Analysis of the Impact of Generation of Housing on the Performance of Soekarno-Hatta Street in Kasongan City

The purpose of this research is to analyze the effect of housing generation on the performance of Soekarno-Hatta Street in Kasongan City. The independent variable that is used as a factor that influences the generation is the number of family members who work, the number of family members who attend school, the number of family members who do not work and do not attend school, the size of the household, the number of car ownership in the vehicle unit, the number of motorcycle ownership, the number of bicycle ownership, total ownership of vehicles in one house, total income per family per month, and type of house. The research sample was taken at Wengga Housing, Katingan Karya Citra Housing, and Griya Cipta Housing. Analysis is using multiple regression methods. From the results of the research, it was found that the trip generation produced by a housing (pcu / hour) was influenced by the average of total ownership of vehicles in one house and the number of houses occupied in one housing. The form of the generation equation is y = -165,791 + 41,850 x (average of total ownership of vehicles in one house) + 0,321 x (number of occupied houses). To maintain the performance of Jalan Soekarno-Hatta is still stable (LOSC) with the assumption that the external traffic flow growth is 3.5%, then in the fifth year housing development should not exceed 1.8 times (2350 units) or addition of houses can only be 1002 units.


I. INTRODUCTION
The increasing number of population that is growing rapidly at this time must be able to go hand in hand with an increase in the business of fulfilling life needs. The growing number of requests for housing needs attract investors to build a new residential area that provides comfort, security and affordable prices. Housing location can be said to have been well arranged if it has been able to meet the requirements including good accessibility and then reach the place of work [1]. Katingan Regency as one of the cities located in the Central Kalimantan Province cannot be separated from urbanization. With urbanization, the need for housing continues to increase. A housing area can be said to be good if it has good and easy accessibility and is safe to reach the destination. This means that the transportation system in the housing area must be properly regulated. Soekarno-Hatta Street is the main road used for traffic from the Wengga Housing area, Katingan Karya Citra Housing, Griya Cipta Housing of Kasongan City. The development of these three housing will certainly cause loading problems on Soekarno-Hatta Street. Therefore, it is necessary to do research on how the trip generation model and its effect on the performance of the main road in the housing area. From the generated generation model, it can be predicted the recommended housing development limits so that the performance of a good main road can still be maintained.

II.
THEORETICAL REVIEW 2.1 Transportation Planning Transportation means moving or transporting something from one place to other place [2]. In a transportation system activity, there are several components that influence. These components can have different functions according to the type and form of the component itself. The component can be in the form of infrastructure and facilities [3]. Transportation means moving or transporting something from one place to other place [2]. In a transportation system activity, there are several components that influence. These components can have different functions according to the type and form of the component itself.
The component can be in the form of infrastructure and facilities [3]. Transportation planning is basically to predict transportation needs in the future which are related to economic, social and environmental aspects [4]. Transportation planning is a dynamic and responsive process to changes in land use, economic conditions, and traffic patterns. The current popular transportation planning concept is the four-stage transportation planning model [5]. This planning model is a combination of several sub -model series, each of which must be conducted separately and sequentially, namely: accessibility, trip generation and trip attraction, trip distribution, modal selection, route selection and traffic flow on the network (dynamic traffic flow).

