Preliminary Research on Air Passenger Volume Variation in Kilimanjaro International Airport Tanzania

Airport Management needs data on Passenger Volume. Passenger Volume varies annually, monthly, weekly, daily, and hourly. A different aspect of Airport Management needs different data on Pasengger Volume Variation. Each airport has its Variation Characteristics. Kilimanjaro International Airport is a unique airport, as it is located close to a Mountain Kilimanjaro. Knowing it’s Passenger Volume Variation is important. Three Variations have been observed, the monthly, the weekly, and the hourly. The monthly variation shows that the high season happens from July to October. Based on three months of data, the weekly variation shows the inconsistency of the variation pattern. The weekly index of variation could not be developed. While the daily variation shows that high daily volume exists from Friday to Sunday.

required in making decisions and formulating policies for airport development. (Sefrus, et.al, 2020). Wang and Pitfield (1999) indicate that airport size leads to a different impact on the use of air passenger data in design peak hour derivation. Examining a linear model of arriving traffic offers a poorer explanation for small airports although a better one for large airports.
This paper presents the result of mini-research to develop a passenger variation pattern at Kilimanjaro International Airport, located in Tanzania.

RESEARCH METHOD
This Preliminary Research was done by following these steps. The research was started by understanding the Use of Air Passenger Demand Variation Characteristics, followed by underlining the Kilimanjaro International Airport, after that collecting the secondary data, and subsequently developing the index of variation calculation. The analysis of passenger volume variation was still simple, by using the Index of Variation. Its formula is simple arithmetic purpose-developed formula.

The Use of Air Passenger Demand Variations
Air Passenger Demand Variation is needed for Airport Development Plan, the Runway Operation Plan, the Terminal Development Design, the Terminal Operation Plan, and other aspects related to Airport (Alblaster, 2018;Çuhadar, 2014;Sartono, Dewanti & Rahman, 2019;Suprayitno, 2020;Supayitno, 2020a).
Airport Master Plan, normally, is developed based on Annual Passenger Traffic Volume predicted for the Airport Master Plan stage year and horizon year (Madhwal & Avdeeva, 2017;Suprayitno, 2020;Suprayitno, 2020a). Terminal Development Design has to incorporate the seasonal factor, which means based on Monthly Passenger Demand (Mao et al, 2015;Robertson & Wallin, 2014;Suryani et al, 2010). Terminal Operation Plan, includes the Facilities needed, normally is based on Daily and Hourly Passenger Demand Variation (Ahyudanari & Vendebona, 2009;Çuhadar, 2014;Hamzah, Dewanti, Muthohar, 2020), the same as for Sea Passenger Terminal (Suprayitno, Pambudi, Cahyono, 2017). Therefore, Air Passenger Demand Variations are needed. These are from the annual growth, the monthly (seasonal) variations, the weekly variations, the daily variations, down to the hourly variations.

Kilimanjaro International Airport
Kilimanjaro International Airport is strategically located between regions of Kilimanjaro and Arusha in East Africa, Tanzania. It is 23 NM ESE from the city of Arusha and 16 NM WSW from Moshi. (TCAA, 2020). The airport opened in 1971 and famously known as The Gateway to Africa's Wildlife Heritage.
Kilimanjaro International Airport operated by Kilimanjaro Airport Development Company Ltd (KADCO), a company now fully owned by the government of Tanzania since 1998 in the 110km 2 area of land. The airport located between Arusha and Moshi due to the potential of tourism and business opportunities for the two cities. It is few kilometers away from the highest standing mountain in Africa Mount Kilimanjaro and famous national parks such as Ngorongoro Crater, Serengeti, Lake Manyara, Mkomazi, Tarangire to mention the few. It is important also for cargo flight due to the climates condiction and fertile land hence the exportation of crops and animal products such as maize, onions, coffee, beans, banana, flowers to Europe, Middle East, and the Far East. Passenger and freight flows are the consequences of spatial interaction between various regions. Various socio-economic activities in a society, as well as land-uses, highly influence transportation demand (Teodorovic & Janic, 2017).
Airports in Tanzania are categorized into three categories, category A, category B, and Category C. Kilimanjaro International Airport is in category A which contains International airport (TCAA, 2020). The airport is operating in 24 hours and the services provided are Customs and Immigration, Aeronautical Information Services (AIS), Air Traffic Services (ATS), fueling, MET Briefing Office, Handling, Health, and Sanitation.
Passenger facilities available are hotels, restaurants, shuttle, taxi to Moshi and Arusha, medical Facilities, tourist office, bank, and Post office. There are cargo and one passenger terminal which serves domestic and international flights. Daily domestic flight to Zanzibar, Mwanza, Dar es Salaam, Arusha, and national parks such as Seronera, Lake Manyara, Ndutu, etc. The Airport has direct flights to Kenya, Netherland, Rwanda, Turkey, Ethiopia, Oman, Qatar, and Uganda.
The runway length at KIA is 3600m long and 45m wide. Elevation and reference temperature are 2932ft and 33 0 C, airspace classification is Class D, ICAO, and IATA code HTKJ and JRO respectively (TCAA, 2020; ICAO Doc. 7910).
Source : Google Earth

Monthly Variation of Passenger Volume
Two years of 2018 and 2019 data were used to analyze for the Monthly Variation Characteristics. The monthly variation characteristics must be seen based on the average monthly data. To make the monthly variation characteristics be seen more clearly, the Index of Variation for Monthly Passenger Volume was calculated by using the following Formula.

Weekly Variation of Passenger Volume
Three Months Passenger Volume data were used to analyze the Weekly Variation of Passenger Volume. The Weekly Variation characteristics must be seen based on the average weekly data. To make the characteristics be seen more clearly, the Index of Variation for Weekly Passenger Volume was calculated by using the following Formula. Observation on Weekly Passenger Volume, for these three months, shows that the Weekly Passenger Volume Variation is not consistent. In January 2019, the weekly volumes are higher in the first and second weeks of the month. While, in February and March 2019, the weekly volumes are lower during the first and second weeks of the months. Therefore, the Index of Variation for Weekly Passenger Volume was not calculated. More data are needed to see the general tendency of the Weekly Passenger Volume Variation. The data are presented in Table  2 and Figure 4 as follows.

Daily Variation of Passenger Volume
The passenger volume data of January 2019 were used to develop the Daily Variation Characteristics. The daily variation characteristics must be seen based on the average daily data. To make characteristics be seen more clearly, Index of Variation for Daily Passenger Volume was calculated by using the following Formula. Observation and calculation of the Daily Passenger Volume gave us these following variations. Friday to Sunday has relatively high passenger volume. While daily passenger volume is normally low from Monday to Thursday. The data and calculation results are presented in Table 3, Figure 5, and Figure 6 as follows.

CONCLUSION
The index of variation was used to analyze the passenger demand volume variations at Kilimanjaro International Airport (KIA). The main conclusions can be drawn from this collaborative small research as following.
 The important variable of air passenger demand volume variations consist of the annual growth, the monthly variation, the weekly variation, the daily variation, and the hourly variation.  In general, the Monthly Variation analyzes result show that high passenger volumes exist in July -October, and always low in April and May, the other months have average monthly passenger volume.  For these three months of data, the Weekly Variation analyzed result show that the Weekly Passenger Volumes Variation are not consistent. The variation pattern of January is different from the February and March pattern. An additional period of data of other months is needed to get the general weekly variation characteristic.