Article citation information:
Aderibigbe,
O.O., Gumbo, T. Modelling the variation in the mobility pattern of
households in the urban and rural areas of Nigeria. Scientific Journal of Silesian University of Technology. Series
Transport. 2022, 117, 5-21.
ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2022.117.1.
Oluwayemi-Oniya ADERIBIGBE[1],
Trynos GUMBO[2]
MODELLING THE VARIATION IN THE MOBILITY PATTERN OF HOUSEHOLDS IN THE URBAN
AND RURAL AREAS OF NIGERIA
Summary. There is
evidence that rural areas are disadvantaged in mobility compared to urban
areas. Hence, this study examined spatial variation in the travel pattern of
households in urban and rural areas of Nigeria. This study used primary data
obtained through questionnaire administration on household heads in the
residential zones of both urban and rural areas studied, using the multi-stage
sampling technique. Findings revealed that variations exist for age, education level,
income level, and occupation in urban and rural areas, and household's average
daily mean trip frequency showed a level of fewer trips being generated in the
rural area than those in the urban area. Furthermore, the result of the
stepwise multiple regression analysis showed that transport mode, household
size, number of workers in the house, and occupation of household head were
significant variables influencing trip making in urban areas while age,
household size, the income of household head and number of employed people were
significant in the rural areas. This study concludes that differences exist in
the mobility pattern of urban and rural households, and as such, equal
consideration and attention should be given to them in policy formulations.
Keywords: trip
generation, socio-economic characteristics, mobility, urban, rural
1. INTRODUCTION
The
role of transportation in the development of any economy cannot be
overemphasised, it is very pivotal in the overall development of any society,
be it a rural or urban society, transportation constitutes the main avenue
through which different parts of the society are connected. This aligns with
the submission of Stead et al. [1], who opined that transport is inevitable to
human development whether in urban or rural areas, as it enables people to
participate in social and economic activities, thus, improving their overall
health.
Travel
represents an expression of an individual's behaviour and, as such, has the
characteristics of being habitual. As a habit, it tends to be repetitive, and
the repetition occurs in a definite pattern [2]. Studies by Solanke, [3-4,]
Oyesiku, [5], and Osoba [6], asserted that individuals in various locations
generate different mobility patterns, and almost all the world's urban areas
face difficulties coping with the variety of travel, which are made in response
to individual needs and desires. The descriptions of such travel activity
patterns provide considerable insight into the nature of daily life and
variations in the quality of life experienced by different groups of people. An
important observation from existing works on urban travel is that the
relationship between travel and individual characteristics implies, among other
things, that individuals with the greatest extent, variety, and frequency of
travel are those with the fewest constraints imposed upon them. Constraints can
be imposed by one’s socio-economic status, household and societal roles,
and location vis-à-vis the size and density of settlements. Although
research on mobility patterns of urban or rural dwellers exist, this study
advances such discussions by examining a comparative study of mobility pattern
as well as identifying factors influencing trip making in Nigeria's urban and
rural areas. In this study, a detailed investigation and review of factors
influencing household travel behaviour and mobility pattern were carried out.
2. LITERATURE REVIEW
2.1.
Travel Situation in Developed and Developing Countries
Transport
is essential in both developing and developed countries, although it is often
taken for granted. Macroeconomic facts about transport are indeed impressive.
Transport accounts for 3 to 8% of countries' GDP in Asia and the Pacific [7].
Over time, cities and towns have served as centres for entertainment, shopping,
banking, and other activities. Due to the growing population in the twentieth
century, most of these activities have also extended to the periphery.
Beginning as early as the 1940s, downtown stores and banks, for example, found
that they could serve their customers more conveniently by locating a branch in
the suburbs [8]. Bert et al. [9] believed that land-use and transport
policies’ sole aim is to improve accessibility. This reflects in the
ability of producers to transport finished goods to and between different
locations.
In developing
countries, urbanisation rates have led to a higher dependence on motorized
travel, hence, leading to a deterioration of transport infrastructures since
demand outweighs supply. [10]. This notion of dependence on motorised transport
is supported by Mackenzie and Walsh [11], who asserted that there has been an
increase in the number of vehicles globally. For instance, in 1950, about 53
million cars were on the world's roads, four decades later, this number rose to
over 400 million, with an average growth of 9 million motor vehicles per year.
