Directionality Influences in Spatial Processes

by

JD Hunt, University of CalgaryM Thériault, Université LavalP Villeneuve, Université Laval

PROCESSUS Second International Colloquium

Toronto ON, CanadaJune 2005

Overview• Introduction

• Context• Motivations• Approach

• Evidence• Disaggregate Observations of Choice

Behaviour• Aggregate Patterns of System Behaviour

• Conclusions• Modelling• Expectations regarding urban form• Planning and design

Introduction• Context

• Modelling spatial decisions, representation relative location

• Travel time components• Travel cost components• Comfort and convenience

• What of ‘directionality’?• ‘together’, ‘on the way’ rather than ‘out of the way’• Anchored relative to reference locations: work, CBD, etc• Nature of perception beyond times and costs• System reinforcing directional tendencies

• Motivation• Adding representation of directionality – simple

form• Seeing in results in various forms• Improved understanding• Higher fidelity• Increased accuracy?• Faster processing

Introduction

• Approach• Draw on previous work in several locations

• Evidence of directionality effects• Representation of these effects

• Disaggregate behavioural evidence• Parking location choice in Edmonton• Commercial vehicle stop location choice in Calgary• Intermediate shopping stop choice in Quebec City

• Aggregate system behavioural evidence• Trip durations in Quebec City• Historical development patterns in Quebec City• Recent employment dynamics in Montréal

Parking Location ChoiceEdmonton

• 1983: Hunt• Develop mode choice model for regional

travel demand model• Include parking location choice for CBD-

destined auto driver alternative• composite utility for parking• parking demand allocation• support mode and parking policy analysis

Data

• 1983 Morning Commuter Survey• 80 employers (business & government

establishments)• 1702 travellers• 468 drivers, selecting parking locations

• Employer parking policy regarding travellers• 124 publicly available off-street parking facilities• Unmetered on-street parking areas, aggregated

into 12 areas

1 km

107 Ave

104 Ave

Jasper Ave

97 S

t

N

Jasp

er A

ve

10

5 S

t

10

1 S

t

109

St

A

I

J

C

H

E

F

G

B

D

K

L

area of on-street parking

CBD boundary

Legend:

Parking Location Choice Model

attributes in utility functions:• Walking distance to final destination• Parking charge per day• Number of stalls• Surface treatment – if paved or not• Adjacent land use – if residential or not• Security• Cleanliness• Angle relative to CBD and home, ANG

Parking Location Choice Model Nesting Structure

employerarranged

on-street off-street

individualoff-streetlocations

individualon-streetlocations

…. ….

ANG Measurehome

workplace

parkinglocation

ANG

ANG Measurehome

workplace

parkinglocation

ANG

and ANG = 90° when• Walk distance < 450 m for off-street• Walk distance < 700 m for on-street

Parking Location ChoiceEdmonton

Parking Location Choice Model Coefficients t-ratio < 1.96 ; 1.96 < t-ratio < 3.29 Choice Level Money

Cost Walk

Distance Res land use

Number Stalls

Surface Type

ANG On-street Nest

Off-street Nest

On-street Constant

Emp Arranged Constant

Money Cost for

Emp Arranged

Walk Distance for Emp

Arranged Individual on-street locations

-.01300 -4.12 -.02760

Individual off-street locations

-1.18 -.00955 .00313 1.31 -.00678

Parking types 0.352 0.380 2.18 3.85 -.762 -.000494

Parking Location ChoiceEdmonton

Parking Location Choice Model Coefficients t-ratio < 1.96 ; 1.96 < t-ratio < 3.29 Choice Level Money

