E. ALMASRI, M. AL-JAZZAR

244

The second phase is network building and data enter-

ing. An aerial photo of Gaza was geo-referenced and

digitized using ArcGIS. The resulted ESRI shape file was

transferred into TransCAD and used as a background to

draw the network and the zones. Zoning system for Gaza

City was very essential for the O-D matrix estimation

and traffic assignment. For that purpose, the land use

characteristics of the City was studied.

The third phase is matrix estimation for the base year.

It is done using TransCAD Model which is based on

Nielsen’s Model [13], who independently developed it as

a procedure for TransCAD 2.1. The model has the ad-

vantages of handling counts as stochastic variables, as

well as working with any traffic assignment method.

Thus, it is appealing for use with the Stochastic User

Equilibrium Assignment method, as well as with User

Equilibrium Assignment. Nielsen’s model is an iterative

(or bi-level) process that switches back and forth be-

tween a traffic assignment stage and a matrix estimation

stage [14]. To use the O-D Matrix Estimation procedure,

the base O-D matrix and a geographic file of both area

and line laye rs are prepar ed. In line geographic file, each

link data should be input, and a network for the line layer

should be created. It should include all the relevant at-

tributes. In area geographic file, connectors of zones’

centroids must be created to connect line and area geo-

graphic layer. Because there is no available prior O-D

matrix, a unit matrix (each O-D pair has a value of 1)

was used as initial matrix. Based on traffic counts at the

intersections, the O-D matrix was estimated and vali-

dated. A base O-D matrix serves two purposes. The first

purpose is to set the dimensions for the output matrix.

The second purpose is to provide initial values for the

estimated O-D matrix.

The fourth phase is model calibration to ensure good

representation of the traffic network. The aim is to esti-

mate O-D matrix that should be as real as possible.

Model calibration was conducted by adjusting locations

of zones’ connectors, locations of zones’ centroids and

turn penalties. The observed traffic flow and the modeled

traffic flow at the intersections using the traffic assign-

ment should be close to each other. Traffic flow differ-

ence of 10% could be acceptable.

The fifth phase is future O-D matrix estimation. For

that purpose, it is recommended to use uniform growth

factor method by multiplying all O-D pairs by the same

amount as shown in Equation (1) [6]:

ijf ijppf

TTM (1)

where, ijf is trips for O-D pair ij in future year f; ijp

is trips for O-D pair ij in present year p; and

T T

f

is

expected growth in trips between year f and p.

When information about the expected growth in trips

produced in each zone is available, the singly constrained

growth factor method might be used ([6,15]). This is

performed by multiplying different growth factors to

different rows (or columns) in the O-D matrix. Accord-

ing to [6,16], the growth factors might be determined

based on land use or socioeconomic data. Because of

little amount of socioeconomic and land use data, fore-

casting in this research is simply based on uniform

growth factor method. The estimates of rate of growth in

vehicle ownership over the forecasting period are used to

develop the end product of passenger traffic trip matrix.

They are directly input to the modelling process as a

value by which existing base year vehicle trip matrices

are factored to develop future year trip matrices.

The sixth phase is network evaluation. TransCAD

model offers three network performance measures. The

first one is vehicles hours of travel which is the summa-

tion of travel time spent by all vehicles in the network.

The second one is the total vehicle kilometers travelled

which is the summation of the total distance travelled by

all the vehicles over the network in one hour. These two

measures can be used in different scenario evaluation and

comparison, where the best scenario should have the

lowest values. The third performance measure is volume

over capacity ratio which is a direct indication of the

network level of service.

3. Results and Analysis

This section presents the results obtained when applying

the methodology discussed in the previous section. It

includes five parts. The first one presents geometric and

traffic data. The second part concerns the network build-

ing. The third part discusses the obtained results of base

year O-D matrix estimation and model calibration, while

the fourth part discusses the results of future O-D matrix

estimation. The last part presents the results of network

evaluation.

3.1. Data

The geometric data collection focused on the main streets

and intersections which were involved in th e traffic count.

Figure 2 presents the intersectio ns’ names and locations.

Geometric data for some of the intersections are pre-

sented in Table 1 which are the intersections located

along Aljala Street. The first column is intersection serial

number. The second column shows the names of the in-

tersections while the third shows the names of the streets

intersecting Aljala Street. The next columns present the

number of lanes in each approach before arriving at each

intersection. It is noted that the lanes before arriving in-

tersections is less than at intersections because of either

widening of the intersections or using nearside lane for

parking between intersections. Table 2 presents the car-

riageway widths of the streets.

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