Based on the above, it can be said that there has been an increase in car
dependence, which may influence travel, especially in developed countries.
However, despite the increase in car ownership and reliance on private
automobiles, it is pivotal to know if this has resulted in changes in the
mobility of people irrespective of the location. Hence, it is expedient to
identify spatial variation in travel of urban and rural dwellers to identify
factors influencing their trip making.
2.2. Factors Influencing Travel
Generally, it
has been discovered that different factors ranging from demographic attributes,
level of transport infrastructure, government policy, city structure and
location of households, among others, affect household travel behaviour
[12-15]. Socio-economic characteristics have a significant impact on travel
behaviour and must be adequately represented at a disaggregate level in models
that attempt to estimate the impact of the built environment on travel
behaviour. Age, household composition, income, gender, and car ownership are
the most important socio-demographic variables influencing travel behaviour. It
can be ascertained from the aforementioned studies that different factors
account for changes in travel patterns and that people have different travel
characteristics significant to them.
Household
Composition, Car Ownership and Income
A
study by Ryley [16] on the composition of households in Edinburgh found that
households with children have distinct travel behaviour characteristics. These
households are highly dependent on cars as their primary source of travel mode,
own but do not often use cycles, and favour cycle trips predominantly for
leisure rather than work journeys. Key stages within the household life cycle
that impact travel behaviours include gaining employment, having children and
retirement. Thus, households consisting of students and the unemployed are most
likely to use non-motorised forms of transport. Conversely, families consisting
of retirees and high-income owners are least likely to use non-motorised forms
of transport. According to Fadare and Alade [17], car ownership depends largely
on the income level of an individual and could significantly influence trip
making.
In addition to the above factors, psychosocial attributes also affect travel behaviour. These
psychosocial attributes include:
i.
Safety
ii.
Protection from socially undesirable groups
iii.
Feeling of prestige within the peer group
iv.
Identification with selected peer group
v.
Feelings of greater autonomy
Hiscock et al.
[18] studied the perceived psychosocial benefits of car use and
ownership. The study revealed some
psychosocial benefits to car users. They felt that they gained protection,
autonomy and prestige from their car and that car ownership is a form of high
social status. Their car provided them with protection from 'undesirable'
people, autonomy, convenience, and greater access to a greater range of
destinations than public transport.
In addition, Cullinane [19] observed similar psychosocial perceptions
amongst students attending universities in Hong Kong. It was discovered from
the study that car ownership was extremely low amongst the participants.
Respondents felt that public transport was cheaper, readily available and also
allowed them to interact with friends, hence, there is a perception that people
consider these psychosocial attributes when making trips, thus influencing their
mobility pattern.
Location of Land Users and Distance
These
factors are very important to people who choose their travel characteristics,
as their home and workplace are at two different locations. According to
Johansson et al. [20], time and distance influence travel behaviour in a
non-linear way. People get tired and bored in daily long-distance travel; this
discourages them from embarking on trips.
Urban Structure
The
urban structure is another aspect that defines travel characteristics. Census
data for the Houston metropolitan area shows that from 1990 to 2000, the share
of commuters driving alone to work rose from 75.7 to 76.6%, while the share
that carpools declined from 14.6 to 14.4%, and those who ride transit fell from
3.8 to 3.5% [21]. This happened because of the urban growth wherein the
high-density area, transit, and buses are available, but for the city sprawl,
there is a big area to cover, and it is difficult for transit and buses to
cover all the area. In another study by Boarnet and Crane [22], it was
discovered that land use and design proposals would influence the price of
travel and hence the type of trip undertaken. However, a study by Boarnet and
Sarmiento [23] in Southern California on the relationship between land-use
variables and travel behaviour was found to be statistically insignificant.
Congestion
Factor
Traffic
congestion issues have led to more complex problems such as delays, accidents,
and increased travel costs, to mention just a few, hence discouraging people
from making trips. Congestion indicator is on trip chaining: workers who
commuted in peak periods and non-work trips among alternative chains [24].
Subsequently, people may reduce or not embark on trips if there are road
congestions.
Stress/Health /Psychological Factor
Road users stuck in traffic congestions
easily feel stressed and pressured, which negatively affects their health.
Thus, all users tend to self-seeking behaviour like frustration and sometimes
anger [25]. According to Koslowsky et al.
