HYBRID TRANSPORTATION MODELING AS A TOOL FOR … · Hybrid Transportation Modeling as a Tool for Developing a Sustainable City-Scenario ... engine-load when driving at various road

HYBRID

TRANSPORTATION

MODELING AS A

TOOL FOR

DEVELOPING A

SUSTAINABLE CITY-

SCENARIO

R. Katoshevski-Cavari

D. Katoshevski

T.A. Arentze

H.J.P. Timmermans

Conference CODATU XV

The role of urban mobility in (re)shaping cities

22 to 25 October 2012- Addis Ababa (Ethiopia)

CODATU XV - Le rôle de la mobilité urbaine pour (re)modeler les villes 1


Conference CODATU XV

« The role of urban mobility in (re)shaping cities »

22 to 25 October 2012- Addis Ababa (Ethiopia)

Hybrid Transportation Modeling as a Tool for Developing a Sustainable

City-Scenario

R. Katoshevski-Cavari *

D. Katoshevski **

T.A. Arentze***

H.J.P. Timmermans***

* Urban Planner and, POB 4045, Omer, Israel, [email protected], Tel:+972(0)506204309

** Environmental Eng. Unit, Ben-Gurion University of the Negev,

Beer-Sheva 84105, Israel, [email protected], Tel:+972-86479094

*** Department of Architecture, Building and Planning,

Technical University Eindhoven,

Den Dolech 2, P.O Box 513, 5600 MB Eindhoven, The Netherlands,

H.J.P.Timmermans@t ; Tel: +31(0)402472274

Abstract

A hybrid system for evaluating the effect of city scenarios and planning ideas is presented. it combines

a computerized model and the planner's practical inputs. The system includes several sub-models for

establishing land use maps and facilities for combinations of two city forms in terms of main roads

structure and two planning ideas. The outcome city-maps that are created by the numerical procedure

have a major impact on the air pollution associated with aspects of travel within the city, combined

with the characteristics of the specific population which is addressed in planning. These computerized

maps set the basis for a second stage in which the planner is manually changing the maps and re-

evaluates the effect of its input on the pollution emission from transportation. These combined/hybrid

actions of a computerized model and the interference of the planner bring to an optimized city plan in

terms of pollution emission on one hand and optimized mobility and sustainability on the other.

1. Introduction

The relationships between transportation, land-use, and the environment are key factors for developing

a sustainable built environment. The fundamental question is: How can planners reach equilibrium

between these three aspects so that an attractive built environment is developed, travelling is

minimized, but still conducting all daily-life activities, and environmental quality is reached?

Different studies deal with the connection between these aspects, Hall (1996) for example elaborated

the connection between the two first aspects, and claimed that planners should consider land uses and

a spo a io as o m sh, a d ha d h wo i som f agi combi a io O’M a a (1998) a so

dealt with the connection between the first two elements. Newman and Kenworthy (1992, p.360)

Tel:+972(0)506204309

Tel:+972-86479094


abo a d h co c io b w a d s a d vi o m a asp c s: “ h g a cha g i o

cities is to protect individual freedom in locating land uses and to provide access to them, while

mai ai i g h p b ic q a i i s of c a ai , saf s s, a d a ac iv p b ic spac s” I ms of ai

quality, it is only natural and commonly used notion that emission of gaseous species (such as CO and

NO2) and particulate matter (PM) per day from all kinds of vehicles with an internal combustion

engine is first of all calculated per driven kilometres and number of trips (cold starts) This is the way

we approach that issue here as a first approximation. Other aspects may come into play in the next

level of calculation where we can incorporate an additional model/software (such as Mobile-6, or

EPA-Aermod) accounting for the vehicle specifics, speed, engine-load when driving at various road

conditions, meteorological consideration, the fuel which is used, etc.

