Adi Soemarmo Airport Train Demand Modelling Based on Google Cloud Big Data

Ma’ruf Tsaghani Purnomo (Faculty of Engineering, Universitas Diponegoro)
Amelia Kusuma Indriastuti (Faculty of Engineering, Universitas Diponegoro)
Kami Hari Basuki (Faculty of Engineering, Universitas Diponegoro)
Diondi Toto Kusuma (Faculty of Engineering, Universitas Diponegoro)
Radhevio Izza Aghna (Faculty of Engineering, Universitas Diponegoro)

Jurnal Teknik dan Science (JTS)
Vol. 4 No. 3 (2025): Oktober: Jurnal Teknik dan Science

The development of the Adi Soemarmo Airport Train route is necessary to optimise its services by increasing the load factor. One of the efforts that can be made to enhance the load factor includes demand modelling. Big Data provided by Google Cloud Big Data is utilised for its capacity to provide fast and large-scale trip data. This approach supports demand modelling carried out across regencies and cities as study areas for route development. The data is modelled with a four-stage transportation model, adopting zones based on sub-districts within the regencies and cities in the study area. The results indicate the emergence of potential demand through changes in transit points to transfer points in zones along the Madiun, Klaten, Wonogiri, and Gundih. These zones that have potential demand are chosen as the guidelines for developing the Adi Soemarmo Airport Train route.