[26], industrial and
organisational psychologists are generally concerned with more indirect
effects, such as attitudinal and emotional outcomes, which indirectly affect
the mobility pattern of people.
Impact
A densely populated city suffering from heavy
traffic experiences negative consequences like air pollution, fuel consumption,
long travel time and stress [27-28, 21]. There are positive and negative
impacts during travel, and traffic congestion is one of the negative impacts,
resulting in delays and increased travel. Hence, discouraging people from
embarking on a trip.
Cost of Trip
Travel cost is another factor that could influence travel
behaviour, and it is one of the issues highlighted when people commute. Every
single commute needs cost, but the difference is the high or low cost, which
depends on the type of commute and the distance of commuting. This will also
influence the use of either motorised or non-motorised transport, as the high
cost of trips may influence the use of non-motorised trips such as cycling or
walking in a friendly environment, and vice versa [29]. Hensher and King [30] studied the availability of parking spaces
and the cost of parking on travel behaviour in Sydney. It was found that in 97%
of the responses, the cost of the parking option was the most significant
factor that determined travel mode and, as such, influences trip making.
Based
on all these, trip making is a function of the socio-economic characteristic
and demographic characteristics, such as location, and other travel-related
impacts such as stress and fatigue, which could affect the health of an
individual. As a result, people have different travel patterns due to
variations in their socio-economic characteristics. This implies that
differences in location, socio-economic characteristics, and travel
characteristics of households could influence the mobility pattern of
individuals and households.
2.3.
Theoretical Review
The
theoretical link between transport and location is explained from the outlined
perspectives: Spatial Mismatch Theory, Social Exclusion Theory, and Social
Justice Theory.
The
spatial mismatch theory explores the relationship
between transport and poverty from a geographical perspective. It was developed
primarily in North America in 1960 by John F. Kain, and it explains the
relationship between location, accessibility, and poverty. This explains the spatial
barriers poorer people face to access jobs and services in the context of
suburbanisation and high car dependency [31]. As explained by the theory,
cheaper, more affordable housing tends to be in areas with poor transport
connectivity and poor service provision; thus, it becomes increasingly
difficult for those of lower income and without a car to access jobs and
quality services, hence, negatively affecting their quality of life. This can
be likened to the situation in the rural areas, where most households are
low-income earners who may not be able to afford the cost of living in urban
areas with quality services for transport and housing, thus, settling in
environments with poorer transport connectivity which disconnects them from
accessing good and quality services and transport infrastructures which may
impact their wellbeing negatively.
Hence, the location of an individual can be linked to his status, given
such socio-economic characteristics as income, level of education, and
occupation, among others, and these factors influence one's travel behaviour.
Overall, this theory explains the relationship between the locational pattern
of people and their socio-economic status. It emphasises the reason the poor
resides in areas where there are poor services due to their inability to afford
the luxury of urban living.
Social
exclusion theory: This examines relationships between transport
and poverty, and contrary to the spatial mismatch theory, social exclusion
literature focuses more on the consequences of transport deprivation than on
the processes leading to it. The
spatial mismatch theory is the process leading to the consequences being
explained by the social exclusion theory. Mainly a theory from the social
sciences, it is based on a term first developed in France by Red Lenoir in the
early 1970s, which refers to the loss of the ability to connect with the
services and facilities needed to participate in society fully. It explains the
consequences or the impacts of poor transport facilities on the poor, who live
in locations with poor transport connectivity; they become disadvantaged
because of their socio-economic conditions and their location. Research on
transport builds upon this general conceptualisation to define
transport-related social exclusion as the process by which people are prevented
from participating in the community's economic, political, and social life.
This is typical of the experience in rural areas, and Akure North Local
Government Area dwellers may not escape these consequences as most of them are
deprived of accessibility to good transport infrastructures to enhance their
mobility.
Social
justice theory: This approach was developed by John Rawls,
author of the seminal in the 1970s. His thought centred on the idea of fair
distribution and equality of opportunity. It examined transport-related
disadvantages and their relation to poverty from a perspective of inequality.
This approach relates mainly to the underlying idea of equality of access and
thus suggests that policies should focus on offering the greatest benefit to
the least advantaged members of society [32-33]. This approach allows equality
in the distribution of infrastructures and facilities to disadvantaged
communities and populations to enhance their mobility. This is particularly
relevant to those living in poor transport connectivity environments like rural
areas.
3.
