Article citation information:
Korkmaz, E., Akgungor, A.P. Estimation of passengerkilometer and tonnekilometer values for highway transportation in Turkey using the flower pollination algorithm. Scientific Journal of Silesian University of Technology. Series Transport. 2018, 98, 4552. ISSN: 02093324. DOI: https://doi.org/10.20858/sjsutst.2018.98.5.
Ersin KORKMAZ [1], Ali
Payidar AKGUNGOR[2]
ESTIMATION
OF PASSENGERKILOMETER AND TONNEKILOMETER VALUES FOR HIGHWAY TRANSPORTATION IN
TURKEY USING THE FLOWER POLLINATION ALGORITHM
Summary. Within the scope of this study, intercity passenger
and freight movements in Turkey are estimated by using the flower pollination
algorithm (FPA), while demand forecasts are performed on transport systems
considering possible future scenarios. Since the passenger and freight
transport system in Turkey mainly involves road transport, passengerkilometer
and tonnekilometer values of this system are estimated. By relying on three
independent parameters, models were developed in three different forms: linear,
force and semiquadratic. Population (P) between 1990 and 2016, gross domestic
product per capita (GDPperC) in US dollars and the number of vehicles were used
as input parameters for the development of the models. When the passengerkilometer
models were created, the number of cars, buses and minibuses that are
predominantly used for passenger transportation was preferred for the number of
vehicles, while the number of trucks and vans used for cargo transportation
were taken into consideration in the tonnekilometer models. The coefficients
of the models were determined by FPA optimization, with models developed to
estimate passengerkilometer and tonnekilometer values. The model results were
compared with the observation values and their performance was evaluated. Two
different scenarios were created to estimate passengerkilometer and tonnekilometer
in 2030. Parallel to the increase in population and welfare level, it is
predicted that demand for passenger and freight transport will increase. In
particular, the higher input parameter values in Scenario 1 significantly
affect the increase in demand, leading to a demand increase of around 50%. In
addition, the FPA has demonstrated effective performance in predicting the demand
for passenger and freight transport and that it can be used in many different
areas.
Keywords: passengerkilometer,
tonnekilometer, flower pollination algorithm
1. INTRODUCTION
Transport, defined as the rapid, economic and
secure displacement of people and goods, is a service activity created by other
sectors of demand, with industry, commerce, agriculture and tourism among the
most important sectors that generate demand. In the main, Turkey has a highway
transport system, with approximately 90% of passenger and freight transport [1]
carried out via this transportation system. The highway transportation system
brings about many problems, especially traffic accidents. It is possible to
obtain many benefits and to avoid transportation problems with the development
of transportation systems with a specific plan for and the coordination of each
transport system. For this purpose, it is necessary to determine future
transportation demands in order to create the right plans and policies.
Many researchers have been working on
forecasting transport demand for years. Different approaches have been applied
to estimate the demand for passenger and freight transport, and more realistic
models have been put forward. Garrido and Mahmassani [2] have applied the multinomial
probit model with a spatially and temporally correlated error structure to
estimate freight transport demand. The model has been successfully applied to
the actual transport data set presented by a large truck load carrier. In
addition to the substantive information obtained from the estimation results,
the predictive tests were performed to assess the predictive ability of the
model for operational purposes [11]. Haldenbilen and Ceylan [3] used
socioeconomic data and developed demand prediction models using genetic
algorithms. They attempted to estimate freight and passenger movements in
intercity roads in Turkey for the period up to 2025 by using the proposed
models. Çelikoğlu and Cığızoğlu [4] conducted an
estimation of passenger flows with a generalized regression neural network and
compared them with a stochastic model. It has been shown that the proposed
model gives better results than observational results and performs better than
the statistical model. Semeida [5] developed forecasting models for travel
demand for less populated places in northeastern Egypt with multiple linear
regression and generalized linear modelling. Demand models can provide
acceptable statistics within regions and are conceptually suitable. In
addition, this study found that the generalized linear modelling approach is
more appropriate and accurate than the regression approach. Nuzzolo and Comi
[6] developed a model for predicting the demand for urban freight in Rome,
depending on the quantity, delivery and vehicle. The developed modelling system
is multistage and considers a separate selection approach for each decision
level. The model has been tested using traffic counts and interviews with
retailers and truck drivers in the inner area of Rome. Yang [7] developed demand
prediction models for regional freight transport by applying simple linear
regression, multiple linear regression and nonlinear regression approaches.
The latter approach outperformed others. Toole et al. [8] estimated travel
demand using mobile phone call records in conjunction with open and crowded
geographical data, census records and surveys. The flexibility of the developed
system has been analysed in various cities around the world.
The aim of this study is to develop simple and
practical transportation demand forecasting models based on population, GDPperC
and the number of vehicles. In addition, it seeks to demonstrate that the FPA,
one of the artificial intelligence techniques, is applicable to the estimation
of transportation demand.
2. FLOWER POLLINATION ALGORITHM
The FPA, developed by XieShe Yang
[9] in 2012, is inspired by the reproductive behaviour of flowering plants. The
pollination method is used in the maintenance of optimal biological viability
and reproduction. There are two important forms of pollination: biotic and
abiotic. The biotic form, which takes place with pollens transferred by
pollinators, such as flying insects, has been used in the reproduction of 90%
of flower plants. The abiotic form, in which no pollinator is required, is used
in 10% of plants. This model has been developed with certain assumptions and
rules. The FPA has four basic rules and looks for the most appropriate solution
according to these rules:
Given that insects can fly for a
long time, pollen can be transported over long distances. This situation
guarantees the best reproduction possible. The mathematical expression of
flower constancy is shown in Equation 1.
_{} (1)
where _{} is solution vector at iteration t and g_{∗} is the current best. Here, γ is a scaling factor to control the
step size.
The Lévy distribution is used to
correspond to the strength of pollination. When insects travel long distances,
the movement of insects can be represented by the Lévy distribution. Lévy’s
mathematical expression is shown in Equation 2.
_{} (2)
where Γ (λ) is the standard gamma
function and s is the step size. This distribution is valid for “s>0” large
steps. In theory, s0»0 is required; but, in practice, s0 can be as small as
0.1. For local pollution, both Rule 2 and Rule 3 are shown in Equation 3.
_{} (3)
where _{} are pollen from different flowers of the same
plant species.
3. PASSENGERKILOMETER AND TONNEKILOMETER
MODELS USING THE FLOWER POLLINATION ALGORITHM
Population (P), GDPperC and the number of vehicles (V) were used as input
variables in the development of the models. These data were obtained from the
Turkey Statistical Institute [10]. During the creation of the passengerkilometer
models, the number of cars, buses and minibuses that are generally used for
passenger transportation were used for the number of vehicles, while the number
of trucks and vans used for cargo transportation were taken into consideration
in the tonnekilometer models. Twentytwo of the 27yearold input parameters
between 1990 and 2016 were randomly divided and used as training data, while the rest were used as test data. The models
developed in linear, force and semiquadratic forms are shown in Equations 46.
Linear form:
_{} (4)
Power form:
_{} (5)
Semiquadratic form:
_{} (6)
Here, x_{1}, x_{2} and x_{3}
are population, GDPperC and the number of vehicles, respectively. W_{i}s
are the coefficients of the models.
After the models were optimized
according to the FPA, the coefficients of models were obtained, as shown in
Table 1.
Tab. 1
Coefficients of the models
Tonnekilometer 

