7%

IntersectionDensity

9x9 12x12 15x15

Block Length 660’ 480’ 375’

1 Sq. MileGrid Size

81 144 225

Risk of Severe Inj.

(non-highway)

3.3% 1.9% 1.8% 1.5%

Risk of Fatality(non-highway)

0.5% 0.3% 0.2% 0.2%

Statistical Analysis

What do we want to know?

How are street network measures associated

(correlated) with road safety outcomes?


Response Variables:

Model 1: Total No. of Crashes

Model 2: Total No. of Severe Injury Crashes

Model 3: Total No. of Fatal Crashes


Built crash prediction models using a generalized linear regression

Crash Model ResultsVariables

= Significant with Positive Association (More Crashes)

= Significant with Negative Association (Fewer Crashes)

= Not Significant

Model 1 Model 2 Model 3

Variables

Street Network MeasuresStreet Pattern Type (categorical)


Macro-Intermediate Intersection Density

Dead End Node Density

Link to Node Ratio

Curvilinear (0, 1)

Street Level DataAvg. # of Lanes

Avg. Width of Outside Shoulder

Raised Median (0, 1)

Painted Median (0, 1)

% of Macro Road Length with On-Street Parking

% of Macro Road Length with Bike Lanes

% of Macro Road Length with Curbs

ExposureVMT

Proxy for Activity

MiscellaneousDistance from City Center

Avg. Income

Adjacent Limited Access Highway

Mixed Land Uses

Severe Injury Crashes

Fatal Crashes


Variables





Link to Node Ratio

Curvilinear (0, 1)








ExposureVMT

Proxy for Activity


Avg. Income


Mixed Land Uses

Total CrashesSevere Injury

Crashes


Variables





Link to Node Ratio

Curvilinear (0, 1)








ExposureVMT

Proxy for Activity


Avg. Income


Mixed Land Uses

Total Crashes


Variables





Link to Node Ratio

Curvilinear (0, 1)








ExposureVMT

Proxy for Activity


Avg. Income


Mixed Land Uses

Total Crashes (Model 1)

% Change % Change % Change

Intersection Density81 14.15% 20.05% 53.75%144 (reference value) - - -225 -15.64% -20.94% -42.48%324 -31.48% -40.67% -70.74%

Link to Node Ratio1.1 -14.29% -12.20% -28.21%1.25 (reference value) - - -1.4 16.67% 13.90% 39.29%1.55 36.13% 29.73% 94.02%

Total No. of Lanes on Macro Roads2 (reference value) - - -4 65.17% 33.96% 34.15%6 172.81% 79.46% 79.95%

Distance from City Center (miles)0 41.48% 23.71% -12.86%1 18.95% 11.23% -6.65%2 (reference value) - - -3 -15.93% -10.09% 7.12%4 -29.32% -19.17% 14.75%

% of Macro Road Length with On-Street Parking0% (reference value) - - -50% 18.26% 19.49% -100% 39.86% 42.79% -

% of Macro Road Length with Bike Lanes0% (reference value) - - -50% - - -14.29%100% - - -26.53%


Severe Crashes (Model 2)

Total Fatal Crashes (Model 3)

































FULL NETWORK CRASH MODELS






Link to Node Ratio1.1 - - -24.30%1.25 (reference value) - - -1.4 - - 32.10%1.55 - - 74.50%

Total No. of Lanes on Macro Roads2 -45.82% -30.54% -23.08%4 (reference value) - - -6 84.56% 43.96% 30.01%


% of Macro Road Length with On-Street Parking0% -18.12% -15.10% 19.93%50% (reference value) - - -100% 22.13% 17.78% -16.62%

% of Macro Road Length with Bike Lanes0% - - 20.42%50% (reference value) - - -

CITYWIDE MACRO CRASH MODELS

LT GT


Link to Node Ratio

90

1.09

140

1.15

130

1.18

160

1.24

Expected Total Crashes

Expected Fatal Crashes

290

1.2

202

0.9

275

1.1

213

1.0


Link to Node Ratio

-

-

225

1.34

289

1.37

265

1.40

Expected Total Crashes

Expected Severe Injury Crashes

-

-

191

3.1

211

3.3

209

3.1

(Non-HW Crashes)

RTTT

LG GGRGTG

Expected Severe Injury Crashes 5.5 3.8 4.1 5.2

Expected Fatal Crashes - 0.8 0.6 0.7

LT GT

% Walking

% Biking

2.9%

1.6%

3.5%

2.5%

1.9%

0.9%

2.9%

1.7%

% Public Transit

% Driving

3.3%

92.2%

4.3%

89.7%

2.1%

95.1%

2.9%

92.5%

% Walking

% Biking

N/A

N/A

4.8%

3.3%

4.0%

4.2%

9.5%

4.6%

% Public Transit

% Driving

N/A

N/A

4.3%

87.6%

10.2%

81.6%

10.9%

75.0%

RTTT

LG GGRGTG

MODE CHOICE

MODE CHOICE MODEL

Variables

BASELINE BY STREET PATTERN TYPE 3.66% 2.28% 1.71% 92.35% 4.18% 3.93% 3.39% 88.51% 9.00% 8.79% 4.09% 78.13%Intersection Density

