land use-transport models in regional development planning

Socio-Econ. Plan. Sci. Vol. IO, pp. 47-55. Pergamon Press 1976. Printed in Great Britan

LAND USE-TRANSPORT MODELS IN REGIONAL

DEVELOPMENT PLANNING

B. G. HUTCHINSON

Department of Civil Engineering, University of Waterloo, Waterloo, Ontario, Canada

(Receioed 9 July 1975)

Abstract-A land use-transport model of the Lowry-type is described along with several applications of the model to regional planning problems in the Toronto region of Ontario. The particular model used has several unique features. It may be disaggregated by socio-economic group, the population serving employment sub-model may be disaggregated into a number of employment sectors and transport mode-specific travel behaviour estimated. Applications of the model to public policy program formUlatiOh within the region are illustrated by three examples.

INTRODUCTION

In 1970 the Government of Ontario adopted as generaI’ policy a broad structure plan for a large region centred on Metropolitan Toronto[l]. This structure plan, which is known as the Toronto-Centred Region Plan, is divided into three large sub-areas or zones. Zone 1 is located along the north shore of Lake Ontario and is intended to contain the bulk of the urbanization expected in the Region. The preferred articulation of this urbanization within Zone 1 is illustrated in Fig. 1. Zone 2 which is largely rural is intended to be preserved as agricultural land. Zone 3 contains the primary recreational elements of the region such as the Niagara Escarpment and the lake country in the Canadian shield to the north. It is expected that by the year 2000 some 8 million people will live in the region with 5.7 million being concentrated in Zone 1. The 1971 population of Zone 1 was just over 3 million.

In 1973 the Government of Ontario established a task force to refine the structure plan for Zone 1 and the sub-region of concern to this task force was referred to as the Central Ontario Lakeshore Urban Complex (COLUC). The COLUC Task Force was an inter- ministerial group consisting of representatives from the Ontario Government Ministries of Agriculture and Food, Natural Resources, Transportation and Communications,

Environment, Housing, and Treasury, Economics and Inter-Governmental Affairs. The primary responsibility of the COLUC Task Force was to refine the Toronto- Centred Region concept so that it might be used as a common guide-line by regional municipalities and various provincial government agencies in formulating development policies and programs [2].

A second initiative by the Government of Ontario in implementing the Toronto-Centred Region plan is the North Pickering Project which is an undertaking of the Ontario Ministry of Housing. The aim of this project is to create a new community on a 25,000 acre site located to the northeast of Metropolitan Toronto as shown in Fig. 2. This new community along with the associated new Toronto International Airport represent the first major steps in implementing the structure plan. The North Pickering community represents an integration of the communities labelled as Cedarwood and Brock in Fig. 1.

The objective of this paper is to describe several related applications of a land use-transport model of the Lowry type to regional development planning issues within the Toronto-Centred Region. The applications described in this paper are based on studies performed by the author for the COLUC Task Force and the North Pickering Project.

Fig. 1. Structure plan for Toronto centred region.

47 SEPS VOL. IO NO, Z-A

B. G. HUTCHINSON

Fig. 2. Oshawa sub-region.

LAND USE-TRANSPORT MODEL STRUCTURE

The land use-transport demand analysis model which forms the basis of the analyses described in this paper is derived from the Lowry land use model[3]. A number of forms of the Lowry model have been developed and applied to planning problems, Wilson[4], Hutchinson [S], Goldner [6] and Batty [7] have summarized the majority of this work.

While the Lowry model is well described in the literature it is important to describe very briefly the particular structure of the model used in the studies described in this paper. It has a number of unique features that permit its application to a wide range of development planning problems.

Figure 3 illustrates the broad structure of the model and its relationship to policy inputs, model parameters and model outputs. The model consists of a residential sub-model and a service employment sub-model. The residential sub-model calculates a spatial distribution of household demands created by an exogenously specified distribution of basic employment and an internally calculated distribution of population-serving employment. The service employment sub-model calculates a spatial distribution of population-serving employment created by the spatial distribution of households calculated by the residential sub-model.

