13,008 research outputs found
A deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources
A deep learning model is applied for predicting block-level parking occupancy
in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to
extract the spatial relations of traffic flow in large-scale networks, and
utilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to
capture the temporal features. In addition, the model is capable of taking
multiple heterogeneously structured traffic data sources as input, such as
parking meter transactions, traffic speed, and weather conditions. The model
performance is evaluated through a case study in Pittsburgh downtown area. The
proposed model outperforms other baseline methods including multi-layer LSTM
and Lasso with an average testing MAPE of 10.6\% when predicting block-level
parking occupancies 30 minutes in advance. The case study also shows that, in
generally, the prediction model works better for business areas than for
recreational locations. We found that incorporating traffic speed and weather
information can significantly improve the prediction performance. Weather data
is particularly useful for improving predicting accuracy in recreational areas
Real time Traffic Flow Parameters Prediction with Basic Safety Messages at Low Penetration of Connected Vehicles
The expected low market penetration of connected vehicles (CVs) in the near
future could be a constraint in estimating traffic flow parameters, such as
average travel speed of a roadway segment and average space headway between
vehicles from the CV broadcasted data. This estimated traffic flow parameters
from low penetration of connected vehicles become noisy compared to 100 percent
penetration of CVs, and such noise reduces the real time prediction accuracy of
a machine learning model, such as the accuracy of long short term memory (LSTM)
model in terms of predicting traffic flow parameters. The accurate prediction
of the parameters is important for future traffic condition assessment. To
improve the prediction accuracy using noisy traffic flow parameters, which is
constrained by limited CV market penetration and limited CV data, we developed
a real time traffic data prediction model that combines LSTM with Kalman filter
based Rauch Tung Striebel (RTS) noise reduction model. We conducted a case
study using the Enhanced Next Generation Simulation (NGSIM) dataset, which
contains vehicle trajectory data for every one tenth of a second, to evaluate
the performance of this prediction model. Compared to a baseline LSTM model
performance, for only 5 percent penetration of CVs, the analyses revealed that
combined LSTM and RTS model reduced the mean absolute percentage error (MAPE)
from 19 percent to 5 percent for speed prediction and from 27 percent to 9
percent for space-headway prediction. The statistical significance test with a
95 percent confidence interval confirmed no significant difference in predicted
average speed and average space headway using this LSTM and RTS combination
with only 5 percent CV penetration rate.Comment: 16 pages, 15 figures, 4 table
Bayesian Particle Tracking of Traffic Flows
We develop a Bayesian particle filter for tracking traffic flows that is
capable of capturing non-linearities and discontinuities present in flow
dynamics. Our model includes a hidden state variable that captures sudden
regime shifts between traffic free flow, breakdown and recovery. We develop an
efficient particle learning algorithm for real time on-line inference of states
and parameters. This requires a two step approach, first, resampling the
current particles, with a mixture predictive distribution and second,
propagation of states using the conditional posterior distribution. Particle
learning of parameters follows from updating recursions for conditional
sufficient statistics. To illustrate our methodology, we analyze measurements
of daily traffic flow from the Illinois interstate I-55 highway system. We
demonstrate how our filter can be used to inference the change of traffic flow
regime on a highway road segment based on a measurement from freeway
single-loop detectors. Finally, we conclude with directions for future
research
Flow: A Modular Learning Framework for Autonomy in Traffic
The rapid development of autonomous vehicles (AVs) holds vast potential for
transportation systems through improved safety, efficiency, and access to
mobility. However, due to numerous technical, political, and human factors
challenges, new methodologies are needed to design vehicles and transportation
systems for these positive outcomes. This article tackles technical challenges
arising from the partial adoption of autonomy: partial control, partial
observation, complex multi-vehicle interactions, and the sheer variety of
traffic settings represented by real-world networks. The article presents a
modular learning framework which leverages deep Reinforcement Learning methods
to address complex traffic dynamics. Modules are composed to capture common
traffic phenomena (traffic jams, lane changing, intersections). Learned control
laws are found to exceed human driving performance by at least 40% with only
5-10% adoption of AVs. In partially-observed single-lane traffic, a small
neural network control law can eliminate stop-and-go traffic -- surpassing all
known model-based controllers, achieving near-optimal performance, and
generalizing to out-of-distribution traffic densities.Comment: 14 pages, 8 figures; new experiments and analysi
Deep Learning for Short-Term Traffic Flow Prediction
We develop a deep learning model to predict traffic flows. The main
contribution is development of an architecture that combines a linear model
that is fitted using regularization and a sequence of layers.
The challenge of predicting traffic flows are the sharp nonlinearities due to
transitions between free flow, breakdown, recovery and congestion. We show that
deep learning architectures can capture these nonlinear spatio-temporal
effects. The first layer identifies spatio-temporal relations among predictors
and other layers model nonlinear relations. We illustrate our methodology on
road sensor data from Interstate I-55 and predict traffic flows during two
special events; a Chicago Bears football game and an extreme snowstorm event.
