Modeling Trip Generation Pattern by Adopting Multiple Linear Regression in at Existing Traffic Condition

doi:10.21203/rs.3.rs-1959512/v1

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Modeling Trip Generation Pattern by Adopting Multiple Linear Regression in at Existing Traffic Condition

https://doi.org/10.21203/rs.3.rs-1959512/v1

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The beginning of this study is made with the objective of developing the predicted household trip generation models for Hossana town that involve the socioeconomic characteristics and land use trends. For the purpose of this study, Addis sub town was used in the analysis. In this sub town, 9 nodes of the road segment were also selected for travel flow analysis. Totally 384 forms were distributed in the town for home interview purpose. The data collected was analyzed and classified in order to qualify the social and economic features in each zone using principal component analysis method. Finally, the regression analysis taken under four components reduced by principal component analysis. The relationship between daily household trips and socioeconomic characteristics were developed using Multiple Linear Regression technique under four components reduced by principal component analysis using SPSS software package. P – Values of all variables are less than 0.05 as summarized in the table below. Which show all the variables/components/ are statistically significant within a given confidence interval. R ² = 0.519 shows that 51.9% of the variation of the dependent variable explained by the explanatory/independent/ variables. The value of goodness of fit indicates that the model is well defined and the trips are a function of the variables chosen for this study.

Household Trip

Multiple Linear Regression

Principal Component Analysis

Trip Generation Model

The mobility of people and materials of any nation across the world are mainly depending upon the transportation facilities that are planned and provided by the respective nations to the public. Cites constructed without transportation planning are suffering from many traffic and insurance problems. To overcome these problems; transportation studies must be conducted and their conclusion must be activated. The value of the land anywhere mainly depends upon the transportation facilities that exist for the better movement of traffic and transportation. Better transportation facilities in any area will enable the people to move freely to different places for various transactions[4]. These transactions will help the people to develop various social, cultural, economic and business activities across the world and will make them to grow in a faster rate. The administration in any place needs better network connectivity for easy mobilization. The better network will make the products to be utilized more and more by many people across the country that reduces the cost of the product too[12]. This will also encourage the people to make a greater number of trips from one place to other place for various activities. Better movement in any place needs better network connectivity. The nodes of every place have to be well connected by other places by good road network. The number of vehicles moving on the road is to be controlled by various traffic and transportation rules and regulations in case of road transportation [5].

According to [1] Transport planning is an important part of overall town and country planning. Any change in the transportation system is reflected in number of impacts. Transportation planning is thus meant to understand the nature of these problems and to formulate proposals for the safe and efficient movement of goods and people from one place to another place. To come up with efficient movement of both people and goods, understanding trip generation is ultimate solution. The objective of trip generation is to estimate the number of trips originated in each zone within the study area, which it is usually correlated to the socioeconomic characteristics of the resident population in that zone[10].

1.1 BACKGROUND

1.1.1 Trip and traffic models

A trip is a one- way person movement by a mechanized mode of transport having two trip ends, an Origin and a Destination. An origin is called as the start of the trip/generation of trip, where as a destination is called as the end of the trip/attraction of trip. Trips are usually divided into Home based Trips and Non-Homed Trips. Home based trips are the trips having one end of the trip (either origin or destination) at the home of the persons making the trip. Non homebased trips are the trips having neither end at the home of the person making the trip. The trip ends are again classified as Generations and Attractions. A generation is the home end of any trip that has one end at the home and is the origin of the trip. An attraction is the non-homebased trip and is the destination of a trip. In urban areas, the demand for the travel is very high. The rate of population is high followed by a greater number of vehicular populations. This will not only affect the environment but also creates lot of traffic problems. The traffic moving from one place to other place on every day will suffer with lots of congestion, frustration that deals with many unknown accidents. Hence the traffic moving in the urban areas is having a lot of travel demand and planning for smooth movement of traffic from one place to the other place[3].