In our study we use a multi-agent model, as a decision support tool to develop different versions of

master plans which include land uses and facilities, and are evaluated based on transportation and

environmental aspects. These developed master plans can be further modified, "fine–tuned", by the

planner to improve the overall plan. In the course of this paper, we discuss aspects of planning for a

sustainable city- addressing also the perspective of pollution emission from transportation in

conjunction with travelling in the city, then we describe the methodology combined with the planner

manual input and the description of the developed planning scenarios. Finally, the results of the

simulations are presented and discussed

2. Planning for a sustainable city

A common hypothesis is that different policies which include the encouragement of people to live in

higher-density residential areas, mixed land-use, transit accessibility, and pedestrian friendliness,

create an environment where people drive less (Cervero 1989). This reduction in travelling may result

from fewer trips, shorter ones, or shifting from single-occupancy vehicles to public transportation,

walking, and/or cycling. Cervero and Kockelman (1997), Newman and Kenworthy (1989 & 1999),

Holtzclaw (1990), Frank and Pivo (1994), Kitamura et al. (1997), Badoe and Miller (2000), and

Roodra et al. (2009) are examples of studies that assume that living in higher density neighborhoods

contributes to the reduction of motorized level. These assumptions have led some regions to try and

implement different policies, for example, transit-oriented development, mixed land-use, and different

concentration schemes. Bagley and Mokhtarian (2000) give an overview of early empirical studies of

these policies and their effect on transportation. Activity-based models as a tool for modelling

behavioural response to such issues have been estimated and applied in different studies (e.g.,

Kitamura et al, 1996; Rossi and Shiftan, 1997; Gunn and Van der Hoorn, 1998; Shiftan, 1999; Algers

and Beser., 2000; Salvini and Miller, 2005; Katoshevski-Cavari, 2007; Shiftan, 2008; and

Katoshevski-Cavari et al., 2009)

The extension of this line of research to include pollution is not yet accomplished. One of the

exceptional studies in this respect is of Marquez and Smith (1999) which discussed the effect of

different possible future city scenarios to decrease in pollution in the city.

In parallel to the discussion concerning the possible influence of different planning policies, it is

understood that good location choices for housing, work places and facilities (that is, shops, clinics,

schools, etc.) are essential. Hence, an optimized city form is the essence for creating a sustainable built

environment. In the general view we deal with planning as a tool for the development of the most

sustainable city, with a focus on the reduction of the total travel in the city as well as number of trips

and hence emission from transportation.

3. Methodology

The study includes two major processes for planning a sustainable city form. The first is a

computerized model- the multi-agent system, which is responsible for the development of a master

plan that includes land uses and facilities, based on surveys data. The second process is the adjustment

of the plan by the planner, that is, the "fine- tuning". We will now detail both process. The multi agent planning support system/algorithm is described in detail in Arentze and Timmermans (2000; 2007), and Katoshevski-Cavari (2007), and hence here we describe it briefly for the sake of


convenience, and focus on its first stage, the suitability function that is responsible for the land use map. The other stages are only theoretically explained. The basis of this model is the assumption that urban dynamics are driven by decisions of at least three groups of actors: (1) The planning authority, (2) Supplier agents (agent that develop services as education facilities, leisure facilities and more), and (3) Individuals and households. The assumption is that the behaviour, decisions and interaction of these three actors are the drivers for the development of the built environment. The system is comprised of several stages and consists of co-evolving models for each of these actors. That is, the multi-agent system consists of three sub-models. 1) Land use model, 2) Facility location model and 3) Facility use model. These sub-models generate city-maps which i c d a d s s a d h oca io of faci i i s h i p da a is bas d o p op ’s preferences and observed activity-travel patterns (conjoint study and time-use survey, see Katoshevski and Timmermans (2001), and Katoshevski-Cavari(2007)).