Both cases have sharp traffic flow regime changes, occurring very suddenly, and
we show how deep learning provides precise short term traffic flow predictions
Self-Driving Cars: A Survey
We survey research on self-driving cars published in the literature focusing
on autonomous cars developed since the DARPA challenges, which are equipped
with an autonomy system that can be categorized as SAE level 3 or higher. The
architecture of the autonomy system of self-driving cars is typically organized
into the perception system and the decision-making system. The perception
system is generally divided into many subsystems responsible for tasks such as
self-driving-car localization, static obstacles mapping, moving obstacles
detection and tracking, road mapping, traffic signalization detection and
recognition, among others. The decision-making system is commonly partitioned
as well into many subsystems responsible for tasks such as route planning, path
planning, behavior selection, motion planning, and control. In this survey, we
present the typical architecture of the autonomy system of self-driving cars.
We also review research on relevant methods for perception and decision making.
Furthermore, we present a detailed description of the architecture of the
autonomy system of the self-driving car developed at the Universidade Federal
do Esp\'irito Santo (UFES), named Intelligent Autonomous Robotics Automobile
(IARA). Finally, we list prominent self-driving car research platforms
developed by academia and technology companies, and reported in the media
Learning Traffic Flow Dynamics using Random Fields
This paper presents a mesoscopic traffic flow model that explicitly describes
the spatio-temporal evolution of the probability distributions of vehicle
trajectories. The dynamics are represented by a sequence of factor graphs,
which enable learning of traffic dynamics from limited Lagrangian measurements
using an efficient message passing technique. The approach ensures that
estimated speeds and traffic densities are non-negative with probability one.
The estimation technique is tested using vehicle trajectory datasets generated
using an independent microscopic traffic simulator and is shown to efficiently
reproduce traffic conditions with probe vehicle penetration levels as little as
10\%. The proposed algorithm is also compared with state-of-the-art traffic
state estimation techniques developed for the same purpose and it is shown that
the proposed approach can outperform the state-of-the-art techniques in terms
reconstruction accuracy
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent
years, with notable achievements such as Deepmind's AlphaGo. It has been
successfully deployed in commercial vehicles like Mobileye's path planning
system. However, a vast majority of work on DRL is focused on toy examples in
controlled synthetic car simulator environments such as TORCS and CARLA. In
general, DRL is still at its infancy in terms of usability in real-world
applications. Our goal in this paper is to encourage real-world deployment of
DRL in various autonomous driving (AD) applications. We first provide an
overview of the tasks in autonomous driving systems, reinforcement learning
algorithms and applications of DRL to AD systems. We then discuss the
challenges which must be addressed to enable further progress towards
real-world deployment.Comment: Accepted for Oral Presentation at VISAPP 201
User-centric interdependent urban systems: using time-of-day electricity usage data to predict morning roadway congestion
Urban systems are interdependent as individuals' daily activities engage
using those urban systems at certain time of day and locations. There may exist
clear spatial and temporal correlations among usage patterns across all urban
systems. This paper explores such a correlation among energy usage and roadway
congestion. We propose a general framework to predict congestion starting time
and congestion duration in the morning using the time-of-day electricity use
data from anonymous households with no personally identifiable information. We
show that using time-of-day electricity data from midnight to early morning
from 322 households in the City of Austin, can make reliable prediction of
congestion starting time of several highway segments, at the time as early as
2am. This predictor significantly outperforms a time-series predictor that uses
only real-time travel time data up to 6am. We found that 8 out of the 10
typical electricity use patterns have statistically significant affects on
morning congestion on highways in Austin. Some patterns have negative effects,
represented by an early spike of electricity use followed by a drastic drop
that could imply early departure from home. Others have positive effects,
represented by a late night spike of electricity use possible implying late
night activities that can lead to late morning departure from home
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Deep Learning methods have been proven to be flexible to model complex
phenomena. This has also been the case of Intelligent Transportation Systems
(ITS), in which several areas such as vehicular perception and traffic analysis
have widely embraced Deep Learning as a core modeling technology. Particularly
in short-term traffic forecasting, the capability of Deep Learning to deliver
good results has generated a prevalent inertia towards using Deep Learning
models, without examining in depth their benefits and downsides. This paper
focuses on critically analyzing the state of the art in what refers to the use
of Deep Learning for this particular ITS research area. To this end, we
elaborate on the findings distilled from a review of publications from recent
years, based on two taxonomic criteria. A posterior critical analysis is held
to formulate questions and trigger a necessary debate about the issues of Deep
Learning for traffic forecasting. The study is completed with a benchmark of
diverse short-term traffic forecasting methods over traffic datasets of
different nature, aimed to cover a wide spectrum of possible scenarios. Our
experimentation reveals that Deep Learning could not be the best modeling
technique for every case, which unveils some caveats unconsidered to date that
should be addressed by the community in prospective studies. These insights
reveal new challenges and research opportunities in road traffic forecasting,
which are enumerated and discussed thoroughly, with the intention of inspiring
and guiding future research efforts in this field.Comment: 25 pages, 7 figures, 2 table
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