Cost Walk

Distance Res land use

Number Stalls

Surface Type

ANG On-street Nest

Off-street Nest

On-street Constant

Emp Arranged Constant

Money Cost for

Emp Arranged

Walk Distance for Emp

Arranged Individual on-street locations

-.01300 -4.12 -.02760

Individual off-street locations

-1.18 -.00955 .00313 1.31 -.00678

Parking types 0.352 0.380 2.18 3.85 -.762 -.000494

Parking Location ChoiceEdmonton

• Findings:• ANG appears to have influence• driving time differences not included,

potential for bias• consider in future location choice modelling

Commercial Stop Location ChoiceCalgary

commercial vehicle movements

• 2005: Hunt, Stefan, McMillan, Abraham, et al

• Develop model of ehicles operated for commercial purposes• As opposed to household, personal

movements• Includes ‘non-commercial’ non-household

purposes (government, not-for-profit)• Comprise 10-15% of total urban traffic

Commercial Vehicle Movements

• Vehicles operated for commercial purposes• As opposed to household, personal

movements• Includes ‘non-commercial’ non-household

purposes (government, not-for-profit)• Comprise 10-15% of total urban traffic

Some Examples

Commercial• Hauling freight for

a company• Service workers

visiting clients• Sales meetings• Mail• Delivering parcels

Personal• Travel to work• Travel to school• Shopping• Leisure trips• Social visits

Data

• 2001 Commercial Movement Study • All commercial movements

• Not just freight• Not just trucks

• 3,100 establishments in Calgary• 4,300 establishments in Edmonton• 24 hour stop diary• Firmographics

• Employment structure• Vehicle fleet

Next Stop Location• Assigns location for each subsequent, non-

establishment stop on each tour in list• by 13 commercial model segments

(industry, vehicle and tour purpose categories)

• Monte Carlo, probabilities based on Logit• Single-level Logit among locations (zones)

for next stop, total of 1,447 zones in model

Next Stop Location Choice Model

attributes in utility function:• Travel gen cost to potential stop location• Travel gen cost for return to establishment

from potential stop• Population and employment accessibilities• Land use coefficients (5 land uses)• Average income for households at potential

stop• Population and employment size terms• Enclosed Angle

Angle Measure

CURRENTSTOP

ESTABLISHMENT

NEXTSTOP

ANGLE

CURRENTSTOP

ESTABLISHMENT

NEXTSTOP

ANGLE

Angle Measure

CURRENTSTOP

ESTABLISHMENT

NEXTSTOP

ANGLE

CURRENTSTOP

ESTABLISHMENT

NEXTSTOP

ANGLE

and ANGLE = 0 when• starting tour• NEXT STOP = CURRENT STOP

Commercial Stop Location ChoiceCalgary

Next Stop Location Choice Model Coefficients t-ratio < 1.96 ; 1.96 < t-ratio < 3.29 Firm-Tour-Veh

Types Low Den land use

CommRet land use

Ind land use

EmpNode land use

AveInc

(10-6)

GenCost travel to

next stop

GenCost return to

base

Pop Access (10-6)

Emp Access (10-6)

Enclosed Angle

(10-3)

Size Term

Emp Size Term ratio

All-Other-LMH -.7902 .0270 -.1595 -.6126 -11.490 .3039 .1310 -7.651 -9.696 -2.346 .2800 6.779 PS-S-L -.0898 -.2755 .2152 -.4623 1.676 .3283 -10.83 -2.653 -1.884 .3094 .087 PS-S-MH .7250 -.1057 .4655 -.7546 9.476 .0848 .1229 -44.65 9.296 3.684 .2219 PS-G-LMH -.3327 .5674 .4926 .2062 .5688 5.717 -16.54 -6.348 .1588 R-S-LMH -.9676 -.2310 .1547 -.5132 .3601 .03662 -17.35 0 -1.241 .2841 .633 R-G-LMH -.1707 -.0256 .8014 -.1840 .3734 .09158 -13.32 -1.682 1.914 .2067 1.633 I-S-L -1.144 -.2361 .0503 -.4182 .2869 -24.98 5.477 -3.067 .2371 1.231 I-S-MH -.3231 .2789 -.8438 .1627 .1279 -13.25 -16.96 2.934 .1205 1.012 I-G-LMH -.1497 .5575 -.2042 .2581 .09615 -10.68 -5.139 -2.146 .2722 W-S-LMH -.9340 -.2130 .1440 -.4410 2.367 .3849 .04300 -11.81 -27.71 1.761 .2426 2.138 W-G-L -.6668 .9271 -.2688 .4495 .1075 -32.84 -67.74 .892 .2248 W-G-MH -.1226 .1445 -.1183 .3123 .03430 -31.84 5.950 -1.431 .3021 2.313 T-X-LMH -.5279 .1004 .6275 .0267 -4.691 .3792 -11.72 -5.984 3.109 .0087

