3,538 research outputs found
Stigmergy-based modeling to discover urban activity patterns from positioning data
Positioning data offer a remarkable source of information to analyze crowds
urban dynamics. However, discovering urban activity patterns from the emergent
behavior of crowds involves complex system modeling. An alternative approach is
to adopt computational techniques belonging to the emergent paradigm, which
enables self-organization of data and allows adaptive analysis. Specifically,
our approach is based on stigmergy. By using stigmergy each sample position is
associated with a digital pheromone deposit, which progressively evaporates and
aggregates with other deposits according to their spatiotemporal proximity.
Based on this principle, we exploit positioning data to identify high density
areas (hotspots) and characterize their activity over time. This
characterization allows the comparison of dynamics occurring in different days,
providing a similarity measure exploitable by clustering techniques. Thus, we
cluster days according to their activity behavior, discovering unexpected urban
activity patterns. As a case study, we analyze taxi traces in New York City
during 2015
A high resolution spatiotemporal model for in-vehicle black carbon exposure : quantifying the in-vehicle exposure reduction due to the Euro 5 particulate matter standard legislation
Several studies have shown that a significant amount of daily air pollution exposure is inhaled during trips. In this study, car drivers assessed their own black carbon exposure under real-life conditions (223 h of data from 2013). The spatiotemporal exposure of the car drivers is modeled using a data science approach, referred to as microscopic land-use regression (mu LUR). In-vehicle exposure is highly dynamical and is strongly related to the local traffic dynamics. An extensive set of potential covariates was used to model the in-vehicle black carbon exposure in a temporal resolution of 10 s. Traffic was retrieved directly from traffic databases and indirectly by attributing the trips through a noise map as an alternative traffic source. Modeling by generalized additive models (GAM) shows non-linear effects for meteorology and diurnal traffic patterns. A fitted diurnal pattern explains indirectly the complex diurnal variability of the exposure due to the non-linear interaction between traffic density and distance to the preceding vehicles. Comparing the strength of direct traffic attribution and indirect noise map-based traffic attribution reveals the potential of noise maps as a proxy for traffic-related air pollution exposure. An external validation, based on a dataset gathered in 2010-2011, quantifies the exposure reduction inside the vehicles at 33% (mean) and 50% (median). The EU PM Euro 5 PM emission standard (in force since 2009) explains the largest part of the discrepancy between the measurement campaign in 2013 and the validation dataset. The mu LUR methodology provides a high resolution, route-sensitive, seasonal and meteorology-sensitive personal exposure estimate for epidemiologists and policy makers
Delays, Inaccuracies and Anticipation in Microscopic Traffic Models
We generalize a wide class of time-continuous microscopic traffic models to
include essential aspects of driver behaviour not captured by these models.
Specifically, we consider (i) finite reaction times, (ii) estimation errors,
(iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal
anticipation. The estimation errors are modelled as stochastic Wiener processes
and lead to time-correlated fluctuations of the acceleration.
We show that the destabilizing effects of reaction times and estimation
errors can essentially be compensated for by spatial and temporal anticipation,
that is, the combination of stabilizing and destabilizing effects results in
the same qualitative macroscopic dynamics as that of the respectively
underlying simple car-following model. In many cases, this justifies the use of
simplified, physics-oriented models with a few parameters only. Although the
qualitative dynamics is unchanged, multi-anticipation increase both spatial and
temporal scales of stop-and-go waves and other complex patterns of congested
traffic in agreement with real traffic data. Remarkably, the anticipation
allows accident-free smooth driving in complex traffic situations even if
reaction times exceed typical time headways.Comment: Major revision of the model and the simulations. Particularly, the
number of model parameters has been reduce
Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs
Deep neural networks can be powerful tools, but require careful
application-specific design to ensure that the most informative relationships
in the data are learnable. In this paper, we apply deep neural networks to the
nonlinear spatiotemporal physics problem of vehicle traffic dynamics. We
consider problems of estimating macroscopic quantities (e.g., the queue at an
intersection) at a lane level. First-principles modeling at the lane scale has
been a challenge due to complexities in modeling social behaviors like lane
changes, and those behaviors' resultant macro-scale effects. Following domain
knowledge that upstream/downstream lanes and neighboring lanes affect each
others' traffic flows in distinct ways, we apply a form of neural attention
that allows the neural network layers to aggregate information from different
lanes in different manners. Using a microscopic traffic simulator as a testbed,
we obtain results showing that an attentional neural network model can use
information from nearby lanes to improve predictions, and, that explicitly
encoding the lane-to-lane relationship types significantly improves
performance. We also demonstrate the transfer of our learned neural network to
a more complex road network, discuss how its performance degradation may be
attributable to new traffic behaviors induced by increased topological
complexity, and motivate learning dynamics models from many road network
topologies.Comment: To appear at 2019 IEEE Conference on Intelligent Transportation
System
Autonomous Accident Monitoring Using Cellular Network Data
Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
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