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IMPORTANT SERVICE INDICATORS OF PUBLIC TRANSPORT SYSTEM IN

AKURE-OWO, NIGERIA

Adeniran Adetayo Olaniyi and Olorunfemi Samuel Oluwaseyi

*1Department of Transport Management Technology, Federal University of Technology Akure

Corresponding Author Email:adeniranao@futa.edu.ng

Abstract: Among the major goals of all actors involved in the public transport business is the formation of a well organized transit system, within which citizens can find a sufficient level of mobility and satisfy their important need for the efficient movement under safe and comfortable conditions. This study elucidates the model and approach of factor analysis, and focus on its application in identifying the most important public transport system indicators in Akure-Owo axis. Exploratory factor analysis was used because there is no conception of theoretical hypothesis, and the underlying data involved is a common structure. One hundred and twelve (112) questionnaires were administered to the passengers across the bus terminals of Akure-Owo axis with purposive and random sampling techniques. The findings revealed that relationship with drivers ranked 1st, overloading and over-speeding ranked 2nd, compliance with road safety rule and order ranked 3rd, cost charge per trip ranked 4th, and condition of vehicle ranked 5th. The identified factors constitute key indicators of public transport system along Akure-Owo axis. The behaviour of the important indicators was examined with a correlation matrix. The important service indicators of public transport system were identified among several indicators to enhance the effectiveness of adopting economic and social theory in delivering quality service of the public transport. Finally, it is recommended that the operators of public transport system along Akure-Owo axis should properly and cautiously relationship with their drivers and passengers, and reduce the cost charge per trip. For safety reasons, the operators should avoid overloading and over-speeding, comply with road safety rule and order, and ensure the vehicle is always in good condition. Keywords: Factor analysis, Importance analysis, Public transport, Performance indicators

Author(s) agree that this article remain permanently open access under the terms of the Creative

Commons Attribution http://creativecommons.org/licenses/by/4.0/

Vol. 5(6), pp. 285-301, September 2019 ISSN 2315-5396 DOI: 10.14412/SRI2019.301 Copyright© 2019 Author(s) retain the copyright of this article Available online at http://www.scienceparkjournals.org/SRI

Scientific Research and Impact

INTRODUCTION

Transportation is concerned with mobility (Jean, Claude and Brian, 2006), particularly how

mobility is taking place in the context of a wide variety of conditions. Mobility in this context is

connected with the movement of people, distribution of goods and services without which

production (finished products that get to the final consumers) cannot be achieved (Akintayo,

2010). Transport is important for the survival of modern society and without it there would be no

life in the city (Onokala, 2001). Transport needs of major cities in Nigeria now present significant

challenges for policy makers as unpredictable shifts in population dynamics in response to the

need for employment, housing, and sustenance continues (Nwachukwu, 2014). The expansion

of the cities in Nigeria, coupled with increasing urban population, results in greater demand for

transport provision. This demand has, however, not always been met, and efforts to provide

adequate transport infrastructural facilities are ad hoc, uncoordinated, and poor (Nwachukwu,

2014). Public transportation has undoubtedly play a vital role in commuting passengers to work

or to places that they desire, and more importantly, to reduce traffic congestion (Rohana et al.,

2012). Public transportation by definition connotes the act or the means of conveying large

number of people “en masse” as opposed to conveyance in individual vehicles carrying very few

people at a time. In other words, public transport or mass transit is a system in which a greater

number of people are moved at a time along principal corridors (Nwachukwu, 2005). It is also

referred to as mass transit which comprises mainly of the rail system, light rail system, tram

ways and monorails, bus system and where possible water transportation (Nwachukwu, 2005).

Recently, experiences show a need for greater variety of public transport modes, but buses are

choice of a majority of the communities and are the only means of mobility that can be afforded

by the poor in developing countries of the World. The choice of any or a combination of the

public transportation systems enumerated above could be influenced by population and

area/size of the city, their transportation demand and characteristics and land use pattern. The

bus system is the transportation system that uses buses that may have a range of passenger

capacities and performance characteristics, and may operate on fixed routes with fixed

schedules, or may be flexibly routed. Bus systems have the capability of extending transport

services to greater proportions of urban residents who do not have private cars and cannot

afford frequent taxi fares (Nwachukwu, 2014). They have the potential of being used as policy

tools to reduce the number of cars on urban roads and thus reduce traffic chaos in cities.

