Engaging Mobility
Project details
Duration
09.2015-03.2018
Sponsor
Singapore National Research Foundation
Staff
M. van Eggermond, P. Fourie, S. Ordonez, C. Anda, T. Maheshwari, J. Kupferschmid, M. Nazemi and M. Joos
Partner
external page URA Urban Redevelopment Authority, Singapore
external page LTA Land Transport Authority, Singapore
Abstract
This project is conducted at the Future Cities Laboratory of the Singapore ETH Centre and is features two strands of research. The team in Singapore also maintains the Engaging Mobility blog with regular updated about the research project.
Engaging Active Mobility
Walking and cycling are not only the most sustainable modes of transport, they are also the most sensitive to the quality of the built environment and climatic conditions. Due to car-oriented street design and modernist urban planning, the potential of active transport modes remains heavily underutilised in many cities. This project aims to understand, model and simulate future mobility solutions for dense urban areas. To understand what is needed to make walking and cycling viable modes of transport in tropical towns and emerging cities, we apply a multi-method approach, including innovative survey approaches, traffic microsimulation of active transport modes and virtual reality applications. These methods allow us to understand current challenges for walking and cycling in Singapore and test how retrofitted and newly designed streetscapes will impact travel choices.
Engaging Big Data
New big data streams generated by public transport smart cards and mobile phones allow one to observe urban mobility at an unprecedented scale. But since this data lacks the level of detail that is required for predictive transport simulation models, the challenge is to enrich such data with behavioural information as obtained from conventional travel surveys and the new insights from the research on active mobility behavior. This research project aims turning passively generated streams of geographic and activity data into predictive models of activity and travel behaviour, in order to test the viability of policy and infrastructure decisions before they are implemented, and guide and inform the urban and transport planning process. The goal is to dramatically improve the turnaround time, and lower the barrier-to-entry for implementing the latest agent-based, activity-oriented approaches to transportation modelling, applied to the open-source agent-based modelling framework, MATSim.
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