3.1 The land use model- The Suitability function. Determining the suitability of an allocation in the context of the land-use allocation process is based on a suitability function. This part of the system is "done" by the planning authorities, in our case based on learned people preferences. The function is defined by the following equations:

i Gh hghj

g

ij

g

ij

g

igl lzlxwz )()( (1)

otherwise

cldcifl

g

iji

g

jig

ij,0

)( ,1)( 1 (2)

Where:

G is the exhaustive set of land use typesg, h G

i =1,…,|G| + 2, is an index of land-use types extended with city center and main road

j =1,…, 6, is an index of cut-of-points used to define distance intervals

l is an index of cells

glz is the suitability of land characteristics of cell l for g

g

iw is the weight of distance to land-use/center/road i for g

g

ijx is a suitability score assigned to the j-th level of distance to i for g

hgz is the suitability of presence of land use h adjacent to g

)(lh equals 1, if land use h is adjacent to l and 0 otherwise

)(ldi is the distance of l from i

g

ijc is the j-th cut-off-point for distance to i defined for land-use g (ci0 = 0, ci6 = )

As described in the above equations, the suitability of a cell l for a particular land-use g in this

model is assumed to depend on three factors. First, accessibility to main roads, to the city center and to

specific land use categories h are measured as a minimum distance across all other cells in the plan


area that contain land-use h. Second, adjacency (land use in neighborhood cells) refers to any direct

negative or positive effect one land use may have on another, adjacent land-use (for example, caused

by noise, traffic load, decrease of visibility etc.). The latter involves the four direct neighboring cells

and four diagonal ones. Finally, land characteristics such as for example slope and soil may have an

influence. It is noted that, given the purpose of our analysis, this latter set of factors is left-out-of

consideration here. The land use map is developed based on these aspects, using an allocation

algorithm. (Katoshevski-Cavari 2007). Then, after the land use map is finalized the system "creates"

population. That is the target population for the developed city and then plot facilities.

3.2 The Facility Location and Use Model. The facility is determined in the system in two stages. First, the facility location model determines the number, type and location of facilities as emergent from supplier agents decisions. Agents evaluate candidate facility locations in terms of the number of visitors a (new) facility would attract in a given time period (e.g., a day) based on a catchment area analysis. For each facility type the system implements an agent, which is concerned with developing and maintaining a network of facilities. Thus, a supplier agent incorporates methods to conduct market analysis and make location decisions. (For further information refer to our previous studies Arentze and Timmermans, 2007 Katoshevski-Cavari 2007)

The result of this stage is a land use map with facilities. However, in this stage, the location

decisions of agents are based on limited information about users. Their behavior is approximated in terms of assumptions regarding activity frequencies, normative expenditures, penetration rates, competition strengths, etc. Uncertainty exists concerning the way the demand will actually be allocated across supply locations by individuals. The latter will be revealed by the facility use mode.

The facility use model is based on Individuals and households (the third group of actors) who

conduct their activities in the planned area, given the locations, sizes and types of facilities, determine the actual needed size and feasibility of facilities. In the system, agents schedule and implement their activities on a daily basis, using the facility use model. A modified version of Albatross ) Arentze and Timmermans, 2000) a model of activity-scheduling behavior, is used to simulate the generation and implementation of daily activity-travel patterns. The version of the model that is implemented was estimated based on an Israeli national time-use data set of the Israeli Central Bureau of Statistics (CBS 1995). The model predicts for a given day and individual a sequence of activity episodes with associated trips on a continuous time scale while taking temporal constraints, some socio-economic variables, day of the week and spatial variables (location and size of facilities) into account. (For more information Katoshvski-Cavari, 2007)

As a consequence of this process, after some time of exploiting the facilities by individuals (adults

and children) the actual size of demand attracted will be known. Based on that, the supplier agents then re-evaluate the performance of their developed facilities and make adaptation decisions, if any, in terms of closing or re-sizing facilities. Adaptations of facilities will have an impact on spatial choice behavior of individuals. Therefore, after some time, the supplier agents again consider whether adaptations are needed, and so on. These cycles of adaptations are repeated until convergence.

This finalizes the whole process of the development of the built environment. The outcome is a map that includes land uses and facilities which are relevant and adapted to the targeted population.

The result of the multi-agent system, as shown in Fig. 1, is a map that includes land uses (developed in the first stage of the model) and facilities (developed in the two other stages of the model).


Figure 1. Land use plan (legend presented in map), including school facilities. 1-

kindergarten+elemetary schools, 2- high schools.