Linear 
Power 
Semiquadratic 
w_{1}=10,185 
w_{1}=0.097 
w_{1}=9,979 
w_{2}=3,429,856 
w_{2}=1.336 
w_{2}=55,146,909 
w_{3}=11,231 
w_{3}=0.285 
w_{3}=175,884 
w_{4}=481,854,122,943 
w_{4}=0.453 
w_{4}=282,809 


w_{5}=5,588 


w_{6}=7,115,182 


w_{7}=371,062,336,694 
Passengerkilometer 

Linear 
Power 
Semiquadratic 
w_{1}=5,747 
w_{1}=0.335 
w_{1}=1,223 
w_{2}=832,688 
w_{2}=1.299 
w_{2}=15,297,166 
w_{3}=31,827 
w_{3}=0.036 
w_{3}=33,060 
w_{4}=394,859,379,208 
w_{4}=0.256 
w_{4}=910,708 


w_{5}=1,255 


w_{6}=3,939,353 


w_{7}=363,813,728,095 
4. FINDINGS AND EVALUATION
The performance evaluation of the proposed models was
performed according to the mean absolute percentage error (MAPE) and the
coefficient of determination (R^{2}) methods. The mathematical
expressions of the comparison criteria are given in Equations 7 and 8.
(7)
(8)
The statistical values of the passengerkilometer and tonnekilometer
estimation models according to training and test data are given in Table 2.
Tab. 2
Statistics for training and
test data

Passengerkilometerm 
Tonnekilometer 



Linear 
Power 
Semiquadratic 
Linear 
Power 
Semiquadratic 
Training 
MAPE 
4.1 
6.64 
3.09 
6.18 
8.15 
5.57 
R^{2} 
96.17 
91.29 
97.74 
95.87 
92.47 
96.53 

