soltani et al-icbedc 2007

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LAND USE PATTERN, TRIP GENERATION AND THE ROLE OF PHYSICALATTRIBUTES: A CASE STUDY OF METROPOLITAN MASHHAD, IRAN 1

SOLTANI, Ali and ZAMIRI, Mahsa and RAMEZANI, Vida

Urban Planning Department, Faculty of Art & Architecture, Shiraz University, Iran

[email protected] , [email protected] , [email protected]

Abstract: The patterns of urban development are correlated with the evolution of transportationsystems. There is a complicated relationship between transportation, land use and urban form.Therefore, any change in one of these attributes will also result in changes in the other twoassures. The ability to predict and display the three-way dynamics between the level of landsupply, urban development and travel behavior would be helpful to decision-makers (CAO,2004.). A large body of studies can be found that land use patterns significantly impact travelbehavior of residents (Cervero and Gorham 1995, Handy 1993, Frank and Pivo 1994).However, the majority of the literature comes from developed countries especially from NorthAmerica and Europe. It is less clear how such a connection stands within developing countries.

This study using an ordinary least squares (OLS) regression on a sample community of Mashhad metropolitan, in Iran made an attempt to discover the relationship between land useand travel behavior. The results of modeling analysis show that consistent with perception,especially for those who work in this area, the proximity of shopping places to the trip-maker'sresidential location increases the likelihood of making home-based non-work trip frequently. Incontrast, population density in the zone of origin has no significant effect.

Key Words: Land use pattern, Trip generation, Mashhad Iran

1. INTRODUCTION"New Urbanism" emphasizes on making a human scale, walkable community with

moderate to high residential densities and a mixed use core. New Urbanism is a

reaction against "urban segregation", which is believed, is unsustainable, in longer

term. The environment is damaged by compulsory daily commutes, and new

developing projects. From a social viewpoint, sprawl segregates people, and

decreases social-cultural communications. The dependence on car threatens society

health. From an economic aspect, urban management system need to spend more on

establishing infrastructure and public services required for them such as sewer, water,

police, fire, streets and roadways (Barton 2000). "New Urbanism" solutions include

increasing the population density within built areas, increasing reliance on public

transportation and other environment-friendly modes such as walking and bicycling

instead of dependence on car (Katz 1994).

There is an increasing number of studies in Western nations has aimed at

improving the knowledge of the travel effects of land use patterns. To help

understand the potential for land use strategies to affect transport system

11Research paper presented at The 1 st International Conference on Built Environment in Developing Countries (ICBEDC 2007), 1-2 December 2007, Universiti Teknologi Malaysia, Pulau Pinang,Malaysia

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mailto:[email protected]








performance in a developing country city, the project presented here, used

conventional methods to an inventory collected data from the North-east of Iran,

Mashhad Metropolitan area. The research is a primary step towards conducting an

empirical research in the developing countries. The findings could help offer analytical

direction on the potential to influence travel through changes in built environment in a

specific developing country city. It would be also useful for establishing groundwork for

future investigating in that city and other similar cities in the developing countries.

2. BACKGROUND STUDIES

The body of academic literature on the influence of urban form on travel behavior has

expanded considerably over the past 15 years. Previous studies have related several

types of land-use factors at the neighborhoods level to travel behavior (Cervero and

Kockelman, 1997; Schwanen, 2003):• Density: typically the number of inhabitants/households or dwellings per

hectare are used.• Land-use mixing: the proximity of different types of land use to each other.• Design: the physical amenities of buildings and streets and the physical layout of

streets, including the provision of sidewalks and parking places.• Proximity to transportation infrastructure: access to public transit (bus, rail) and

highways.

