The Prediction of Optimal Route of City Transportation Based on Passenger Occupancy using Genetic Algorithm: A Case Study in the City of Bandung

TELKOMNIKA, Vol.16, No.3, June 2018, pp. 1201~1207
ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013
DOI: 10.12928/TELKOMNIKA.v16i3.7077  1201
Received November 10, 2017; Revised April 3, 2018; Accepted April 22, 2018
The Prediction of Optimal Route of City Transportation
Based on Passenger Occupancy using Genetic
Algorithm: A Case Study in the City of Bandung
Sri Suryani Prasetiyowati*, Yuliant Sibaroni, Derwin Prabangkara
School of Computing, Telkom University, Bandung, (022) 7564108, Indonesia
*Corresponding author, e-mail: wati100175@gmail.com
1
, yuliant@telkomuniversity.ac.id
2
,
derwin@student.telkomuniversity.ac.id
3
Abstract
Currently, the existence of city transport is increasingly eliminated by private vehicles such as
cars and motorcycles.This situation is further exacerbated by the behavior ofcity transportdrivers who are
less discipline in driving, or in picking up and dropping off their passengers. The bad behavior is partly
caused by the low level of passenger occupancy. The drivers try to search for passengers as much as
possible but often ignore the traffic rules. To overcome this problem, an optimal transport route with high
passenger potential is required.Therefore, this study investigated the optimal route of city transport based
on the passenger occupancy rate in the city of Bandung as the case study. The method employed for
determining the optimal route is Genetic algorithm combined with Ordinary Kriging method used for the
process of passenger prediction and fitness calculation. The optimal routes are those with higher
occupancy rate. The analysis results showed that the use of the Genetic algorithm with a low number of
generations succeed in creating new optimal routes even though the increase is not too high the maximum
only reaches 4%.This result is certainly important enough to be used in making better public transport
routes.
Keywords:optimal route, genetic algorithm,occupancy,kriging
Copyright © 2018 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
The continuous increase in vehicles produced by automotive companies, which are not
offset by the improvement in the capacity and quality of roads, can lead to congestion. In
Indonesia, besides being triggered by the increase in vehicles, congestion is also caused by the
economic growth of the people, thus encouraging them to use various vehicles to fulfill their
needs. They use either private vehicles or public transportation to support their dynamic
mobilization. Public transportation operating in Indonesia is quite a lot such as buses, city
transport, rickshaws, and trains. However, the one which causes traffic jam lately is city
transport (angkot). It is because it has typical characteristics such as having various shape and
size, low passenger capacity, passing through a route which depends on the coverage area so
that each route has different mileage, a different number of modes in each route, and
inexpensive fare. In addition, there are also some other factors affecting the traffic jam due to
city transport including passengers dropping off anywhere (not at the terminals) and drivers
picking up passengers at will.
The low number of passengers of the city transport is one of the causes of undisciplined
drivers. This condition is not in accordance with the government regulation that one of the
provisions in the procurement of modes of transportation is the potential number of passengers
per vehicle (i.e. 250 passengers per day) [1]. The drivers try to make every effort to get his daily
income target fulfilled. At the beginning of the launch of city transport, it was believed to be one
solution to reduce congestion, but its existence currently contributes to congestion. Therefore,
the problem of city transport especially the one related to the determination of optimal city
transport routes is interesting to study. The improvement of quality standard and public
transportation service is one of the solutions and attractions of increasing public awareness to
use public transportation. Some solutions which are believed to increase people’s desire to use
public transport include: public transportation routes should be able to reach all urban areas,

