Analisis Probabilitas Penumpang Bus di Dalam dan di Luar Terminal Oebobo Berbasis Karakteristik Sosial Ekonomi dan Aksesibilitas dengan Metode Revealed Preference

Abstract

This study will find out the characteristics of bus passengers inside the Oebobo terminal and outside the terminal (Oesapa T-junction) as well as the probability of choosing a bus ride location based on socio-economic characteristics and accessibility. This study begins with the collection of primary data obtained from the results of filling out questionnaires by respondents using revealed preference technique. Primary data is then processed using logistic regression analysis to obtain probability values. From the analysis, it can be seen that bus passengers prefer the location to ride outside the terminal (Oesapa T-junction) which is 77.65%. The probability of passengers who choose a ride location is influenced by the variable fare to the bus ride location and bus waiting time. For the rate of <Rp.5000 the probability value is 76.25% and the tariff > Rp.20000 the probability value of 92.39% chooses at the Oesapa T-junction. The greater the tariff towards the hitching location, the higher the potential for bus passengers to choose the location to stay at the Oesapa T-junction. Whereas based on the bus waiting time variable the faster waiting time for the bus, the higher the potential for bus passengers to choose the location to stay at the Oesapa T-junction with a probability value for waiting time <30 minutes 97.09% choosing the location to stay at Oesapa T-junction and for waiting time >120 minutes the probability value of 91.49% chooses the location to take a ride at Oebobo Terminal.

Probabilitas Modal Shift Model Angkutan Umum Di Bandara Adi Soemarmo Solo

“…Analisa probabilitas antara perilaku dalam perpindahan moda dengan variabel yang mempengaruhi dilakukan dengan beberapa skenario analisis regresi logistik biner untuk menganalisa perpindahan moda dari kendaraan pribadi dan angkutan umum ke kereta api bandara [2]. Analisis data model pemilihan moda menggunakan model logit biner dan estimasi parameter model dengan menggunakan analisa regresi [5], [6], [7].…”