4,228 research outputs found
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data
Accurately modeling traffic speeds is a fundamental part of efficient
intelligent transportation systems. Nowadays, with the widespread deployment of
GPS-enabled devices, it has become possible to crowdsource the collection of
speed information to road users (e.g. through mobile applications or dedicated
in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced
speed data also brings very important challenges, such as the highly variable
measurement noise in the data due to a variety of driving behaviors and sample
sizes. When not properly accounted for, this noise can severely compromise any
application that relies on accurate traffic data. In this article, we propose
the use of heteroscedastic Gaussian processes (HGP) to model the time-varying
uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a
HGP conditioned on sample size and traffic regime (SRC-HGP), which makes use of
sample size information (probe vehicles per minute) as well as previous
observed speeds, in order to more accurately model the uncertainty in observed
speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we
empirically show that the proposed heteroscedastic models produce significantly
better predictive distributions when compared to current state-of-the-art
methods for both speed imputation and short-term forecasting tasks.Comment: 22 pages, Transportation Research Part C: Emerging Technologies
(Elsevier
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation
Traffic speed data imputation is a fundamental challenge for data-driven
transport analysis. In recent years, with the ubiquity of GPS-enabled devices
and the widespread use of crowdsourcing alternatives for the collection of
traffic data, transportation professionals increasingly look to such
user-generated data for many analysis, planning, and decision support
applications. However, due to the mechanics of the data collection process,
crowdsourced traffic data such as probe-vehicle data is highly prone to missing
observations, making accurate imputation crucial for the success of any
application that makes use of that type of data. In this article, we propose
the use of multi-output Gaussian processes (GPs) to model the complex spatial
and temporal patterns in crowdsourced traffic data. While the Bayesian
nonparametric formalism of GPs allows us to model observation uncertainty, the
multi-output extension based on convolution processes effectively enables us to
capture complex spatial dependencies between nearby road segments. Using 6
months of crowdsourced traffic speed data or "probe vehicle data" for several
locations in Copenhagen, the proposed approach is empirically shown to
significantly outperform popular state-of-the-art imputation methods.Comment: 10 pages, IEEE Transactions on Intelligent Transportation Systems,
201
Scalable Population Synthesis with Deep Generative Modeling
Population synthesis is concerned with the generation of synthetic yet
realistic representations of populations. It is a fundamental problem in the
modeling of transport where the synthetic populations of micro-agents represent
a key input to most agent-based models. In this paper, a new methodological
framework for how to 'grow' pools of micro-agents is presented. The model
framework adopts a deep generative modeling approach from machine learning
based on a Variational Autoencoder (VAE). Compared to the previous population
synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs
sampling and traditional generative models such as Bayesian Networks or Hidden
Markov Models, the proposed method allows fitting the full joint distribution
for high dimensions. The proposed methodology is compared with a conventional
Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary.
It is shown that, while these two methods outperform the VAE in the
low-dimensional case, they both suffer from scalability issues when the number
of modeled attributes increases. It is also shown that the Gibbs sampler
essentially replicates the agents from the original sample when the required
conditional distributions are estimated as frequency tables. In contrast, the
VAE allows addressing the problem of sampling zeros by generating agents that
are virtually different from those in the original data but have similar
statistical properties. The presented approach can support agent-based modeling
at all levels by enabling richer synthetic populations with smaller zones and
more detailed individual characteristics.Comment: 27 pages, 15 figures, 4 table
Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services
This study develops an online predictive optimization framework for
dynamically operating a transit service in an area of crowd movements. The
proposed framework integrates demand prediction and supply optimization to
periodically redesign the service routes based on recently observed demand. To
predict demand for the service, we use Quantile Regression to estimate the
marginal distribution of movement counts between each pair of serviced
locations. The framework then combines these marginals into a joint demand
distribution by constructing a Gaussian copula, which captures the structure of
correlation between the marginals. For supply optimization, we devise a linear
programming model, which simultaneously determines the route structure and the
service frequency according to the predicted demand. Importantly, our framework
both preserves the uncertainty structure of future demand and leverages this
for robust route optimization, while keeping both components decoupled. We
evaluate our framework using a real-world case study of autonomous mobility in
a university campus in Denmark. The results show that our framework often
obtains the ground truth optimal solution, and can outperform conventional
methods for route optimization, which do not leverage full predictive
distributions.Comment: 34 pages, 12 figures, 5 table
Learning and Generalizing Polynomials in Simulation Metamodeling
The ability to learn polynomials and generalize out-of-distribution is
essential for simulation metamodels in many disciplines of engineering, where
the time step updates are described by polynomials. While feed forward neural
networks can fit any function, they cannot generalize out-of-distribution for
higher-order polynomials. Therefore, this paper collects and proposes
multiplicative neural network (MNN) architectures that are used as recursive
building blocks for approximating higher-order polynomials. Our experiments
show that MNNs are better than baseline models at generalizing, and their
performance in validation is true to their performance in out-of-distribution
tests. In addition to MNN architectures, a simulation metamodeling approach is
proposed for simulations with polynomial time step updates. For these
simulations, simulating a time interval can be performed in fewer steps by
increasing the step size, which entails approximating higher-order polynomials.