 ISSN: 1693-6930
TELKOMNIKA Vol. 16, No. 3, June 2018: 1201-1207
1202
construction of shelters should be done at crowded points, public transport may only pick up or
drop off their passengers at shelters, and vehicles should be upgraded periodically.
One of the ways to overcome the congestion caused by public transportation especially
in Bandung City is by obtaining the optimal variable values from the problem formulation. This
study offers a solution by optimizing the urban transport routes by which the existing routes are
considered not complying with such requirements stipulated in the government regulation. One
of the methods of route optimization commonly done by many researchers is to use an
algorithm with a supporting variable, i.e. distance [2]. The determination of optimal route based
on the distance has a weakness, i.e. unable to consider the potential income of the city
transport drivers appropriately. In other areas such as in the determination of tourism scenic
routes, the determination of an optimal tourist route is designed by considering the constraints
of slope and roughness[3]. Meanwhile in Express Delivery Routing optimization, the value of the
transportation costs is a measure for optimal route determination[4]. The transportation costs
should still be able to ensure the quality of delivery service and customers' satisfaction
remains good.
All research on the determination of optimal route for public transportation above, no
one has paid attention to passenger occupancy factor. Whereas the occupancy of passengers
is a very decisive thing for the sustainability of the availability of public transportation that is
currently its existence is still held by the private sector. Passenger occupancy should be a key
consideration in determining the optimal routes of public transportation. The problem of
determining the efficient transportation cost for the determination of the optimal urban transport
route becomes a consideration in this research. Passengers certainly want the smallest possible
cost, while the provider of urban transport vehicles wants a high tariff to get more income.
The income of drivers can increase if urban transport has routes with high potential
passengers. To overcome this, this study proposes the determination of optimal route based on
the passenger occupancy rate. This is in line with the results of data analysis of city transport
routes that the average passenger occupancy on the existing routes is still relatively low. This
study used a genetic algorithm with the fitness function defined as urban transport occupancy at
a location. the genetic algorithm is chosen based on the consideration that the algorithm is quite
effective in the case of route determination, as did Changqing et al in the determination of Route
Optimization of Stacker [5].This passenger occupancy value is obtained from the results of
prediction using the Kriging method. It is expected that this study can recommend more optimal
city transport routes in Bandung so that the transport becomes more directed at the crowded
points and is able to generate a higher level of passenger occupancy.
2. Related Work
In the field of transportation, one of the generally-addressed issues is route
optimization. Each researcher has a different definition in determining the optimal route,
depending on the problems to be solved. Generally, a transportation route as the focus of
research is the bus route. Today, the determination of the optimal bus route also considers the
arrival and departure of other modes of transportation such as trains. Determining the optimal
public transportation route (e.g. bus route) separately without being connected to the schedule
of other modes of transportation is classified as a less complex issue and able to be solved
using a simple algorithm such as Dijkstra. Nevertheless, the determination of the optimal
transportation routes which are associated with other modes of transportation (e.g. train
schedules) is a complex optimization issue which requires long computation time. Such a model
is called the time-dependent model. One of the most effective approaches to solving such a
route optimization problem is by employing the concept of transfer patterns. Some researchers
have made route optimization in association with the concept.
Shrivastava and Mahony combine genetic algorithm and the specialized heuristic
algorithm to determine optimal routes [6]. They develop feeder routes by using a genetic
algorithm and then use a specialized heuristic algorithm to satisfy the demand of all the nodes.
In relation to the computation of route optimization with respect to the concept of transfer
patterns, [7]-[8] also develop special heuristics algorithm to obtain a feasible pre-computation
time. This is because the use of an algorithm such as Dijkstra is time-consuming.
. In a similar study, Chien et al. used the minimal value of total system cost, including
operator and user costs as a measure for determining an optimal bus route [9]. They developed

TELKOMNIKA ISSN: 1693-6930 
The Prediction of Optimal Route of City Transportation Based on … (Sri Suryani Prasetiyowati)
1203
method which applicable to irregular grid networks. They also show that the optimal route is
sensitive to demand distribution over the service area. The determination of the cost-based
optimal route is also done by Sadrsadat et al. who apply the bus route users’ profit subtracted
by the cost of network operator as an optimal route indicator. The most optimal route is the one
which has the maximum profit value [10].
Another issue which arises in the field of transportation is the determination of transit
route networks. Chakroborty and Dwivedi propose an optimal route based on the link travel
times and transit demand [11]. On the other hand, in determining the optimal route on the
pickup service of travel car passengers, [2] employ the minimum distance weight to determine
the optimal pickup route.
The method or algorithm used to solve transportation problems also varies. One
method the researchers often use is the genetic algorithm. It has long been used by researchers
to solve many complex problems. A genetic algorithm can be used to solve complex
optimization and is suitable to solve the problems of transportation route optimization [11], [12].
In addition, Sadrsadat et al. [2012] employ a genetic algorithm by which the fitness function is
defined as the bus route users' profit subtracted by the cost of the network operator. This fitness
function will maximize the distribution of bus routes in the observed area. Another method to
determine an optimal route is a combination of genetic algorithm and specialized heuristic
algorithm [6]. In another case, the Ant Colony System can also be a suitable method to
determine optimal routes [2]. In the latter case, the optimal routes are those with the shortest
distance.
3. Data Processing and Methods
3.1. Data
Data processed in this study included the occupancy of city transport passengers in
Bandung area as recorded in 2016. As many as 6 routes of city transport were observed from
which the data on each route where passenger occupancy was grouped into two categories, i.e.
weekday and weekend.
3.2 The making of shapefile
Shapefile or commonly known as SHP is a geospatial data format generally used for the
geographic system software with an extension of .shp. A shapefile is depicted in the forms of
lines, extents, and dot geometry which form the mapping of territories, rivers, roads, seas, and
so on. In this study, the shapefile functioned to create a map of Bandung City along with the
streets. The shapefile was created using the QGIS Desktop 2.18.3 application and was
displayed using the ArcMap 10.5 application. The unit of the shapefile was then converted to
UTM, so it turned into meter. Subsequently, the occupancy data on the shapefile was input into
ArcMap. The labels displayed on the shapefile included street names at the crowded points.
The shapefile along with the streets are displayed in Figure 1.
Figure 1. Bandung City Shapefile and the Crowded Points