1

1,2

1,2 1,2

2 1,2

2 2

1,2

1,2

2 1

1,2

2 2

1

3.3 The evaluation criteria. The resulted city versions can be evaluated by using two performance-indicators measuring aspects of sustainability. Analysis of Mobility- transport demand in the system is based on the trips resulting from a one day run of facilities used by the city population The first measure we account for is the total travel distance, implying that in a general view there is an emission rate per kilometer of travel which can be taken as an average across the various types of vehicles. The second measure is the number of trips, implying that a higher number of trips results in more pollution emission, due to more ignition actions of vehicles, and time during which the vehicle is not in motion while emitting pollution. The Accessibility aspect in this study reflects the ease of access to facilities, that is, the convenience of moving from home to different facilities. For each facility type a number of accessibility measures for each housing cell are calculated. These include: the average distance to the first and second nearest facilities and the average number of facilities (of that type) within a several distance categories. Those aspects are considered as fundamental for a sustainable planning.

3.4 The planner adjustments After the computerized development of the city, the planner is invited to suggest changes in the land use map, based on his knowledge/experience with respect to the built environment and the targeted population. This is done for different reasons as: 1) Strengthening the chosen planning policy by artificially modifying the land use map and/or facilities replacing specific cells, in case a specific planning ideology is implemented, and 2) Improve the overall plan, that is, after the master plan is finalized, the planner adjusts the developed city to common planning norms and perceptions. This additional stage aims at using the planner heuristics to improve the computerized results in the practical sense The planner can choose from the developed city-plans, the version/s that show the best performance concerning the evaluation criteria, and to "fine-tune" the outcome maps, by manually

Housing- H Housing- L Industry- H Industry- L Comercial Recreation Nature


changing the land use configuration (which may lead to changes in the facilities distributions). This comes to improve the overall performance of the city, or to improve the overall planning using planner's knowledge, such as "plotting" a strip of green/open cells between low-tech industry and housing. The planner can also change facilities distribution for the same mentioned reasons. The adjustment of land-uses to improve the Commercial cells distribution for strengthening the trend of "mixed uses" is portrayed as an example. Figure. 2 includes two maps: the left one is generated by the model, where the mixing was created only at the central part. The other map, on the right side is the map resulting from the planner's additional input, showing that Commercial cells are distributed all over the city.

Figure 2. Land use maps for two options: a) left, produced by the computerized model, b) right

outcome map including planner's input.

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Figure 3 Outcome total travel distance, with and without the planner's additional input

(changed/computerized, correspondingly).

292000

294000

296000

298000

300000

302000

304000

City versions

Dis

tan

ces

Computerized map

Canged map

We examined the two city configurations, using the described indicators. As can be seen from Fig. 3 the planner's intervention with respect to the distributing commercial cells in different areas, is increasing the total travel in the city compared to the purely computerized version. However it is the planner's task to decide whether the new configuration is still his preferred version. Hence, in terms of


pollution emission from vehicles, the computerized map, in this case, is resulting in minimization in that respect, and hence, any intervention of the planner would increase that. In turn, other aspects of the overall planning would improve for the targeted population, based on plann ’s k ow dg Now, after we had described the planning system and the planning-related idea we move to describe the case study.

The case study

The multi-agent system is employed here for two fundamental city forms, the "compact city", which is based on previous studies, and the "multi-nuclear city" which is new in this respect. We will now shed light on these two forms and compare the results of the simulations for their land use distributions.

The land use and population settings. The following six land use categories were included in the

planning: (i) Housing High density (labelled as Housing-H); (ii) Housing Low density (labelled

Housing-L); (iii) Industry; (iv) Commercial; (v) Recreation, and (vi) Nature. The plan area consists of

a grid of 5000 cells of 125 x 125m in size divided as follows: 1520 cells for Housing H, 800 cells for

Housing-L, 192 cells for Industry 384 cells for Commercial land use, 160 cells for Recreation and

1944 cells for Nature. The total size of the area and proportional land use requirements are derived

from an anticipated population size of 300,000 people and planning standards. The size of a cell was

determined such that it is small enough to accurately represent facilities and not smaller to avoid

excessive computation times.