Commercial Stop Location ChoiceCalgary

Next Stop Location Choice Model Coefficients t-ratio < 1.96 ; 1.96 < t-ratio < 3.29 Firm-Tour-Veh

Types Low Den land use

CommRet land use

Ind land use

EmpNode land use

AveInc

(10-6)

GenCost travel to

next stop

GenCost return to

base

Pop Access (10-6)

Emp Access (10-6)

Enclosed Angle

(10-3)

Size Term

Emp Size Term ratio

All-Other-LMH -.7902 .0270 -.1595 -.6126 -11.490 .3039 .1310 -7.651 -9.696 -2.346 .2800 6.779 PS-S-L -.0898 -.2755 .2152 -.4623 1.676 .3283 -10.83 -2.653 -1.884 .3094 .087 PS-S-MH .7250 -.1057 .4655 -.7546 9.476 .0848 .1229 -44.65 9.296 3.684 .2219 PS-G-LMH -.3327 .5674 .4926 .2062 .5688 5.717 -16.54 -6.348 .1588 R-S-LMH -.9676 -.2310 .1547 -.5132 .3601 .03662 -17.35 0 -1.241 .2841 .633 R-G-LMH -.1707 -.0256 .8014 -.1840 .3734 .09158 -13.32 -1.682 1.914 .2067 1.633 I-S-L -1.144 -.2361 .0503 -.4182 .2869 -24.98 5.477 -3.067 .2371 1.231 I-S-MH -.3231 .2789 -.8438 .1627 .1279 -13.25 -16.96 2.934 .1205 1.012 I-G-LMH -.1497 .5575 -.2042 .2581 .09615 -10.68 -5.139 -2.146 .2722 W-S-LMH -.9340 -.2130 .1440 -.4410 2.367 .3849 .04300 -11.81 -27.71 1.761 .2426 2.138 W-G-L -.6668 .9271 -.2688 .4495 .1075 -32.84 -67.74 .892 .2248 W-G-MH -.1226 .1445 -.1183 .3123 .03430 -31.84 5.950 -1.431 .3021 2.313 T-X-LMH -.5279 .1004 .6275 .0267 -4.691 .3792 -11.72 -5.984 3.109 .0087

Commercial Stop Location ChoiceCalgary

base

1

2

34

5

+ve

base

1

2

3

4

5

-ve

• Findings:• Enclosed angle has strong influence• Sign changes for different segments

• Displaying different spatial patterns

• Driving gen cost differences included for next location and base location

• Stronger case for directionality influence• Include in future location choice modelling• Expect to find aggregate impacts

Commercial Stop Location ChoiceCalgary

HBW Intermediate Shopping Choice

Quebec City• 2005: Thériault• Model decision to make an intermediate

shopping stop on trip from work• Consider influence of locations relative to

work - home axis• ‘on the way’ vs ‘out of the way’

from work to home

Data

• 2001 OD Survey• 29,249 workers with fixed workplace

(not working at home)• 825 intermediate shopping stops made

during HBW trips• 323 to large store• 222 to small shop• 270 to grocery

Stop to Shop Choice Model

attributes in utility function:

• Gender• Age• Household size• Household auto ownership• Distance from home to central axis (Grande