The travel demand is more common in urban areas and is required to take lot of precautions to forecast the travel demand so as to control the urban traffic. Trip Generation Modal, Trip Distribution Modal, Traffic Assignment Modal, Modal Split Analysis the four stage/ FSM/ basic traffic models that can be used to forecast the future traffic in urban areas by considering their influencing parameters and by certain assumptions. Each modal is having its own assumptions and methodology of calculating the future traffic in urban areas. Trip Generation Modal is the general term used in the transportation planning process to cover the field of calculating the number of trips ends in a given area [6].

[7] stated that the first trip model phase of the transportation planning process deals with surveys, data collection and inventory. The next phase is the analysis of the data so collected and building modals to describe the mathematical relationships that can decide the trip making behavior. The analysis and modal building phase start with the step commonly known as Trip generation.

Multiple Regression Analysis

This technique can be applied for solving various transportation problems that exists in the urban areas. In the case of trip generation equations, the dependent variable is the number of trips generated and the independent variables are the various measurable factors that influence trip generation. These independent variables are the land use and socio-economic characteristics. Multiple Regression equation is of two types. Multiple Linear Regression (MLR) Equation and Multiple Non-Linear Regression Equation. For the research work Multiple Linear Regression Equation used to analyze the trip generation modeling. Typical model used to estimate trips originating in a typical household for a given residential area

$${y}_{p}={a}_{0}+{{a}_{1}x}_{1}+{{a}_{2}x}_{2}+{{a}_{3}x}_{3}+---+{{a}_{n}x}_{n} \left(1\right)$$

Where y_p = Number of trips generated for a particular purpose, x ₁, x₂, x₃, -----------, x_n are the independent variables, a₁, a₂, a₃, ----, a_n are the coefficients of respective independent variables and a_o is the regression constant.

1.2 STATEMENT OF PROBLEM

In urban areas, the demand for the travel is very high. Since Hossana town is on the way of rapid growing in urbanization, activities of people in the area increases from time to time. As result a greater number of trips generated to attractive areas of the town. Hence the traffic moving in the urban areas is having a lot of travel demand and planning for smooth movement of traffic from one place to the other place. In trip generation modeling in addition to personal trips, freight trips are also of interest. Although the latter comprises about 20 percent of trips, their contribution to the congestion is significant. Freight trips are a critical component of traffic impact analyses and very important information to understand how goods move within an urban area. Freight trip generation refers to different areas or particular objects and may be estimated either the number of vehicles (truck-based model) or by the quantity of commodities measured in tons or values (commodity-based model) and influenced by several factors. In order to obtain reduced traffic problem and advanced generation/attraction models in the town, effective trip generation play ultimate role. Accurate trip generation results will lead to more accurate trip assignments when forecasting travel demands. Therefore, developing trip generation model by carrying out a clear analysis using new statistical approach known as principal component analysis which play significant role in the improvement of the existing traffic movement.

1.3 Objectives

1.3.1 The General Objective

The general objective of the research is to study and to develop model for trip generation Pattern of Hossana Town at current Condition.

1.3.2 The specific objectives

To perform detail study of project areas by their corresponding zone division with respect to factors making trip behind the trip generation.
To know the trip pattern and contributing factor in relation to generation
To identify the major independent variables that affect the trip generation rate.
To develop mathematical relationships which synthesis the trip making pattern on the basis of observed trips.

1.4 Scope of the study area

Hossana is located at the southern part of Ethiopia and at 231km far from Addis Ababa which is the capital of Ethiopia. The geographic position of the town falls between 734'59.988"N and 3752'59.880"E longitude with an average altitude 2300m above sea level. The total population of the town was 126,786 with 61,728 male and 65,058 females according to Federal Democratic Republic of Ethiopia Central Statistical Agency in 2013. From the Housing Units of Towns by Type of Housing Unit: 2007; All housing unit = 16,082, Conventional = 15,607, Improvised = 475. There are three sub – towns in Hosanna town namely, (Sech Duna, Gofer Meda and Addis). Now our study was limited to one sub – town known as Addis sub town. The study area Addis sub town is one among three sub towns (Sech Duna, Gofer Meda and Addis).