METHODOLOGY
In
the second stage, buildings in the selected wards were identified, ranging from
semi-detached houses to hut structures made of traditional materials, among
others. Information from the National population commission revealed that there
are a total of 12,365 registered buildings in the selected wards, and 4% of
these buildings were selected for questionnaire administration. The number of
questionnaires administered in each settlement was based on a sample size of 4%
of the listed households, and the use of a 4% sample size in this kind of study
is not new. For example, Olawole [36], in a study of rural mobile phone usage
and travel behaviour in Nigeria, suggested a maximum of 10% sample of rural
household heads in studies involving rural residents. The last stage involved
the selection of respondents for questionnaire administration. The systematic
random sampling technique was adopted in selecting respondents, and an adult
not below the age of 18 years was surveyed. Further, 1 of every 25th and 10th
building was systematically selected, representing 4 and 10% of the total
building in the rural and urban areas, respectively. Using this procedure, a
total of 495 and 512 respondents were surveyed.
The
formula for the sample size in the study location:
K=
N/n…………………………i
Where K= sample size
N= number
of registered building/dwelling unit/household
n= represents 10 and 4% of all
households per settlement in the urban and rural areas, respectively.
Study
Area
The study locations, as presented in Figures 1 and
2, are Akure North Local Government Area (Rural) and Akure South Local
Government Area (Urban). The choice
of the study locations was influenced by the population size, occupation,
administrative activities, infrastructural provision, and development.
Akure
South Local Government Area is the capital of Ondo State in 1976 and the
headquarters of Akure South Local Government Council since its creation. The
socio-economic activities in the town have made it a convergent point for
people from different parts of the state. Also, the multifarious activities
performed in the local government have given rise to improvements in transport
facilities by influencing the state government to construct new roads and
rehabilitate the old ones. Akure North Local Government Area was created on the
1st of October, 1996. The local government is blessed with fertile land that is
good for agriculture. This has made farming the major occupation of the people.
This typically depicts the area as rural as most of the populace engage in
agricultural activities. Even though both local
governments are approachable by road, it is unfortunate that most communities
in some parts of the local government, such as Ala- Igabatoro, and Elefosan,
among others, are usually cut off from other parts of the local government,
especially during the rainy season as most of the footbridges in the area are
over flooded, making transportation a big task.
Fig.
1. Study Location (Akure North Local Government Area- Rural Areas)
Source:
ARCGIS 2021
Fig.
2. Study Location (Akure South Local Government Area- Urban Areas)
Source: ARCGIS 2021
4. RESULTS AND DISCUSSIONS
Information
and results on the socio-economic characteristics of respondents, travel
characteristics of respondents and factors influencing the trip generation of
households are discussed in this section.
4.1.
Socio-economic Characteristics of Respondents
Polk [37] found a significant
relationship between sustainable travel patterns and gender. Information on the
gender of respondents revealed that the majority (54.1%) of respondents in the
urban areas were male, while the majority (52.5%) of those in the rural areas
were females. The result on the age distribution of households showed variation
in the age of urban and rural households. From the study, 63.7% of respondents
in the urban area were between the ages of 18-40 years, while the elderly,
which were from 60-69 years old, accounted for the largest (43.7%) proportion
in the rural areas. This agrees with the assertions of the World Bank [38] that
the aged, who are from 60 years upwards, dominate the rural areas. The study
further identified the median age for respondents in the rural areas as 51
while that of the urban as 45.
The
result on education distribution also found variation in the distributional
pattern of households in both areas. While a greater number (72.1%) of
respondents in the urban area had tertiary education, only 33.3% of their
counterparts in the rural area fell into that category. Most of the rural
households (54.4%) had secondary education. A study by Gardiner [39] on
variation in the educational level and performance of both urban and rural
areas showed that most people between the ages of 25 and 34 in urban areas had
completed matriculation than their rural counterparts and more than double the
number of urban people has achieved a post-school qualification (tertiary
education) than the rural people have done, thus corroborating the findings of
this study. Occupation of residents or the profession an individual engages in
is a determinant of their level of income [40-41]. We found out that most of
the urban respondents were civil servants (42.5%) against 10.6%
of their counterparts within the same category in the rural areas. Households
in rural areas engage in farming activities, with 40.3% of the respondents in
this occupational category, while only 4% of their counterparts in the urban
area engage in farming. It is noteworthy that the variation in occupation of
urban and rural dwellers reflects their education level. The assertion by Ahn
[42] and Kyeremeh and Fiagborlo [43] that one’s level of education could
determine the type of job one could engage in upholds this fact. Also,
Nwachukwu [44] argued that farming is one of the dominant activities of households
in rural areas.