81 3.81% 1.94% 1.29% 92.96% 5.94% 4.69% 2.72% 86.64% 8.93% 5.08% 2.84% 83.15%144 3.65% 2.30% 1.74% 92.31% 5.10% 4.35% 3.00% 87.55% 8.98% 6.14% 3.23% 81.65%225 3.44% 2.85% 2.56% 91.15% 4.19% 3.93% 3.38% 88.50% 9.01% 7.81% 3.79% 79.39%324 3.18% 3.69% 4.06% 89.07% 3.27% 3.47% 3.91% 89.35% 8.96% 10.40% 4.56% 76.08%

Link to Node Ratio1.1 3.42% 2.40% 1.74% 92.44% 2.58% 2.87% 1.59% 92.95% 8.40% 9.93% 3.21% 78.47%1.25 4.17% 2.05% 1.65% 92.13% 3.49% 3.50% 2.55% 90.46% 8.69% 9.35% 3.62% 78.34%1.4 5.06% 1.75% 1.55% 91.63% 4.67% 4.22% 4.05% 87.06% 8.99% 8.80% 4.08% 78.13%1.55 6.14% 1.50% 1.46% 90.91% 6.16% 5.01% 6.32% 82.52% 9.29% 8.28% 4.59% 77.85%

Total No. of Lanes on Macro Roads2 4.48% 2.34% 1.72% 91.46% 7.15% 6.38% 3.80% 82.66% 8.28% 8.57% 3.45% 79.70%4 2.80% 2.19% 1.68% 93.32% 2.10% 2.10% 2.85% 92.95% 10.16% 9.09% 5.27% 75.48%6 1.74% 2.05% 1.63% 94.58% 0.57% 0.64% 1.97% 96.82% 12.26% 9.48% 7.93% 70.33%

Distance from City Center (miles)0 3.30% 4.03% 3.18% 89.49% 3.88% 5.47% 4.86% 85.79% 8.39% 11.10% 5.28% 75.23%1 3.48% 3.06% 2.36% 91.11% 4.12% 4.19% 3.63% 88.06% 9.04% 8.62% 4.00% 78.33%2 3.65% 2.31% 1.74% 92.30% 4.35% 3.18% 2.69% 89.77% 9.65% 6.62% 3.00% 80.72%3 3.82% 1.74% 1.27% 93.17% 4.57% 2.41% 1.99% 91.04% 10.22% 5.05% 2.23% 82.50%4 3.98% 1.31% 0.93% 93.78% 4.78% 1.81% 1.46% 91.95% 10.75% 3.82% 1.65% 83.78%

MODE CHOICE MODEL Transit Mode Share

Pedestrian Mode Share

Biking Mode Share

Automobile Mode Share

(all other variables held at mean)




Variables


81 3.81% 1.94% 1.29% 92.96% 5.94% 4.69% 2.72% 86.64% 8.93% 5.08% 2.84% 83.15%144 3.65% 2.30% 1.74% 92.31% 5.10% 4.35% 3.00% 87.55% 8.98% 6.14% 3.23% 81.65%225 3.44% 2.85% 2.56% 91.15% 4.19% 3.93% 3.38% 88.50% 9.01% 7.81% 3.79% 79.39%324 3.18% 3.69% 4.06% 89.07% 3.27% 3.47% 3.91% 89.35% 8.96% 10.40% 4.56% 76.08%




TT


Pedestrian Mode Share

Biking Mode Share

Automobile Mode Share





Variables


81 3.81% 1.94% 1.29% 92.96% 5.94% 4.69% 2.72% 86.64% 8.93% 5.08% 2.84% 83.15%144 3.65% 2.30% 1.74% 92.31% 5.10% 4.35% 3.00% 87.55% 8.98% 6.14% 3.23% 81.65%225 3.44% 2.85% 2.56% 91.15% 4.19% 3.93% 3.38% 88.50% 9.01% 7.81% 3.79% 79.39%324 3.18% 3.69% 4.06% 89.07% 3.27% 3.47% 3.91% 89.35% 8.96% 10.40% 4.56% 76.08%






Variables


81 3.81% 1.94% 1.29% 92.96% 5.94% 4.69% 2.72% 86.64% 8.93% 5.08% 2.84% 83.15%144 3.65% 2.30% 1.74% 92.31% 5.10% 4.35% 3.00% 87.55% 8.98% 6.14% 3.23% 81.65%225 3.44% 2.85% 2.56% 91.15% 4.19% 3.93% 3.38% 88.50% 9.01% 7.81% 3.79% 79.39%324 3.18% 3.69% 4.06% 89.07% 3.27% 3.47% 3.91% 89.35% 8.96% 10.40% 4.56% 76.08%




GG

LT GTRTTT

LG GGRGTG

VMT in Block Group per capita per day 66 28 27 51

VMT in Block Group per capita per day - 21 23 24

VMT

24 x

1925VM

T (b

illions

)

Population

1935 1945 1955 1965 1975 1985 1995 2005

2.5 x

Effect on VMT?

8 x

Street DesignCommunity DesignStreet ConnectivityMore Sustainable Places

Safer &

Alternative Modes

Road Safety & Mode Choice

Getting things right requires a more comprehensive approach that considers:

Redefining the Problem

Street Network Density

Street Patterns

All of which will help inform our efforts toward creating:

marshall - cnu street network presentation

Education

year of development

d s tate s

s ults

road safety outcomes

street network densityiii

micro road fatal

macro road fatal

international road safety