Despite the vital role that buses are able to play in any urban area, their services in Nigerian

cities are often insufficient to meet demand, and the services provided suffer from low output

(Ali, 2010; Onokala, 2001). This study however examines factor analysis of the important public

transport indicators in Akure-Owo, Nigeria. It has been argued that provision of public transport

services in a large number of cities in the

developing world often does little to meet the travel needs of the population, particularly

residents of low income areas (Wright, 2004; Lorita, 2011). The road-based public transport in

developing world cities is characterized by many informal and formal vans, mini-buses, and full-

sized buses (Wright, 2004), and the services rendered are generally poor and often regarded as

inadequate. Standards of safety, security, comfort, convenience, regularity, punctuality (where

schedules apply), reliability, and speed are not encouraging and low incomes also lead to

problems of affordability of prices (Behrens et al, 2004; Iles, 2005; Wright, 2004; Lorita,2011).

According to Iles (2005), there is also general public dissatisfaction in many developing

countries with the quality of public transport services. Meanwhile, Lorita (2011) quoting Wright

(2004) identified the following paucities in the present services that the public transport

customers or passengers usually complain about and this are; inconvenience in terms of

location of stations and frequency of services; failure to service key origins and destinations;

fear of crime at stations and within public transport vehicles; lack of safety in terms of driver

ability and roadworthiness of public transport vehicles; service is much slower than private

vehicles, especially when public transport vehicles make frequent stops; overloading of vehicles

makes ride uncomfortable; poor-quality or non-existent infrastructure (e.g., lack of shelters,

unclean vehicles, etc.); lack of organized system structure and accompanying maps and

information make the systems difficult to use; and Low status of public transit services.

Several variables are proposed to explain the complex situation, interconnections and

interrelationships between independent variables. In this regard, the few basic variables and

propositions central to understanding remain to be determined. The systematic dependencies

and correlations among these variables are charted only on presence-absence or rank order

scales data. In a situation where the researcher is being confronted with entangled behaviour,

unknown interdependencies, masses of qualitative and quantitative variables, and bad data,

many social scientists turn towards factor analysis to uncover characteristic features of major

social and international phenomena (Nimalathasan, 2009).

Factor analysis is, at its core, a statistical technique designed to inform how the underlying

structure of phenomena is understood. The foundations of factor analyses are now over a

century old (Spearman, 1904), and yet they remain a relevant and ever-developing set of

techniques. Despite the central role they have played in the various field, they are often

misinterpreted and misapplied (Aidan and Wright, 2017). Although factor analytic methods are

well-known techniques, they have the potential to continue to generate important insights into

the debate of importance analysis. Factor analysis model belongs to the General Linear Model

(GLM) family of procedures bearing same assumptions as multiple regressions e.g. linear

relationships, interval or near -interval data, latent variables, proper specification including

relevant variables and excluding extraneous ones, lack of high multicollinearity, and multivariate

normality. It is a generic term for a family of statistical techniques concerned with the reduction

of a set of observable variables in terms of a small number of latent factors. It has been

developed primarily for analysing relationships among a number of measurable entities (such as

survey items or test scores) (Nimalathasan, 2009).

This is possible because factor analysis can simultaneously manage several variables,

compensate for random error and invalidity, and disentangle complex interrelationships into

their major and distinct regularities. Factor analysis involves the following terminologies such as

correlation matrix, communality, eigenvalues, factor rotation, factor loadings, total variance

explained and others. In importance study of the service industry, service indicators are

benchmarked to assess the performance of services rendered; organization may decide to set

priority on some services that was earlier benchmarked. In view of identifying the most

significant services, some studies adopt relative importance index which seems to be devoid of

strong statistical analysis. Many studies have been conducted on factor analysis; most of these

articles are carried out in western countries. Few of the abridged studies were found available in

Nigeria and other similar countries, but no detailed study is seen on assessing passengers’

satisfaction. Therefore, this will somewhat cover the research gap by reducing the bus services

with a view to identifying the most important services of the public transport system in Akure-

Owo axis with the use of exploratory factor analysis.