The income of residents is
another important variable in the explanation of trip making. To present this,
the income group for federal tax rating was adopted to illustrate the income
distribution of respondents. The minimum monthly income in the urban areas is N1000,
the maximum is N400,000, and the average monthly income for the
respondents is N51, 686.8k. Also, 54.8% of the urban dwellers earned between N60,000 - N79,999, thus constituting the
highest in that category. In the rural
area, majority (34.8%) of
the respondents earned below the federal government adopted minimum wage of N20,000.
This implied that the average income for rural households is low compared to
those in urban areas. The mean income for the rural dwellers stood at N35,215.9k,
while that of the urban areas was N51,686.8k.
Car ownership is a form of income earned by an individual, and it is another
socio-economic variable influencing mobility patterns. The study revealed that
about half (49.6%) of the respondents in the rural areas do not own a car; on
the contrary, almost half of the households, precisely, 49.3% of urban
households, own a car. This supports the assumption of Giuliano [45], which
argued that the rate of car ownership among low-income earners is low compared
to the high-income earners in the society, which may be typical of a rural
environment.
4.2. Travel
Characteristics of Households
The travel characteristics of respondents to
the trip frequency, travel cost, transport mode and trip purpose are discussed
here. Analysis of the trip frequency of households in the study areas revealed
that the urban dwellers made more trips than the rural dwellers. From the
study, 50.8% of respondents in the urban areas made an average of 4 daily trips
while 71.9% of their counterparts in the rural area made an average of 1 daily
trip. The mean trip stood at 2.29 and 1.36 for the urban and rural dwellers,
respectively. The reason for the differences in the trip generation rate of
households in both urban and rural areas of the study is not far-fetched, as
the majority of those in the rural areas do not own a car; hence, may be
discouraged from embarking on numerous trips. Our finding corroborates the
assertions from the 2017 National household travel survey [46], which stated
that rural dwellers make fewer trips compared to those in the urban areas. The
reason for the fewer trips by rural dwellers was attributed to factors ranging
from poor road conditions in the rural areas, lack of organized public
transport, and the low level of income, among others. Information on travel mode also
showed significant differences among the urban and rural respondents, while
50.7% of urban dwellers used a private car for their trips; this is not the
case in the rural area as a greater proportion (43.2%) used the non-motorised
travel (walking/cycling) for most of their trips. This is not surprising as
studies by Starkey [47] and Towner [48] upheld the use of
non-motorised transport as the dominant mode for most rural dwellers.
The
travel cost of respondents was also examined, and it was discovered that school
trips accounted for the highest cost by rural dwellers as 43.3% averagely spent
N4286. The majority of
them attributed this to distant learning due to the poor quality of education
in the neighbourhood; hence, travel to the urban centres for good, quality
education. Contrarily, the trip to work had the highest amount compared to
other trip types in the urban centres. It was discovered from the study that
travel costs, as explained by the respondents, includes money spent on fuelling
the vehicles, especially for those who made use of their private automobiles,
public transport fares and other cost incurred especially during peak hour
travels and traffic congestion. On average, 80% of the respondents spent N73,485 averagely on work
trips.
Overall,
the summary of socio-economic and travel characteristics of respondents showed
variation in age, education, occupation, income distribution, car ownership,
trip frequency and transport mode. This can be likened to the spatial mismatch
and social exclusion theory which explains the impact of one’s
socio-economic characteristics in determining the location (urban/rural) due to
the affordable housing and transportation cost in most remote locations; hence,
individuals with low income tend to settle in places with a lower cost of
living and vice versa. This, in turn, excludes them from better opportunities
that could positively affect their overall quality of life because of social
exclusion.
4.3. Analysis of Factors Influencing Trip
Generation of Respondents in the Urban Areas
A
stepwise regression analysis determining the factors influencing the trip
generation of households was carried out to identify factors influencing trip
generation.