Allee)• Distances from home to workplace

• Straight-line• North-south and east-west components separately

distances from home to work with directionality

components

workplace

home

east-westcomponent

distanceX-axis

N

straight-li

ne

distance

nort

h-so

uth

com

pone

ntdi

stan

ceY

-axi

s

Stop to Shop ChoiceQuebec City

Variables in the Equation

,806 ,076 113,565 1 ,000 2,240

,739 ,142 27,085 1 ,000 2,093

-,196 ,035 31,955 1 ,000 ,822

-,108 ,052 4,331 1 ,037 ,897

,013 ,005 5,931 1 ,015 1,013

,076 ,031 5,998 1 ,014 1,079

-6,269 ,561 125,013 1 ,000 ,002

Woman versus Man

Natural logarithm of Age (Years)

Number of persons in the household

Number of cars owned by the household

Euclidean distance from home to central axis (Km)

Log of Euclidean distance from home to Workplace (Km)

Constant

Step1

B S.E. Wald df Sig. Exp(B)

Stop to Shop ChoiceQuebec City

Variables in the Equation

,806 ,076 113,565 1 ,000 2,240

,739 ,142 27,085 1 ,000 2,093

-,196 ,035 31,955 1 ,000 ,822

-,108 ,052 4,331 1 ,037 ,897

,013 ,005 5,931 1 ,015 1,013

,076 ,031 5,998 1 ,014 1,079

-6,269 ,561 125,013 1 ,000 ,002

Woman versus Man

Natural logarithm of Age (Years)

Number of persons in the household

Number of cars owned by the household

Euclidean distance from home to central axis (Km)

Log of Euclidean distance from home to Workplace (Km)

Constant

Step1

B S.E. Wald df Sig. Exp(B)

Stop to Shop ChoiceQuebec City

Variables in the Equation

,791 ,076 109,399 1 ,000 2,207

,728 ,142 26,273 1 ,000 2,072

-,199 ,035 32,737 1 ,000 ,820

-,103 ,052 3,899 1 ,048 ,903

,017 ,005 9,371 1 ,002 1,017

-,014 ,005 7,182 1 ,007 ,986

,095 ,028 11,172 1 ,001 1,100

-6,143 ,559 120,575 1 ,000 ,002

Woman versus Man

Natural logarithm of Age (Years)

Number of persons in the household

Number of cars owned by the household

Euclidean distance from home to central axis (Km)

Absolute distance between Home and Workplace on X axis of the Map (Km)

Log of Absolute distance between Home and Workplace on Y axis of the Map (Km)

Constant

Step1

a

B S.E. Wald df Sig. Exp(B)

Variable(s) entered on step 1: GENDER, LNAGE, NBPERS, NBAUTO, DEUCREAX, ADX, LNADY.a.

Stop to Shop ChoiceQuebec City

Variables in the Equation

,791 ,076 109,399 1 ,000 2,207

,728 ,142 26,273 1 ,000 2,072

-,199 ,035 32,737 1 ,000 ,820

-,103 ,052 3,899 1 ,048 ,903

,017 ,005 9,371 1 ,002 1,017

-,014 ,005 7,182 1 ,007 ,986

,095 ,028 11,172 1 ,001 1,100

-6,143 ,559 120,575 1 ,000 ,002

Woman versus Man

Natural logarithm of Age (Years)

Number of persons in the household

Number of cars owned by the household

Euclidean distance from home to central axis (Km)

Absolute distance between Home and Workplace on X axis of the Map (Km)

Log of Absolute distance between Home and Workplace on Y axis of the Map (Km)

Constant

Step1

a

B S.E. Wald df Sig. Exp(B)

Variable(s) entered on step 1: GENDER, LNAGE, NBPERS, NBAUTO, DEUCREAX, ADX, LNADY.a.

• Findings:• Home location relative to central axis has +ve

impact – more chaining of shopping with work travel when home location has relatively less nearby

• Home to work distance has +ve impact – more ‘on the way’ intermediate opportunities

• Directionality components of home to work distance have different impacts

• Y-Axis (N-S) +ve linear impact• X-Axis (E-W) –ve logrithmic impact• Perhaps related to highway network, with more high-speed

capacity N-S

• Travel times & costs not included, potential for bias?

