Urban Studies Lunchtime Workshop: Sustainable Transport and ICT

Monday, 5th October 2015
1:00pm - 2:00pm
Urban Studies Boardroom, 29 Bute Gardens, University of Glasgow, Glasgow, G12 8RS

This event is part of the Urban Studies Lunchtime Workshop Series.

UBDC PhD students, Chris Wu and Ashwini Venkatasubramaniam, will be presenting at the 5th October session of the Urban Studies Lunchtime Workshops. Their presentation will focus on 'Sustainable Transport and ICT: Implications from household survey data' and 'spatial-temporal methods of clustering for traffic data from heterogenous sources'.



Chris Wu, 'Sustainable transport and ICT: implications from household survey data'.

In the UK, the focus of transport policy-making has been increasingly placed on promoting changes in people's travel behaviour to deliver sustainability. Due to its potential to substitute individuals' physical travel and to enhance travel efficiency, ICT has been viewed as a facilitator of such behavioural changes by planners and policy-makers. This study attempts to explore the relationship between ICT use and personal activity-travel behaviour, and how such relationship change over time and vary across different social, household and individual groups. The longitudinal and cross-sectional analyses are based on the Scottish Household Survey data.


Ashwini Venkatasubramaniam, 'Spatial-temporal methods of clustering for traffic data from heterogeneous sources'.

Sensor technologies and the resulting availability of complex information allows for a data driven modelling approach to urban planning, traffic  operations and design. Traditional data sources such as loop detectors as well as increasingly ubiquitous sources such as GPS devices and accelerometers generate detailed traffic data on flow and occupancy. We seek to examine these data types from multiple sources and the relationships between flow and occupancy to identify an appropriate set of clusters for a given traffic network. The data presents both spatial and temporal constraints and we typically utilize this time-variant data for links to cluster junctions of the network. We discuss methodologies, preliminary results as well as challenges inherent to this data.