2.1 Introduction

The study area Addis sub town is one among three sub towns (Sech Duna, Gofer Meda and Addis). Addis sub town was selected for traffic analysis among the other zones for the following special cases. The section is currently categorized under major central business district. In addition, the traffic movement in the area is larger relatively to other sections of the town. A traffic analysis zone or transportation analysis zone (TAZ) is also the unit of geography most commonly used in conventional transportation planning models. The O-D travel data is collected from each selected block from different traffic zones and the total number of trips made by household members for each categories of trips was identified. Finally, multiple linear regression (MLR) technique has been incorporated to develop the trip generation after important statistical analysis completed and models for transportation forecasting and planning was built.

2.2 Study Participants

In order to achieve the objective stated in chapter one, numerous groups were participated in this study. Hosanna town municipality officers, Hosanna town Transport officers, Hosanna town statistics officers were the most common contributors among all the others.

2.2.1 Determination of sample size and selection process

The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In this study sample sizes chosen by using a confidence level, i.e., the larger the required confidence level, the larger the sample size (given a constant precision requirement) using Cochran formula (Cochran, 1977).

If the target population is unknown;

$$n=\frac{{Z}^{2}Pq}{{d}^{2}} \left(2\right)$$

Or $n=\frac{{Z}^{2}P(1-P)}{{d}^{2}}$ Where;

n = desired sample size (when the population > 10,000)

Z = standard normal deviate; usually set at 1.96 which correspond to 95% confidence level

p = proportion in the target population estimated to have a particular characteristic. If there is no reasonable estimate use 50% (i.e 0.5)

q = 1-p (proportion in the target population not having the particular characteristics)

d = degree of accuracy required, usually set at 0.05 level

If the target population is known;

$$n=\frac{{NZ}^{2}P(1-P)}{{d}^{2}\left(N-1\right)+{NZ}^{2}P(1-P) }$$

Hence target population of the study area (Addis sub town) is not known, Eq. (1) was used to determine the sample size to make home interview.

Here; using the formula$n=\frac{{Z}^{2}Pq}{{d}^{2}}$ by assuming the proportion of a target population with certain characteristic is 0.50, Z statistics is1.96 and we desire accuracy at 0.05 level, then the

Sample size was calculating as;$n=\frac{{1.96}^{2}*0.5*0.5}{{0.05}^{2}}$ = 384.

Once the number of sample to be collected was known, the random/probability sampling technique was used hence each of the variables has an equal chance of being selected among population.

2.3 Method of data collection

Based on the strategy and necessity of data, both primary and secondary data were collected in this study. The primary data obtained through home interview survey and direct record. Household interview survey was carried out to collect data on trip and socio-economic characteristics. A questionnaire form was prepared for this purpose. The form included questions on income level, number of persons in the family, number of daily trips, mode of transportation, purposes of trips and car ownership. Households were randomly selected and interviewed at their homes. Direct personal interviews with respondents were made and the obtained data recorded in the prepared questionnaire sheet. All the data was collected for every 15 minutes time interval. This interval is chosen because Highway Capacity Manual (HCM) uses this interval as the base unit for capacity calculation, and also it is rather practical from the standpoint of the person collecting the data. Total person counts, vehicle counts, trips by mode, and mode shares were then related to characteristics of the site.

Zoning the Study Area

To facilitate the data collection in transportation planning processes, the area under consideration is divided into a number of zones. Totally, traffic flow data from 9 selected segments namely; “St. Mariam church to Heto, Mubarek Garaj to Heto, Hosanna Maremia to Heto, St. Mariam to Taywan, Menharia to Luci, Mesgid to Maremia, Adebabay to Maremia, Total to Luci, and 18 Mazoria to Luci” was used in the study. This road segment was selected based on the traffic flow, land use and geographic information.