The
formula for this is:
Y
= a + b1 x1 + b2 x2 +.........+ bn
xn + e (1)
Where Y represents the dependent variable. The
dependent variables, in this case, represents Y= Trip frequency,
x1,
x2, x3........xn represent the
independent variables,
a,
b are constants,
e is
the error term.
A
total of 10 predictors/independent variables listed below were employed in both
the urban and rural areas: age, the income of respondents, gender, education,
occupation, household size, cars in the household, employed people/workers in
the house, and travel cost and transport mode. As revealed in Tables 1 and 2
from the stepwise multiple regression analysis for the urban area, four
variables were significant in influencing trips. These variables include transport
mode, household size, workers in the house, and occupation of the household
head. The model summary result showed that the coefficient of determination (R2)
is 0.349, implying that about 34.9% of trip frequency is explained by the
combined influence of the four independent variables selected by the stepwise
regression model. Findings from this study corroborate earlier studies [15, 29]
that found a similar result of household size, travel cost, and transport mode
influencing trip generation.
In
addition, the ANOVA result with F=10.100, 7.495, 10.727 and 9.786 at ‘P=
0.02, 0.01, 0.00 and 0.00’ similarly concludes that there is a
significant overall regression using all the variables in the model.
Furthermore, the coefficient of the regression provides information on the
regression coefficient, standard error of the estimates and the t-tests. The
estimated coefficients are given under the heading 'Standardised (Beta)
coefficients'; the predicted change in the dependent variable when each
explanatory variable is increased by one unit, conditional upon the fact that
all other variables in the model remain constant.
The summary of
the regression coefficients was used to develop a multiple linear regression
model as:
Y= 3.153-0.280(MODE)+0.625(HHS)-0.570(EMPLOYED)+0.216(OCCU)
(2)
Prediction Ability of the Model: Coefficient
of Correlation "R", R = 0.591 means that there is a 59.1% linear
relationship between the dependent and independent variables, while
"Coefficient of Determination R2", R2 = 0.349
means that 34.9% of the dependent variable is explained by the independent
variables. From the model equations, the positive sign in the coefficient of
HHS (household size) and OCCU (occupation) indicates that an increase in their
numbers is also associated with an increase in trip generation for the
respondents. For instance, an increase in the number of the household by one
additional member will lead to an additional trip. However, the negative sign
in the coefficient of MODE indicates a reduction in trip generation upon
utilization of either walking/cycling. This implies that a reduction in the
accessibility to a private car or public transport for a member of the
household in the urban area will automatically reduce the trip generated by
such an individual and household. We can then deduce from this that the
availability of a car or accessibility to a motorized mode of transport such as
public transport for an individual or household will motivate travel; hence,
improving their mobility pattern and overall quality of life since mobility
enables interactions with people and places.
Tab. 1
|
Model summary for trip generation in the urban area
|
||||
|
Model |
R |
R square |
Adjusted R square |
Std.
error of the estimate |
|
1 |
.343a |
.117 |
.106 |
5.57582 |
|
2 |
.408b |
.167 |
.144 |
5.45396 |
|
3 |
.551c |
.303 |
.275 |
5.02097 |
|
4 |
.591d |
.349 |
.313 |
4.88568 |
a. Predictors: (Constant), Dominant mode of
transportation used
b. Predictors: (Constant), Dominant mode of
transportation used, Household size
c. Predictors: (Constant), Dominant mode of transportation
used, Household size, Number of employed people in the household
Source: SPSS OUTPUT/Authors’ Survey 2021
Tab. 2
|
Coefficients for Regression analysis of Trip Generation in
the Urban area |
||||||
|
Model |
Unstandardised coefficients |
Standardised coefficients |
T |
Sig. |
||
|
B |
Std. error |
Beta |
||||
|
1 |
(Constant) |
22.312 |
3.254 |
|
6.857 |
.000 |
|
Dominant
mode of transportation used |
-3.140 |
.988 |
-.343 |
-3.178 |
.002 |
|
|
2 |
(Constant) |
19.631 |
3.428 |
|
5.727 |
.000 |
|
Dominant
mode of transportation used, |
-3.198 |
.967 |
-.349 |
-3.308 |
.001 |
|
|
Household size |
.575 |
.273 |
.222 |
2.106 |
.039 |
|
|
3 |
(Constant) |
19.992 |
3.157 |
|
6.332 |
.000 |
|
Dominant
mode of transportation used, |
-2.635 |
.902 |
-.287 |
-2.920 |
.005 |
|
|
Household size, |
1.547 |
.358 |
.597 |
4.317 |
.000 |
|
|
Number of
employed people in the household |
-3.011 |
