PREDICT OF LOSS OF FUEL DURING THE ROAD RECONSTRUCTION IN INDONESIA.
PERSONAL INFORMATION
First Name: DEWA KETUT SUDARSANA
Family Name:
Whether attend the Conference: Yes
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Position: Professor/ Associate Professor/ Assistant Professor/ Lecturer/ Ph.D Candidate/ Postgraduate/ etc..
Organization or University: Brawijaya Univesity, Malang (Ph.D student), Udayana, Bali university (lecturer), Indonesia
Detailed Post Address (Important!):
Jalan Cenigan Sari IV-A , gang Mawar No.9, Sesetan Denpasar, Bali, Indonesia (80223)
City: Denpasar
State: Bali
Country: Indonesia
Fax: 623618950520
Mobile: +6281558683131
Telephone: +623618950520
Paper ID Code: GBMCE-3014
Paper Title: Predict of Loss of Fuel During The Road Reconstruction in Indonesia
Paper Pages: 5
Additional Page: 1
Postcode: 80223
Email: dksudarsana@gmail.com
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PREDICT OF LOSS OF FUEL DURING THE ROAD RECONSTRUCTION IN
INDONESIA
Dewa Ketut Sudarsana
Student in Doctoral Civil Engineering Program, University of Brawijaya, Lecturer at
University of Udayana, Indonesia
Harnen Sulistio, Achmad Wicaksono and Ludfi Djakfar
Lecturer in Civil Engineering Department, Faculty of Engineering, University of
Brawijaya, Malang, East Java, Indonesia
ABSTRACT: National Roads in Indonesia are still in substandard in order continues to be
reconstructed to establish connectivity Trans National and supports the Trans Asian. During
the reconstruction period has negative impacts such as loss of road user costs, loss of fuel
energy, and the environment. Prediction model of the fuel loss has not been studied. National
road reconstruction project in Bali, Indonesia is used as a case study. Analysis of fuel
consumption refers to the guidelines of Highways Pd-T-15-2005-B, which adapts from HDM
IV, 2000. Characteristics traffic during reconstruction, predicted based on the traffic
characteristics on pre reconstruction condition by multiplying the adjustment factor Fq for
traffic volume, Fv for speed and Fds for the degree of saturation. Average fuel loss on each
road link is predicted to 144.6 liters/day. Loss of fuel during duration schedule for
completing each road reconstruction predictability of 11,128 to 55,772 liters, and the loss of
fuel for the 6 the link road studied is 164,075 liters.
Keywords: road reconstruction, negative impact, loss of fuel, predict
1 INTRODUCTION
In order to establish connectivity Trans
Asian, Indonesian National roads are substandard condition still continues to do
the
reconstruction.
During
the
reconstruction period has negative impact
on road users and the environment. In
Negara-Pekutatan road reconstruction in
Bali province in fiscal year 2013 founded
the capacity of the road decreased by
32%, the vehicle's speed at peak hours
down from 40 km/h to 35 km/hour, the
traffic volume decreases to 18% [4]. The
decline of the performance traffic
characteristics resulting in the loss of
road users cost. Research on the loss of
the road users cost
during the
implementation of the National road
reconstruction project in the province of
Bali fiscal year 2012 founded 1.37% of
the contract of physical value per day [5].
Fuel consumption cost is a component of
the road users cost. In Indonesia,
Predicted loss of fuel during road
reconstruction cannot be done. Prediction
of a loss of fuel has not been conducted,
so that needs to be studied.
2 MATERIALS AND METHODS
Location of the research conducted by the
Executive of the National Road Office, or
Balai Pelaksana Jalan Nasional (BPJN)
VIII”, province of Bali, Indonesia. Case
studies
are
the
National
road
reconstruction project, 2-lane 2-way,
urban and interurban, for fiscal year 2013.
The link road be studied are presented in
Table-1. Descriptive and statistical
analysis used in this analysis [1]. This
paper specifically analyzes predicted a
loss of fuel during the road
reconstruction.