2.4 Methods of data processing and analysis

2.4.1 Variables included in the study

There are multiple factors which affecting the trip generation as explained earlier in the introduction section.

Land-use
Socioeconomic characteristics
Car ownership

Data collected from both primary and secondary sources were processed and analyzed by using a statistical technique known as principal component analysis (PCA) in SPSS software package. PCA is a modern powerful statistical tool for data analyzing (Paul, et al., 2013).

3.1 Analysis of socio-economic and demographic data

3.1.1 Demographic Characteristics of Households

According to the survey, population in age group 15–19 shares the highest number 15.7 ≈ 16% among the total population. The home interview data collected also indicates that the population with in age group 15–25 shares the highest number among the total household members. This was due to the population in this age groups are students. Hence, they make trip in the day to the minimum 3 times per day through going to school and coming to home. Out of school and home-based trip, peoples in this age group also make trip for other purpose to conduct family’s task especially. In contrast, population in age group 70+ (both men and women) make a few trips in a day. They stay at the home rather making trip for different purpose. The population aged 0–14 years is only little less than half of the overall population, while the ‘youth labor force’ (15–29 years) is about one fifth of the entire population. However, in urban areas migrations, reproductive behavior, and mortality have been changing the population structure so that, for example, in Addis Ababa the population aged 0–14 is about 30 per cent and the population aged 15–29 is 40 per cent[2].

Table 2

Household population by age and sex
Age group	Male	Female	Total
0–4	12.3	11.7	11.9
5–9	12.3	10.9	11.5
10–14	14.2	13	13.6
15–19	15.2	16.1	15.7
20–24	9.8	10.9	10.4
25–29	8	9.8	9
30–34	5.9	5.9	5.9
35–39	5.7	5.6	5.7
40–44	4.6	3.7	4.1
45–49	3.3	3.1	3.2
50–54	1.8	1.9	1.8
55–59	2	2.1	2.1
60–64	1.6	1.9	1.8
65–69	1	1.4	1.3
70–74	0.8	0.9	0.8
75–79	0.5	0.5	0.5
80 +	0.9	0.6	0.7
Total	100	100	100

The following Tables 3, 4, and 5 indicates demographical characteristics of Hosanna town taken from 2007 Population and Housing Census of Ethiopia conducted under the auspices of the Population Census Commission.

Table 3

Population, Households and Housing Units for Hosanna town by Sex: 2007
Population			Number of Households	Number of housing unit
Both sex	Male	Female	Number of Households	Number of housing unit
69,995	35,523	34,472	16,962	16,337

Table 4

Housing Units of Hosanna Town by Number of Households and Average Number of Households per Housing Unit: 2007
All Housing Unit	Number of Households Per Housing Unit			Total number of Households	Average Households Per Housing Unit
All Housing Unit	1 household	2 household	≥ 3 household	Total number of Households	Average Households Per Housing Unit
16082	15519	522	41	16706	1.039

According to the federal democratic republic of Ethiopia central statistical agency statistical report on the 2012 urban employment unemployment survey, the average household size of SNNPR computed from the total population to that of the total households was 1, 936, 997/520, 587 = 3.72.

3.1.2 Socio-economic characteristics

A. Household Education

According to Percent distribution of the urban household population by five-year age group, according to sex and residence, Ethiopia 2000, the majority of Ethiopians have little or no education, with females much less educated than males. Sixty-two percent of males and 77 percent of females have no education, and 27 percent of males and 17 percent of females have only some primary educations.

Less than 3 percent of males and 1 percent of females have completed primary education only, and 6 percent of males and 4 percent of females have attended, but not completed, secondary school. 1 only 3 percent of males and 1 percent of females have completed secondary school or higher. The male female gap in education is more obvious at lower levels of education primarily because the proportion of males and females attending higher levels of education is so small.