Test 
MAPE 
4.36 
8.81 
3.7 
6.07 
9.28 
6.26 
R^{2} 
95.98 
92.42 
97.74 
97.39 
95.06 
97.29 
When the results of the
statistics given in Table 2 are analysed as training and test data, it is
understood that the semiquadratic model gives the best result in terms of MAPE
and R^{2} values and provides the closest estimate to the observation
values with minimum error. In the passengerkilometer forecast, the performance
of the model was better than the tonnekilometer forecast, and could be
estimated with an average error of 3%. It has been shown that, although the
performance of the linear model is worse than the semiquadratic model, it can
be an alternative method because it is a practical and useful form. The
performance of the force model has been unsuccessful compared to other models,
while it has been observed that the estimations of this form for the passengerkilometer
and the tonnekilometer model are very different from the observation results
with a 9% error.
5. PASSENGERKILOMETERM AND TONNEKILOMETER
PROJECTION
Passengerkilometer and tonnekilometer values for the
future are estimated with two possible scenarios. In Scenario 1, the population
is predicted to increase by 1.7% per year on average, and it is assumed that it
will reach about 100 million in 2030. The increase in GDPperC is determined as
4% by considering the economic growth data for Turkey. The scenario is
also set by assuming that vehicle numbers will increase by 3%. In Scenario 2,
the projection of the Turkey Statistical Institute data has been used for
population growth, with the population estimated to be approximately 89 million
in 2030. The increase in GDPperC and the number of vehicles is determined by
considering the 27year growth rate. Thus, while the number of vehicles used
for passenger transport in 2030 is approximately 19 million vehicles, it is
predicted that the number of vehicles used for freight transport will be 5.5
million. Tables 3 and 4 show the projection values of the input parameters in
Scenario I and Scenario II, respectively.
Tab. 3
Scenario I: input variable
future forecast
Years 
Future
projection 


Population 
GDPperC 
Number of vehicles for passengers 
Number of vehicles for freight 
2017 
81,171,724 
9,739 
12,422,372 
4,353,173 
2018 
82,551,643 
10,128 
12,857,155 
4,440,237 
2019 
83,955,021 
10,533 
13307,156 
4,529,042 
2020 
85,382256 
10,955 
13,772,906 
4,619,622 
2021 
86,833,755 
11,393 
14,254,958 
4,712,015 
2022 
88,309,929 
11,848 
14,753,881 
4,806,255 
2023 
89,811,197 
12,322 
15,270,267 
4,902,380 
2024 
91,337,988 
12,815 
15,804,727 
5,000,428 
2025 
92,890,734 
13,328 
16,357,892 
5,100,436 
2026 
94,469,876 
1,3861 
16,930,418 
5,202,445 
2027 
96,075,864 
14,415 
17,522,983 
5,306,494 
2028 
97,709,154 
14,992 
18,136,287 
5,412,624 
2029 
99,370,209 
15,592 
18,771,057 
5,520,876 
2030 
101,059,503 
1,6215 
19,428,044 
5,631,294 
Tab. 4
Scenario II: input variable
future forecast
Years 
Future
projection 


Population 
GDPperC 
Number of vehicles for passengers 
Number of vehicles for freight 
2017 
80,550,000 
10,670 
12,087,653 
4,127,815 
2018 
81,320,000 
10,760 
12,154,523 
4283,771 
2019 
82,080,000 
10,853 
12,753,633 
4,439,727 
2020 
82,820,000 
10,949 
13,369,193 
4,595,684 
2021 
83,540,000 
11,050 
14,001,201 
4,751,640 
2022 
84,250,000 
11,154 
14,649,657 
4,907,596 
2023 
84,940,000 
11,263 
15,314,562 
5,063,552 
2024 
85,570,000 
11,381 
15,995,916 
5,219,508 
2025 
86,180,000 
11,504 
16,693,718 
5,375,465 
2026 
86,780,000 
11,631 
17,407,968 
5,531,421 
2027 
87,350,000 
11,762 
18,138,668 
5,687,377 
2028 
87,900,000 
11,899 
18,885,815 
5,843,333 
2029 
88,430,000 
12,040 
19,649,412 
5,999,289 
2030 
88,930,000 
12,188 
20,429,456 
6,155,245 
According to both scenarios, the passengerkilometer and tomkm demand for
the time period up to 2030 was forecasted using the semiquadratic model, which
offered the best performance. The distribution graph of these estimates is
given in Figure 1.
Fig. 1. Passengerkilometer
and tonnekilometer prediction values
In the passengerkilometer
estimation, both scenarios showed parallel predictions, while the tonnekilometer
estimation was expected to show a different trend according to the scenario.
6. CONCLUSION
In this study, the applicability of the FPA for the
estimation of passengerkilometer and tonnekilometer values in Turkey has been
demonstrated. Three different forms of road passenger and freight demand
forecasting models have been developed with statistical data covering 27 years,
and the results are presented. The best performance has been observed with the
semiquadratic model, compared to other models. When looking at the simplicity
and suitability of the forms, it can be seen that the linear model could be
used as an effective alternative approach, even though it delivered worse
results than the semiquadratic model.
According to different scenarios, it is expected that
the demand for passenger and freight transport will increase in parallel with
an increase in the population and the level of prosperity. In particular,
according to the input parameter values in Scenario 2, the increase in freight
and passenger demand is more than in the case of Scenario 1, while it is
predicted that demand will increase by 50%. In addition, the rate of increase
for trucks and pickups in Scenarios 1 and 2 is also reflected in the demand
increase, and it is understood from the graph in Figure 1 that there is a
linear relationship between them.
The FPA has demonstrated effective performance in predicting
passenger and freight transport demand and that it can be used in many
different areas. The effectiveness of the FPA approach will be highlighted
in future studies by comparing it to other artificial intelligence techniques.
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Received 18.10.2017; accepted in revised form 12.01.2018
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