The Integration of Multi-Criteria Evaluation and Least Cost Path Analysis for Bicycle Facility...

The Integration of Multi-Criteria The Integration of Multi-Criteria Evaluation and Least Cost Path Evaluation and Least Cost Path Analysis for Bicycle Facility Analysis for Bicycle Facility PlanningPlanning

Greg Rybarczyk, M.S.Greg Rybarczyk, M.S.Department of GeographyDepartment of GeographyUniversity of Wisconsin-MilwaukeeUniversity of Wisconsin-Milwaukee

Greg RybarczykSeptember 5, 2006

Presentation Outline Bicycle transportation planning in

Milwaukee Is there a problem? Research objectives Methods Results Conclusions


StatisticsPlace Population % Bike Commuters % Pedestrian Commuters

Madison, Wisconsin 207,525.00 3.29% 10.99%Milwaukee, Wisconsin 596,956.00 0.34% 4.79%

Source: U.S. Census, 2000

Milwaukee is listed as one of the top ten worst cities for utilitarian walking and bicycling, and in the top ten for recreational bicycling and walking, as stated by Medical News Today, February 28, 2005


Bicycle Planning in Wisconsin WIDOT Bicycle Facility Planning

Guidelines Bicycling origins-destinations should

be located near parks, commercial facilities, employment centers, and, recreational facilities

Safety should be minimized Bicycle Planning in Wisconsin

follows 2 paradigms “Ad=hoc” planning-constructing

bicycle facilities wherever possible Utilize a Bicycle Level of Service

(BLOS) or Bicycle Compatibility Index

(Huber, 2005 and Wisconsin Department of Transportation-September, 1993)


Research Objectives: Implement a Multi-Criteria Evaluation (MCE) and

Simple Additive Weighting (SAW) methodology towards bicycle facility planning in the City of Milwaukee

Utilize a value function to relate attribute worth for the criteria under consideration

Produce a neighborhood level optimum bicycle network analysis

Conduct trade-off analysis


Methodology Determine BLOS for each road segment in the

study area Collect all performance data for each road

segment Conduct an inverse ranking and weighting of

performance criteria Establish a decision rule for each criterion under

consideration Assess aggregated performance of each road

segment via shortest path analysis Utilize GIS for display and trade-off analysis


Milwaukee, Wisconsin, Bayview Neighborhood

Oklahoma Ave E

Lincoln Ave E

BAY VIEW

FERNWOOD

CLOCK TOWER ACRES

^ Bike Crash

õôó Recreation Area

n School

Crime

Park

Business:

!(

LakeMichigan

N


Constraint Map and Aggregated Criteria

Performance criteria Crime Bicycle Crashes Population Parks Schools Recreation areas Businesses

Through query process reduced road network to existing and viable roads

Summarized criteria per road segment

Wisconsin Department of Transportation-September, 1993


Criteria Ranking and Normalized Weighting

criterion theofposition rank theis

)1,2,.... (ion consideratunder criteria ofnumber theis

criterionth for the weight normalized theis

)1(1

j

j

kj

r

nkn

jw

rnrjnw

Criterion RankNormalized

Weight

Population 7 0.035714

Parks 6 0.071429

Recreation Areas 5 0.107143

Schools 4 0.142857

Businesses 3 0.178571

Crime 2 0.214286

Crashes 1 0.250000

Utilized a reversed rank and sum method

Assigned the most weight to negative criteria

Multiplied weight by criteria values then summed all criteria

Goal is to derive the lowest cost (maximum benefit) for each road segment

(Malczewski, 1999)

1.0


Value Function Decision Rule

(Malczewski, 1999)

score attribute raw

min and max

criteria negative ofset theis

criteria positive ofset theis

)()(

)()(

* *

,*** )(

,*** )(

ij

iji

jiji

j

jjjijijj

jjjijijj

x

xxxxx

C

C

Cjxxxxxv

Cjxxxxxv j

Vi = ∑ wjvj(xij) j = 1

Vi = Total value of each road segmentwjvj = Criterion value function and weighted summationxij= Criterion attribute value from i to j


Trade-off Analysis Value function applied to

summarized criteria Attractiveness (ATTR) and BLOS

BLOS and ATTR were weighted to equal 1

Weighting schemes were re-assigned as “cost” for shortest path analysis

Weighting Scheme = wj

BLOS x 1.0 BLOS x .9 + ATTR x .1 BLOS x .8 + ATTR x .2 BLOS x .7 + ATTR x .3 BLOS x .6 + ATTR x .4 BLOS x .5 + ATTR x .5 BLOS x .4 + ATTR x .6 BLOS x .3 + ATTR x .7 BLOS x .2 + ATTR x .8 BLOS x .1 + ATTR x .9 ATTR x 1.0)........1(