 ISSN: 1693-6930
1204
3.3 Processing of location data
This process is the combination between of a shapefile map created in ArcGIS and
passenger occupancy data as the observation results. The labels or attributes displayed in the
shapefile were the occupancy value in crowded points and street names in Bandung City. The
shapefile read the occupancy which were displayed based on the coordinate points (x,y) -
latitude (x) and longitude (y) at all routes. Table 1 shows the observation of occupancy on one
of the routes in Bandung.
Table 1. The Observation of Occupancy in Abdul Muis - Cicaheum (Via Aceh) Route
Nbr Street Location of the crow d latitude(X) longitude(Y) Occupancy
1 Terminal Kebon Kalapa Terminal Kebon Kalapa 787978.00 9233470.00 1.03
2 Jl. Dew iSartika
SMP 10 Bandung 787933.00 9233298.00 1.34
SMP 3 Bandung 787918.00 9233312.00 1.84
3 Jl. Kautamaan Istri SMP 43 Bandung 788020.00 9233674.00 1.77
4 Jl. Balong Gede SMK Pasundan 1 Bandung 788061.00 9233556.00 2.07
5 Jl. Pungkur Pasar ancol 788448.00 9233263.00 3.00
6 Jl. Karapitan Universitas Langlangbuana 789045.00 9233059.00 1.75
7 Jl. Sunda
Gedung Lippo 789251.30 9233964.66 1.94
Toserba Yogya 789318.00 9234452.00 1.47
8 Jl. Lombok Stadiun Siliw angi 789398.81 9235378.02 1.59
9 Jl. Taman Pramuka Baltos 788353.83 9236649.25 3.14
SMPN 14 Bandung 790683.00 9235603.00 2.35
SMPN 22 Bandung 791067.00 9235138.00 2.58
11 Jl. Jend. Katamso
Ganesha Bimbingan Belajar 790741.56 9235936.56 1.79
Plaza Fleksi 790739.00 9235882.00 2.05
PUSSENIF 790950.00 9236136.00 2.26
12 Jl. Pahlaw an
Griya Pahlaw an 791109.00 9236474.00 3.23
ITENAS 791100.00 9236763.00 3.05
Taman Makam Pahlaw an 791239.89 9237289.08 3.41
13 Jl. Cikutra Universitas Widyatama 792178.44 9236751.83 2.40
14 Jl. Hasan Mustofa Terminal Cicaheum 793600.34 9236200.40 1.77
3.4 Prediction of kriging value
This process is the prediction of occupancy value at other locations in Bandung city
map, outside the observation data. The result showed that all of the location points on the
streets which were potential to be new alternative routes would have the occupancy data
(prediction) to be the reference in determining alternative routes. The prediction process
employed ArcGIS 10.5 tool while the Kriging method used was the Ordinary Kriging. This
Ordinary Kriging method was chosen with the consideration that the passenger occupancy
value tended to be stationary, or had no up or down trends. The prediction result of Kriging
value and its location information were then stored in an adjacency matrix. The location points
were subsequently codified in the forms of location numbers to simplify the writing.
3.5. Searching for new route solutions using genetic algorithm
The next process was to search for solutions using genetic algorithm. The solution
offered was an alternative route which had the following conditions:
1) Passing through certain location points
2) Having a higher predicted value of passenger occupancy than the original route
Stages of genetic algorithm conducted in this study included:
a. Initialization of N Random Population
b. Reproduction Process
c. Selection Process
d. Cross-over Process
e. Mutation Process
f. Evaluation and Termination of Regeneration
a. Initialization of random population
This process is a process for generating individuals in a population size of N. Each
individual is expressed as a chromosome that has the first cell containing the mode starting
point and the last cell in the forms of the mode destination point. The searched individuals were

1205
those who had a high fitness value. This initialization process was only done once. The formula
of fitness function can be seen in equation (1).
routeaonoccupancyaverage_=fitness (1)
b. Reproduction process
The reproduction process means a process for generating new offspring by passing on
the same traits of the parent chromosome. This process aims to keep the good parents from
disappearing. The process also contains the elitism, i.e. a process of maintaining the best
individuals.
c. Selection process
In the selection process, there are two steps to be done, i.e. calculating Linear Fitness
Ranking (LFR) and making the selection using the Roulette Wheel method. After all individuals
were arranged in order by the LFR value, the next process was to select individuals based on
the LFR value using the Roulette Wheel method. Individuals who had a larger LFR value would
have a higher chance of being selected. The LFR formula can be seen in equation 2[13].
  