Understanding how the densities, settlement patterns, land-use compositions, and

urban designs of cities and neighborhoods influence travel patterns is of vital

importance to urban and transport planners and decision makers. Since 1990s,

American scholars have made some efforts to testing the impacts of land-use patterns

and the physical elements of urban form on travel characteristics. For detailed reviews

of such studies, the reader can refer to Crane (2000); stead and Marshall (2001) and

Handy (1996). Briefly, results from the most disaggregated studies suggest thateffects on trip generation rates depend mainly on household socioeconomic

characteristics and that travel demand is inelastic with respect to accessibility (Ewing

and Cervero 2001). Likewise, one common finding that comes from these studies is

that the built environment has a greater impact on trip lengths than on trip

frequencies. Nonetheless, some studies have also shown that urban environments

with higher densities, a mix of land uses, and grid-style street configurations are

associated with higher frequencies of walking/cycling and other nonwork-based trips

(Handy 1993; Friedman et al. 1994; Cervero and Gorham 1995). Studies focusing onmode choice have found that this decision depends as much on built environment

attributes as on socioeconomic characteristics. The association effects of built

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environment attributes with other travel-related outcomes, such as vehicle-kilometers

traveled, have been documented as small but statistically significant.

Farthing et al. (1996) argue that local accessibility does help to reduce the length of

car journeys to those facilities. On the other hand, personal / householder attitudes

and life-style related issues appear to be more significant than accessibility in affecting

modal choice. When disaggregated data are applied, attitudinal variables show much

more significant effects on travel behavior than land use patterns (Gordon and

Richardson1997). Kitamura et al (1997) found that individual attitudes were more

significant predictors of travel behavior than either urban form or other socio

demographic factors. The five selected neighborhoods were different on several

dimensions including density, transit access, and sidewalk availability. Data on

attitudes toward topics like transit, and environment were obtained. The regression

analysis showed was that urban and attitudinal variables contributed significantly to

the model’s explanatory power. In addition, personal attitudes are more associated

with travel behavior than is urban form. Giuliano and Small (1993) investigated travel

trends and land use characteristics in both US and British cities and suggested that

improved income, demographics and economics explain travel trends. Hence, urban

spatial characteristics have little impact on travel behavior. The study by Giuliano and

Small considered aggregated attributes and measures of land use and travel, failing

to consider the finer characteristics of neighborhoods (such as provisions for various

forms of travel, neighborhood character and its conduciveness for non-motorized

forms of travel), the socio-demographics of the population (such as the difference in

travel patterns of individuals and households), and the type of travel being

undertaken. (Soltani and Allan, 2006).

Findings in this regard are not consistent. Researchers have variously found the total

number of trips to be higher in urban neighborhoods (Kitamura et al., 1997), to rise

with shopping accessibility but decline if parks/green/playgrounds are present (Meurs

and Haaijer, 2001). One of the reasons for this ambiguity – in addition to differences in

theoretical and methodological frameworks or geographic setting – is that the impact

of land use depends on the type and purpose of trip. Stratification by purpose

demonstrates the varying impacts of land use on trip frequency. Some authors argued

that shopping frequency is affected more by land use factors (Handy, 1993, 1996).

These studies suggest that good local access to commercial facilities encourages

people to make more shopping trips. A comparable relationship may exist for

recreation trip purposes, such as eating out and leisure trips. Hence, local availability

of key facilities may motivate trip makers do such trips fewer.

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In contrast, the number of trips for subsistence and maintenance activities (other than

shopping) will largely depend on the household activity agenda including the activities

household members need or prefer to perform during a certain time period, and the

allocation of tasks to individual household members (Bhat and Koppelman, 1993).

3. RESEARCH METHODOLOGY, DATA AND CASE STUDY AREA

Data used in the analysis come primarily from the household travel survey conducted

specifically for this research for the case study area, Ahmad-abad district in Mashhad,

Iran early 2007 by the authors. Ahmad-abad is located around 20 km west of

Mashhad's CBD area. Regarding the social-economic characteristics, Ahmad-abad

accommodates well-educated people (40% of residents have post-secondary

certificate) with relatively high level of income (Figure 1).