HBW Intermediate Shopping Choice

Quebec City

Trip Duration InfluencesQuebec City

• 2003: Vandermissen, Villeneuve and Thériault• Examine how trip duration is influenced by

alignment of trip origin and destination with CBD• Hypothesis 1: Trip duration will decrease as

alignment of origin and destination with CBD increases

• Hypothesis 2: Influence of alignment with CBD on trip duration will decrease as city becomes less monocentric

• ‘directionality’ here is relative to CBD

Data• 1991 OD Survey (n=29,046); 2001 OD Survey

(n=46,664)

• Congested network travel times - from model• Trip purposes

• Work• Study• Shopping• Leisure• Other

• Traveller characteristics• Gender• Age

• Trip Characteristics• Mode• Time of Travel (peak vs off-peak)• Distance from origin to CBD• Distance from destination to CBD

Measure of DirectionalityCBD

origin

destination

Path of trip in street network

Dperpen

Dperpen is the length of theperpendicular between thedestination point and the locusof the straight line passing throughthe origin of the trip and the CBD

Measure of DirectionalityCBD

origin

destination

Path of trip in street network

Dperpen

Dperpen is the length of theperpendicular between thedestination point and the locusof the straight line passing throughthe origin of the trip and the CBD

Origin and destination are aligned with CBD when Dperpen = 0;

As Dperpen increases alignment decreases

Measure of DirectionalityCBD

origin

destination

Path of trip in street network

Dperpen

Hypothesis 1: Trip duration increases as Dperpen increases

Dperpen is the length of theperpendicular between thedestination point and the locusof the straight line passing throughthe origin of the trip and the CBD

Origin and destination are aligned with CBD when Dperpen = 0;

As Dperpen increases alignment decreases

Trip Duration Model

independent variables in regression:

• Gender• Age• Mode• Time of travel (peak vs off-peak)• Distance from origin to CBD• Distance from destination to CBD• Dperpen

• Measure of directionality of O-D relative to CBD• Increase in Dperpen less aligned with CBD; increase in trip

duration• +ve coefficient

1991 2001 1991 2001 1991 2001 1991 2001 1991 2001 1991 2001

adjR2 0.518 0.493 0.586 0.608 0.540 0.523 0.529 0.452 0.493 0.448 0.562 0.549

Sex 0,041 0.029 0.034 0.026 0.024 0.029Age -0.016 -0.065 -0.039 -0.070 -0.083 -0.056 -0.022 -0.065

Mode 0.461 0.382 0.648 0.602 0.612 0.377 0.541 0.430 0.493 0.248 0.543 0.414Peak 0.172 0.218 0.050 0.064 0.065 0.095 0.021 0.093 0.093 0.126 0.135 0.214

Dorigcen 0.363 0.342 0.302 0.394 0.315 0.256 0.367 0.289 0.262 0.212 0.335 0.316

Ddestcen -0.398 -0.359 -0.164 -0.113 -0.247 -0.210 -0.330 -0.336 -0.276 -0.301 -0.328 -0.315

Dperpen 0.509 0.545 0.408 0.400 0.442 0.601 0.467 0.524 0.542 0.621 0.459 0.508

N 14031 17730 3402 3067 3869 5402 1214 2987 6530 17478 29046 46664

Dependent variable : ln of trip duration Directionality is measured through Dperpen

Only coefficients significant at the 0,001 level are shown Dorigcen : Distance between origin of trip and CBD

Return home trips are not included Ddestcen : Distance between dest. of trip and CBCSex: male = 1; female = 0 Age in yearsMode: car driver = 0; bus rider = 1 Peak: 7h00-9h00&16h00-18h00 = 1; other = 0

Standardized regression coefficients (betas)

Isolating the effect of directionality on trip duration

Work Study Shopping Leasure Other Total

Trip Duration InfluencesQuebec City

1991 2001 1991 2001 1991 2001 1991 2001 1991 2001 1991 2001

adjR2 0.518 0.493 0.586 0.608 0.540 0.523 0.529 0.452 0.493 0.448 0.562 0.549

Sex 0,041 0.029 0.034 0.026 0.024 0.029Age -0.016 -0.065 -0.039 -0.070 -0.083 -0.056 -0.022 -0.065

