Welcome

Ronald A Halim is a manager and principal transport economist from Equitable Maritime Consulting. Ronald has been a lead author and project manager of various transport policy analysis projects, academic publications, in the area of freight transport decarbonization. He has worked as a key transport consultant with various international organizations such as the United Nations Conference on Trade and Development (UNCTAD), the World Bank, Asian Development Bank, and as a freight transport specialist with Organization for Economic Cooperation and Development (OECD). He has also served in the capacity of National Transport Consultant at the Indonesian Ministry of Transport, Indonesia and a technical advisor for the French delegates for trade and shipping policy measures at the International Maritime Organization. Ronald holds a PhD. in modeling global freight transport from Delft University of Technology.

Srirama has over 18 years of expertise in several transport modelling and GIS modeling projects. His profile includes projects on infrastructure development with local and National Governments in Asia, Africa, and EU. He also has over 8 years of experience in spatial simulations (dynamic GIS) that is currently missing in software like ArcGIS, QGIS. He has professional experience in programming and scripting to automate data analysis, model validation, mapping and to extend functionality of software, to build quick custom spatial GIS simulations in various domains: e.g., infrastructure maintenance, pedestrian movements, evacuations, spread of pandemic. His recent projects include introduction of cluster-computing into workflow for calibration of spatial and traffic models and spatial analytics for rural bus transport in Asia. He is also part of international teams for open-source simulation software. He additionally holds a IBM Data Science Professional certification and uses these skills for enriching insights and decision making. 

Shuainan Liu is a Transport Engineer with a degree in Transport & Planning from TU Delft. She is experienced in developing data-driven prediction systems using machine learning models. In her graduation project, she successfully developed a machine learning model to estimate and predict urban traffic flow for the municipality of The Hague, the Netherlands. Her programming platform of choice is Python and its libraries such as pandas and momepy. She is also an avid user of open-source Geographical Information Systems software such as QGIS. Areas of Shuiainan’s expertise includes quantitative analysis, traditional four-step transport modeling, transport network optimization, and empirical analysis. In her free time Shuianan likes to go on an adventure to discover new places and cultures.