The data. The implemented model was estimated based on two data sources, both from Israel. The conjoint study which was held in 8 different cities in Israel, studied people's preferences concerning different aspects of their built environment, aspects as distances (walking or driving) to various facilities (shopping areas, kindergarten or work), aspects of the dwelling (size, kind of dwelling, etc.) or different aspects of the neighbourhood (Katoshevski & Timmermans, 2001). This data served as indicators for determining the suitability parameters for the development of the land use maps. For the facility distribution, a model of activity-scheduling behavior, is used to simulate the generation and implementation of daily activity-travel patterns. The version of the model that is implemented was estimated based on an Israeli national time-use data set of the Israeli Central Bureau of Statistics (CBS, 1995). The sample consists of 3082 people in the age 14 and older spread across 86 localities living permanently in Israel.

The scenarios The study is dealing with a comparison between two city forms- compact city form and the multi-nuclear form in two different planning ideas, and we term them "planning and form

scenarios", they are as follows:

The planning scenarios

Each one of the two considered scenarios deals with distinctive planning concepts/ideologies.

The "Mixed City": this scenario deals with mixed land uses. Based on that, the setting supports the land use mixture of Commercial cells, Housing and/or Industry-High-Tech. The "Nature City": this denotes a city with "green cells". The idea of this scenario is to develop a city which includes a few areas of Nature cells.

The form scenarios -the road structure

The "Connected/Compact City": two main roads intersect at the centre of the planned area,


forming an "x" shape and in addition several circular main roads, dividing the city into sections surrounded by roads (Fig 4). Such a layout is likely to result in a constrained development and hence the emergence of a compact city.

Figure 4. The Compact City form

The "Multi-Nuclear City": This form, is based on the compact city form. However, this city land use development will be characterized by double-centre city (Fig.5) comprised of a major cener and a smaller one. Cocerning the road structue it is based on the same compact form but includes two of these road systems or one full compact-form and a second partial one, that is "1.5 compact forms". The actual model that was examined was ha “1 5 fo m” which is va fo a c a io of a ci y withone major center and another smaller one.

Figure 5. The Multi-Nuclear City form

The results - the city land use configurations

The above described planning scenarios and form scenarios were combined and used to develop

different city alternatives, all of the same city size, and based on the same population sample.


Figure 6: Mixed uses city forms: (a) the compact city, (b) multi-nuclear city. Colors: red-

Housing-H, pink- Housing-L, purple- Industry, blue-Commercial, light green- Recreation, dark

green- Nature

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4 4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 2 2 1 1 1 5 5 1 1 1 2 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 1 1 1 1 1 2 1 1 1 1 1 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 2 2 1 1 1 5 5 2 2 2 2 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 1 1 1 1 1 2 1 1 1 1 1 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 2 2 1 1 1 5 5 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 1 1 1 1 1 2 1 1 1 1 1 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 6 6 6 6 6 6 6 6 6 6 6 6 6 2 2 1 1 1 5 5 1 1 1 7 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 5 5 5 5 1 1 1 1 2 1 1 1 1 1 1 1 1 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 4 6 6 6 6 6 6 6 6 6 6 6 6 2 2 1 1 1 5 5 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 5 1 1 1 1 1 1 1 2 1 1 1 1 5 5 5 5 5 1 1 1 1 2 1 2 1 1 1 1 1 1 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 4 4 6 6 6 6 6 6 6 6 6 6 6 2 2 1 1 1 5 5 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 6 6 6 2 2 1 1 1 5 5 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 6 6 2 2 1 1 1 5 5 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 7 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 6 2 2 1 1 1 5 5 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 2 2 1 1 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 2 1 1 1 1 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 2 2 1 1 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 2 1 1 1 1 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 2 1 1 1 1 5 5 5 1 1 1 1 1 1 1 1 5 5 5 5 1 1 1 1 2 2 2 2 2 2 1 7 7 7 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 1 1 1 1 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 2 1 1 1 1 5 5 5 1 1 1 1 1 1 5 5 5 5 5 1 1 1 1 2 2 2 2 2 2 2 7 7 7 7 7 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 1 1 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 6 2 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 2 2 2 2 2 2 7 7 7 7 7 7 7 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 2 2 2 2 2 2 7 7 7 7 7 7 7 7 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 4 4 4 4 4 4 4 4 4 4 4 4 4 7 2 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 4 4 4 4 4 4 4 4 4 4 4 4 2 2 1 1 1 1 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 4 4 4 4 4 4 4 4 4 4 4 4 2 2 1 1 1 1 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 7 7 7 7 7 7 7 7 7 7 7 7 1 2 2 2 2 2 2 1 1 1 1 1 1 1 1 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 7 4 4 4 4 4 4 4 4 4 4 4 2 2 1 1 1 1 5 5 5 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 5 1 7 7 7 7 7 7 7 7 7 7 7 7 1 5 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

