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Excess Commuting and Job-Housing Imbalance in Warren County, Kentucky Abstract: Excess commuting (EC) is a concept first developed by Hamilton (1982) to measure the degree of commute distance explained by the spatial separation of job sites and households. The method is applied to a smaller metropolitan area and compared to previous studies done in larger urban areas. Methodology: EC can be defined as the portion of all workers’ commute as a whole that is over and above the minimum required by the spatial distance between their residences and the job sites. It is a “benchmark” for evaluating jobs- housing imbalance and characteristics of urban form. It is calculated via a linear programming process that swaps workers to “newhousehold locations in the most efficient manner by minimizing the total travel distance for all workers. In this study, we also conducted a worst-case baseline analysis by allocating workers in the most inefficient way (maximizing the total travel distance). This measure gives the commute potential of a region and is determined by both transportation network and distribution of jobs and workers. Caitlin Hager and Jun Yan, Center For GIS, Western Kentucky University Comparison Analysis: The analysis is conducted at TAZ level in the Bowling Green Warren County MSA using CTPP 2000 data. The above maps show the actual commutes (A), minimized commutes (B), and maximized commutes (D) by drive-alone commuters (the most common mode in small-size cities). As shown in B, a large number of shorter commuting trips is increased drastically and the cross-town trips are minimized. On the other hand, a large number of longer trips rises under maximization (D), indicating the increased cross-town trips. In addition, under optimum minimization, intra-zonal trips are increased (C) but totally eliminated under maximization. Compared with other places in U.S., Bowling Green has relatively inefficient commutes (F), with 6.47 miles actual, 4.2 miles minimum optimal, and 9.16 miles maximum optimal mean travel distance . Minimize or Maximize T req = n i m j ij ij X C 1 1 , 1 n i Dj Xij , ,..., 1 m j m j Si Xij 1 , , ,... 1 n i , 0 Xij , , j i subject to: A B C D Discussion: Excess Commuting (minimization) and Commute Potential (maximization) can be respectively calculated as follows: Warren County has an EC of 35%, which means, as a whole, 35% of total current total travel distance is unnecessary under an optimal scenario. Compared to previous studies (E), EC of Boise, ID is 48%; Omaha, WI 64%; Baltimore, MD 62%; Boston, MA 67%; Atlanta, GA 57%, Warren County, as a small-size metro, has a relatively small EC, Job-Housing Imbalance: Ratio of jobs to matched workers (JHB) for all and selected industries. Job- poor areas generate commutes; job-rich areas attract commutes. Blue zones indicate a low JHB; yellow - balanced; red - a high proportion of jobs to matched workers. Reference: Hamilton, B. 1982. Wasteful commuting. The Journal of Political Economy 90(5): 1035-53. E xcess C om m ute P ercentage In O rderofIncreasing W ork Trips 0% 10% 20% 30% 40% 50% 60% 70% 80% Bow ling G reen Boise W ichita O m aha Las Vegas M em phis R ochester C harlotte San Antonio Colum bus Sacram ento Cincinnati Portland M ilwaukee Miami Pittsburgh Cleveland Phoenix D enver Baltim ore St. Louis San D iego Seattle M in/St. Paul Atlanta Boston Philadelphia Com posite C om m uting A nalysis (adapted from Horner2002) 0 5 10 15 20 25 30 S eattle P hiladelphi B oston P ortland San D iego A tlanta Cleveland P ittsburgh C harlotte Milwaukee D enver S t.Louis Min/St.Paul Baltim ore Sacram ento Cincinnati P hoenix Colum bus R ochester Miami S an A ntonio M em phis Las V egas O m aha W ichita Bow ling Boise A verage M iles es M axim um Average M iles Minim um Average M iles ActualAverage M iles E F 100 * r m r a T T T T CP 100 * ) ( a r a T T T EC

Excess Commuting and Job-Housing Imbalance in Warren County, Kentucky Abstract: Excess commuting (EC) is a concept first developed by Hamilton (1982) to

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Page 1: Excess Commuting and Job-Housing Imbalance in Warren County, Kentucky Abstract: Excess commuting (EC) is a concept first developed by Hamilton (1982) to

Excess Commuting and Job-Housing Imbalance in Warren County, Kentucky

Abstract: Excess commuting (EC) is a concept first developed by Hamilton (1982) to measure the degree of commute distance explained by the spatial separation of job sites and households. The method is applied to a smaller metropolitan area and compared to previous studies done in larger urban areas.