Mode 0.461 0.382 0.648 0.602 0.612 0.377 0.541 0.430 0.493 0.248 0.543 0.414Peak 0.172 0.218 0.050 0.064 0.065 0.095 0.021 0.093 0.093 0.126 0.135 0.214

Dorigcen 0.363 0.342 0.302 0.394 0.315 0.256 0.367 0.289 0.262 0.212 0.335 0.316

Ddestcen -0.398 -0.359 -0.164 -0.113 -0.247 -0.210 -0.330 -0.336 -0.276 -0.301 -0.328 -0.315

Dperpen 0.509 0.545 0.408 0.400 0.442 0.601 0.467 0.524 0.542 0.621 0.459 0.508

N 14031 17730 3402 3067 3869 5402 1214 2987 6530 17478 29046 46664

Dependent variable : ln of trip duration Directionality is measured through Dperpen

Only coefficients significant at the 0,001 level are shown Dorigcen : Distance between origin of trip and CBD

Return home trips are not included Ddestcen : Distance between dest. of trip and CBCSex: male = 1; female = 0 Age in yearsMode: car driver = 0; bus rider = 1 Peak: 7h00-9h00&16h00-18h00 = 1; other = 0

Standardized regression coefficients (betas)

Isolating the effect of directionality on trip duration

Work Study Shopping Leasure Other Total

Trip Duration InfluencesQuebec City

• Findings:• Trip duration decreases as alignment of origin

and destination with CBD increases • Supports Hypothesis 1• For full range of trip purposes• More of a network effect, supply vs demand

• Combined with other work, this influence is increasing

• Potential for reinforcing directionality aspects of choice behaviour

Trip Duration ModelQuebec City

Historical Development Patterns

Quebec City• 2004: Thériault and Bourel• Examine growth patterns

• Axes of development, impact of ‘on the way’ increasing activity at intermediate locations

• Role of CBD as reference

Data

• History of development• Various points starting in 1830

• Range of modal influences• Rail encourages linear patterns in Quebec City Auto

encourages more radial expansion in all directions with some clustering along high-speed roads

• Configuration of land parcels (normal to river)

0 5 10

kilometres

High DensityUrbanised Areas

1 8301 8801 9201 9451 9611 9711 9781 9852 000

Old QuebecOld QuebecOld QuebecOld QuebecOld QuebecOld QuebecOld QuebecOld QuebecOld Quebec

CharlesbourgCharlesbourgCharlesbourgCharlesbourgCharlesbourgCharlesbourgCharlesbourgCharlesbourgCharlesbourg

BeauportBeauportBeauportBeauportBeauportBeauportBeauportBeauportBeauport

LorettevilleLorettevilleLorettevilleLorettevilleLorettevilleLorettevilleLorettevilleLorettevilleLoretteville

SillerySillerySillerySillerySillerySillerySillerySillerySillery

Sainte-FoySainte-FoySainte-FoySainte-FoySainte-FoySainte-FoySainte-FoySainte-FoySainte-Foy

LévisLévisLévisLévisLévisLévisLévisLévisLévis

CharnyCharnyCharnyCharnyCharnyCharnyCharnyCharnyCharny

Ancienne LoretteAncienne LoretteAncienne LoretteAncienne LoretteAncienne LoretteAncienne LoretteAncienne LoretteAncienne LoretteAncienne Lorette

Historical Development of Quebec City

(1) Early 19th CenturyOld City Core and Villages

(2) Mid 19th CenturyDevelopment of “Faubourgs”

(3) Turn of 20th CenturyExtension of “Faubourgs”

(4) 1920 -1945 – Axes appearNew Neighbourhoods

(5) 1945 -1960 – Axes confirmBeginning of urban sprawl

(6) 1960-1975 – Axes consolidatePeak of urban sprawl – Remote towns

(7) 1975 - 2000 – Filling gapsSome extension of axes – Suburbanization

• Findings:• Distinct axes appear

• Radial out from CBD• Before sprawl

• Consistent with linear impact of ‘on the way’ directionality influence

• Also consistent with rail then auto impacts• Parcel orientation also a factor• Difficult to separate influences