The above developed maps, presented in figure 6, are the two city forms elaborated based on the mixed uses scenario. They include 6 land uses: housing high, housing low, industry, commercial, nature and recreation. In the multi focal city there are two commercial centers-areas and in the compact form one large center is developed in the center as well as along the concentric roads. However in both cases the commercial cells are not developed as an enclosed area but combined with housing cells so that a mixed used area is developed. One recreation area and one industrial area are developed in both cities. Housing-high in both scenarios is concentrated in the center whereas housing-low cells are spread in the city but mainly in the edges of the cities on the border between the housing area and nature. The difference between the two city forms is clear and significant. While in the compact city the commercial cells are spread along the city, in the multi focal version the commercial cells are mainly in the two centers. The travelling time, number of trips as well as distances are analyzed based on the facility distribution that are developed as another layer of the land use map, as explained above.

a

b


Figure 7: Nature city forms: (a) the compact city, (b) multi-nuclear city. Colors: red- Housing-

H, pink- Housing-L, purple- Industry, blue-Commercial, light green- Recreation, dark green-

Nature

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7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 2 2 2 2 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 7 7 1 5 5 1 7 2 2 2 2 7 7 7 7 7 2 2 2 2 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 7 2 2 2 2 7 7 7 7 7 2 2 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 7 2 7 2 2 7 7 7 7 7 2 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 7 7 7 2 2 7 7 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 7 7 7 2 2 2 7 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 7 7 7 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 7 7 7 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 2 2 5 5 5 5 2 2 2 2 2 7 7 7 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 6 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 2 2 2 2 2 7 1 1 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 5 2 2 2 2 5 2 2 2 2 2 7 1 5 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 5 2 2 2 2 5 2 2 2 2 2 7 1 1 2 2 2 2 7 7 7 2 2 2 2 2

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 5 2 2 2 2 5 2 2 2 2 2 7 7 7 2 2 2 2 7 7 7 2 2 2 2 2

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 5 2 2 2 2 5 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 7 7 7 7 1 5 5 5 5 5 5 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 1 1 1 1 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 1 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 1 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 5 2 2 2 2 5 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 5 2 2 2 2 5 2 2 2 2 2 7 7 7 2 2 2 2 7 7 7 2 2 2 2 2

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 5 2 2 2 2 5 2 2 2 2 2 7 1 1 2 2 2 2 7 7 7 2 2 2 2 2