More specifically the residential sub-model allocates employees of socio-economic group k working in

DEVELOPMENT

POLICY lNP”TS MODEL PARAMETERS

employment zone i to residential opportunities in zone j that are compatible with the preferences and ability to pay of this group. The residential sub-model produces an n x n square matrix showing the households in zone j that are occupied by employees working in zone i for each of the k socio-economic groups. The entries in this matrix are calculated from:

/F = e,“a ‘prk”’ [ h,L exp (- u,*dZ)/T h,” exp (- n:d!J]

(1)

where II;“’ = the number of zone i employees of socio- economic group k who live in zone j and travel there by mode m, e,k = the total number of employees of socio- economic group k who work in zone i, a’ = the inverse of the activity rate for socio-economic group k in terms of households per employee (or population per employee), prk” = the probability that socio-economic group k will choose mode m for the journey to work, hr = the number of residential opportunities in zone j that are compatible with socio-economic group k, a,' = the work-zone i specific parameter which reflects the influence that travel time dii has on the residential location selections of employees in socio-economic group k where ok is established by calibration of the residential sub-model to survey data, d 1: = the travel time between zones i and j on the mode of transport that is most important to socio-economic group k in the selection of a household location.

The number of households of socio-economic group k allocated to each zone are calculated by summing the columns of the linkage matrix calculated by eqn (1):

p;m = T II;” (2)

where P,~“’ = the number of households allocated to zone j which are occupied by group k employees travelling by mode m

p,“=cpll” m (3)

MODEL

INTERNAL STRUCTURE MODEL OUTPUTS

r---- 7

I BASIC

1 EMPLOYMENT

’ ALLOCATION

I

SUE-MODEL

r

POPULATION

EMPLOYMENT

SUB-MODEL

It ! 1 EMPLOYMENT 1 1

j 1 ALLOPION 1

/ ;i (

BASIC 8 SERWCE

/ ( EMPLOYMENT?

EMPWYMENT DISTRIBUTION

Fig. 3. Basic structure of land use-transport demand analysis model.

Land use-transport models in regional development planning 49

where p; = the number of households allocated to zone j which are occupied by group k employees.

The allocation of households to zones calculated by eqns (l)-(3) are subject to the following constraints:

pk ,pCk (4)

where P‘~ = 1 x n vector of the residential holding capacities of each of the n zones partitioned into k socio-economic groups.

The service sub-model allocates the demand for r-types of household serving employment to competing service centre locations using the following equation:

lp=p;bkrprkm s,‘exp(-/?‘d$ [ In

zs,‘exp(-P’ds) 1 (5) where 1 Fp = the number of population-serving employees of type r in zone i serving households of socio-economic group k in zone j where the trip is by mode m, p: = the number of households of group k allocated to zone j by the residential sub-model, b” = the type r population- serving employment per household generated by households in socio-economic group k, prkmr = the probability that group k households will choose mode m for type r service trips, s,’ = the attractivity of zone i for type r service trips.

The type r service employment allocated to each zone is obtained from:

Allocations of population-serving zones produced by eqns (5) and (6) following constraint equation:

esr 2 esrmln

(6)

employments to must satisfy the

(7)

where e”‘“‘” = 1 x r vector of the population-serving employment thresholds for type r service employment that are considered to be viable for any one zone.