Table 1. Name and length of link road
No
cases
S7
S8
S9
S10
S11
S12
Source:
Name of link road
Cekik-Batas Kota Negara
Antosari-Batas Kota Tabanan
Kota Singaraja dan Kubutambahan
Batas Kota Singaraja-Kubutambahan
Jimbaran-Uluwatu
Denpasar-Tuban
[6]
Case studies
Operating Cost (VOC) Pd-T-15-2005-B
by Department of Public Works [3].
These guidelines are adapted some
equations and parameters of HDM IV in
2000. Analyzes traffic performance using
Indonesian Highway Capacity Manual
(IHCM) 1997 guidelines [2]. The
framework of the analysis phase is
presented in Figure 1.
3 DATA AND ANALYSIS
The characteristics of traffic related to the
prediction of fuel consumption are the
volume (Q), speed (V) and the degree of
saturation (DS). DS is the ratio between
the volumes (Q) with a capacity (C). The
capacity of urban road can be determined
in equation (1) [2].
C = Co x FCw x FCsp x FCsf x FCcs . (1)
Characteristics of traffic: traffic volume (Q),
speed (V), capacity (C), Degree of Saturation
(DS)
Pre-Reconstruction
Conditions : (Qp),
(Vp),(Cp), (DSp)
Reconstruction
Period Conditions:
(Qm), (Vm), (Cm),
(DSm)
Predicting models
Vm*. Qm*, DSm*
Observed :
loss of fuel
Predict: loss of
fuel
Predict of loss of fuel
(fit model)
Fig. 1 Framework of analysis
The formulation of fuel consumption,
refer to the guidelines of Vehicle
Where:
C
= actual capacity (pcu/h)
Co
= capacity of the ideal
conditions (pcu/h)
FCw
= road width adjustment factors
FCsp
= separator adjustment factor
FCsf
= side friction adjustment factor
FCcs
= city size adjustment factor
Traffic
characteristics
on
pre
reconstruction denoted by p, so it can be
written Qp, Vp, DSp. During the
reconstruction period is denoted by m, so
it can be written Qm, Vm, DSm. Average
of traffic characteristic values for 6 link
roads case study, denoted by r, so that the
pre reconstruction conditions can be
written Qpr, Vpr and DSpr, while during
reconstruction are Qmr, Vmr and DSmr.
The graph in Figure-2 it can be seen the
average of the volume value of Qpr and
Qmr. Figure 3 is a graph of the speed
average value of Vpr and Vmr, and
Figure 4 is a graph of the degree of
saturation average value of DSpr and
DSmr.
seen in Figure 2 and can be determined as
in equation (2).
Qmr = Qpr x Fq .............. (2)
.
Or
Fq = Qpr/ Qmr
Where:
Fq = traffic volume adjustment factors’
Fig.2 Average traffic volume Qpr (pcu/ h),
Qmr (pcu/h) and the volume
adjustment factor Fq, by hour of day
Fq values are presented in Figure-2.
Based on the Fq values, then the traffic
volume during reconstruction (Qm*) can
be predicted as in equation (3).
Qm* = Qp x Fq ............ (3)
3.2 Speed Vp-Vm relationship
The average of vehicle speed on pre
reconstruction
(Vpr)
with
during
reconstruction (Vmr) relationship can be
seen in Figure 3 and can be determined
as in equation (4)
Vmr = Vpr x Fv ............ (4)
Fig.3 Average speed Vpr (km/h), Vmr (km/h)
and the speed adjustment factor Fv, by
hour of day
Or
Fv = Vmr / Vpr
Where:
Fv = speed adjustment factor
The value of the speed factor Fv is
presented in Figure 3. Based on Fv values
it can be predicted the speed on during
reconstruction (Vm*) as in equation (5).