B. Employment Status

Employment status of a person indicates the level of involvement and degree of decision-making in respective activity. In the census, employment status is classified as paid employees (Government employees, NGO’s employees and domestic employees), self-employed, unpaid family workers, apprentice, members of cooperatives and others. Employment status was asked for all employed and unemployed persons aged ten years and over and those who had prior work experience. In this 2007 population and housing census, statistical data on activity status, activity rate, reason for inactivity, employed, unemployed, unemployment rate and employment status are presented in Tables 6 and 7.

3.2 Principal component analysis results

Principal component analysis was based on correlation matrix of the variables and the correlation usually need a large sample size before they stabilize. KMO value greater than 0.8 can be considered goods i.e., an indication that component or factor analysis will be useful for these variables. KMO value less than 0.5 require remedial action either by deleting offending variables or by including other variables related to offenders. Table 8 show the correlation between independent variables. Correlation coefficient (r) of most variable is greater than or equal to 0.3. Variables with correlation coefficient closed to 1 has a strong correlation to each other. While variables whose correlation coefficient closed to zero shows their less correlation to each other. For example, Economically Active persons and Family size has a correlation coefficient 0.981 that indicates the variables are highly correlated to each other. Similarly, Monthly household Income and Distance of the zone from the town center (km) correlated with very less correlation coefficient.

3.3 Regression Results after PCA

The performance of the model is measured by the coefficient of the model (R²). The value of R² gives the proportion of the variability of the response variable accounted for by the explanatory variables. The higher the R² the higher the precision and accuracy of the model to predict the trip generation. The analysis of the model in Table 9 shows that the model is well fitted with (R² = 0.519) and could be used for prediction purposes. R² = 0.519 shows that 51.9% of the variation of the dependent variable explained by the explanatory/independent/ variables. The index is adjusted down to compensate for chance increases in R², with bigger adjustments for larger sets of explanatory variables. Use of this adjusted measure leads to a revised estimate that 51.4% of the variability in trip generation in the population can be explained by the four explanatory variables. High value of goodness of fit indicates that the model is well defined and the trips are a function of the variables chosen for this study.

The “Model Summary” table provides an estimate of the standard deviation of the error term (under “Std. Error of the Estimate”). Here we estimate the mean absolute deviation as 0.978 which is very small.

Table 9

Regression Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.720^a	.519	.514	.978

The equation of the model is then displayed to facilitate the visualization or the reuse of the model. This could be rewritten in the form of; y = 2.995 + 0.166X₁ + 0.373X₂ – 0.583X₃ + 0.112X₄. Y stands for dependent variable average daily trip per day per person and X_1, X_2, X₃ and X₄ stands for independent variables Demographic Characteristics, Household Income and Land Use characteristics, Distance from CBD, Population in age groups (yr.) 25–35 and above 65 respectively.

To estimate the total number of trips for each trip purpose for the future years, this model assumes that the reliable estimates for the significant independent variables are available for the forecast year, and the regressions between the numbers of trips and the independent variables are stable between the calibration and the forecast years.

Table 10

Regression Coefficients
Model	Unstandardized Coefficients		Standardized Coefficients	T	Sig
	B	Std. Error	Beta	T	Sig
(Constant)	2.995	0.05		59.983	0.000
Demographic Characteristics	0.233	0.05	0.166	4.667	0.000
Household Income and Land Use characteristics	0.523	0.05	0.373	10.462	0.000
Distance from CBD	-0.818	0.05	-0.583	-16.36	0.000
Population in age groups (yr.) 25–35 and above 65	0.157	0.05	0.112	3.132	0.002
Dependent Variable: Average number of trip made per/day/person

3.3.1 Accuracy and reliability

A trip generation model should be accurate and reliable. Accuracy means that the model should replicate as closely as possible that the actual number of trips originating or ending in zones and that should be no bias (which is a systematic under – or – over – estimating and trips). Reliability means that the amount of error is minimized.