Traffic Generation
The traffic generation is the amount of traffic generated by a zone or area per unit of time. The amount of traffic depends on the activities of the city, because the cause of traffic is the human need to carry out activities that relate to and transport goods needed. The generation of the trip is assumed that the generation and attraction of trip as a function of some zone-based socio-economic attributes (x1, x2, ... xn) [6]. P = f (x1, x2, …xn) A = f (x1, x2, …xn) where: P = Production/Generation A = Attraction x1, x2, … xn = Variable land use 2.3 Performance of the Road Section Service quality from road sections can be measured using a comparison of the volume of traffic flow to road capacity [7]. According to the Indonesian Highway Capacity Manual (1997), road capacity is defined as the maximum flow through a point on the road that can be maintained per hour at certain condition. For two-way roads (two-way combinations), but for multi-lane roads, the flow is separated by direction and the capacity is determined per lane. Capacity values are observed through field data collection as long as possible, capacity estimated from analysis of traffic conditions, and theoretically assuming mathematical relationship between density, speed and flow [8]. The equation to determine road capacity is as follows [8]; C = C0 x Fcw x Fcsp x Fcsf x Fccs. The level of service of a road section is a comparison between traffic volume and road capacity (V/C). Soekarno-Hatta is a type of primary collector road. Characteristics of road service levels on primary collector roads can be seen in Table 1. between independent variables is greater than 0.60. It is said that there is no multicollinearity if the correlation coefficient between independent variables is smaller or equal to 0.60 (r <0.60). [12].
Heteroscedasticity test aims to test whether in the regression model there is a variance inequality from residuals of one observation to another observation. If the variance from one observation to another observation remains called homoscedasticity or heteroscedasticity does not occur. Or if the variance is different then it is called heteroscedasticity. A good regression model is homoscedasticity or heteroscedasticity does not occur [13]. If there are certain patterns such as the dots that form a certain regular pattern (wavy, widened and then narrowed) it indicates that heteroscedasticity has occurred. If there is no clear pattern and the points spread above and below the number 0 on the Y axis, heteroscedasticity does not occur. According to [14] heteroscedasticity can lead to inefficient estimation of parameters so that they do not have a minimum range. Parameter estimation is considered efficient because it has a minimum variety, so that the variety of tools is constant or also called that the assumption of homoskedasticity is fulfilled. Heteroscedasticity testing uses a graph test; it can be conducted by comparing the distribution between the predicted values of the dependent variable and the residuals, the output of the detection will be printed in the form of data distribution on a scatter plot. A good regression equation is not having autocorrelation problems. If there is autocorrelation, the equation becomes not good or not suitable for prediction. The size in determining whether there is an autocorrelation problem with the Durbin-Watson (DW) test. Provisions of test results are if there is a positive autocorrelation if DW is below -2 (DW <-2) and autocorrelation does not occur if DW is between -2 and +2 or -2 <DW +2. [15]. Normality test is useful to determine the data that has been obtained is normally distributed. The normality test will be conducted using the chi square formula or chi square. Chi squared techniques are used to test the significance of frequency differences. It means that to interpret whether there are significant differences or not between the frequencies obtained with the expected frequency [16].

Data Collection
Primary data from this research are household characteristics (independent variables and characteristics of the population's trip of the Wengga Housing area, Katingan Karya Citra Housing, Griya Cipta Housing of Kasongan City; independent variables (household characteristics) and the dependent variable in this research is total trips per-family per day for activities out of housing. Primary data is obtained by observing questionnaires distributed to each house to be filled in b y the respondents. The numbers of respondents in this research were 300 household samples.

Analysis and Interpretation
In this research, the number of generation generated through cross-category analysis and analysis using SPSS (Statistics Product and Service Solution) program [17]. This research is in the form of quantitative descriptive with the relationship test of all variables including regression normality, multicollinearity test, heteroscedasticity test, and autocorrelation test. Whereas to analyze the load due to access road traffic in the Wengga Housing area, Katingan Karya Citra Housing area, Griya Cipta Housing area, Kasongan City uses the Indonesian Highway Road Capacity Manual approach of 1997.