1
1)(
N
iR
f-ff=f minmaxmaxLR (2)
where
maxf : maximum fitness
maxf : minimum fitness
R(i) :the i-th individual rank
N : Number of chromosome in population
d. Crossover process
The crossover process means a process of producing a new chromosome as a result of
the combination of two parents who have been selected at random. The result indicated new
individuals implying a new route. The ultimate goal was to bring out individuals with better
fitness. The crossover rate used was 0.8. The crossover mechanism used the PPX
(Precedence Preservative Crossover).
e. Mutation Process
The process of mutation in the formation of routes here was done by replacing or
switching genes or points of intersection with each other in the hope of making the fitness value
better than before. The mutation probability used in this study was 0.1. The mutation probability
value was chosen not too high in order that the chromosome of the new offspring did not
change in the extreme. The optimal value of mutation probability is actually different for each
case handled. In some cases of non-transportation fields, the optimal value of mutation
probability is obtained from a higher value [14-15]. While in the field of transportation research,
the used mutation probability value is relatively low [6], [10-11] .
f. Evaluation and Criteria for Termination of Regeneration
This stage includes terminating the generation or epoch according to the desired
conditions. The terminating conditions may be based on either the number of epochs or on the
fitness value (when the new route fitness>the old route fitness). Once the conditions have been
as expected then the process of solution search will stop and will not return to the reproduction
process. Subsequently, the system will provide optimal solution in the forms of more optimal
new routes.
4. Experiment Results and Analysis
Based on the observation data on 6 city transport routes in Bandung, the experiment
was done with the parameter configuration as follows: population size=30, crossover
probability=0.8, and mutation probability=0.1. The number of tested generations included 50,
200, 500, 1000 and 2000. The detailed search results of the optimal routes for weekend and
weekday periods can be seen in Table 2 and Table 3.

 ISSN: 1693-6930
1206
Table 2: Optimal Route Acceleration Prediction using Genetic Algorithm (Weekend)
No. Route
Number of Generation
Real Occupancy Increase
50 200 500 1000 2000
1 AbdulMuis-Cicaheum 3.25 3.25 3.25 3.25 3.25 3.12 4%
2 Cicaheum-Abdul Muis 3.15 3.15 3.16 3.16 3.16 3.12 1%
3 AbdulMuis-Dago 3.28 3.35 3.35 3.36 3.36 3.17 6%
4 Dago-AbdulMuis 3.38 3.37 3.38 3.39 3.39 3.29 3%
5 St.Hall-Dago 3.53 3.53 3.53 3.53 3.53 3.49 1%
6 Dago-St.Hall 3.50 3.50 3.50 3.50 3.50 3.45 1%
Table 3. Optimal Route Acceleration Prediction using Genetic Algorithm (Weekday)
No. Route
Number of Generation
Real Occupancy Increase
50 200 500 1000 2000
1 AbdulMuis-Cicaheum 3.94 3.95 3.95 3.95 3.95 3.80 4%
2 Cicaheum-Abdul Muis 3.85 3.85 3.85 3.86 3.86 3.81 1%
3 AbdulMuis-Dago 3.81 3.88 3.88 3.86 3.88 3.73 4%
4 Dago-AbdulMuis 3.88 3.90 3.93 3.93 3.93 3.83 3%
5 St.Hall-Dago 3.95 3.95 3.95 3.95 3.95 3.84 3%
6 Dago-St.Hall 3.94 3.94 3.94 3.94 3.94 3.80 4%
Table 2 and Table 3 show that the passenger occupancy rate on city transport on
weekdays is higher than that on weekends. This implies that urban communities in Bandung
use more city transport services for daily work activities. The use of genetic algorithm to search
for new, more optimal routes is successful with the highest increase reaching 6% for the
weekend and 4% for the weekday. In this study, the determination of optimal route can be
commonly achieved in the 50th generation although in some cases, the new optimal route is
obtained in the 500th and 1000th generations.
5. Conclusion
This study discusses the determination of optimal route of city transport based on the
passenger occupancy rate in Bandung City. The method used is genetic algorithm combined
with Ordinary Kriging method for the passenger prediction process and fitness calculation. The
determination of route optimality according to the passenger occupancy rate is based on the
data that the city transport operators’ income is decreasing due to the decreasing rate of
passenger occupancy.
The analysis results show that the use of genetic algorithm with a low number of
generations succeeds in producing new, more optimal routes even though the increase is not
that high. Factors affecting the passenger occupancy rate such as schools, markets,
supermarkets, and others may be considered for inclusion in the prediction of passenger
occupancy rate to produce a better predictive occupancy model.
Acknowledgments
This research is supported by Telkom University and Ministry of Research, Technology
and Higher Education of the Republic of Indonesia.
References
[1] Departemen Perhubungan RI. Pedoman Teknis Penyelenggaraan Angkutan Penumpang Umum di
Wilayah Perkotaan dalam Trayek Tetap dan Teratur. no. SK.687/AJ.206/DRJD/2002. pp. 2–69,
2002.
[2] L. Samudra and I. Mukhlash. Penentuan Rute Optimal Pada Kegiatan Penjemputan Penumpang
Travel Menggunakan Ant Colony System. J. SAINS DAN SENI POMITS. 2013; 2(1): 1–6.
[3] L. Xiaofang and S. Hui. IntelligentPlanning ofTourism Scenic Routes Based on Genetic Algorithm in
Coal WANBEI Mining Subsidence. TELKOMNIKA (Telecommunication Computing Electronics and
Control). 2016; 14(2A): 69-76.
[4] X. Luo, J. Tu, and L. Huang. Optimization of Express Delivery Routing Problem. TELKOMNIKA
(Telecommunication Computing Electronics and Control). 2016; 14(3A): 380-388.
[5] C. Changqing and W. Yiqiang. Route Optimization of Stacker in Automatic Warehouse based on

