972 research outputs found
Data science applications to connected vehicles: Key barriers to overcome
The connected vehicles will generate huge amount of pervasive and real time data, at very high frequencies. This poses new challenges for Data science. How to analyse these data and how to address short-term and long-term storage are some of the key barriers to overcome.JRC.C.6-Economics of Climate Change, Energy and Transpor
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks
Connected automated driving has the potential to significantly improve urban
traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative
behavior planning can be employed to jointly optimize the motion of multiple
vehicles. Most existing approaches to automatic intersection management,
however, only consider fully automated traffic. In practice, mixed traffic,
i.e., the simultaneous road usage by automated and human-driven vehicles, will
be prevalent. The present work proposes to leverage reinforcement learning and
a graph-based scene representation for cooperative multi-agent planning. We
build upon our previous works that showed the applicability of such machine
learning methods to fully automated traffic. The scene representation is
extended for mixed traffic and considers uncertainty in the human drivers'
intentions. In the simulation-based evaluation, we model measurement
uncertainties through noise processes that are tuned using real-world data. The
paper evaluates the proposed method against an enhanced first in - first out
scheme, our baseline for mixed traffic management. With increasing share of
automated vehicles, the learned planner significantly increases the vehicle
throughput and reduces the delay due to interaction. Non-automated vehicles
benefit virtually alike.Comment: 8 pages, 7 figures, 34th IEEE Intelligent Vehicles Symposium (IV),
updated to accepted versio
Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems
Vehicle-to-Everything (V2X) communication has been proposed as a potential
solution to improve the robustness and safety of autonomous vehicles by
improving coordination and removing the barrier of non-line-of-sight sensing.
Cooperative Vehicle Safety (CVS) applications are tightly dependent on the
reliability of the underneath data system, which can suffer from loss of
information due to the inherent issues of their different components, such as
sensors failures or the poor performance of V2X technologies under dense
communication channel load. Particularly, information loss affects the target
classification module and, subsequently, the safety application performance. To
enable reliable and robust CVS systems that mitigate the effect of information
loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled
with a hybrid learning-based predictive modeling technique for CVS systems. The
CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian
Process (HGP) prediction system. Consequently, the vehicle safety applications
use the information from the CA-TC, making them more robust and reliable. The
CAM leverages vehicles path history, road geometry, tracking, and prediction;
and the HGP is utilized to provide accurate vehicles' trajectory predictions to
compensate for data loss (due to communication congestion) or sensor
measurements' inaccuracies. Based on offline real-world data, we learn a finite
bank of driver models that represent the joint dynamics of the vehicle and the
drivers' behavior. We combine offline training and online model updates with
on-the-fly forecasting to account for new possible driver behaviors. Finally,
our framework is validated using simulation and realistic driving scenarios to
confirm its potential in enhancing the robustness and reliability of CVS
systems
Reinforcement Learning with Model Predictive Control for Highway Ramp Metering
In the backdrop of an increasingly pressing need for effective urban and
highway transportation systems, this work explores the synergy between
model-based and learning-based strategies to enhance traffic flow management by
use of an innovative approach to the problem of highway ramp metering control
that embeds Reinforcement Learning techniques within the Model Predictive
Control framework. The control problem is formulated as an RL task by crafting
a suitable stage cost function that is representative of the traffic
conditions, variability in the control action, and violations of a
safety-critical constraint on the maximum number of vehicles in queue. An
MPC-based RL approach, which merges the advantages of the two paradigms in
order to overcome the shortcomings of each framework, is proposed to learn to
efficiently control an on-ramp and to satisfy its constraints despite
uncertainties in the system model and variable demands. Finally, simulations
are performed on a benchmark from the literature consisting of a small-scale
highway network. Results show that, starting from an MPC controller that has an
imprecise model and is poorly tuned, the proposed methodology is able to
effectively learn to improve the control policy such that congestion in the
network is reduced and constraints are satisfied, yielding an improved
performance compared to the initial controller.Comment: 14 pages, 10 figures, 3 tables, submitted to IEEE Transactions on
Intelligent Transportation System
Design and validation of novel methods for long-term road traffic forecasting
132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe
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