The Trip Distribution Analysis of Water Trucks as Heavy Vehicles

with PTV Visum Application

Zulfiani Ar, Anie Tuati and Amy Wadu

Politeknik Negeri Kupang, Kupang City, NTT, Indonesia

Keywords: Trip Distribution, Visum, DCGR Model.

Abstract: Water is the basic need of people and it supports the basic activities of the community in Kupang City.

Therefore, in order to meet the need for clean water, communities purchase water that is delivered by water

trucks and sold by private sectors. These heavy vehicles pass through public roads, increase the traffic volume

and affect the road capacity. This study aims to determine the trip distribution of heavy water trucks using the

Origin-Destination Matrix (ODM). The method used in analyzing the trip distribution is the Double

Constrained Gravity (DCGR) model with a negative exponential impedance function, which is then used to

assign the road network traffic in the PTV Visum Application. The resulting trip distribution model is Tid =

Oi.Dd.Ai.Bd.exp(-0,6181.Cid) and the total trip distribution of water trucks is at the rate of 131 vehicles/day.

The prediction of trip distribution shows that the highest traffic assignment is found on the Liliba village to

Oepura village route.

1 INTRODUCTION

Transportation difficulties, especially in developing

countries, continue to increase as the needs of

adequate transportation grow. Thus, in order to be

better prepared in fulfilling the need for transportation

infrastructure in the future, it is necessary to study the

trip patterns (Suthanaya and Maulidawati, 2019). The

acceleration of digital system also enhances the

opportunity to create a high-quality transportation

system (Maget et al., 2019).

A research conducted in Denpasar, Bali predicts

that the trip distribution will reach the total number of

28,873,490 people/day by 2033 (Suthanaya and

Maulidawati, 2019). In order to describe the trip

distribution of travelers in an area, it is necessary to

generate the Origin-Destination Matrix (ODM)

through conducting the Origin-Destination Survey.

Previous studies have also produced approximations

of trip distribution in West Java Province based on the

results of statistical tests using the Double

Constrained Gravity (DCGR) model with a negative

exponential impedance function, Tid =

Oi.Dd.Ai.Bd.exp

(-0,014159.Cid)

. (Aprilliansyah and

Herman, 2015).

This study aims to determine the trip distribution

of water trucks using the Origin-Destination Matrix

(ODM). The data is based on a survey of the water

trucks’ trip distributions starting from the water

sources, as the origin zones, to the points of delivery,

as the destination zones. Moroever, a research in the

City of El Paso, the United States, offers a model for

transportation planners to calculate the total trip

distribution (Bencomo, 2018). PTV Visum

application is an example of the utilization of

computerized traffic model and it provides modelling

of transport networks and transport demand,

including trip production-attraction and spatial trip

distribution (Jacyna, 2017).

2 LITERATURE REVIEW

2.1 Origin-Destination Matrix

In the transportation system, trip patterns are often

described in terms of the trip flow of vehicles,

passengers, and goods that move from the origin zone

to the destination zone within a certain area and over

a certain period of time. The Origin-Destination

Matrix is often utilized by transport planners to

describe these trip patterns.

Origin-Destination Matrix is a two-dimensional

matrix that contains information of trips between

1056

Ar, Z., Tuati, A. and Wadu, A.

The Trip Distribution Analysis of Water Trucks as Heavy Vehicles with PTV Visum Application.

DOI: 10.5220/0012057300003575

In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2022), pages 1056-1061

ISBN: 978-989-758-619-4; ISSN: 2975-8246

zones in a certain area. The row denotes the origin

zone, the column represents the destination zone, thus

the cells represent the amount of trip from the origin

zone to the destination zone. In this case, the notation





states the amount of trip flow (vehicles,

passengers, or goods) from the origin zone i to the

destination zone d during a certain time interval. Trip

patterns can be generated if the Origin-Destination

Matrix is utilized in a transport network system. By

examining the trip patterns, the transport problems

can be identified and solutions may be immediately

generated.

The Origin-Destination Matrix plays a pivotal

role in transport planning and management as it

provides a detailed explanation of the trips. The

number of zones and the value of each cell are two

most important elements in Origin-Destination

Matrix, as the number of zones indicates the total

number of cells that can be obtained. Each cell

requires information on distance, time, cost, or a

combination of these three, which is used as a

measure of accessibility. The general form of the

Origin-Destination Matrix can be seen in Table 1.

2.2 Trip Distribution

Trip distribution is the flow of trip production from

one zone to a number of other zones, known as zone-

to-zone trip. Several methods can be used to generate

the trip distribution, including the Synthetic Method.