Vm* = Vp x Fv .................. (5)
Fig.4 Average Degree of Saturation DSpr,
DSmr and Degree of Saturation
adjustment factor Fds, by hour of day
3.1 Traffic volume Qp-Qm relationship
The average traffic volume on pre
reconstruction
(Qpr)
with
during
reconstruction (Qmr) relationship can be
Where:
Vm* = prediction of speed during
reconstruction (km/h)
Vp = speed of pre reconstruction (km/h)
Fv = speed adjustment factor
3.3 Degree of Saturation DSp-DSm
relationship
The average degree of saturation on pre
reconstruction (DSpr) with
during
reconstruction (DSmr) relationship can be
seen in Figure 4 and can be formulated as
in equation (6).
AR
= Average of acceleration
SA
= Standard deviation of
acceleration
BK
= Weight Vehicles
i
= Type of vehicle
DSmr = DSpr x Fds .................... (6)
AR is average of acceleration can be
calculated by equation (9):
Or
Fds = DSmr / DSpr
Where:
Fds
= degree of saturation adjustment
factor.
The adjustment factor of degree of
saturation Fds values is presented in
Figure-4. Based on the Fds values, then
the degree of saturation of
during
reconstruction
(DSm*)
can
be
formulated as in equation (7).
DSM * = DSP x Fds ....................... (7)
Where:
DSm* = Prediction of degree of
saturation on during
reconstruction
DSp = Degree of Saturation on pre
reconstruction
Fds = adjustment factor of the degree
of saturation, see Figure -4.
3.4 Fuel consumption
The fuel consumption for each vehicle
can be calculated by equation (8) [3].
KBBMi = (+ 1/VR + 2 x VR2 + 3 x
RR + 4x FR + 5 x FR2 + 6 x
DTR + 7 x AR + 8 xSA + 9
x BK + 10 x BK x AR + 11 x
BK x SA)/1000 ........ (8)
Where:
= Constant
1… 11 = Parameter coefficients
VR
= Average of speed
RR
= Average of road ramp up
FR
= Average sloop down the road
DTR
= Degree of average curve road
AR = 0.0128 x DS ...................... (9)
Standard deviation of acceleration (SA) is
determined from the equation (10):
SA = SA max (1.04/(1+ e (a0 + a1)*DS)
…………(10)
Where:
SA max = 0.75 (m/s2) (default).
a0, a1
: a0 = 5.14; a1 = - 8:26
(default).
3.5 Prediction of the Loss of Fuel
Model
Loss or increase of fuel consumption
(KBBM) can be determined from the
difference in fuel consumption on during
reconstruction (KBBMm) reduced by the
fuel consumption on pre reconstruction
(KBBMp), and can be formulated as in
equation (11).
KBBM = KBBMm = KBBMp ......... (11)
By substituting the traffic characteristics
on pre reconstruction (Qp, Vp, DSp) and
the model prediction of the reconstruction
(Qm*, Vm*, DSm*), into the equation
(8), (9),) (10) and (11), then KBBM
prediction equation obtained as in
equation (12).
KBBM = { 1 (1-Fv)/(Vp.Fv) + 2.Fv (Vp2
1) + 0.128 (7+10. BK).(Fds-1)
(+ 0.78 (8 + 11 x BK). (1/(1+e
3.1540)* Fds*DSp
(- 3.1540)*
) – 1/(1+e
DSp
))}/1000 ………(12)
2
3.6 Analysis of loss of fuel
The loss of fuel per day by using
predictive models (LoF*) and loss of fuel
with observational (LoF) is presented in
Table-2. The average of LoF* prediction
found was 144.6 liters/day, while the
average of loss of fuel by observed was
145.3 liters/day. In Table-2 also presented
the ratio between LoF with LoF* (RLoF
= LoF/LoF*). Average ratio RLoF found
was 1.02 (there is a 2% difference). It can
be said that the model prediction equation
(12) is the fit model. The total loss of fuel
prediction
each link road for the
scheduled duration of the project D (days)
is presented in Figure-5. The total loss of
fuel each link road ranged from 11,128 to
55,772 liters, and the total loss for the 6
link roads is 164,075 liters.