.791 |
-.531 |
-3.807 |
.000 |
|
|
4 |
(Constant) |
18.377 |
3.153 |
|
5.827 |
.000 |
|
Dominant
mode of transportation used, |
-2.563 |
.879 |
-.280 |
-2.918 |
.005 |
|
|
Household size, |
1.619 |
.350 |
.625 |
4.624 |
.000 |
|
|
Number of
employed people in the household, |
-3.232 |
.776 |
-.570 |
-4.166 |
.000 |
|
|
Occupation of the household head |
.486 |
.214 |
.216 |
2.270 |
.026 |
|
|
Source: SPSS Output 2021 a. Dependent
variable: average number of trips |
||||||
|
(Significant
at P≤ ‘0.05’) |
||||||
4.4. Analysis of Factors Influencing the Trip
Generation of Respondents in the Rural Areas
The
result of the stepwise regression analysis in Tables 3 and 4 for the rural
respondents revealed that four significant socio-economic variables: age,
household size, the income of household head and the number of employed people
in the house, were significant in influencing trip making. The R2 value,
which represents the coefficient of determination, shows 0.202 and 0.273 for
age and household size, respectively. This implies that age with R2 value
of 0.202 and household size, 0.273, contributes about 20.2 and 27.3% to
influencing the average trip making of respondents in the rural areas. Further
to this, the coefficient of multiple determination for income of household head
shows 0.326, implying that 32.6% of factors influencing the trip making of respondents
is influenced by the income of the household head while the number of employed
people in the household has an R2 of 0.368; thus, suggesting that the
average number of employed people in the house contribute 36.8% to factors
influencing the trip making of respondents in the urban area of the study.
The
ANOVA result with F=33.436, 24.577, 20.986 and 18.761 for the four significant
independent variables at ‘P =0.01’ for age, household size, the
income of household head and the number of employed people in the house further
revealed the significant overall regression.
The
coefficient of regression also explains the influence of the
predictors/independent variables in influencing the dependent variables. From
the table, it was discovered that the negative standardised coefficient for age
implies that a unit increase in age will portend a decrease in the trip
frequency of respondents in the urban area, while a unit increase in the
household size will lead to an additional increase in trips. Likewise, the result
of the regression analysis also established that the positive standardised
regression coefficient of 0.232 and 0.250 for the income of the household head
and the number of employed people, respectively, in the household explains that
a unit increase in the income of respondents and number of employed people in
the household by at least 0.232 and 0.250, respectively, will increase the trip
rate for the household. The model result also supports earlier assertions that
there is a likelihood for households with more seniors or elderly to generate
fewer trips than households with other age cohorts. This was attributed to the
deteriorating health condition of the aged, which may not encourage or allow
them to embark on numerous trips. Also, the majority of the elderly are
retired, thus reducing their number of trips.
The
summary of the regression coefficients was used to develop a multiple linear
regression model as:
Trip
Frequency=3.934 -0.284(Age)+0.368(HousSize)+0.231(INC)+0.250(EmpMM) (3)
Prediction
Ability of the Model: "Coefficient of Correlation
"R", R = 0.606 means that there is a 60.6% linear relationship
between the dependent and independent variables, while "Coefficient of
Determination R2", R2 = 0.368 means that 36.8% of the dependent
variable is explained by the explanatory variables.
Conclusively,
the common factors influencing trip generation of households in both urban and
rural areas are the household size and the number of employed people in the
house. From the model, an increase in the household size in both the urban and
rural areas will result in additional trip generation for the households.
Hence, the number of people living and feeding from the same pot portends an
increase in trip generation patterns. However, the impact of the number of
employed people on trip generation in the urban and rural areas varies, while
an additional employed member/worker for the urban dwellers will result in
lesser trip generation, the reverse is the case in the rural area. This implies
that while the addition of an employed member to an urban household may reduce
the number of trips being generated, other members of the household may not
necessarily embark on some trips such as shopping or recreational, to mention
just a few. Contrarily, the addition of an employed member for the rural
dwellers will lead to additional trips.