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Figure 1 . Ahmad-abad district location within the Mashhad Metropolitan area

The household travel survey contains information on 86 households, and 185 tours,

disaggregated into a total of 40 urban blocks covering the studied area –the average

area of a block was 1.3 hectare. Additional data on land uses and density for each

survey block also come from Mashhad's Detailed Plan Published by the metropolitan

authorities. No other block-level data were readily available on urban design

characteristics (such as street layout and connectivity) so their impact is absent.

Gathering data was by conducting face to face interview while choosing people

randomly; it took about 5 days to fill the questionnaire. Because the survey period was

within a Norooz holiday time, a broad range of adults participated in the survey. The

questionnaire involves the personal information, physical attribute of neighborhood

area and travel data included origin, destination, mode of travel and travel time.

The small sizes of the blocks make them as a disaggregated spatial unit of analysis.

For the analysis, data were extracted for all 135 “Home-Based” Non-work including

tours with the purpose of shopping, social, recreation, medical-dentist and school

tours. Home-based tours, for the purpose of this project, refer to tours that originate at

home; i.e. tours that have the trip-maker’s residential zone as the origin block. These

tours comprise approximately 82% of all the trips reported in the travel survey.

Regarding the mode of travel, 325 individuals walked to a destination or used a

bicycle. 485 used a vehicle as shared ride and 115 chose drive alone-mode. Only 9%

caught public bus to reach a destination. A copy of questionnaire survey for gathering

data is enclosed in Farsi (See Appendix 1).

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4. MODELLING ANALYSIS

Four sets of variables were considered for inclusion; household socio-economics,

individual socio-economics, travel characteristics, and land use pattern measures

(table1). The household socio-economics characteristics taken into account here

included household income, number of vehicles in the household, and number of

students in the household. The individual socio-economic attributes that explored

included: gender and age of the individual, student status and employment status.

The travel characteristics represent several dimensions of the tour included origin,

destination, travel time, modal choice and tour purpose. Travel cost however was not

included in the group of travel variables.

The land use pattern measures incorporated in the model included the proximity to

important facility centers and residential density. Proximity was measured for four

major activity centers: grocery shop; local park; education centers and workplace.

Residential density is the next land use variable included in the model specification. It

was defined as the number of people per hectare. Density has often been used to

proxy a large number of excluded physical factors. Table 2 presents the variables

used in the analysis and their basic descriptive statistics. The conceptual model

extracted from the liteture is depicted on Figure 2.

Figure 2. Conceptual model

Travelbehavior (Mode

Choice)

Urban formDensityMix Land useConnectivityAccessibility

Travel charactersTravel TimeTravel CostTravel modeTrip distance

Household charactersHousehold sizeCar ownershipHousehold income

Personal charactersAgePersonal incomeGender Education

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Table 1. Potential factors

Potential Factors Measures Proxy Measures

Socio-economics IncomeEducationHousehold sizeCar ownershipAge, Gender

Employer type(business, retail,professional, etc)

Question responses &location of residence(city/county)

Urban form DensityLand use mixAccessibilityConnectivityPedestrian access

Spatial analysisresults

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Table 2: Descriptive statistics

N Minimum Maximum Mean Std.Deviation

Block area 187 .2 1.7 1.289 .399Employmentstatus

187 1 6 1.33 .859

Number of employed adults

187 1 5 1.64 .676

Personal income 187 1 4 2.63 1.131Household type 187 1 4 2.98 .451Floor (squaremeter )

187 5 280 162.27 46.747

Number of storey

187 1 5 2.85 1.278

Number of unit ineach storey

187 1 2 1.37 .485

Proximity toeducation centre

187 1 4 3.31 .928

Proximity to localpark

187 1 4 2.51 .721

The final model specification was developed through a systematic process of

adding variables to the constants-only model and eliminating statistically

insignificant variables. The power of the model was improved when the measures

of urban form were added. The results of the final regression model are presented

in Tables 3, 4 and 5. These tables summarize the corresponding coefficient

estimates, t statistics , and the statistical significance test for each estimated

coefficient. (The 95% confidence level). The model specification with traditional

explanatory variables for trip generation rates (model 1) helps to explain some of

the variability of frequency of trip-making compared with a model without

regressors.

