378 research outputs found
Where Should Traffic Sensors Be Placed on Highways?
This paper investigates the practical engineering problem of traffic sensors
placement on stretched highways with ramps. Since it is virtually impossible to
install bulky traffic sensors on each highway segment, it is crucial to find
placements that result in optimized network-wide, traffic observability.
Consequently, this results in accurate traffic density estimates on segments
where sensors are not installed. The substantial contribution of this paper is
the utilization of control-theoretic observability analysis -- jointly with
integer programming -- to determine traffic sensor locations based on the
nonlinear dynamics and parameters of traffic networks. In particular, the
celebrated asymmetric cell transmission model is used to guide the placement
strategy jointly with observability analysis of nonlinear dynamic systems
through Gramians. Thorough numerical case studies are presented to corroborate
the proposed theoretical methods and various computational research questions
are posed and addressed. The presented approach can also be extended to other
models of traffic dynamics
Autonomous vehicles: From vehicular control to traffic control
International audienc
Developing A Physics-informed Deep Learning Paradigm for Traffic State Estimation
The traffic delay due to congestion cost the U.S. economy $ 81 billion in 2022, and on average, each worker lost 97 hours each year during commute due to longer wait time. Traffic management and control strategies that serve as a potent solution to the congestion problem require accurate information on prevailing traffic conditions. However, due to the cost of sensor installation and maintenance, associated sensor noise, and outages, the key traffic metrics are often observed partially, making the task of estimating traffic states (TSE) critical. The challenge of TSE lies in the sparsity of observed traffic data and the noise present in the measurements. The central research premise of this dissertation is whether and how the fundamental principles of traffic flow theory could be harnessed to augment machine learning in estimating traffic conditions. This dissertation develops a physics-informed deep learning (PIDL) paradigm for traffic state estimation. The developed PIDL framework equips a deep learning neural network with the strength of the governing physical laws of the traffic flow to better estimate traffic conditions based on partial and limited sensing measurements. First, this research develops a PIDL framework for TSE with the continuity equation Lighthill-Whitham-Richards (LWR) conservation law - a partial differential equation (PDE). The developed PIDL framework is illustrated with multiple fundamental diagrams capturing the relationship between traffic state variables. The framework is expanded to incorporate a more practical, discretized traffic flow model - the cell transmission model (CTM). Case studies are performed to validate the proposed PIDL paradigm by reconstructing the velocity and density fields using both synthetic and realistic traffic datasets, such as the next-generation simulation (NGSIM). The case studies mimic a multitude of application scenarios with pragmatic considerations such as sensor placement, coverage area, data loss, and the penetration rate of connected autonomous vehicles (CAVs). The study results indicate that the proposed PIDL approach brings exceedingly superior performance in state estimation tasks with a lower training data requirement compared to the benchmark deep learning (DL) method. Next, the dissertation continues with an investigation of the empirical evidence which points to the limitation of PIDL architectures with certain types of PDEs. It presents the challenges in training PIDL architecture by contrasting PIDL performances in learning the first-order scalar hyperbolic LWR conservation law and its second-order parabolic counterpart. The outcome indicates that PIDL experiences challenges in incorporating the hyperbolic LWR equation due to the non-smoothness of its solution. On the other hand, the PIDL architecture with the parabolic version of the PDE, augmented with the diffusion term, leads to the successful reassembly of the density field even with the shockwaves present. Thereafter, the implication of PIDL limitations for traffic state estimation and prediction is commented upon, and readers\u27 attention is directed to potential mitigation strategies. Lastly, a PIDL framework with nonlocal traffic flow physics, capturing the driver reaction to the downstream traffic conditions, is proposed. In summary, this dissertation showcases the vast capability of the developed physics-informed deep learning paradigm for traffic state estimation in terms of efficiently utilizing meager observation for precise reconstruction of the data field. Moreover, it contemplates the practical ramification of PIDL for TSE with the hyperbolic flow conservation law and explores the remedy with sampling strategies of training instances and adding the diffusion term. Ultimately, it paints the picture of potent PIDL applications in TSE with nonlocal physics and suggests future research directions in PIDL for traffic state predictions
Sensor Location For Network Flow And Origin-Destination Estimation With Multiple Vehicle Classes
The need for multi-class origin-destination (O-D) estimation and link volume estimation requires multi-class observations from sensors. This dissertation has established a new sensor location model that includes: 1) multiple vehicle classes; 2) a variety of data types from different types of sensors; and 3) a focus on both link-based and O-D based flow estimation. The model seeks a solution that maximizes the overall information content from sensors, subject to a budget constraint. An efficient twophase metaheuristic algorithm is developed to solve the problem. The model is based on a set of linear equations that connect O-D flows, link flows and sensor observations. Concepts from Kalman filtering are used to define the information content from a set of sensors as the trace of the posterior covariance matrix of flow estimates, and to create a linear update mechanism for the precision matrix as new sensors are added or deleted from the solution set. Sensor location decisions are nonlinearly related to information content because the precision matrix must be inverted to construct the covariance matrix which is the basis for measuring information. The resulting model is a nonlinear knapsack problem. The two-phase search algorithm proposed addresses this nonlinear, nonseparable integer sensor location problem. A greedy phase generates an initial solution, feeding into a Tabu Search phase which swaps sensors along the budget constraint. The neighbor generation in Tabu search is a combination of a fixed swapout strategy with a guided random swap-in strategy. Extensive computational experiments have been performed on a standard test network. These tests verify the effectiveness of the problem formulation and solution algorithm. A case study on Rockland County, NY demonstrates that the sensor location method developed in this dissertation can successfully allocate sensors in realistic networks, and thus has significant practical value
Software Defined Networks based Smart Grid Communication: A Comprehensive Survey
The current power grid is no longer a feasible solution due to
ever-increasing user demand of electricity, old infrastructure, and reliability
issues and thus require transformation to a better grid a.k.a., smart grid
(SG). The key features that distinguish SG from the conventional electrical
power grid are its capability to perform two-way communication, demand side
management, and real time pricing. Despite all these advantages that SG will
bring, there are certain issues which are specific to SG communication system.