If the constraint eqns (4) and (7) are violated by an activity allocation then new attractivity factors must be developed for the residential and service sub-models. The approach used in this model is:

hi’** = hj(h,k*l/p;) (8)

where hr *’ = the new residential attractivity of zone j for the next run of the model, h,“*’ = the residential attractivity of zone j in the previous iteration of the residential sub-model; h: *I = hi for the second iteration of the sub-model, p: = the households of group k allocated to zone j in each iteration of the sub-model.

s,‘* = sir for zones in which e,“’ 2 es’“‘” (9)

= 0 for zones in which e,” 5 e ” _I”

The household and service employment distributions produced by the sub-models must satisfy:

e,k = edbk + x eFk. r

The travel demand matrices for a model-allocated

co-distribution of population and employment are calculated by multiplying the work and service linkage matrices defined in eqns (1) and (5) by the appropriate trip rates:

T”km = W”[[F] (11)

T r!+l = R’[[F] (12)

where Twk” = n x n square matrix of t:“” the number of trips by group k employees from jobs in zone i to households in zone j by mode m, Wk = II x n diagonal matrix of the work trip generation rates per employee for group k, Yk”’ = n x n square matrix of tr:‘” the number of trips by group k households in zone j to type r service opportunities in zone i by mode m, Rk = n x n diagonal matrix of the type r service trip generation rates per household.

Information disaggregated by socio-economic group of the type required by the above formulation of the Lowry model is not normally directly available in structure planning studies. Many of the issues raised in structure planning studies require analyses which are disaggregated by socio-economic group. Consequently a technique is required for disaggregating the information normally developed for structure planning studies.

A basic employment vector disaggregated by socio- economic group may be estimated in the following way:

[ehk] = [e”] [eb”] (13)

where [eb’] = 1 x n vector of basic employment partitioned into k person types, [e”] = k x c matrix of the probabilities that employees of group k will be employed in industry category c, [e b’ ] = 1 x n vector of basic employment opportunities partitioned into industry categories c.

A disaggregated population-serving employment vector may be derived in a similar manner:

[e”‘] = [e”] [e”‘] (14)

where [esk] = 1 x n vector of population-serving employment partitioned into k groups, [e”] = k x r matrix of the probabilities that employees of group k will be employed in type r service industries, [e”‘] = 1 x n vector of service employment partitioned into r service industries

As all employees are not household heads it may be necessary to transform the employment vector by socio-economic group into household heads by socio- economic group. Working wives and children in the lower income jobs tend to be from multi-employee households. This transformation may be achieved by multiplying the employment vector by socio-economic group by the following lower triangular conditional probability matrix: !'I1 0 0'.- 0 !(2, yzz 0 .... 0

[i 1 (13 yr, . . . . . . . . . . . . . . . yil where y,, = the probability that an employee will be associated with a household of income group i given that the employee earns a salary in income group j

In the applications of the Waterloo model conducted to date the measure of residential attractivity h: is set equal to the number of housing opportunities oi( available to

50 B. G. HUTCHINSON

group k people in zone j. The housing opportunities for group k may be estimated from:

[ohI = Pkdl WY (16)

where [okI = 1 x n vector of housing opportunities partitioned into k person groups, [h”“] = k X d matrix of the probabilities that group k persons will live in housing density class d, [gd] = 1 x n vector of the number of housing opportunities g partitioned into d housing density classes.

The approach to the disaggregation of housing opportunities described above uses residential density as a proxy for housing quaht nd price. While this is a rather

? crude approach, residentia density is one of the few variables normally contained in structure plan proposals for future time horizons. In certain applications of the model residential density may not be an adequate reflection of the supply characteristics of the housing market and the coarse density groupings suggested previously may have to be sub-divided further into housing quality and price groups.

COLUCLEVELREGIONALANALYSES

The COLUC Task Force identified a system of 23 urban places within the region. These urban places are grouped into four sub-regions which are focussed on Hamilton, Mississauga, Toronto and Oshawa. Each of these sub-regions is intended to be highly diversified and relatively self-sufficient for service employment depend- ing on Toronto for only the most specialized services. Each sub-region is intended to contain several urban places of different sizes which are functionally inter- dependent and Fig. 4 shows the preferred orientations of the urban places within the COLUC system and their functional classes at the mature state. Preferred population and employment targets were established for each of the urban places illustrated in Fig. 4.

The role of the land use model in these region-wide studies is illustrated in Fig. 5. Alternative programs were converted into input variables acceptable to the model.