While our approach is compatible with any simulation with polynomial time step
updates, a demonstration is shown for an epidemiology simulation model, which
also shows the inductive bias in MNNs for learning and generalizing
higher-order polynomials
Modeling Censored Mobility Demand through Quantile Regression Neural Networks
Shared mobility services require accurate demand models for effective service
planning. On one hand, modeling the full probability distribution of demand is
advantageous, because the full uncertainty structure preserves valuable
information for decision making. On the other hand, demand is often observed
through usage of the service itself, so that the observations are censored, as
they are inherently limited by available supply. Since the 1980s, various works
on Censored Quantile Regression models have shown them to perform well under
such conditions, and in the last two decades, several works have proposed to
implement them flexibly through Neural Networks (CQRNN). However, apparently no
works have yet applied CQRNN in the Transport domain. We address this gap by
applying CQRNN to datasets from two shared mobility providers in the Copenhagen
metropolitan area in Denmark, as well as common synthetic baseline datasets.
The results show that CQRNN can estimate the intended distributions better than
both censorship-unaware models and parametric censored models.Comment: 13 pages, 7 figures, 4 table
Towards a Computational Case-Based Model for Creative Planning
This paper describes a computational case-based model for the creative planning process. Our approach is inspired
in Wallas’ model for the creative process in that we consider that creativity involves a sequence of four stages: preparation,
incubation, illumination and verification. Preparation includes problem acquisition and assimilation of background
knowledge, which is represented by cases, i.e., documented past experiences. With the aim of achieving a flexible
knowledge representation, as a means to potentiate specific creative abilities like Fluency, Synthesis and Analysis, we
structure each case as a network of hierarchically and temporally related case pieces. These case pieces can be considered
individually, providing better recombinations of them. These recombinations, rather than made by chance, are guided by
those hierarchical and temporal case piece relations (or explanations). We explain the role of opportunistic knowledge
acquisition at the incubation stage. We sustain that illumination may comprise recursive calls of the sequence of the first
three stages.
This computational model is implemented in the system INSPIRER (ImagiNation1 taking as Source Past and Imperfectly
RElated Reasonings). An application in musical composition domain is presented. We also show how a musical composition
task may be cognitively modelled and treated as a planning task. We also present a short example illustrating how
INSPIRER generates music
From nose to nuisance: A collaborative approach to assess the odour problem in an oilseed plant
Odour nuisance is an ignored environmental problem, an invisible face of air quality analysis and monitoring in Portugal. Local and governmental authorities have been receiving odour complaints, but only in recent years this issue is seen as a growing concern and not just a matter of licensing and inspection. This fact and the lack of specific ambient air odour regulation in Portugal originated a bottom up approach focused on citizens and their contribution to a more comprehensive analysis. Despite the existence of odour measuring instruments, the human nose is a universal sensor with higher sensitivity that allows to assess the impact of discomfort on sensitive receptors. From this point of view, a sensorial method has been conducted with community neighbours of an odour emission source as an integrative approach to the problem and a complementary vector to a quantitative analysis. The human nose used as a "tool", allows to address the issue instantly and at a local level, which is not always possible with other methodologies, even in situations where the detection limit is reduced and therefore not measurable with certain equipment. It should be noted that this olfactory evaluations are the ones responsible for triggering formal complaints to the authorities whether it is the National Guard, the municipality or the environmental regulators. But the lack of a unified form to register the complaints is a mandatory issue to help addressing the correct odour sources and better understand the problem. So, this sensorial approach also aims to develop a tool to aggregate the needed elements to a valid form, to ensure that the complaints can be verified and validated. This would help to make a comparison and create a record history database, at the odour emitter level or at local and national scale. The results obtained with this approach have led to the application of several actions such as the real knowledge of the problem from an industrial operator perspective, inclusion of public stakeholders, and the design and implementation of an odour management plan with the purpose of the establishment of mitigation measures.publishersversionpublishe
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