The widely known, and frequently used, synthetic

method (spatial interaction) is the gravity model (GR)

as it is rather simple and easy to understand. This

model follows the concept of gravity introduced by

Newton in 1686. This method assumes that the

production and attraction of trips are related to several

parameters of the origin zone, such as population and

accessibility, including distance, time, or cost. The

accessibility is regarded as an impedance and is

calculated using a negative exponential function.

Table 1: The general form of Origin-Destination Matrix.

Zona 1 2 3 … N O

1 T

… T

2 T

… T

3 T

… T

. . . . … . .

N T

… T

… D

The β parameter is obtained from the quotient

between the value of K which ranges from 2~3 with





, which is the average of the impedance value

(Aprilliansyah and Herman, 2015).



()

=

()

(1)

Double-constrained Gravity Model (DCGR) is

used in the equation (1) (Tamin, 2000).





=



.







.





()

(2)

Dimana:





= number of trips between zone i and d

(trips/day)





= number of trips from origin zone i

(trips/day)





= number of trips from destination zone d

(trips/day)





= production balancing constant





= attraction balancing constant



()

= impedance function

With the boundary conditions can be seen in

equation (3) and equation (4).





∑

(





.





()





)

(3)





∑

(



.





()





)

(4)

2.3 PTV Visum

PTV Visum is a world's leading software for

macroscopic modelling of transportation and is

currently used by more than 1,000 organizations. It

provides transportation modelling of the traffic

network, as well as a feature to analyze the external

effects of traffic, such as air pollution and noise levels

(AR, 2021).

The traffic assignment can be modelled in the

PTV Visum application and the result can be used as

the measurement for the road segment and the road

network. This stage involves three components,

namely the trip matrix, the road network, and the

assignment mechanism, including the route selection

and the road restriction for goods transport vehicles

(Citra et al., 2019).

Trip distribution is part of four sequential

transportation modelling steps. Trip production and

attraction are calculated by determining the

impedance matrix from the total requested trip. The

elements of the matrix itself are calculated under the

The Trip Distribution Analysis of Water Trucks as Heavy Vehicles with PTV Visum Application

1057

trip distribution procedure. On the one hand, the trip

distribution from the destination zone to the origin

zone is based on the attraction of the trip, on the other

hand the trip distribution from the origin zone to the

destination zone is measured by the matrix of travel

time, fares and other general costs (Visum 15, 2015).

Figure 1: Example of an observation points map in the

VISUM Program (AR, 2021).

3 RESEARCH METHOD

This study begins with a preliminary research on the

volume of heavy vehicles, specifically of water trucks

in Kupang City. After conducting the preliminary

survey, it is necessary to identify how the volume of

the water trucks affects the road capacity. Thus, a

reference matrix is developed based on the topic and

research objectives. Following this, the data

collection process is conducted through direct

surveys, such as observations and interviews, as well

as filling a survey form at the origin zone and the

destination zone. The survey is conducted within five

working days, as the research location consists of five

zones, namely zone 1 (Oebobo), zone 2 (Kayu Putih),

zone 3 (Liliba), zone 4 (Oepura), and zone 5

(Oesapa).

The data summary and data processing steps are

completed through the Origin-Destination Matrix

with the help of Microsoft Excel. Furthermore, the

DCGR Model is used to calculate the trip distribution

and then the results are assigned into the road network

by the PTV Visum application. Once the trip

distribution data is obtained, the future policy on

traffic management may be formulated based on this

finding.

3.1 Trip Distribution Survey

The trip distribution survey is delivered by collecting

five types of data, namely the total number of heavy

vehicles, travel time, travel distance, and travel cost.

3.1.1 Heavy Vehicles Survey

This survey is conducted in five research zones, from

07 AM to 06 PM for five days, namely Oebobo

village, Sikumana village, Liliba village, Oepura

village, and Oesapa village which can be seen in

Table 2. The survey is carried out by identifying

water trucks in each origin zone (water collection

points) and the destination zone (end-consumer

delivery points). This study ensures each truck is

available to participate in the survey, recorded based

on the vehicle number, and shadowed by the surveyor

to the destination zone.

Table 2: Trip distribution zone.

Zone Village

1 Oebobo

2 Sikumana

3 Liliba

4 Oepura

5 Oesapa

3.1.2 Travel Time Survey

Travel time records the time needed for the water

trucks to arrive at the destination zone from the origin

zone. The travel time survey utilizes a basic,

smartphone-based timer application and the data is

taken from each zone and for each water truck.

3.1.3 Distance Survey

This survey records the distance from the origin zone

to the destination zone. The distance survey utilizes a

basic, smartphone-based distance tracker application

and the data is taken from each zone and for each

water truck.