MINISTRY PROGRAMS

TREASURY, ECONOMICS l NEW AIRPORT SCALE

AND INTER-GOVERNMENTAL . MUNlClPAL OFFlClAL

AFFAIRS PLAN APPROVALS

l LOCATION INCENTIVES

ENVIRONMENT . TRUNK SERVICES

. MUNICIPAL SUBSIDIES

TRANSPORTATION AND . REGIONAL HIGHWAY

C~MMUNIC~~TIONS SYSTEM lNVESTMENTS

. COMMUTER RAIL

INVESTMENTS

AGRICULTURE AND FOOD . AGRICULTURAL AND

PRESERVATION

NATURAL RESOURCES . MINERAL AGGREGATE

EXTRACTION

URBAN PLACE

FUNCTlONAL CLASS I\“,o,a,hlewma,kef

01 On 0 = 0 m 0 Y

0 Til

Fig. 4. Preferred orientation or urban places in COLUC region.

These inputs were used along with the appropriate model parameters to calculate population and employment allocations to each of the urban places along with the associated travel demands assigned to a coarse corridor- level regional transport network. The objective of the process illustrated in Fig. 5 was to assist in understanding the probable impacts of the following policy elements on the preferred regional structure plan: (1) The distribution of basic employment particularly the location of airport employment (2) Residential trunk servicing policies and the residential densities of the urban places (3) Urban place population sizes and their influence on service employment distributions and inter-urban place service demand orientations (4) Region-wide transport policies.

The land use-transport model described in eqns (I)-( 12) was applied to the COLUC regional level analyses in an aggregated form. The parameter magnitudes used in these analyses were a = 2.178 persons per employee, b = 0.191 service employees per person, W = 0.85 trips per employee and R = 0.47 trips per person. The (Y and p magnitudes were estimated from calibrations of the model to 1971 trip length frequency distributions and these were developed separately for 8 sub-regions within the COLUC region in order to reflect differences in behaviour between the urban areas. The majority of the analyses

PROGRAM ASSESSMENT

l IS STRUCTURE PLAN

SENSlTlVE TO

AIRPORT ?

l IS SERVICE CENTRE

HIERARCHY CONCEPT

VIABLE ?

l SHOULD DIFFERENT

BASIC EMPLOYMENT

DISTRIBUTIONS BE

ENCOURAGED ?

. WHICH IS BEST

TRUNK SERVICES

PROGRAM

. ARE STRUCTURE

PLAN POPULATION

HOUSING . NORTH PICKERING 7

DISTRIBUTIONS

SCALE REALISTIC ?

. LAND BANKING

Fig. 5. Role of land use-transport demand model in structure plan testing


were conducted relative to a 1986 time horizon although some analyses were conducted relative to 2001.

Table 1 outlines in general terms the characteristics of the public development policies analyzed. These policies included variations in the location of the new Toronto International Airport, alternative locations for the major trunk services extensions and alternative commuter rail networks. Figure 6 illustrates the changes in population from the COLUC Base population allocations calculated by the model for each of the five sub-regions for a number of the policy elements listed in Table 1. Model outputs were obtained on a zone-by-zone basis but have been aggregated to the sub-regional level for illustrative purposes. The impacts of alternative commuter rail policies have not been illustrated in Fig. 6 since they had little impact on population and employment distributions at the sub-regional level.

Fig. 6. Changes in sub-regional population allocations for different development policies.

The concentration of all airport employment at the current airport site at Malton had little impact on population distribution at the regional scale. The shifting of 15,000 employees would influence the residential location decisions of about 32,000 people. The principal residential zones influenced are those located on the fringes of Metropolitan Toronto and those within the Oshawa and Mississauga sub-regions adjacent to the airport sites.

The residential land servicing policies had the greatest influence on the distribution of population and employment within the COLUC Region. Of the individual servicing policies tested the Peel Pipe had the largest impact on the distribution of population and employment within the COLUC region. This might have been expected since land use model analyses of the COLUC Base concept showed that residential zones in the western areas of Metropolitan Toronto and in the Mississauga sub-region are likely to experience continued pressures for development and the provision of serviced land simply strengthened this demand. This servicing policy also had the effect of making Mississauga an important service employment centre which in turn created an additional demand for residential location within the Mississauga sub-region by the service employees. Figure 6 demonstrates that similar effects were produced by the other servicing policies in that residential demand was encouraged to locate in the areas influenced by the particular servicing policy along with a certain amount of population-serving employment.

The regional transport policy alternatives tested all represented improvements in the public transporuystem with the exception of the completion of one freeway link within the boundaries of Metropolitan Toronto. These policies influenced the populations allocated to only a few of the urban places in the COLUC region. These were the urban places whose access times to the Toronto central business district were increased dramatically by the introduction of a high speed commuter rail service. However, these effects were insignificant when viewed at the regional scale.

52 B. G. HUTCHINSON

NORTHPICKERINGSERVICEEMPLOYMENT ANALYSIS

A second series of analyses were performed which were directed towards a detailed analysis of inter-urban place service employment allocations within the Oshawa sub-region of the COLUC area. The particular interest of these analyses were the service employment allocations to the North Pickering new town under 3 development scenarios for the sub-region. The characteristics of these three development scenarios are shown in Table 2. Scenarios A and B have the same population targets for the Oshawa sub-region but differ from each other in terms of the activity rate and the proportions of basic and population-serving employment. With scenario C a higher sub-regional target population exists and the activity rate is higher. Table 3 summarizes the service employment demand rates for each service employment sector. The rates for scenarios A and C are based on 1971 census data while those for scenario B are forecasts developed by the COLUC Task Force.

A number of development alternatives were postulated for the North Pickering community within each of these sub-regional scenarios and these alternatives are: (1) North Pickering developed to a scale of 34,000 persons in 1986 (2) North Pickering developed to a scale of 80,000 persons in 1986 (3) New Toronto International Airport constructed with a total employment of 22,000 in 1986 (4) New Toronto International Airport not constructed with all traffic using the existing airport.

This combination of sub-regional development scenarios and North Pickering development alternatives were subject to analyses by the land use model using a disaggregated service employment sub-model operated at the COLUC region scale.

Figure 7 summarizes the population and service employment allocations to each of the urban places within the Oshawa sub-region for four of the conditions analyzed. This diagram illustrates that under scenario A,

with Oshawa providing only 180,000 residential opportunities, the population targets at North Pickering are exceeded for both scales of development of the new town. However, it is interesting to note that the service employment targets for North Pickering are not met. For a scale of development of 80,000 persons 83% of the service employment target is met while only 59% of the target is achieved with the smaller scale of development. The service employment targets for Oshawa are just exceeded and Ajax, being the focus of the sub-region, receives an over-allocation of service employment.

Figure 7 illustrates that under scenario C, with Oshawa providing some 300,000 residential opportunities, the population and service employment allocations to North Pickering are of a similar character to those just described for scenario A. Population targets are achieved but the service employment targets are not achieved with the lakeshore communities of Ajax and Oshawa receiving over-allocations of service employment. These analyses provided by the North Pickering developing as a relatively self-contained service community.

Figure 8 shows the variation in the percentage of the target service employment that is achieved at the North Pickering community under a variety of the development assumptions. This diagram demonstrates clearly that the best strategy for the balanced development of North Pickering is that which involves sub-regional scenario A, North Pickering developed to a scale of 80,000 and the construction of the airport. This diagram also demonstrates that it is difficult to develop a viable community at North Pickering under scenario C unless the airport is constructed. With scenario C, Oshawa dominates the entire sub-region and Oshawa and Ajax attract a significant amount of service employment from North Pickering and the smaller places of the sub-region.