Historical Development Patterns

Quebec City

Recent Employment DynamicsMontréal

• 2005: Barbonne• Examine employment patterns 1981 and

2001• Axes of development, impact of ‘on the way’

increasing activity at intermediate locations• Role of CBD as reference

Source: Barbonne,2005

Centrographic analyis suggests elongation of local labour markets

• Findings:• Distinct axes appear

• Radial out from CBD• Before sprawl

• Consistent with linear impact of ‘on the way’ directionality influence

• Following roads oriented out form CBD

• Aggregate emergent behaviour, combining• disaggregate choice behaviour• system supply characteristics

Recent Employment DynamicsMontréal

Conclusions• For modelling:

• There is a directionality component to spatial choice behaviour

• Relevant demand and choice models should include representation of ‘on the way’ vs ‘out of the way’ relative to various reference locations along with usual time and cost elements

• Expectations regarding urban form:• Axes of development apparent

• Planning and design:• Acknowledge potential for CBD-based directionality in

provision of transportation supply and in resulting travel times

• Consider longer term impacts of such directionality working in combination with demand and associated spatial choice behaviour

• Build on existing axes orientation

Further Material

Modelling the effect of directionality on trip duration

• In this section of the paper, directionality is defined with regard to CBD

• Hypothesis 1: Alignment of origin and destination of a trip with CBD should decrease duration

• Hypothesis 2: As cities become less monocentric, the effect of alignment should decrease

• First results, using an angular measure with small travel zones as the georeference, support Hyp 1 & 2 for work trips in Quebec City, comparing 1977 and 1996 (Vandersmissen, Villeneuve, Thériault, 2003).

• Now, we look at trip purposes other than work, using what appears to be a simpler measure of directionality, with a better georeference (6-character postal codes)

Are there planning implications?

• The effect of directionality seems to have increased during de 1990s.

• What happens in the aggregate?• Jobs along corridors• Elongated local labour markets

around the CMA• Elongated urban form, mixed-use and

transit

Lévis

Couronnes Travail Étude Com-loisir Résidence Autre Total Entropie C1 920 0 4649 1176 475 7220 0,443238 C2 346 347 239 1288 241 2461 0,584086 C3 558 768 486 1917 485 4214 0,622878 C4 282 367 469 1490 507 3115 0,609198 C5 321 0 59 901 127 1408 0,422429

Total 2427 1482 5902 6772 1835 18418 0,621981 Québec-Sainte-Foy

Segments Travail Étude Com-loisir Résidence Autre Total Entropie S1 2169 197 3828 1437 1233 8864 0,591087 S2 2082 3409 2606 1061 1522 10680 0,666422 S3 768 448 359 5463 933 7971 0,450315 S4 3110 1234 5050 11469 3049 23912 0,591368 S5 5770 513 3385 7110 2785 19563 0,609972

Total 13899 5801 15228 26540 9522 70990 0,647725

-Lévis: concentric rings around CBD-Qc-Ste_Foy: segments of an urban corridor-Entropy measures diversity within rings and segments-Greater diversity in segments than in rings-Perhaps because of land price gradients?-But, here the segments are much more urbanized than the rings

Does an elongated urban form favour mixed land use?

Concluding questions

• Is the directionality effect the same in the center as it is in the periphery? (center: 0.438 in 1991; 0.523 in 2001) (periphery: 0.434 in 91; 0,486 in

01)

• Are there alignments on other poles than the CBD? (or alignments of trips without poles?)

• Are the examples of Curitiba and Ottawa reproducible?

Acknowledgments to:

• Céline Bourel for computing the trip durations• Rémy Barbonne for analyzing the local labour markets

and mapping the jobs• Simon Faucher for compiling data on poles and corridors• RTC (réseau de transport de la Capitale and MTQ

(ministère des transports du Québec) for giving access to the OD surveys