3.1.4 Travel Cost Survey

This survey directly interviews water truck drivers on

the transportation cost needed to travel from the

origin zone to the destination zone. The data is taken

in each zone and for each water truck.

4 RESULTS AND DISCUSSION

The result of Origin-Destination Matrix (ODM) as the

output of the survey can be seen in Table 3. Based on

the matrix, the origin zone with the highest traffic is

zone 3 (Liliba village), which it records the rate of 38

vehicles/day. Meanwhile, zone 1 (Oebobo village) is

the origin zone with the second highest record of trips

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at 29 vehicles/day. Furthermore, Zone 5 (Oesapa

village) is the third highest origin zone with a trip rate

of 25 vehicles/day.

On the other hand, Zone 5 (Oesapa village) is the

destination zone with a highest trip record of 34

vehicles/day. The second position is held by zone 3

(Liliba village) with the value of 29 vehicles/day.

Moreover, zone 4 (Oepura village) is the third busiest

destination zone with a record of 24 vehicles/day.

The origin zone with the least trip distribution is

zone 4 (Oepura village), with a record of 18

vehicles/day. While the destination zones with the

least trip distributions are zone 1 (Oebobo village)

and zone 2 (Sikumana village), both record the value

of 22 vehicles/day.

Meanwhile, the number of trips from the origin

zone to the destination zone with the highest number

of trips (Oesapa village) is 23 vehicles/day. The rate

of 1 vehicle/day from the origin zone to the

destination zone with the least trip distribution, is

shown by Oebobo to Sikumana and Liliba village;

Sikumana to Oebobo, Liliba, Oepura and Oesapa

village; Liliba to Sikumana and Oepura; Oepura to

Liliba village; and Oesapa to Oebobo and Sikumana

village.

Table 3: The result of Origin-Destination Matrix (ODM) of

water trucks.

Zone 1 2 3 4 5 O

1 6 1 1 15 6 29

2 1 17 1 1 1 21

3 3 1 23 1 10 38

4 11 2 1 2 2 18

5 1 1 3 5 15 25

22 22 29 24 34 131

The table shows that Liliba village has a high trip

distribution, as the result of abundant water resources

in this particular zone and adequate road accessibility.

Meanwhile, Oesapa village shows that the community

has a high demand for clean water, although the

demand originates internally or from the same zone.

4.1 Impedance Function Analysis

Distance, as one of the accessibility functions, is

regarded as an impedance function that will illustrate

the longest and shortest distance taken by the water

trucks. As seen from the table 4, the shortest distance

is recorded between zone 2 (Sikumana village) to zone

1 (Oebobo village) because the road access is adequate

enough. On the contrary, the longest distance is

recorded between the zone 2 (Sikumana village) to

zone 5 (Oesapa village) as both points are far apart.

Table 4: Accessibility matrix (



Zone 1 2 3 4 5

1,93 5,40 3,40 0,88 2,13

0,45 1,53 5,45 0,88 12,60

3,43 2,90 1,54 5,50 0,96

2,86 2,55 4,10 0,98 5,60

5,40 3,00 2,83 2,34 2,26

Table 5 displays the value of the impedance

function for each zone of origin and destination using

a negative exponential function with a value of 





of

3.236 and β with the value of 0.6181.

Table 5: Accessibility function matrix (

()

Zone 1 2 3 4 5

1 0,3027

0,0355 0,1223 0,5817 0,2675

0,7572 0,3880 0,0344 0,5823 0,0004

0,1198 0,1666 0,3862 0,0334 0,5542

0,1706 0,2068 0,0793 0,5474 0,0314

0,0355 0,1566 0,1736 0,2354 0,2474

4.2 DCGR Model Analysis

The Origin-Destination Matrix (ODM) is then

analysed with the DCGR model by entering the

calculated distance as the impedance function. The

data is processed using the value of 



 and





alternately for each origin and destination zone.

The iteration process starts by assuming the values of





, 



, 



, 



, dan 



= 1. The iteration is repeated

14 times until the values of each 



and 



reach

convergence, or static, including in the next iteration.

The results then act as the input for the Origin-

Destination matrix using Equation (2). Hence, the

water trucks’ trip distribution in the form of DCGR

model can be seen in Table 6.

The Origin-Destination Matrix, generated from

the DCGR model, illustrates the trip distribution of

water trucks with a distance factor. The results show

that there is a difference between the ODM based on

the preliminary calculation and the ODM based on

the DCGR model. A significant change is seen in

zone 1 (Oebobo village) with a decrease from 15

vehicles/day to 8 vehicles/day due to the long

distance between Oebobo village and Oepura village.