Figure 9 illustrates the composition of the service employment by sector allocated to four of the urban places within the Oshawa sub-region for four develop-

Table 2. Oshawa sub-region characteristics for 3 scenarios in 1986

Land use-transport models in regional development planning

N. PICKERING S. PICKERING AJAX BOWMANVILLE AUDLEY

rl

NEW AIRPORT BUILT 400

SCENARIO C 2QO

r-2 , /-TARGET

80.000 NORTH

30.000 PICKERING 80,000 TARGET 34.000 POPULATION too

r

OSHAWA

Fig. 7. Population and service employment allocations to urban places within the Oshawa sub-region for4 strategies.

I--

ALTERNATIVE DEVELOPMENT STRATEGY

5 NO AIRPORT

SCENARIO A, N.P= 34,000

6 AIRPORT

7 NO AIRPORT

SCENARIO A, NJ? = 80,000

8 AIRPORT

I7 NO AIRPORT

SCENARIO C, N.l? = 30,000

I8 AIRPORT

IS NO AIRPORT

SCENARIO C, N.P. = SO,000

20 AIRPORT

% TARGET SERVICE EMPU)YMENT

0 50 100 t I

I I

I I

I 1 I 1

I 1

I

I 1

I 1

Fig. 8. Percent target service employment achieved in North Pickering for various development strategies.

ment strategies whose properties are described in Fig. 8. This diagram illustrates that the employment sector primarily responsible for the under-allocation of service employment to North Pickering is the retail trade sector. This effect is particularly dramatic for strategies which involve the lower population target for North Pickering and sub-regional scenario C. Figure 9 demonstrates that the bulk of this retail trade is attracted to Ajax and Oshawa under scenario C.

NORTH PICKERING COMMUTER DEMAND ANALYSIS

A third set of analyses were directed towards a more microscopic analysis of the probable demand for residential opportunities within the North Pickering community from employment opportunities located outside of the community. A primary objective of the regional planning efforts within the Toronto-Centred Region is to develop a set of relatively self-contained communities rather than a series of dormitory communities.

Some of the existing communities located on the fringes of Metropolitan Toronto experience high levels of

commuting due to imbalances between the housing and employment opportunities within these communities. For example, the City of Mississauga located on the western fringe of Toronto experiences high levels of commuting in spite of the fact that the total numbers of employment and housing opportunities within the community are roughly in balance. The majority of the housing opportunities that exist in Mississauga cater to white collar workers whereas the majority of employment opportunities located in Mississauga are of the blue collar type. The white collar residents tend to commute to jobs in central Toronto whereas the majority of the blue collar employees commute from residential areas within Metropolitan Toronto. It has been estimated[S] that in 1971 about 60% of the work trips where to and from communities outside of Mississauga.

In analyzing the potential degree of self-containment of the North Pickering community the spatial distribution of employment throughout the COLUC Region was fixed and the household demand was allocated to each of the urban places by the residential sub-model. These analyses were also performed with respect to the location decisions

B. G. HUTCHINSON

Fig. 9. Service employment allocations by sector to urban places within Oshawa sub-region.

of three socio-economic groups labelled as high, medium and low income groups.

The demands for residential opportunities created by employment at each urban place were calculated using eqns (13)-(15). Employment by sector at each urban place was first converted into employment by income group which was then transformed into household demands by household head within each income group. The supply of housing opportunities by income group at each urban place was calculated from eqn (16). Housing opportunities

INTRA-ZONAL

by density class were converted into housing opportunities by income group. The demands for housing opportunities were allocated to compatible housing opportunities using a household allocation constrained version of the residential sub-model. The high income group housing demands were allocated as a function of the expected road system travel times, the low income group demands as a function of the public transport travel times and the middle income group as a function of a weighted average of the road and public transport travel times.

A number of housing development scenarios were advanced for North Pickering and the characteristics of 6 of the scenarios tested are shown in Table 4. These development scenarios consisted of two different development scales and three housing density combinations.