Methodology: EC can be defined as the portion of all workers’ commute as a whole that is over and above the minimum required by the spatial distance between their residences and the job sites. It is a “benchmark” for evaluating jobs-housing imbalance and characteristics of urban form. It is calculated via a linear programming process that swaps workers to “new” household locations in the most efficient manner by minimizing the total travel distance for all workers. In this study, we also conducted a worst-case baseline analysis by allocating workers in the most inefficient way (maximizing the total travel distance). This measure gives the commute potential of a region and is determined by both transportation network and distribution of jobs and workers.

Caitlin Hager and Jun Yan, Center For GIS, Western Kentucky University

Comparison Analysis: The analysis is conducted at TAZ level in the Bowling Green Warren County MSA using CTPP 2000 data. The above maps show the actual commutes (A), minimized commutes (B), and maximized commutes (D) by drive-alone commuters (the most common mode in small-size cities). As shown in B, a large number of shorter commuting trips is increased drastically and the cross-town trips are minimized. On the other hand, a large number of longer trips rises under maximization (D), indicating the increased cross-town trips. In addition, under optimum minimization, intra-zonal trips are increased (C) but totally eliminated under maximization. Compared with other places in U.S., Bowling Green has relatively inefficient commutes (F), with 6.47 miles actual, 4.2 miles minimum optimal, and 9.16 miles maximum optimal mean travel distance .

Minimize or Maximize Treq =

n

i

m

j

ijijXC1 1

,1

n

i

DjXij ,,...,1 mj

m

j

SiXij1

,,,...,1 ni

,0Xij ,, ji

subject to:

A B C

D

Discussion: Excess Commuting (minimization) and Commute Potential (maximization) can be respectively calculated as follows:

Warren County has an EC of 35%, which means, as a whole, 35% of total current total travel distance is unnecessary under an optimal scenario. Compared to previous studies (E), EC of Boise, ID is 48%; Omaha, WI 64%; Baltimore, MD 62%; Boston, MA 67%; Atlanta, GA 57%, Warren County, as a small-size metro, has a relatively small EC, considering that Bowling Green is one of the fastest growing cities in Kentucky and it functions as a regional employment center.

Job-Housing Imbalance: Ratio of jobs to matched workers (JHB) for all and selected industries. Job-poor areas generate commutes; job-rich areas attract commutes. Blue zones indicate a low JHB; yellow - balanced; red - a high proportion of jobs to matched workers.

Reference: Hamilton, B. 1982. Wasteful commuting. The Journal of Political Economy 90(5): 1035-53.

Excess Commute PercentageIn Order of Increasing Work Trips

0%

10%

20%

30%

40%

50%

60%

70%

80%

Bow

ling Green

Boise

Wichita

Om

aha

Las Vegas

Mem

phis

Rochester

Charlotte

San A

ntonio

Colum

busS

acramento

Cincinnati

Portland

Milw

aukee

Miam

i

Pittsburgh

Cleveland

Phoenix

Denver

Baltim

ore

St. Louis

San D

iego

Seattle

Min/S

t. Paul

Atlanta

Boston

Philadelphia

Composite Commuting Analysis (adapted from Horner 2002)

0

5

10

15

20

25

30

Se

attle

Ph

ilad

elp

hi

Bo

ston

Po

rtlan

dS

an

Die

go

Atla

nta

Cle

vela

nd

Pittsb

urg

hC

ha

rlotte

Milw

au

kee

De

nve

rS

t. Lo

uis

Min

/St. P

au

lB

altim

ore

Sa

cram

en

toC

incin

na

tiP

ho

en

ixC

olu

mb

us

Ro

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ster

Mia

mi

Sa

n A

nto

nio

Me

mp

his

La

s Ve

ga

sO

ma

ha

Wich

itaB

ow

ling

Bo

ise

Av

era

ge

Mile

s e

s

MaximumAverage Miles

MinimumAverage Miles

Actual AverageMiles

E F

100*

rm

ra

TT

TTCP100*)(

a

ra

T

TTEC