)( BLOS1

Ii

xvwVJ

j

ijjji

)........1(

)( ATTR1

Ii

xvwVJ

j

ijjji

+


Bayview Neighborhood Route Analysis

Potential Route ATTR 1.0 Only

Potential Bicycle Route BLOS .9 ATTR .1

Potential Bicycle Route BLOS 1.0

Bayview Existing Bike Lane

Oklahoma Ave E

Lincoln Ave E

BAY VIEW

FERNWOOD

CLOCK TOWER ACRES

§̈¦794

Oklahoma Ave E

Lincoln Ave E

BAY VIEW

FERNWOOD

CLOCK TOWER ACRES

§̈¦794

Oklahoma Ave E

Lincoln Ave E

BAY VIEW

FERNWOOD

CLOCK TOWER ACRES

§̈¦794



FIGURE 2 Shortest Path Routes and Comparisons to Existing Bicycle Route: (a) existing route vs. BLOS 1.0 only; (b) existing route vs. BLOS ..9 ATTR .1: (c) existing route vs. ATTR 1.0 only

. . .(a) (b) (c)

Potential Route ATTR 1.0 Only

Potential Bicycle Route BLOS .9 ATTR .1Potential Bicycle Route BLOS .9 ATTR .1

Potential Bicycle Route BLOS 1.0Potential Bicycle Route BLOS 1.0

Bayview Existing Bike LaneBayview Existing Bike Lane

Oklahoma Ave E

Lincoln Ave E

BAY VIEW

FERNWOOD

CLOCK TOWER ACRES

§̈¦794

Oklahoma Ave E

Lincoln Ave E

BAY VIEW

FERNWOOD

CLOCK TOWER ACRES

§̈¦794

Oklahoma Ave E

Lincoln Ave E

BAY VIEW

FERNWOOD

CLOCK TOWER ACRES

§̈¦794

Oklahoma Ave E

Lincoln Ave E

BAY VIEW

FERNWOOD

CLOCK TOWER ACRES

§̈¦794

Oklahoma Ave E

Lincoln Ave E

BAY VIEW

FERNWOOD

CLOCK TOWER ACRES

§̈¦794

Oklahoma Ave E

Lincoln Ave E

BAY VIEW

FERNWOOD

CLOCK TOWER ACRES

§̈¦794



FIGURE 2 Shortest Path Routes and Comparisons to Existing Bicycle Route: (a) existing route vs. BLOS 1.0 only; (b) existing route vs. BLOS ..9 ATTR .1: (c) existing route vs. ATTR 1.0 only

. . .(a) (b) (c)

Lake Michigan

Lake Michigan

Lake Michigan


Bayview Criteria Analysis

“A” “A” “A” “C”BLOS

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0n

o. cri

teri

a

Crime 116.00 50.00 48.00 99.00

Crash 1.00 1.00 1.00 3.00

Park 11.00 0.00 0.00 0.00

School 15.00 3.00 3.00 27.00

Recreation 61.00 36.00 35.00 37.00

Business 47.00 57.00 58.00 55.00

ATTR 25.24 15.84 14.80 16.66

Ave BLOS 0.03 0.09 0.33 2.96

BLOS 1.0 Blos .9 ATTR .1 ATTR 1.0 Existing Route


Bayview Criteria Analysis Cont.

17,902

8,1817,660

12,903

0.0

2,000.0

4,000.0

6,000.0

8,000.0

10,000.0

12,000.0

14,000.0

16,000.0

18,000.0

20,000.0

BLOS 1.0 Blos .9 ATTR .1 ATTR 1.0 Existing Route

po

pu

lati

on


Bayview Neighborhood Results Optimum bicycle facility placement

combines BLOS and social factors! As ATTR increases crime is reduced and #

of businesses increase BLOS paths only contain elevated # of all

negative criteria Trade-off analysis reveals that an

acceptable BLOS can be reached when incorporating “other” bicycle data


Conclusions Multi-Criteria Evaluation in a GIS environment can

quantify several competing bicycling planning criteria

Careful analysis is needed by the decision maker during the trade-off analysis

A combination of supply-side and demand-side bicycle transportation criteria can be assimilated

Interdependency between criteria may justify other criteria to measure road performance

Further inclusion of directness, slope, weather?

Thank You

Special Thanks to:University of Wisconsin-Milwaukee

Bicycle Federation of Wisconsin

The Integration of Multi-Criteria Evaluation and Least Cost Path Analysis for Bicycle Facility...

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