Figure 10 shows the variations in the components of the work trip commuter travel demands at North Pickering for each of the six development scenarios identified in Table 4. The components of travel demand identified in Fig. 10 are the intra-zonal trips within North Pickering, trips to residential opportunities within North Pickering by airport employees, trips to residential opportunities in North Pickering by employees working in other parts of the COLUC Region and trips by employees working in North Pickering to residential opportunities at other locations throughout the Region.

The information summarized in Fig. 10 demonstrates a number of effects which are important to planning decisions about North Pickering. Firstly, the degree of self-containment of the North Pickering community may

Table 4. Characteristics of North Pickering development scenarios

FROM AIRPORT r

i

:

REGION TO

NORTH PICKERING

i 34 5 6 123456 I23456

SCENARIO NUMBER

Fig. 10. Variations in work trip commuter demands under different development assumptions.


be expected to increase with increasing development scale of the community and with housing policies which emphasize low income housing opportunities. This is illustrated by scenario 4 in Fig. 10 in which the degree of self-containment is about 83%. Secondly, if the new Toronto International Airport at North Pickering were cancelled then the degree of self-containment of the community might be expected to decrease to about 70%. The information presented in Fig. 10 also illustrates that for the range of housing density proportions examined the amount of extra-community commuting does not vary dramatically.

CONCLUSIONS

This paper demonstrates several sample applications of a land use-transport model of the Lowry type to issues encouped in regional strategic development planning. Analysis models used for this type of planning must have very modest data requirements, be adaptable to a variety of issues and have quick computer turn-around time.

The first application of the model described in this paper was as an aid to the formulation of a consistent and desirable set of public development policies. In this application the behavioural parameters of the mode1 were disaggregated spatially but the residential sub-model was aggregated over all socio-economic groups and the service sub-model was aggregated over all service sectors.

The second application of the model involved detailed analyses of the distribution of service employment by sector within one sub-region. In this application the service employment sub-mode1 was disaggregated into a number of service employment sectors.

The final application of the model described in this paper involved an application of a constrained form of the residential sub-model but where the sub-model was disaggregated by socio-economic group. The purpose of this third application was to explore the sensitivity of the

commuting demands to and from a proposed new town to housing density policies in that community.

Clearly, each of these three types of analyses could have been conducted simultaneously. However this was prevented by limitations in the availability of planning inputs at various stages throughout the study. It is also a reflection of the way in which strategy planning proceeds. A broad articulation of development is established initially and this development concept is refined gradually as additional planning decisions are made.

Acknowledgements-The studies described in this paper were performed by the author for the North Pickering Community Development Project and the COLUC Task Force both of which are undertakings of the Government of Ontario. The contributions of James Lucei, Beryl Dymond and Gerry O’Hearn of the North Pickerine Proiect and Niael Richardson, Chairman of the COLUC Working-Grolp are gratefully acknowledged. The analyses on which this paper is based were performed by John Freeman, John Tofflemire, Gustav0 Esguerra and Hazel Austin.

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REFERENCES

Government of Ontario, Design for Development : The Toronto Centred Region. Toronto, Ontario (1970). Central Ontario Lakeshore Urban Comalex Task Force, Reuort to the Aduisory Committee on Urban hnd Regional Plan?&. Toronto, Ontario (Dec. 1974). I. S. Lowry, A model of metropolis, Tech. bfemo. Rm-4035 RC, The Rand Corporation, Santa Monica, California (1964). A. G. Wilson, Urban and Regional Models in Geography and Planning. Wiley, London (1974). B. G. Hutchinson, Principles of Urban Transport Systems Planning. McGraw-Hill, New York (1974). W. Goldner, The Lowry model heritage, J. Am. Inst. Plan. 37 (1971). M. J. Batty, Recent developments in land use modelling: A review of British research, Urban Studies 9 (1972). W. R. McDougall and R. B. Rebeiro. The Commuting problems of a dormitory community, Annual Conf. Roads and Transportation Association of Canada (Sept. 1974).

SEPS VOL. IO NO. Z-B

land use-transport models in regional development planning

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