Table 2 Loss of fuel (liters/day)
Link road
Qp (pcu/day)
LoF*
LoF
LoF/LoF*
S7
29,806
46.4
52.5
1.13
S8
26,322
74.0
75.0
1.01
S9
27,720
120.0
114.2
0.95
S10
20,663
59.8
61.5
1.03
S11
44,935
229.2
232.4
1.01
S12
67,920
338.0
335.9
0.99
Average
36,228
144.6
145.3
1.02
Fig 5. Los of Fuel on Link Road
4 CONCLUSION
The characteristics of traffic during road
reconstruction correlates with the
prediction of a loss of fuel such as
volume, speed and degree of saturation
can be predicted based on the traffic
characteristics on pre reconstruction by
multiplying the volume adjustment factor
Fq, the speed adjustment factor Fv and
degree of saturation adjustment factor
Fds. The results of a loss of fuel using
the prediction model compared with the
observed, differences were found 2%
only, it can be said that the prediction
model has the fit model. An average of a
loss of fuel by prediction on each road
link was found as 144.6 liters/day. Total
of losses of fuel prediction during
reconstruction by 6 link roads is 164.075
liters.
REFERENCES
[1] Bhattacharyya, G.K. and Johnson,
R.A., 1977. Statistical Concepts and
Methods. John Wiley and Sons.
[2] DPU (Departemen Pekerjaan Umum)
1997. Manual Kapsitas Jalan di
Indonesia (MKJI).
[3] DPU
(Departemen Pekerjaan
Umum). 2005. Pedoman Perhitungan
Biaya Operasi Kendaraan, Nomor:
Pd.T-15-2005-B,
Puslitbang
Prasarana Transportasi
[4] Sudarsana Dewa Ketut, Harnen
Sulistio, A Wicaksono dan Ludfi
Djakfar. 2013. Kajian Kinerja Jalan
Akibat Adanya Zona Kerja (Work
Zone). Prosiding The 16th FSTPT
International Symposium, UMS
Surakarta, 1 – 3 Nov 2013
[5] Sudarsana Dewa Ketut, Sulistio
Harnen, Wicaksono Achmad and
Djakfar Ludfi, 2014. The Analysis
Of Work Zone Road User Costs Due
To The Delay Completion Of The
Road Maintenance Project. Adv. in
Nat. Appl. Sci., 8(3): 103-108
[6] Sudarsana Dewa Ketut. 2014. Model
Kerugian Pengguna Jalan Pada Masa
Pelaksanaan Proyek Rekonstruksi
Jalan. Draft Naskah Disertasi,
Program Doktor Teknik Sipil,
Fakultas
Teknik,
Universitas
Brawijaya Malang.
First Name: DEWA KETUT SUDARSANA
Family Name:
Whether attend the Conference: Yes
or
No
(If you can’t attend, we will send the proceeding and the receipt to you free of charge after the conference.)
Position: Professor/ Associate Professor/ Assistant Professor/ Lecturer/ Ph.D Candidate/ Postgraduate/ etc..
