85 research outputs found
City Indicators for Geographical Transfer Learning: An Application to Crash Prediction
The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users’ mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution
Individual and Collective Stop-Based Adaptive Trajectory Segmentation
Identifying the portions of trajectory data where movement ends and a significant stop starts
is a basic, yet fundamental task that can affect the quality of any mobility analytics process.
Most of the many existing solutions adopted by researchers and practitioners are simply
based on fixed spatial and temporal thresholds stating when the moving object remained still
for a significant amount of time, yet such thresholds remain as static parameters for the user
to guess. In this work we study the trajectory segmentation from a multi-granularity perspec tive, looking for a better understanding of the problem and for an automatic, user-adaptive
and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the
specific user under study and to the geographical areas they traverse. Experiments over
real data, and comparison against simple and state-of-the-art competitors show that the
flexibility of the proposed methods has a positive impact on results
Ranking places in attributed temporal urban mobility networks
Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of “hotspots” of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of “hotspots” and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.This work is supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P. It is also funded by the EU H2020 programme under Grant Agreement No. 780754, “Track & Know”
Speed Limit: Obey, or Not Obey?
It is commonly expected that drivers maintain a driving speed that is lower
than or around the posted speed limit, as failure to obey may result in safety
risks and fines. By taking randomly selected road segments as examples, this
study compares the percentages of speeding vehicles in five countries
worldwide, namely, two European countries (Germany and Italy), two Asian
countries (Japan and China), and one North American country (the United
States). Contrary to expectations, our results show that more than 80% of
drivers violate the posted speed limits in the studied road segments in Italy,
Japan, and the United States. In particular, a significant portion (45.3%) of
drivers in Italy exceed the posted speed limit by a substantial margin (30
km/h), while few speeding vehicles are observed in the road segment examined in
China. Meanwhile, it is found that drivers on low-speed-limit roads are more
likely to exceed the posted speed limit, particularly when there are fewer
on-road vehicles. The comparison of different countries' speeding fines
indicates that for the purpose of preventing speeding, increasing fines (as
Italy has done) is less effective than enhancing supervision (as China has
done). The findings remind law enforcement agencies and traffic authorities of
the importance of the supervision of driver's behavior and the necessity of
revisiting the rationale for the current speed limit settings
Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks
A fundamental problem of interest to policy makers, urban planners, and other
stakeholders involved in urban development projects is assessing the impact of
planning and construction activities on mobility flows. This is a challenging
task due to the different spatial, temporal, social, and economic factors
influencing urban mobility flows. These flows, along with the influencing
factors, can be modelled as attributed graphs with both node and edge features
characterising locations in a city and the various types of relationships
between them. In this paper, we address the problem of assessing
origin-destination (OD) car flows between a location of interest and every
other location in a city, given their features and the structural
characteristics of the graph. We propose three neural network architectures,
including graph neural networks (GNN), and conduct a systematic comparison
between the proposed methods and state-of-the-art spatial interaction models,
their modifications, and machine learning approaches. The objective of the
paper is to address the practical problem of estimating potential flow between
an urban development project location and other locations in the city, where
the features of the project location are known in advance. We evaluate the
performance of the models on a regression task using a custom data set of
attributed car OD flows in London. We also visualise the model performance by
showing the spatial distribution of flow residuals across London.Comment: 9 pages, 5 figures, to be published in the Proceedings of 2020 IEEE
International Conference on Smart Computing (SMARTCOMP 2020
Hydraulic contacts identification in the aquifers of limestone ridges: tracer tests in the Montelago pilot area (Central Apennines)
The investigated area, located in the inner part of the Marche region (central Italy) and belonging to the carbonate Umbria- Marche ridges in the central Apennines, is characterised by very complex geo-structural setting and widespread karst phenomena that make difficult the definition of the relation among the aquifers basing only on the hydrogeological survey. Hence, the presence of different flowpaths among aquifers of the Umbria-Marche hydrostratigraphic sequence and of tectonic contacts among the different structures is verified using tracer tests. In particular, the tests showed that the Calcare Massiccio and the Maiolica aquifers are connected under certain tectonic conditions. A new tracer given by a single stranded DNA molecule and traditional fluorescent dyes have been injected into the Montelago sinkhole in different periods (during the recharge and during the discharge) and recovered in several points along the expected hydrogeological basin, using either manual and automatic sampling. Fluorescent traps were positioned in creeks, rivers and springs. The DNA molecule is useful to trace surface water and groundwater, is detectable even at very low concentrations, no significant change in water density and viscosity can be observed and its use is not dangerous for the environment. The results stress the suitability of DNA as hydrogeological tracer, capable to identify connections among aquifers and study different flowpaths even in high flow conditions when traditional tracers are more and more diluted. Moreover, fluorescein tracer allowed for the transport parameter determination, giving mean velocities ranging from 100 to 3000 m/day and mean residence time from some tens to hundreds of hours, and determining the aquifer volumes
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