Exploratory factor analysis was used because there is no conception of theoretical hypothesis,

and the underlying data is a common structure. It is believed that this study will assist those who

need to read and comprehend research articles on factor analysis, as well as those that might

find it desirable to apply factor analysis of the important study. The specific objectives are to

elucidate the model and approach of factor analysis and to focus on its application in

importance study of the public transport system.

METHODOLOGY

The Akure-Owo expressway is a major road connecting the western and northern parts of the

country and thus a very busy federal road (Adeyemo and Omosuyi, 2012). According to

FGNRSDT (2010), the road is one of the categories of road constructed from Benin to Ilesha in

1965 as surface dressed and in 1978 it was converted to asphaltic concrete and had its last

major intervention from Owo to Akure in 1998. The Owo – Akure section of the road

commences from Ikare junction in Owo and terminates at Akure.

This section of the road has experienced major rehabilitation recently thus clarifying the good

condition of the road. Although, according to the report of FGNRSDT (2010), it was discovered

that culverts, drains and bridges are experiencing various levels of siltation and scouring and

will require various types of repairs and maintenance including painting, cleaning of bridge

bearings, joints and weep holes. The distance from Akure to Owo covers 51 kilometres. The

major towns along the axis include Iluabo, Ogbese, Uso and Emure. The major parks identified

in the axis are Beinin-Ado Park in Akure and Post Office Park in Owo. The map below shows

the Akure-Owo in its national setting (Olorunfemi and Adeniran, 2018).

Figure1: Map of areas in Akure showing Akure-Owo road Modified from Eke, Oyinloye and Olamiju (2017); Olorunfemi and Adeniran (2018)

Research Design

This research is descriptive in nature simply because the statistical data types are ordinal and

nominal in nature. Data were sourced using primary and secondary sources. The populations

under study were the passengers of public transport (minibuses) along Akure-Owo axis in Ondo

State, Nigeria. Well-structured questionnaires were used to collect primary data from

passengers who travelled along the axis on daily basis (Olorunfemi and Adeniran, 2018).

In order to determine the sample size for the study, the daily record of the movement was

sourced from National Union of Road Transport Workers (NURTW), coordinating Akure-Owo

axis. Based on the record, at least fourteen (14) minibuses operate to and fro along the axis and

each has a carrying capacity fourteen (14) passengers. The numbers of the minibuses operate

on a daily basis were multiplied by the number of passengers, which amounted to one hundred

and twelve (112) (Olorunfemi and Adeniran, 2018). However, purposive and random sampling

methods were used during the course of the study. The purposive sampling method was

needed to ensure that only the passengers who are travelling between Akure-Owo were

surveyed. The random technique was adopted to ensure that individual respondents

(passengers) have equal rights to be sampled for the purpose of the study (Olorunfemi and

Adeniran, 2018). The data collected for the study were analyzed using exploratory factor

analysis

Model Specification

If the observed variables are X1, X2 …. Xn, the common factors are F1, F2 … Fm and the unique

factors are U1, U2 …Un , the variables may be expressed as linear functions of the factors:

X1 = a11F1 + a12F2 + a13F3 + … + a1mFm + a1U1

X2 = a21F1 + a22F2 + a23F3 + … + a2mFm + a2U2

…

Xn = an1F1 + an2F2 + an3F3 + … + anmFm + anUn …………… (1)

Each of these equations is a regression equation; factor analysis seeks to find the coefficients

a11, a12 … anm which best reproduce the observed variables from the factors. The coefficients

a11, a12 … anm are weighted in the same way as regression coefficients because of some

reasons: the variables are standardised, and the constant is zero, therefore it is not shown.

An example is the coefficient a11 that shows the effect on variable X1 of a one-unit increase in

F1. In factor analysis, the coefficients are called loadings (a variable is said to load on a factor)

and, when the factors are uncorrelated, they also show the correlation between each variable

and a given factor. In the model above, a11 is the loading for variable X1 on F1, a23 is the loading

for variable X2 on F3, etc. When the coefficients are uncorrelated, that means the factors are

uncorrelated, and the sum of the squares of the loadings for variable X1, namely a112 + a122 + …

+ a132, shows the proportion of the variance of variable X1 which is accounted for by the

common factors. This is called the communality. The larger the communality for each variable,

the more successful a factor analysis solution is. Also, the greater the discrepancy, the less

successful the factor solution will be in preserving the information in the original correlation

matrix.

RESULTS

The main question is “how important are the following public transport system indicators?”

Respondents rate the five items, such as “Highly important “, “Important”, “Neutral”, “Less

important, and “Not important at all”.

Social-Economic Characteristics of Respondents

For socio-economic characteristic of the respondents sampled for the study, the variables

considered were: gender, marital status, age, educational status. The gender of the

respondents revealed that 66.1% were male and 33.9% were female. This indicates that

majority of the respondents moving along Akure-Owo route are male. This may be as a result of

mobility variation. The activities of women in terms of mobility or travel vary differently along

gender lines (Olorunfemi and Adeniran, 2018). There is observable gender differentiation in

travel behaviour in the space which can be traced to numerous factors that range from age,

income level, societal perception, time, distance, family size, etc.

The marital status indicates that 27.7% were single, 48.2% were married, 17.9% were divorced

and 6.3% were widowed. The analysis shows that majority of the respondents were married.

This implies that married people make more trips to the area than other marital status class.

This could be to the fact that married people need to move around in searching for one

opportunity or the other for the purpose of meeting their daily needs and to provide for their

household. The investigation into the age status of the respondents shows that that 27.7% of

respondents fall within the age bracket of 20-30 years, 44.6% were within 30-40 years, 18.8%

were between ages of 40-50 years and 8.9% were above 50 years. Hence, the majority of the

respondents fall within the age bracket of 30-40 years. This implies that these age bracket

greatly influence the quest for a living. It is an active working age bracket capable of coping with

the complex challenges of the human environment (Fadare and Adeniran, 2018).

The educational status of the passenger reveals that majority of the respondents (44.6%) were

the holder of higher institution certificate. This implies the respondents were knowledgeable

enough to provide information on the level of service importance from patronizing public

transport along Akure-Owo axis. Meanwhile, the majority (54.5%) of the respondents reveals

that low transport rate along the axis propels them to patronize public transport in the area.

Their occupational status shows that majority (40.2%) were traders, 22.3% were students, and

22.3% were a professional/artisan.

Factor Analysis Output

For the purpose of easy identification of indicators, it is important to note that X1 is safety, X2 is

comfortability, X3 is availability of seat, X4 is time delay at bus stop, X5 is relationship with

drivers, X6 is competence of drivers, X7 is condition of vehicle, X8 is reliability, X9 is overloading

and over-speeding, X10 is drivers altitude, X11 is compliance with road safety rule and order, and

X12 is cost charge per trip.

The Coefficients

According to the hypothetical solution in table 1,

X1 = 0.427F1 + (-0.013) F2 + (-0.601) F3 + 0.032F4 + (-0.159) F5

X2 = 0.192F1 + 0.180F2 + 0.507F3 + (-0.152) F4 + 0.493F5

X3 = 0.571F1 + (-0.044) F2 + (-0.198) F3 + 0.279F4 + 0.268F5

…..

X12 = (-0.327) F1 + 0.119F2 + (-0.065) F3 + 0.523F4 + 0.019F5

As well as being weights, the coefficients above show that the correlation between X1 and F1 is

0.427, between X1 and F2 is -0.013, between X1 and F3 is -0.601, between X1 and F4 is 0.032,

between X1 and F5 is -0.159, and so on.

Communality

The quantities in the communality column show the proportion of the variance of each variable

accounted for by the common factors. According to Child (2006), the variance is equal to the

square of the factor loadings. In many methods of factor analysis, the goal of communality

(extraction) is to remove as much common variance in the first factor as possible (Child, 2006).

By default, communality is the squared multiple correlations obtained when each variable is

regressed on all the other variables. In other words, the amount of the variance of variable X1

explained by all the other variables is taken as a reasonable first estimate of the amount of X1’s

variance accounted for by the common factors.

For X1 this quantity is 0.4272 + (-0.013)2 + (-0.601)2 + 0.0322 + (-0.159)2 = 0.570, for X2 it is

0.1922 + 0.1802 + 0.5072 + (-0.152)2 + 0.4932 = 0.592, and so on.

Account of Variance

The quantities at the bottom of each factor column are the sums of the squared loadings for that

factor, which show how much of the total variance of the observed variables is accounted for by

that factor. For Factor 1, the quantity is 0.4272 + 0.1922 + 0.5712 + 0.2092 + 0.0772 + 0.3692+

(0.177)2 + 0.5752 + 0.2142 + 0.4932 + 0.6702 + (-0.327)2 = 1.94. Because in the factor analysis

discussed here the total amount of variance is equal to the number of observed variables (the

variables are standardised, so each has a variance of one), the total variation here is twelve, so

that Factor 1 accounts for1.94

12∗ 100 = 16.2% of the variance.

Sampling Adequacy and Significance

Kasier Meyer Olkin (KMO) is a measure of whether or not the distribution of value is adequate

for conducting factor analysis. A measure of KMO between 0.9 and 0.8 is excellent, KMO

between 0.7 and 0.6 is very good, KMO between 0.6 and 0.5 is good, KMO below 0.5 if not

good (unacceptable) (George and Mallery, 2003; Sergejs and Ksenija, 2015). In table 1, the

data returned a value sampling adequacy of 0.576 (57.6%) indicating a good measure of the

multivariate normality of the set of distributions. The significance value of 0.026 is less than 0.05

indicates that the data do not produce an identity matrix and are thus appropriately multivariate

normal and acceptable for factor analysis; this is supported by (George and Mallery, 2003).

When the original twelve variables were analysed, five variables were extracted from the

analysis with eigenvalues greater or equal to 1, which explained 56.872 per cent of the total

variance. The result of the factor analysis is presented in table 1. The full result of factor

loadings is presented in table 1. The factors retained ranged from 0.514 to 0.670 without taking

into account their signs whether it is positive or negative as shown in table 2. The variable label

that gets the highest loading becomes the title of each group of variables.

Table 1: Hypothetical solution of factor analysis

Variables Communalities Eigenvalues %Variance Cumulative% Loadings

X1 X2 X3 X4 X5 KMO

X1 .570 1.937 16.141 16.141 .427 -.013 -.601 .032 -.159 .576

X2 .592 1.519 12.662 28.802 .192 .180 .507 -.152 .493 Sig

X3 .516 1.212 10.098 38.900 .571 -.044 -.198 .279 .268 0.026

X4 .587 1.128 9.401 48.301 .209 -.662 .006 .191 .262 Bartlett's

Test

X5 .670 1.029 8.571 56.872 .077 -.451 .359 .024 -.576 89.849

X6 .652 .983 .369 .301 .039 .514 -.400

X7 .523 .883 -.177 .657 -.014 .230 .086

X8 .660 .789 .575 .483 -.059 -.301 -.045

X9 .562 .700 .214 .103 .603 .376 -.016

X10 .463 .690 .493 -.247 -.071 .271 .284

X11 .630 .642 .670 .053 .217 -.306 -.196

X12 .399 .488 -.327 .119 -.065 .523 .019

ΣX2 1.94 1.52 1.21 1.13 1.03

Source: Authors’ work

DISCUSSIONS

Indicators group I: Compliance with road safety rule and order: This indicator was

represented by three indicators with factor loadings ranging from 0.670 to 0.571. They were

compliance with public transport operatives with road safety rules and order; availability of seat

in public transport; and reliability of public transport system. This indicator accounted for

16.141% of the rated variance.

Indicators group 2: Condition of vehicle: This indicator was represented by two indicators

with factor loadings ranging from 0.657 to 0.662. They were a condition of vehicle used for

public transport; and time delay at a bus stop. This indicator explained 12.662% of the rated

variance.

Indicators group 3: Overloading and over-speeding: This indicator was represented by two

indicators with factor loadings ranging from 0.603 to 0.601. They were overloading and over-

speeding of public transport; and safety. This indicator explained 10.098% of the rated variance.

Indicators group 4: Cost charge per trip: This indicator was represented by two indicators

with factor loadings ranging from 0.523 to 0.514. They were cost charge per trip; and

competence of drivers. This characteristic explained 9.401% of the rated variance.

Indicators group 5: Relationship with drivers: One indicator with 0.576 belonged to a

relationship with public transport drivers. This characteristic explained 8.571% of the rated

variance.

Table 2: Model and classification

Variables X1 X2 X3 X4 X5 Variables Group 1 Group 2 Group 3 Group 4 Group 5

X1 .427 -.013 -.601 .032 -.159 X1 -.601 X2 .192 .180 .507 -.152 .493 X2 X3 .571 -.044 -.198 .279 .268 X3 .571 X4 .209 -.662 .006 .191 .262 X4 -.662 X5 .077 -.451 .359 .024 -.576 X5 -.576

5

X6 .369 .301 .039 .514 -.400 X6 .514 X7 -.177 .657 -.014 .230 .086 X7 .657

2

X8 .575 .483 -.059 -.301 -.045 X8 .575 X9 .214 .103 .603 .376 -.016 X9 .603

3

X10 .493 -.247 -.071 .271 .284 X10 X11 .670 .053 .217 -.306 -.196 X11 .670

1

X12 -.327 .119 -.065 .523 .019 X12 .5234

Eigenvalues 1.937 1.519 1.212 1.128 1.029

% Variances 16.141 12.662 10.098 9.401 8.571

%

Cumulative 16.141 28.802 38.900 48.301 56.872

Source: Authors’ work

Identification of Indicators in Order of Importance

Ranking of the above indicators in order of their importance, along with factor score, is shown in

table 3. The importance of these characteristics, as perceived by the respondents, has been

ranked on the basis of factor score. As depicted in table 3, the indicators: “Relationship with

drivers” got the 1st rank, “Overloading and over-speeding” ranked 2nd, “Compliance with road

safety rule and order” ranked 3rd, “Cost charge per trip” ranked 4th, and “Condition of vehicle”

ranked 5th which are the key indicators of public transport system along Akure-Owo axis.

Olorunfemi and Adeniran (2018) conducted a similar study by using gap analysis to assess

passengers' satisfaction of the public transport system in Akure-Owo axis. Their findings

revealed that passengers were not satisfied with the competence of drivers, condition of the

vehicle, overloading and over-speeding, attitude of drivers, driver's compliance with road safety

rules and order, and the cost charge per trip. Among the important indicators of public transport

system identified in this study, it was revealed by Olorunfemi and Adeniran (2018) that

passengers were not satisfied with competence of drivers, condition of the vehicle, overloading

and over-speeding, attitude of drivers, driver's compliance with road safety rules and order, and

the cost charge per trip. It is therefore pertinent to note that the essence of identifying the

important indicators of public transport system among several indicators is to enhance the

effectiveness of adopting a scale of preference in providing quality service and enhancing

passengers’ satisfaction of public transport system.

Table 3: Ranking of characteristics according to their importance

Key Indicators Score Rank

Compliance with road safety rule and order 0.720 3

Condition of vehicle 0.621 5

Overloading and over-speeding 0.766 2

Cost charge per trip 0.671 4

Relationship with drivers 0.890 1

Source: Authors’ work

Behaviours of Indicators/ Reproducing the Correlations

The correlation between variables X1 and X2 as derived from the factor solution is equal to

(0.427 x 0.192) + (-0.013 x 0.180) + (-0.601 x 0.507) + (0.032 x -0.152) + (-0.159 x 0.493) = -

0.308, while the correlation between variables X3 and X5 is equal to (0.571 x 0.077) + (-0.044 x -

0.451) + (-0.198 x 0.359) + (0.279 x 0.024) + (0.258 x -0.576) = -0.155. These values are shown

in table 4.