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 5 2 2 2 2 5 2 2 2 2 2 7 1 5 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 5 2 2 2 2 5 2 2 2 2 2 7 1 1 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 2 2 2 2 2 7 7 7 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 1 1 1 1 1 1 1 2 2 2 2 2 2 2 5 5 5 5 2 2 2 2 2 7 7 7 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 7 7 7 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 7 7 7 2 2 2 2 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 7 7 7 2 2 2 7 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 1 7 7 2 2 7 7 7 7 7 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 7 1 7 2 2 7 7 7 7 7 2 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1 2 1 2 2 7 7 7 7 7 2 2 2 2 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 7 1 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 5 5 5 5 5 1 7 2 2 2 2 7 7 7 7 7 2 2 2 2 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 2 2 2 2 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 2 2 2 2 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 2 2 2 2 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 7 7 7 2 2 2 2 7 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 7 7 7 7 7 1 1 1 1 1 1 5 5 5 5 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 7 7 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 7 7 7 7 7 7 7 7 7 7 7 7 4 4 4 4 5 5 5 5 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 7 2 5 2 2 2 2 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 5 5 5 5 5 1 1 1 1 1 1 7 7 7 7 7 7 7 7 7 7 4 4 4 4 4 4 4 5 5 5 5 5 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 5 5 5 5 5 1 7 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 7 7 7 4 4 4 4 4 4 4 4 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 7 7 7 4 4 4 4 4 4 4 4 4 4 4 1 1 1 5 5 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 7 7 4 4 4 4 4 4 4 4 4 4 4 7 7 1 1 5 5 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 7 7 7 7 7 7 7 1 7 1 7 7 7 7 7 7 7 7 7 4 4 4 4 4 4 4 4 4 4 4 7 7 7 1 1 5 5 5 5 5 5 5 5 1 1 1 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

Figure 7 shows again a comparison of two planning scenarios and planning forms. In this case the city is developed to include some nature-green cells in the built area. It clearly shows the profound effect of a double-focal scenario, that is, the multi-nuclear city is not a simple edition of a second major intersection but rather attracts a different kind of an area to it, namely a natural area, which usually is outside of the city center when only one center is considered. Whether the natural area will be found inside or outside the single-nuclear compact city, the multi-nuclear city shows a different behavior for each of the focal points. The above land-use maps served as a basis for calculating the facility maps and from there we evaluated the travel distance in each scenario, as well as other indicators which include number of trips, number of facilities in various determined distances from housing, and more. Hence, the above presented land use maps have the highest impact on travel in the city and thus on the air pollution emission from transportation. The facility maps are created according to the targeted population and their daily activity patterns. Their presences may vary from one specific population to the other, based on average age, household size/type education and more (Ellias and Katoshevski 2011). These population parameters can lead to a different end-result in terms of total travel in the city that is minimized by the computational procedure.

a

b


These different cities in terms of land use patterns, as explained, provide distinct platforms for suppliers to locate their facilities, and for individuals/ households to conduct their activities. For each one of the city plans the supplier agents developed facility networks. This process includes several cycles of adaptation, as elucidated in the modelling part of the paper. The result of this process after convergence is a city with a particular spatial distribution of facilities. For each city version the model creates 9 facility maps (one for each facility class: Daily shopping, Non daily shopping, Schools (elementary), Schools (high), Medical, Leisure, Services, Sport and Parks) These maps present the facilities distribution in the city (as presented in Fig. 1 above). As can be seen kindergartens and elementary schools are not spread homogenously in all city areas, they have higher number-density in the inner areas of the city than at the outskirts.

4. Results and Discussion

The outcome of the numerical simulation is in the form of mobility and accessibility measures: 1) Total travel distance per day, 2) Number of trips per day, 3) Number of facilities per distance category, and 4) Distance to the 1

st and 2

nd nearest facility. The overall analysis, based on the performance

indicators, show some advantage to the Multi-nuclear city, compared to the Compact city form, in term of travelling in the city, that is lower number of trips and hence less "cold starts" of vehicle engines. That has an advantage in terms of pollution emission. In the current comparison, between the two city forms, the differences are very small and can only indicate tendency of the Multi-nuclear city to be a more efficient form for people travel in the city and for conducting their daily activities. At this stage it is possible for the planner to manually change the maps of the land uses and facilities according to his ideas/norms at the expense of somewhat higher car emission.

To summarize, the study deals with the essence of the combination between creating a master plan

based on a computerized/numerical model and planner adjustments, while taking into account

transportation pollution aspects. This combination brings to an optimized planning. It improves the

commonly employed planning procedure which is based solely on the planner. The numerical

procedure is based on sub-models for land use and facility distributions enabling a comparison

between various city versions in terms of their basic road-net configurations, such as the compact city

and the multi-nuclear one, and planning concepts, such as mixed use and nature/green city. They are

analyzed by the planner and the optimized scenario is chosen for his further modifications, to reach a

sustainable plan.

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