3.3.2 Validation Analysis

Model verification tests the ability of the model to predict future behavior. This requires comparing estimated model results to survey observations. A random sample of 100 observations was selected from all regions to check the performance of the developed regression and cross-classification approaches. These 100 observations were not used in regression analysis or in rate development. The average values of actual (observed) and estimated trips using regression and rate approaches were summarized to check the validity of a model. Although the outcomes of both approaches were comparable and consistent with the actual field data, the rate approach provides better estimates. Thus, models are applicable to estimate residential trips, provided that the data are within the limits and context of the town. Since the estimated model results and survey observation are in acceptable agreement the model developed is valid.

3.4 Travel demand and trip characteristics for OD matrix

Since the project study area lies within several TAZs, an analysis of the nine (9) project area TAZs was completed. Different transport trip rates between one node to another node. Trip generated and attracted to selected nodes depend up on different demographic factors, living conditions, economic and other related factors. The following 9 nodes below is a summary of the TAZs selected for the analysis and the changes in trips generated selected based on the above listed factors.

1. St. Mariam to Heto

2. Mubarek Garaj to Heto

3. Hossana Maremia to Heto

4. St. Mariam to Taywan Market Center

5. Menaharia to Luci

6. Total to Luci

7. 18 Mazoria to Luci

8. Mesjid /Handa Mall/ to Hossana Maremia

9. Adebabay Fitona Hotel to Hossana Maremia

3.4.1 Land uses

Trip generated from a particular segment depend on land use with several purposes like; Residential, Retail, Garage, office, parking, school, hotel and shops-based trips. Among the above particular nodes, St. Mariam to Heto, Mubarek Garaj to Heto, And Hossana Maremia to Heto nodes are bordered by residential. Except this node all the other nodes/ road segment/ cross through commercial areas.

3.5 Travel mode experienced in the study area

Two-wheeler (motor cycle), three-wheeler (Bajaj), and walk only are most common travel mode experience in the study area particularly. Table 11 below shows travel mode of trip generated from particular area. This table summarizes the total trip generated from particular nodes with in peak 15-minute time interval. As per the land use value, the trip made in different nodes vary to each other. Because land use one factor to determine the trip generated from and attracted to particular area. Since the node from St. Mariam to Taywan Market Center and Menhariya to Luci are commercial areas, the trip made in these particular segments greater than that of trip made in other segments as shown below.

Trip made in the other nodes like, Mubarek Garaj to Heto, Total & Luci and Hossana Maremia to Heto is relatively low that is the trip affected by the land use of the area. As shown in the table, walk only shares the large extent of the trip generated from all areas. Trips categorized under “others” is the data of trip taken from vehicle count out of two and three wheelers. Among the nodes described below, Menhariya to Luci node is the only segment where three-wheeler (Bajaj) is ordered by transport sectors. Except Menhariya to Luci node, there was no order of transportation system in all other nodes listed in the table. The traffic flow in the node b/n St. Mariam & Taywan Market Center needs immediate order of transportation system in the current status. Other segments like, St. Mariam & Heto, Mesjid /Handa Mall/ & Hossana Maremia and 18 Mazoria & Luci needs the order of transportation system with in short period of time. In the current status the road segment from Menhariya to Luci can accommodate three wheelers safely. However, having the consideration of growth rate 7%, at the end of 2025 the transportation system shall be shifted in to other modal split to accommodate smooth flow in this particular road segment.

