5 research outputs found
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
A key aspect of a sustainable urban transportation system is the
effectiveness of transportation policies. To be effective, a policy has to
consider a broad range of elements, such as pollution emission, traffic flow,
and human mobility. Due to the complexity and variability of these elements in
the urban area, to produce effective policies remains a very challenging task.
With the introduction of the smart city paradigm, a widely available amount of
data can be generated in the urban spaces. Such data can be a fundamental
source of knowledge to improve policies because they can reflect the
sustainability issues underlying the city. In this context, we propose an
approach to exploit urban positioning data based on stigmergy, a bio-inspired
mechanism providing scalar and temporal aggregation of samples. By employing
stigmergy, samples in proximity with each other are aggregated into a
functional structure called trail. The trail summarizes relevant dynamics in
data and allows matching them, providing a measure of their similarity.
Moreover, this mechanism can be specialized to unfold specific dynamics.
Specifically, we identify high-density urban areas (i.e hotspots), analyze
their activity over time, and unfold anomalies. Moreover, by matching activity
patterns, a continuous measure of the dissimilarity with respect to the typical
activity pattern is provided. This measure can be used by policy makers to
evaluate the effect of policies and change them dynamically. As a case study,
we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin
Guest Editorial: Introduction to the Special Issue on Advances in Smart and Green Transportation for Smart Cities
According to a recent UN report, continuing population growth and urbanization are expected to increase the world’s urban population by 2.5 billion people by 2050, with 2.9 billion extra vehicles. This massive growth in both population and number of vehicles, together with urban transformation and a trend toward mega cities, creates greater and more challenges for achieving smart transportation goals in smart cities. Therefore, new and more integrated modes of transportation, and environment friendly solutions are required to accommodate the rising demands of high liveability in smarter cities that offer safe, secure, affordable, reliable and sustainable transportation in old and new markets alike
Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City
Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications.
Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics
Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City
Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications.
Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics