| Summary: |
Transportation systems are not only a key factor for economic sustainability and social welfare, but also a key dimension in the
smart city agenda. Around 80% of the European citizens live in urban areas, which is where 85% of European Gross National
Product is generated (EC, 2006), however these areas are currently faced with the challenges of growing car ownership, vehicle
travel, and energy consumption. These issues can only be addressed with an integrated approach, where the efforts must converge
in order to promote the deployment of ITS and address sustainable urban transport planning by combining land-use with innovative,
efficient, clean transport systems and human centred modes, such as cycling and walking.
The concept of sustainable mobility involves the consideration of transport dimensions that are not strictly limited to the field of
energy in transport and its impacts on the environment: mobility must be understood as the mode and frequency with which people
move to satisfy their several needs - which range from "mandatory" (work/school) to optional/non-routine (leisure, social, etc.). So,
although it remains an essential aspect of transportation planning, studying commuting to and from work or school has ceded
ground to studying travel for several other non-routine purposes. But providing quality public transportation services may be
extremely expensive when demand is low, variable and unpredictable. Usually, transport supply design criteria require satisfying the
highest demand hours, typically occurring during the work commute. Understanding personal travel patterns and modelling travel
demand is essential to plan sustainable urban transportation systems to fulfil citizens' mobility needs. To do this effectively and
timely, urban and transportation planners need a dynamic way to profile the movement of people and vehicles.
Profiling of urban movements has traditionally relied on the knowledge of land use patterns, but, w  |
Summary
Transportation systems are not only a key factor for economic sustainability and social welfare, but also a key dimension in the
smart city agenda. Around 80% of the European citizens live in urban areas, which is where 85% of European Gross National
Product is generated (EC, 2006), however these areas are currently faced with the challenges of growing car ownership, vehicle
travel, and energy consumption. These issues can only be addressed with an integrated approach, where the efforts must converge
in order to promote the deployment of ITS and address sustainable urban transport planning by combining land-use with innovative,
efficient, clean transport systems and human centred modes, such as cycling and walking.
The concept of sustainable mobility involves the consideration of transport dimensions that are not strictly limited to the field of
energy in transport and its impacts on the environment: mobility must be understood as the mode and frequency with which people
move to satisfy their several needs - which range from "mandatory" (work/school) to optional/non-routine (leisure, social, etc.). So,
although it remains an essential aspect of transportation planning, studying commuting to and from work or school has ceded
ground to studying travel for several other non-routine purposes. But providing quality public transportation services may be
extremely expensive when demand is low, variable and unpredictable. Usually, transport supply design criteria require satisfying the
highest demand hours, typically occurring during the work commute. Understanding personal travel patterns and modelling travel
demand is essential to plan sustainable urban transportation systems to fulfil citizens' mobility needs. To do this effectively and
timely, urban and transportation planners need a dynamic way to profile the movement of people and vehicles.
Profiling of urban movements has traditionally relied on the knowledge of land use patterns, but, while land use and transportation
infrastructures tend to remain in the same form for a long time once they are put in place, urban movements, on the other, often
change. Transport planning input information mostly comes from traditional survey methods that are expensive and time
consuming, giving planners only a picture of what has happened and needing an active involvement. In contrast, an emerging field
of research uses mobile phones for ''urban sensing'' (Cuff, 2008). The wide deployment of pervasive computing devices (cell phone,
smart card, GPS devices and digital cameras) and transport system records (e.g. ticket validation counts; traffic counts) provide
unprecedented digital footprints, telling where and when people are. Moreover, the composition of social networks and human
interactions is crucial not only for understanding social activities, but also for travel patterns. The past few years have witnessed an
overwhelming increase in the adoption and use of social media. Social media services and platforms offer a wide array of digital
channels for expression and interaction, ranging from forums/message boards, weblogs, and micro blogging, to wikis and social
networking services. With millions of users around the world, transportation researchers have also realized the potential of Social
Network Analysis (SNA) for travel demand modelling and analysis (Carrasco, 2008). To date there is little published research,
however, on how to realize this opportunity for the sector by capturing the views, needs and experiences of the travelling public in a
timely and direct fashion through social media. All these mobility data, together with modern techniques for geo-processing, SNA,
and data fusion, offer new possibilities for deriving activity destinations and allowing us to link cyber and physical activities through
user interactions, in ways that can be usefully incorporated into models of land use and transportation interactions.
In this project we propose to study individual's mobility for mining non-routine (leisure, social, etc.) mobility patterns from multiple
data sources. The following mobility patterns are of great interest: locations of significance, modes of transport, trajectory patterns
and location-based activities for destination choice modelling. Data collected via ubiquitous devices and smart metering combined
with data from social media platforms provides a range of new close-to-real-time information that can be combined with the data
from more traditional sources (surveys, transport system records and static data) for urban efficient mobility planning and
management. When considered in isolation, each of these data sources has gaps/missing observations, so the matching of multiple
data sources can facilitate transport analysis, and enable operators to better tune - even on the fly - public transportation within
cities with the aim of travelling at lower costs, faster and producing a smaller carbon footprint. |