1207
Genetic Algorithm. TELKOMNIKA. 2013; 11(11): 6367–6372,.
[6] P. Shrivastava and M. O’Mahony. Design of Feeder Route Network Using Combined Genetic
Algorithm and Specialized Repair Heuristic. J. Public Transp. 2007; 10(2): 109–133.
[7] R. Geisberger. Advanced Route Planning in Transportation Networks. Karlsruher Instituts für
Technologie, 2011.
[8] H. Bast, E. Carlsson, A. Eigenwillig, R. Geisberger, C. Harrelson, V. Raychev, F. Viger. Fast Routing
in Very Large Public Transportation Networks using Transfer Patterns. ESA. 2010.
[9] S. I. Chien, B. V Dimitrijevic, and L. N. Spasovic. Optimization of Bus Route Planning in Urban
Commuter Networks. J. Public Transp. 2003; 6(1): 53–79.
[10] E. Sadrsadat, H., Poorzahedi, H., Haghani, A., & Sharifi. Bus Network Design Using Genetic
Algorithm. in 53rd annual transportation research forum, Tampa. 2012: 1–16.
[11] P. Chakroborty and T. Dwivedi. Optimal Route Network Design For Transit Systems Using Genetic
Algorithms. Eng. Optim. 2002; 34(1): 83–100.
[12] P. Chakroborty. Brics Optimal Routing and Scheduling in Transportation :Using Genetic Algorithm to
Solve Difficult Optimization Problems. 2002.
[13] Suyanto, Soft Computing: Membangun Mesin Ber-IQ Tinggi. Bandung: Informatika, 2008.
[14] Y. Sibaroni, Fitriyani, and F. Nhita, “The Optimal High Performance Computing Infrastructure for
Solving High Complexity Problem,” TELKOMNIKA (Telecommunication Computing Electronics and
Control). 2016; 14(4): 281–288.
[15] M. M. Navarro. Evaluations ofCrossover and Mutation Probability ofGenetic Algorithm in an Optimal
Facility Layout Problem. 2016: 3312–3317.

The Prediction of Optimal Route of City Transportation Based on Passenger Occupancy using Genetic Algorithm: A Case Study in the City of Bandung

More Related Content

What's hot (18)

Similar to The Prediction of Optimal Route of City Transportation Based on Passenger Occupancy using Genetic Algorithm: A Case Study in the City of Bandung (20)

More from TELKOMNIKA JOURNAL (20)

Recently uploaded (20)

The Prediction of Optimal Route of City Transportation Based on Passenger Occupancy using Genetic Algorithm: A Case Study in the City of Bandung