Tab. 3
|
Model summary of regression analysis of rural respondents |
||||
|
Model |
R |
R square |
Adjusted R square |
Std.
error of the estimate |
|
1 |
.450a |
.202 |
.196 |
4.97857 |
|
2 |
.522b |
.273 |
.262 |
4.77086 |
|
3 |
.571c |
.326 |
.311 |
4.60986 |
|
4 |
.606d |
.368 |
.348 |
4.48289 |
a. Predictors: (Constant),
age
b. Predictors: (Constant), age, household size
c. Predictors: (Constant), age, household size, average monthly income
of the household head
d. Predictors: (Constant), age, household size, average monthly income
of the household head, number of employed people in the household
Source: SPSS OUTPUT/Authors’ Survey 2021
Tab. 4
|
Coefficients for regression analysis of trip generation in the rural
area |
||||||
|
Model |
Unstandardised coefficients |
Standardised coefficients |
T |
Sig. |
||
|
B |
Std. error |
beta |
||||
|
1 |
(Constant) |
.844 |
1.867 |
|
.452 |
.652 |
|
Age |
-.278 |
.048 |
-.450 |
5.782 |
.000 |
|
|
2 |
(Constant) |
-3.167 |
2.113 |
|
-1.499 |
.136 |
|
Age, |
-.255 |
.047 |
-.412 |
5.480 |
.000 |
|
|
Household size |
1.047 |
.293 |
.269 |
3.570 |
.001 |
|
|
3 |
(Constant) |
-.394 |
2.217 |
|
-.178 |
.859 |
|
Age, |
-.244 |
.045 |
-.394 |
5.400 |
.000 |
|
|
Household size, |
.998 |
.284 |
.256 |
3.518 |
.001 |
|
|
Average
monthly household income of the household head |
2.569E-005 |
.000 |
.232 |
-3.211 |
.002 |
|
|
4 |
(Constant) |
3.934 |
2.619 |
|
1.502 |
.136 |
|
Age, |
-.176 |
.050 |
-.284 |
3.544 |
.001 |
|
|
Household size, |
1.434 |
.314 |
.368 |
4.567 |
.000 |
|
|
Average
monthly income of the household head, |
2.557E-005 |
.000 |
.231 |
-3.286 |
.001 |
|
|
Number of
employed people in the household |
1.709 |
.587 |
.250 |
-2.910 |
.004 |
|
|
a.
Dependent variable:
trip frequency Source: SPSS OUTPUT/Authors’ Survey 2021 (Significant
at P≤ ‘0.05’) |
||||||
5. CONCLUSION
This study examined the
variation in households’ trip generation pattern of urban and rural
dwellers. It was discovered that variations exist in the socio-economic
characteristics and travel characteristics of urban and rural dwellers. This
difference is seen in the age, income, education level, occupation, trip
frequency and transport mode of households, to mention just a few. The
regression analysis was used to identify significant factors influencing trip
generation in the rural and urban areas. From the analysis, variables such as
household size and the number of employed people in the house were both
significant in the urban and rural areas. Transport mode and occupation were
the other two significant variables influencing trip generation in the urban areas,
while the income of respondents and age were also significant for the rural
dwellers. The findings from this study show the extent of demographic
attributes of households in influencing the mobility pattern of households. In
addition to this, the travel pattern for the dominant transport mode goes a
long way in determining the trip frequency rate of individuals or households.
Given this, researchers need to be careful in relying on only socio-economic
attributes as measures of determinants of travel behaviour, especially in
developing countries. Considerations should be given to travel characteristics
such as the influence of transport mode, and transport cost, among others, in
measuring or determining household travel, or mobility pattern. Furthermore, we
proposed that equal privilege should be given to the rural areas as those of
their counterparts in the urban areas when formulating policies, especially
transport policies. These privileges could be in public transport provision and
the provision of relevant road transport infrastructures such as pedestrian
walkways that could enhance their mobility, thus improving their quality of
life. We conclude that the mobility pattern of respondents in the urban and
rural areas varies; these variations are observed in the trip generation rate,
travel characteristics and variations in factors influencing their travel.
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[1] Department of Urban and Regional Planning, University of Johannesburg South Africa. Email: oboniya@uj.ac.za. ORCID: https://orcid.org/0000-0002-8914-323X
[2] Department of Urban and Regional Planning, University of Johannesburg South Africa. Email: tgumbo@uj.ac.za. ORCID: https://orcid.org/0000-0003-3617-4996