Organization or University: Brawijaya Univesity, Malang (Ph.D student), Udayana, Bali university (lecturer), Indonesia
Detailed Post Address (Important!):
Jalan Cenigan Sari IV-A , gang Mawar No.9, Sesetan Denpasar, Bali, Indonesia (80223)
City: Denpasar
State: Bali
Country: Indonesia
Fax: 623618950520
Mobile: +6281558683131
Telephone: +623618950520
Paper ID Code: GBMCE-3014
Paper Title: Predict of Loss of Fuel During The Road Reconstruction in Indonesia
Paper Pages: 5
Additional Page: 1
Postcode: 80223
Email: dksudarsana@gmail.com
REGISTRATION FEE by US Dollar***（以下金额为到帐金额，不含手续费）
Registration Fee
Your Choice (Use Arabic
Numerals)
Authors (Attend the meeting, non-student)*:
450 USD
-
Authors (Attend the meeting, student)*:
400 USD
-
Authors (Nonattendance)*
360 USD
360 USD
Listeners:
250 USD
-
Additional Paper(s)**
300 USD
-
35USD/One Page
35 USD
100 USD
-
Additional Page
Extra Proceeding
Total
395 USD
PREDICT OF LOSS OF FUEL DURING THE ROAD RECONSTRUCTION IN
INDONESIA
Dewa Ketut Sudarsana
Student in Doctoral Civil Engineering Program, University of Brawijaya, Lecturer at
University of Udayana, Indonesia
Harnen Sulistio, Achmad Wicaksono and Ludfi Djakfar
Lecturer in Civil Engineering Department, Faculty of Engineering, University of
Brawijaya, Malang, East Java, Indonesia
ABSTRACT: National Roads in Indonesia are still in substandard in order continues to be
reconstructed to establish connectivity Trans National and supports the Trans Asian. During
the reconstruction period has negative impacts such as loss of road user costs, loss of fuel
energy, and the environment. Prediction model of the fuel loss has not been studied. National
road reconstruction project in Bali, Indonesia is used as a case study. Analysis of fuel
consumption refers to the guidelines of Highways Pd-T-15-2005-B, which adapts from HDM
IV, 2000. Characteristics traffic during reconstruction, predicted based on the traffic
characteristics on pre reconstruction condition by multiplying the adjustment factor Fq for
traffic volume, Fv for speed and Fds for the degree of saturation. Average fuel loss on each
road link is predicted to 144.6 liters/day. Loss of fuel during duration schedule for
completing each road reconstruction predictability of 11,128 to 55,772 liters, and the loss of
fuel for the 6 the link road studied is 164,075 liters.
Keywords: road reconstruction, negative impact, loss of fuel, predict
1 INTRODUCTION
In order to establish connectivity Trans
Asian, Indonesian National roads are substandard condition still continues to do
the
reconstruction.
During
the
reconstruction period has negative impact
on road users and the environment. In
Negara-Pekutatan road reconstruction in
Bali province in fiscal year 2013 founded
the capacity of the road decreased by
32%, the vehicle's speed at peak hours
down from 40 km/h to 35 km/hour, the
traffic volume decreases to 18% [4]. The
decline of the performance traffic
characteristics resulting in the loss of
road users cost. Research on the loss of
the road users cost
during the
implementation of the National road
reconstruction project in the province of
Bali fiscal year 2012 founded 1.37% of
the contract of physical value per day [5].
Fuel consumption cost is a component of
the road users cost. In Indonesia,
Predicted loss of fuel during road
reconstruction cannot be done. Prediction
of a loss of fuel has not been conducted,
so that needs to be studied.
2 MATERIALS AND METHODS
Location of the research conducted by the
Executive of the National Road Office, or
Balai Pelaksana Jalan Nasional (BPJN)
VIII”, province of Bali, Indonesia. Case
studies
are
the
National
road
reconstruction project, 2-lane 2-way,
urban and interurban, for fiscal year 2013.
The link road be studied are presented in
Table-1. Descriptive and statistical
analysis used in this analysis [1]. This
paper specifically analyzes predicted a
loss of fuel during the road
reconstruction.
Table 1. Name and length of link road
No
cases
S7
S8
S9
S10
S11
S12
Source:
Name of link road
Cekik-Batas Kota Negara
Antosari-Batas Kota Tabanan
Kota Singaraja dan Kubutambahan
Batas Kota Singaraja-Kubutambahan
Jimbaran-Uluwatu
Denpasar-Tuban
[6]
Case studies
Operating Cost (VOC) Pd-T-15-2005-B
by Department of Public Works [3].
These guidelines are adapted some
equations and parameters of HDM IV in
2000. Analyzes traffic performance using
Indonesian Highway Capacity Manual
(IHCM) 1997 guidelines [2]. The
framework of the analysis phase is
presented in Figure 1.
3 DATA AND ANALYSIS
The characteristics of traffic related to the
prediction of fuel consumption are the
volume (Q), speed (V) and the degree of
saturation (DS). DS is the ratio between
the volumes (Q) with a capacity (C). The
capacity of urban road can be determined
in equation (1) [2].