Generation Model
The generation model is built in two data approaches, namely; 1) household-based generation models, and 2) zone-based generation models. By means of "trial and error" by eliminating the insignificant "x" variable, the equation for the household-based generation model is as follows: y = 0.308 + 0.326x4+ 0.206x8+ 2.010x9 where; y = total trips per family per day x4 = household size unit of people in the family x8 = the total number of vehicle ownership in one house x9 = income scale per family per month Furthermore, the estimation and classical test results are shown in Table 2, Table 3 Table 3 it can be seen that partially there is a significant influence between X4, X8, and X9 on Yi. From the Sig value. the three independent variables show a value of <0.05. The resulting Durbin-Watson (DW) value is 2,285 ( Table  2). From DW Table with 0.05 significance and number of data (n) equal to 300, and k by 3 (k is the number of independent variables), the DW value (2,285) is between dU and 4-dU, meaning there is no autocorrelation. The normality test in Figure 1 shows the normal curv e line (mean≈0), the points tend to approach the diagonal line, so it can be said to be normally distributed. Figure 2 shows the heteroscedasticity test graphically where the points are not patterned and spread above and below the y axis (number 0), meaning that there is no heteroscedasticity problem in the regression model. Multicollinearity test is that all the coefficient of determination (R 2 ) <R 2 value model, so it is concluded that there is no multicollinearity problem in the regression model. In the same way for the equation of the zone-based generation model, the equation of the generation model can be as follows: y = -165.791 + 41.850x8+ 0.321 x13 where; y = total trips per family per day x8 = total vehicle ownership in one house x13 = number of inhabited houses Furthermore, the estimation and classical test results are shown in Table 4, Table 5, Figure 3, and Figure 4.  The Durbin-Watson (DW) value generated is 2,522. From Table DW with 0.05 significance and number of data (n) equal to 300, and k by 2 (k is the number of independent variables), DW values (2,522) are between dU and 4-dU, meaning there is no autocorrelation. The normality test in Figure 3 shows the normal curve line (mean≈0), the points tend to approach the diagonal line so it can be said to be normally distributed. Figure 4 shows the heteroscedasticity test graphically where the points are not patterned and scattered above and below the y axis (number 0), meaning there is no heteroscedasticity problem in the regression model. Multicollinearity test that all of value determination coefficient (R2) <R 2 value model, then concluded there was no multicollinearity problem in the regression model. The classic test of the two generation model approaches shows results that have met the requirements. However, when viewed from the correlation value, the zone-based generation model shows a better value than the household-based generation model.

Analysis of Road Performance in Existing Conditions
Road performance analysis is carried out at peak hours using the value of degree of saturation (DS) that occurs. From the traffic data taken for 12 hours, the peak hours occur at 06.00 -07.00 at 676.9 pcu/hour. Fluctuations in traffic flow then can be seen in Figure 5. the trip can be divided into two, namely the flow coming from the internal zone (settlement) and the external zone (trajectory/outside the settlement). At the same peak hour, the contribution of traffic loads originating from the reviewed generation zone is as shown in Table 6. Based on the data from Table 6, the contribution of traffic flow burden from the reviewed settlement is 62.24% while the rest comes from the external zone which is 255.6 pcu/hour. Furthermore, the increase of traffic flow in the future for the internal zone uses the zone-based generation model obtained and the external zone uses the regional traffic growth rate of 3.5%.
Assuming the traffic impact due to settlement development is up to 5 years [18] Table 7, it can be seen that the performance of Soekarno Hatta Street still can be maintained in a stable traffic flow condition (LOS ≤ C) if the housing development is not more than 1.8 x (2350 units) or an additional of 1002 houses occur.
V. CONCLUSION several tests on the relationship between total trips on Soekarno-Hatta Street as the dependent variable (y) and as an independent variable (x), the zone-based generation model resulted a better model compared to the householdbased generation model. The best form of regression equation obtained is: y = -165,791 + 41,850x8 + 0,321x13 where y is total trips in one zone, x8 is the total vehicle ownership on average in one house, and x13 is the number of inhabited houses. Assuming that the rate of regional traffic growth is 3.5%, the average number of vehicle ownership is 4 units, and the traffic flow conditions on Soekarno-Hatta Street are still stable in the next 5 years, the allowable housing development is no more than 1.8 x (2350 units) or the addition allowed is only 1002 housing.