The model was then developed in this principal component. This could be rewritten in the form of; y = 2.995 + 0.166X₁ + 0.373X₂ – 0.583X₃ + 0.112X₄. Y stands for dependent variable average daily trip per day per person and X_1, X_2, X₃ and X₄ stands for independent variables Demographic Characteristics, Household Income and Land Use characteristics, Distance from CBD, Population in age groups (yr.) 25–35 and above 65 respectively. The main factors that affect the household trip production rate were analyzed based on their coefficients. A unit change in Demographic Characteristics variables makes 0.166 estimated change in trip generation. The main factors that affect the household trip production rate in demographic characteristics was gender, employee rate per household, household age groups, car ownership and number of students in the household. A unit change in Household Income and Land Use characteristics makes 0.373 estimated change in trip generation and others interpreted in the same manner. P – Values of all variables are less than 0.05 as summarized. Which show all the variables/components/ are statistically significant within a given confidence interval. R² = 0.519 shows that 51.9% of the variation of the dependent variable explained by the explanatory/independent/ variables. The value of goodness of fit indicates that the model is well defined and the trips are a function of the variables chosen for this study.

Travel mode experienced in the study area is commonly two-wheeler (motor cycle), three-wheeler (Bajaj), and walk only particularly. There were nine nodes selected to analysis origin and distribution of traffic. As per the land use value, the trip made in different nodes vary to each other. The traffic flow in the node b/n St. Mariam & Taywan /Market Center/ needs immediate order of transportation system in the current status. Other segments like, St. Mariam & Heto, Mesjid /Handa Mall/ & Hossana Maremia and 18 Mazoria & Luci needs the order of transportation system with in short period of time. In the current status the road segment from Menhariya to Luci can accommodate three wheelers safely. However, having the consideration of growth rate 7%, at the end of 2025 the transportation system shall be shifted in to other modal split to accommodate smooth flow in this particular road segment.

Competing interests: We declare there is no competing and conflict interests in this original paper of "MODELING TRIP GENERATION PATTERN BY ADOPTING MULTIPLE LINEAR REGRESSION AT EXISTING TRAFFIC CONDITION".

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Tables 1, 5, 6, 7, 8, and 11 are available in the Supplementary Files section.

Table1.png
Table 1: the initial and end point of three-wheeler trip ordered
Table5.png
Table 5: Housing Units of Hosanna Town by Number of Persons and Average Number of Persons per Housing Unit: 2007
Table6.png
Table 6: Population Aged 10 Years and Above by Sex, Economic Activity Status, Age Group and Economic Activity Rates, Hosanna town: 2007
Table7.png
Table 7: Economically Active Persons of Hosanna Town Aged 10 Years and Above by Sex, Employed, Unemployed and Unemployment Rates: 2007
Table8.png
Table 8: Correlation Matrix
Table11.png
Table 11 travel mode and corresponding trip generated from particular area

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Modeling Trip Generation Pattern by Adopting Multiple Linear Regression in at Existing Traffic Condition

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Abstract

Figures

Introduction

1.1 BACKGROUND

1.1.1 Trip and traffic models

1.2 STATEMENT OF PROBLEM

1.3 Objectives

1.3.1 The General Objective

1.3.2 The specific objectives

1.4 Scope of the study area

Method And Material

2.1 Introduction

2.2 Study Participants

2.2.1 Determination of sample size and selection process

2.3 Method of data collection

2.4 Methods of data processing and analysis

2.4.1 Variables included in the study

Results And Discussion

3.1 Analysis of socio-economic and demographic data

3.1.1 Demographic Characteristics of Households

B. Employment Status

3.2 Principal component analysis results

3.3 Regression Results after PCA

3.3.1 Accuracy and reliability

3.3.2 Validation Analysis

3.4 Travel demand and trip characteristics for OD matrix

1. St. Mariam to Heto

2. Mubarek Garaj to Heto

3. Hossana Maremia to Heto

4. St. Mariam to Taywan Market Center

5. Menaharia to Luci

6. Total to Luci

7. 18 Mazoria to Luci

8. Mesjid /Handa Mall/ to Hossana Maremia

9. Adebabay Fitona Hotel to Hossana Maremia

3.4.1 Land uses

3.5 Travel mode experienced in the study area

Conclusions

Declarations

References

Tables 1, 5, 6, 7, 8, and 11

Supplementary Files

Status:

Version 1