Among the household socio-economic variables, the number of employed adults

was found to be significant. The results indicate that employment status, household

structure and Household income are another important factor in explaining non-

work trip generation. Males were found to be associated with higher likelihood of

making frequent non-work tours.

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Table 3: The final model

Model

UnstandardizedCoefficients

StandardizedCoefficients

tstat.

Sig.

B Std.Error

Beta

(Constant) .003 .608 .004 .996Employment status -.437 .072 -.375 -6.079 .000Number of unit ineach storey

1.296 .272 .630 4.764 .000

Proximity toeducation centre

.351 .067 .326 5.257 .000

Householdstructure

.517 .135 .234 3.821 .000

Floor area(square meter)

-.010 .003 -.448 -3.434 .001

Number of storey -.124 .050 -.159 -2.469 .015Personal income .167 .052 .189 3.205 .002

Proximity to localpark -.245 .082 -.177 -2.979 .003

Number of employed adults

.235 .092 .159 2.546 .012

Block area (ha) .293 .147 .117 1.993 .048

Table 4: Model Summary (only socio-economic attributes as explanatory factors)

Model

R RSquare

AdjustedR

Square

Std. Error of the

Estimate1 .483(a) .234 .162 .914

Table 5: Model Summary (socio-economic attributes and land use characteristics asexplanatory factors)

Model R R Square AdjustedR Square

Std. Error of the Estimate

1 .698(a) .487 .397 .776

Consistent with perception, the closeness to local park and education centre were

found to increase the likelihood of making HB non-work trip frequencies. This

proves "New Urbanism" idea that creating diverse facilities in walking distance

increase the level of activities. This model obtained from Enter regression

methodology ; therefore this R- square value obtained from all variables.

Table 6 . Result of regression analysis

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variable Regression FormulaEmployment status -Number of unit in each storey +Proximity to education centre +Household type +Floor area (square meter) -

personal income +Proximity to local Park -Number of employed adults +Block area (ha) +

Because the number of storey variable had linear correlation with household type

and floor area variable, eliminated from final model. Because the governmental

employees are working a long time of day, their trip generation limits to the work

travel and more time other purpose for travel eliminated from the daily travel; for

this reason, this group of people generated a few trip. Therefore the relationship

between trip generation and employment status is negative and by increasing the

time of work, trip generation decrease. The result of the regression show that the

proximity to educational centre increases the trip generation. The main reason is

that the proximity to educational centre results in chauffeuring the children to

school by parents; then increased trip generation. So the relationship between the

trip generation and proximity to educational centre is positive. The other variable

that has a positive relationship with the trip generation is the number of unit in each

storey. An increase in this variable leads to an increase in population density which

is in turn makes fewer trips happened. The higher the personal income, the greater

the car ownership and the likelihood of driving a car lead to increase the trip

generation.

Proximity to the local park and trip generation has a negative relationship in this

case study. The reason for this might be the low quality of local park in the study

area.

5. CONCLUSION AND DIRECTIONS FOR FURTHER INVESTIGATION

The results of modeling analysis show that consistent with perception, especially

for those who work in this area, the proximity of shopping places to the trip-maker's

residential location increases the likelihood of making home-based non-work trip

frequently. In contrast, population density in the zone of origin has no significant

effect.

This study showed that proximity to main activities is a basic point but importantone in the design of cities. For achieving a sustainable travel agenda, one way is to

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pay more attention to the physical order of urban features.

Only a small part of community has been studied and a modest survey response

rate has been obtained, so these results are not necessarily generalisable unless

they are replicated in other contexts and for populations with different

socioeconomic attributes. A low variation between land use variables in

geographical areas, due to small sample size, plus lesser variation within the

studied area make the land use measures less insensate to detect the effect of

urban form on travel behavior. This study is a primary step in Iranian context with

applying a disaggregated approach; considerable additional analysis is possible

and is encouraged. The results would help to identify practical means to

incorporate built environment aspects in local demand travel forecasting systems,

to better understanding of the connection between land use patterns and travel

behavior.

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