Additionally, zone 2 (Sikumana village) also

experiences a change with a decrease from 17

vehicles/day to 6 vehicles/day. This is because the trip

distribution is more evenly generated based on the

distance.

The Trip Distribution Analysis of Water Trucks as Heavy Vehicles with PTV Visum Application

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Table 6: Origin-Destination Matrix of water trucks generated from DCGR model.

Zone 1 2 3 4 5 o

1 7 1 5 8 8 29 29 0,0330 1,00

2 9 6 1 5 0 21 21 0,0256 1,00

3 2 5 14 0 16 38 38 0,0240 1,00

4 3 5 3 7 1 18 18 0,0442 1,00

5 1 5 7 4 8 25 25 0,0415 1,00

22 22 29 24 34

131

22 22 29 24 34

131

1,0226 1,3871 1,3686 0,6334 0,9589

1,00 1,00 1,00 1,00 1,00

1,00

Furthermore, zone 3 (Liliba village) also

experiences a change of trip distribution due to the

distance factor between zones, from 10 vehicles/day

to 16 vehicles/day.

The largest change occurs in the destination zone

1 (Oebobo village) with a decreasing value from 11

vehicles/day to 3 vehicles/day due to a long distance

between these two zones. Also, the most significant

trip distribution value change in zone 5 (Oesapa

village) occurs inside the destination zone 5 itself

where the value of 15 vehicles/day decreases to 8

vehicles/day. This is because the trip distribution is

more evenly generated based on the distance.

The comparison of the Origin-Destination Matrix

based on the preliminary calculation and the one

generated by the DCGR model can be seen in Figure

2. The results of the comparison then create the

equation Y = 3.074 + 0.413X with a correlation

coefficient value (r) = 0.629. This illustrates that the

Origin-Destination Matrix based on the preliminary

calculation and the origin-destination matrix based on

the DCGR model has a fairly close correlation.

4.3 Assigning with PTV Visum

The origin-destination matrix based on the DCGR

model is utilized to assign the trip distribution to the

road network using the PTV Visum application. The

data needed in the application is the zone data for each

village which is stored as node, link, zone, and

connector, as well as the Origin-Destination Matrix

of the DCGR model. The traffic characteristics such

as the initial capacity and initial speed is obtained

from secondary data. Meanwhile, data on travel time

is obtained from surveys.

The assignment of the Origin-Destination Matrix

is calculated using the Equilibrium Assignment

method. The map of water trucks’ trip distribution,

with a line of demand connecting the origin zone and

the destination zone, can be seen in Figure 3.

Figure 2: Comparison of ODM with preliminary calculation

and ODM with DCGR model.

Figure 3: The results of assigning trip distribution with the

PTV Visum Application.

0 5 10 15 20 25 30

DCGR Model (vehicle/day)

Calculation (vehicle/day)

0 5 10 15 20 25 30

Model DCGR (kend/hari)

Pengukuran (kend/hari)

iCAST-ES 2022 - International Conference on Applied Science and Technology on Engineering Science

1060

In addition to the map of trip distribution, the PTV

Visum application also generates the data of vehicles

volume at each link that connects origin zone and

destination zone. Based on Table 7, the largest

volume is shown on the trip from origin zone 3 to the

destination zone 4, which is from Liliba village to

Oepura village, at the rate of 21 vehicles/day.

Meanwhile, the smallest volume is located on the trip

from origin zone 1 (Oebobo village) to destination

zone 2 (Sikumana village), origin zone 2 (Sikumana

village) to destination zone 1 (Oebobo village), origin

zone 2 (Sikumana village) to destination zone 3

(Liliba village), at the rate of 11 vehicles/day.

Table 7: Vehicles volume for each origin zone and

destination zone generated by the PTV Visum Application.

Origin

Zone

Destination

Zone

Volume

(vehicle/day)

1 2 11

2 1 11

2 3 11

3 2 12

3 4 21

4 3 15

4 5 20

5 4 19

1 5 16

5 1 9

5 CONCLUSIONS

The total trip distribution of water trucks from all

zones combined is at the rate of 131 vehicles/day.

Furthermore, the origin-destination matrix

calculation based on the DCGR model generate a trip

distribution model Tid = Oi.Dd.Ai.Bd.exp

(-0,6181.Cid)

The prediction of trip distribution shows that the

highest traffic assignment is found on the Liliba

village to Oepura village route, at the rate of 21

vehicles/day. Furthermore, alternative options, in the

form of modeling and traffic engineering, are crucial

in order to anticipate the increasing number of heavy

vehicles, such as water trucks, on certain routes.

ACKNOWLEDGEMENTS

DIPA Kupang State Polytechnic.

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