Data fusion for the planning of sustainable urban services

Use cases in transport, environment and tourism

Abstract

The planning and management of sustainable cities requires understanding the
behavioural patterns of the population: transport planning and operation requires
accurate, reliable and updated travel demand information; the definition of effective

strategies to mitigate the exposure to air pollutants needs information on the spatio-
temporal distribution of the population along the day; tourist’s activity and mobility

patterns are essential to design a sustainable touristic offer. Traditional data collection
methods, such as household travel surveys, provide rich travel and demographic data,
but they also suffer from shortcomings: they depend on users’ willingness to answer,
people may provide incorrect or imprecise answers, and they are expensive and
require months to complete, which limits the size of the sample and the frequency with
which information is updated.
New digital data sources make it possible to complement and/or replace traditional
travel surveys, overcoming some of their main limitations. In particular, mobile phone
records can give access to a sample that is usually at least one order of magnitude
higher than the one provided by traditional sources, and that is also well distributed
across the different socioeconomic segments, given the high penetration of mobile
phone services. Additionally, the high temporal granularity of mobile phone data allows
us to determine in detail the location of the device along the day and their spatial
resolution is in general suitable to study population’s behaviour at urban and
metropolitan scale.
This contribution focuses on the analysis of anonymised mobile phone data and their
fusion with other data sources to provide information on population’s activity and
mobility. We discuss the need to enrich the information obtained from mobile telephone
data with different data sources and we described the methodology followed in three
use cases: the study of mobility, including public transport mobility, in the city of Málaga
using mobile phone data and intelligent transport card data; the study of population
exposure to air pollutants in the city of Madrid using dynamic population maps and an
air pollutants dispersion model; and the characterisation of the visitors to the Parque
Nacional de la Sierra de Guadarrama using mobile phone data and people counts.