For indicators in group 1, there is a positive and very weak relationship between compliance

with road safety rule and the availability of seat in public transport. This implies that road safety

rules have nothing to do with the availability of a seat in public transport, as it is ideal that for

public transport to operate, the operator must ensure the seat is available. Also, there is a

positive and strong relationship between compliance with road safety rule and the reliability of

public transport system. This implies that when public transport operator complies with road

safety rules, the passenger will pass a vote of the reliability of the system.

For indicators in group 2, there is a negative and a very weak relationship between the condition

of vehicle used for public transport and time delay at a bus stop. Although there is a very weak

correlation, the negative direction implies that when vehicles are in poor condition, there will be

a frequent delay at a bus stop.

For indicators in group 3, there is a negative and a very weak relationship between overloading/

over-speeding and safety. Although there is a very weak correlation, the negative direction

implies that when overloading/over-speeding is high, the safety of public transport is less and

there is a high probability of accident occurrence. This corroborates the studies of Sumaila

(2001); Gbadamosi (2003); Gbadamosi (2005); Yusuff (2015); Atubi and Gbadamosi (2015). For

indicators in group 4, there is a positive and a very weak relationship between the cost charge

per trip and the competence of drivers. This indicates that the cost charge per trip cannot

necessarily be because of the competence of drivers. For indicator in group 5, there is one

indicator which has to do with the relationship of passengers with public transport drivers. Since

it is a single indicator, there is no need for correlation.

Table 4: Correlation matrix showing the behaviour of indicators

Variable X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12

X1 1.000

X2 -.308 1.000

X3 .230 .061 1.000

X4 .039 -.007 .159 1.000

X5 .008 .011 -.015 .114 1.000

X6 .094 -.017 .126 -.033 .023 1.000

X7 -.025 .036 -.017 -.250 -.175 .079 1.000

X8 .171 .149 .170 -.129 -.138 .202 .046 1.000

X9 -.089 .091 .084 .021 .053 .146 .070 .012 1.000

X10 .130 .039 .200 .169 -.012 .095 -.129 .048 .058 1.000

X11 .099 .105 .155 .016 .123 .120 -.101 .371 .096 .217 1.000

X12 -.083 .000 -.077 -.073 -.010 .056 .082 -.113 -.072 -.022 -.186 1.000

Source: Authors’ work.

CONCLUSION AND POLICY IMPLICATIONS

Factor analysis has been noted as the aggregation of statistical methods for reducing correlated

data into a smaller number of dimensions or factors. It has been used in various studies such as

psychology, health, social science, and management sciences. It is adopted in this study to

identify important indicators of the public transport system in Akure-Owo axis.

It reveals that the indicators: “Relationship with drivers” was ranked 1st, “Overloading and over-

speeding” ranked 2nd, “Compliance with road safety rule and order” ranked 3rd, “Cost charge per

trip” ranked 3rd, and “Condition of vehicle” ranked 5th as they constitute the key indicators of

public transport system along Akure-Owo axis.

The essence of identifying important service indicators of public transport system among

several indicators is to enhance the effectiveness of adopting economic and social theory (e.g.

opportunity cost, and scale of preference) in providing quality service and enhancing

passengers’ satisfaction of public transport system.

It is recommended that the operators of public transport system along Akure-Owo axis should

educate their drivers on establishing good relationship with passengers, and reduce the cost

charge per trip. For safety reasons, the operators should avoid overloading and over-speeding

which is in compliance with road safety rule and order, and also ensure their vehicles are

always in good condition.

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Cite this article as:

Adeniran and Olorunfemi (2019). IMPORTANT SERVICE INDICATORS OF PUBLIC TRANSPORT SYSTEM IN

AKURE-OWO, NIGERIA

SRI 5(6)285-301

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