115 research outputs found
One-Shot Traffic Assignment with Forward-Looking Penalization
Traffic assignment (TA) is crucial in optimizing transportation systems and
consists in efficiently assigning routes to a collection of trips. Existing TA
algorithms often do not adequately consider real-time traffic conditions,
resulting in inefficient route assignments. This paper introduces METIS, a
cooperative, one-shot TA algorithm that combines alternative routing with edge
penalization and informed route scoring. We conduct experiments in several
cities to evaluate the performance of METIS against state-of-the-art one-shot
methods. Compared to the best baseline, METIS significantly reduces CO2
emissions by 18% in Milan, 28\% in Florence, and 46% in Rome, improving trip
distribution considerably while still having low computational time. Our study
proposes METIS as a promising solution for optimizing TA and urban
transportation systems
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
City Indicators for Mobility Data Mining
Classifying cities and other geographical units is a classical task in
urban geography, typically carried out through manual analysis
of specific characteristics of the area. The primary objective of
this paper is to contribute to this process through the definition
of a wide set of city indicators that capture different aspects
of the city, mainly based on human mobility and automatically
computed from a set of data sources, including mobility traces
and road networks. The secondary objective is to prove that such
set of characteristics is indeed rich enough to support a simple
task of geographical transfer learning, namely identifying which
groups of geographical areas can share with each other a basic
traffic prediction model. The experiments show that similarity in
terms of our city indicators also means better transferability of
predictive models, opening the way to the development of more
sophisticated solutions that leverage city indicators
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
On the pursuit of Graph Embedding Strategies for Individual Mobility Networks
An Individual Mobility Network (IMN) is a graph
representation of the mobility history of an individual that
highlights the relevant locations visited (nodes of the graph) and
the movements across them (edges), also providing a rich set of
annotations of both nodes and edges. Extracting representative
features from an IMN has proven to be a valuable task for
enabling various learning applications. However, it is also a
demanding operation that does not guarantee the inclusion
of all important aspects from the human perspective. A vast
recent literature on graph embedding goes in a similar direction,
yet typically aims at general-purpose methods that might not
suit specific contexts. In this paper, we discuss the existing
approaches to graph embedding and the specificities of IMNs,
trying to find the best matching solutions. We experiment with
representative algorithms and study the results in relation to
IMN characteristics. Tests are performed on a large dataset of
real vehicle trajectories
A Bag of Receptive Fields for Time Series Extrinsic Predictions
High-dimensional time series data poses challenges due to its dynamic nature,
varying lengths, and presence of missing values. This kind of data requires
extensive preprocessing, limiting the applicability of existing Time Series
Classification and Time Series Extrinsic Regression techniques. For this
reason, we propose BORF, a Bag-Of-Receptive-Fields model, which incorporates
notions from time series convolution and 1D-SAX to handle univariate and
multivariate time series with varying lengths and missing values. We evaluate
BORF on Time Series Classification and Time Series Extrinsic Regression tasks
using the full UEA and UCR repositories, demonstrating its competitive
performance against state-of-the-art methods. Finally, we outline how this
representation can naturally provide saliency and feature-based explanations
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 EconomĂa y Competividad, grant number TIN2017-84821-P. It is also funded by the EU H2020 programme under Grant Agreement No. 780754, “Track & Know”
From fossil fuel to electricity: studying the impact of EVs on the daily mobility life of users
Electric Vehicles (EVs) currently provide a major
opportunity to decarbonize urban areas and improve their
quality of life, however, the mass transition towards electric
mobility requires understanding and solving the potential issues
that they might cause to users. In this work, we propose a
process that, through a mix of mobility data analytics, efficient
trip planning, and simulation heuristics, is able to analyze the
current fuel-based mobility of a user and quantitatively describe
the impact of switching to EVs on their mobility lifestyle. We
apply our process to a large dataset of real trips, analyzing both
the impact of EVs on the collectivity and on the individuals,
providing a case study with insights at the level of single users
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