C = Co x FCw x FCsp x FCsf x FCcs . (1)
Characteristics of traffic: traffic volume (Q),
speed (V), capacity (C), Degree of Saturation
(DS)
Pre-Reconstruction
Conditions : (Qp),
(Vp),(Cp), (DSp)
Reconstruction
Period Conditions:
(Qm), (Vm), (Cm),
(DSm)
Predicting models
Vm*. Qm*, DSm*
Observed :
loss of fuel
Predict: loss of
fuel
Predict of loss of fuel
(fit model)
Fig. 1 Framework of analysis
The formulation of fuel consumption,
refer to the guidelines of Vehicle
Where:
C
= actual capacity (pcu/h)
Co
= capacity of the ideal
conditions (pcu/h)
FCw
= road width adjustment factors
FCsp
= separator adjustment factor
FCsf
= side friction adjustment factor
FCcs
= city size adjustment factor
Traffic
characteristics
on
pre
reconstruction denoted by p, so it can be
written Qp, Vp, DSp. During the
reconstruction period is denoted by m, so
it can be written Qm, Vm, DSm. Average
of traffic characteristic values for 6 link
roads case study, denoted by r, so that the
pre reconstruction conditions can be
written Qpr, Vpr and DSpr, while during
reconstruction are Qmr, Vmr and DSmr.
The graph in Figure-2 it can be seen the
average of the volume value of Qpr and
Qmr. Figure 3 is a graph of the speed
average value of Vpr and Vmr, and
Figure 4 is a graph of the degree of
saturation average value of DSpr and
DSmr.
seen in Figure 2 and can be determined as
in equation (2).
Qmr = Qpr x Fq .............. (2)
.
Or
Fq = Qpr/ Qmr
Where:
Fq = traffic volume adjustment factors’
Fig.2 Average traffic volume Qpr (pcu/ h),
Qmr (pcu/h) and the volume
adjustment factor Fq, by hour of day
Fq values are presented in Figure-2.
Based on the Fq values, then the traffic
volume during reconstruction (Qm*) can
be predicted as in equation (3).
Qm* = Qp x Fq ............ (3)
3.2 Speed Vp-Vm relationship
The average of vehicle speed on pre
reconstruction
(Vpr)
with
during
reconstruction (Vmr) relationship can be
seen in Figure 3 and can be determined
as in equation (4)
Vmr = Vpr x Fv ............ (4)
Fig.3 Average speed Vpr (km/h), Vmr (km/h)
and the speed adjustment factor Fv, by
hour of day
Or
Fv = Vmr / Vpr
Where:
Fv = speed adjustment factor
The value of the speed factor Fv is
presented in Figure 3. Based on Fv values
it can be predicted the speed on during
reconstruction (Vm*) as in equation (5).
Vm* = Vp x Fv .................. (5)
Fig.4 Average Degree of Saturation DSpr,
DSmr and Degree of Saturation
adjustment factor Fds, by hour of day
3.1 Traffic volume Qp-Qm relationship
The average traffic volume on pre
reconstruction
(Qpr)
with
during
reconstruction (Qmr) relationship can be
Where:
Vm* = prediction of speed during
reconstruction (km/h)
Vp = speed of pre reconstruction (km/h)
Fv = speed adjustment factor
3.3 Degree of Saturation DSp-DSm
relationship
The average degree of saturation on pre
reconstruction (DSpr) with
during
reconstruction (DSmr) relationship can be
seen in Figure 4 and can be formulated as
in equation (6).
AR
= Average of acceleration
SA
= Standard deviation of
acceleration
BK
= Weight Vehicles
i
= Type of vehicle
DSmr = DSpr x Fds .................... (6)
AR is average of acceleration can be
calculated by equation (9):
Or
Fds = DSmr / DSpr
Where:
Fds
= degree of saturation adjustment
factor.
The adjustment factor of degree of
saturation Fds values is presented in
Figure-4. Based on the Fds values, then
the degree of saturation of
during
reconstruction
(DSm*)
can
be
formulated as in equation (7).
DSM * = DSP x Fds ....................... (7)
Where:
DSm* = Prediction of degree of
saturation on during
reconstruction
DSp = Degree of Saturation on pre
reconstruction
Fds = adjustment factor of the degree
of saturation, see Figure -4.
3.4 Fuel consumption
The fuel consumption for each vehicle
can be calculated by equation (8) [3].
KBBMi = (+ 1/VR + 2 x VR2 + 3 x
RR + 4x FR + 5 x FR2 + 6 x
DTR + 7 x AR + 8 xSA + 9
x BK + 10 x BK x AR + 11 x
BK x SA)/1000 ........ (8)
Where:
= Constant
1… 11 = Parameter coefficients
VR
= Average of speed
RR
= Average of road ramp up
FR
= Average sloop down the road
DTR
= Degree of average curve road
AR = 0.0128 x DS ...................... (9)
Standard deviation of acceleration (SA) is
determined from the equation (10):
SA = SA max (1.04/(1+ e (a0 + a1)*DS)
…………(10)
Where:
SA max = 0.75 (m/s2) (default).
a0, a1
: a0 = 5.14; a1 = - 8:26
(default).
3.5 Prediction of the Loss of Fuel
Model
Loss or increase of fuel consumption
(KBBM) can be determined from the
difference in fuel consumption on during
reconstruction (KBBMm) reduced by the
fuel consumption on pre reconstruction
(KBBMp), and can be formulated as in
equation (11).
KBBM = KBBMm = KBBMp ......... (11)
By substituting the traffic characteristics
on pre reconstruction (Qp, Vp, DSp) and
the model prediction of the reconstruction
(Qm*, Vm*, DSm*), into the equation
(8), (9),) (10) and (11), then KBBM
prediction equation obtained as in
equation (12).
KBBM = { 1 (1-Fv)/(Vp.Fv) + 2.Fv (Vp2
1) + 0.128 (7+10. BK).(Fds-1)
(+ 0.78 (8 + 11 x BK). (1/(1+e
3.1540)* Fds*DSp
(- 3.1540)*
) – 1/(1+e
DSp
))}/1000 ………(12)
2
3.6 Analysis of loss of fuel
The loss of fuel per day by using
predictive models (LoF*) and loss of fuel
with observational (LoF) is presented in
Table-2. The average of LoF* prediction
found was 144.6 liters/day, while the
average of loss of fuel by observed was
145.3 liters/day. In Table-2 also presented
the ratio between LoF with LoF* (RLoF
= LoF/LoF*). Average ratio RLoF found
was 1.02 (there is a 2% difference). It can
be said that the model prediction equation
(12) is the fit model. The total loss of fuel
prediction
each link road for the
scheduled duration of the project D (days)
is presented in Figure-5. The total loss of
fuel each link road ranged from 11,128 to
55,772 liters, and the total loss for the 6
link roads is 164,075 liters.
Table 2 Loss of fuel (liters/day)
Link road
Qp (pcu/day)
LoF*
LoF
LoF/LoF*
S7
29,806
46.4
52.5
1.13
S8
26,322
74.0
75.0
1.01
S9
27,720
120.0
114.2
0.95
S10
20,663
59.8
61.5
1.03
S11
44,935
229.2
232.4
1.01
S12
67,920
338.0
335.9
0.99
Average
36,228
144.6
145.3
1.02
Fig 5. Los of Fuel on Link Road
4 CONCLUSION
The characteristics of traffic during road
reconstruction correlates with the
prediction of a loss of fuel such as
volume, speed and degree of saturation
can be predicted based on the traffic
characteristics on pre reconstruction by
multiplying the volume adjustment factor
Fq, the speed adjustment factor Fv and
degree of saturation adjustment factor
Fds. The results of a loss of fuel using
the prediction model compared with the
observed, differences were found 2%
only, it can be said that the prediction
model has the fit model. An average of a
loss of fuel by prediction on each road
link was found as 144.6 liters/day. Total
of losses of fuel prediction during
reconstruction by 6 link roads is 164.075
liters.
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