36 research outputs found
Predicting episodes of non-conformant mobility in indoor environments
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
Fusing WiFi and video sensing for accurate group detection in indoor spaces
Singapore National Research Foundation under IDM Futures Funding Initiativ
Multimodal Classification of Urban Micro-Events
In this paper we seek methods to effectively detect urban micro-events. Urban
micro-events are events which occur in cities, have limited geographical
coverage and typically affect only a small group of citizens. Because of their
scale these are difficult to identify in most data sources. However, by using
citizen sensing to gather data, detecting them becomes feasible. The data
gathered by citizen sensing is often multimodal and, as a consequence, the
information required to detect urban micro-events is distributed over multiple
modalities. This makes it essential to have a classifier capable of combining
them. In this paper we explore several methods of creating such a classifier,
including early, late, hybrid fusion and representation learning using
multimodal graphs. We evaluate performance on a real world dataset obtained
from a live citizen reporting system. We show that a multimodal approach yields
higher performance than unimodal alternatives. Furthermore, we demonstrate that
our hybrid combination of early and late fusion with multimodal embeddings
performs best in classification of urban micro-events
Exploiting the interdependency of land use and mobility for urban planning
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
Inferring accurate bus trajectories from noisy estimated arrival time records
National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ
The Role of Urban Mobility in Retail Business Survival.
Economic and urban planning agencies have strong interest in tackling the hard problem of predicting the odds of survival of
individual retail businesses. In this work, we tap urban mobility data available both from a location-based intelligence platform,
Foursquare, and from public transportation agencies, and investigate whether mobility-derived features can help foretell the
failure of such retail businesses, over a 6 month horizon, across 10 distinct cities spanning the globe. We hypothesise that the
survival of such a retail outlet is correlated with not only venue-specific characteristics but also broader neighbourhood-level
effects. Through careful statistical analysis of Foursquare and taxi mobility data, we uncover a set of discriminative features,
belonging to the neighbourhood’s static characteristics, the venue-specific customer visit dynamics, and the neighbourhood’s
mobility dynamics. We demonstrate that classifiers trained on such features can predict such survival with high accuracy,
achieving approximately 80% precision and recall across the cities. We also show that the impact of such features varies
across new and established venues and across different cities. Besides achieving a significant improvement over past work on
business vitality prediction, our work demonstrates the vital role that mobility dynamics plays in the economic evolution of a
city