For instance, network management of current SG systems is complex, time
consuming, and done manually. Moreover, SG communication (SGC) system is built
on different vendor specific devices and protocols. Therefore, the current SG
systems are not protocol independent, thus leading to interoperability issue.
Software defined network (SDN) has been proposed to monitor and manage the
communication networks globally. This article serves as a comprehensive survey
on SDN-based SGC. In this article, we first discuss taxonomy of advantages of
SDNbased SGC.We then discuss SDN-based SGC architectures, along with case
studies. Our article provides an in-depth discussion on routing schemes for
SDN-based SGC. We also provide detailed survey of security and privacy schemes
applied to SDN-based SGC. We furthermore present challenges, open issues, and
future research directions related to SDN-based SGC.Comment: Accepte
Density and flow reconstruction in urban traffic networks using heterogeneous data sources
International audienceIn this paper, we consider the problem of joint reconstruction of flow and density in a urban traffic network using heterogeneous sources of information. The traffic network is modeled within the framework of macroscopic traffic models, where we adopt Lighthill-Whitham-Richards model (LWR) conservation equation characterized by a piecewise linear fundamental diagram. The estimation problem considers two key principles. First, the error minimization between the measured and reconstructed flows and densities, and second the equilibrium state of the network which establishes flow propagation within the network. Both principles are integrated together with the traffic model constraints established by the supply/demand paradigm. Finally the problem is casted as a constrained quadratic optimization with equality constraints in order to shrink the feasible region of estimated variables. Some simulation scenarios based on synthetic data for a manhattan grid network are provided in order to validate the performance of the proposed algorithm
Machine Learning Solutions for Transportation Networks
This thesis brings a collection of novel models and methods that result from a new look at practical problems in transportation through the prism of newly available sensor data. There are four main contributions: First, we design a generative probabilistic graphical model to describe multivariate continuous densities such as observed traffic patterns. The model implements a multivariate normal distribution with covariance constrained in a natural way, using a number of parameters that is only linear (as opposed to quadratic) in the dimensionality of the data. This means that learning these models requires less data. The primary use for such a model is to support inferences, for instance, of data missing due to sensor malfunctions. Second, we build a model of traffic flow inspired by macroscopic flow models. Unlike traditional such models, our model deals with uncertainty of measurement and unobservability of certain important quantities and incorporates on-the-fly observations more easily. Because the model does not admit efficient exact inference, we develop a particle filter. The model delivers better medium- and long- term predictions than general-purpose time series models. Moreover, having a predictive distribution of traffic state enables the application of powerful decision-making machinery to the traffic domain. Third, two new optimization algorithms for the common task of vehicle routing are designed, using the traffic flow model as their probabilistic underpinning. Their benefits include suitability to highly volatile environments and the fact that optimization criteria other than the classical minimal expected time are easily incorporated. Finally, we present a new method for detecting accidents and other adverse events. Data collected from highways enables us to bring supervised learning approaches to incident detection. We show that a support vector machine learner can outperform manually calibrated solutions. A major hurdle to performance of supervised learners is the quality of data which contains systematic biases varying from site to site. We build a dynamic Bayesian network framework that learns and rectifies these biases, leading to improved supervised detector performance with little need for manually tagged data. The realignment method applies generally to virtually all forms of labeled sequential data
An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques
Origin-destination~(OD) flow modeling is an extensively researched subject
across multiple disciplines, such as the investigation of travel demand in
transportation and spatial interaction modeling in geography. However,
researchers from different fields tend to employ their own unique research
paradigms and lack interdisciplinary communication, preventing the
cross-fertilization of knowledge and the development of novel solutions to
challenges. This article presents a systematic interdisciplinary survey that
comprehensively and holistically scrutinizes OD flows from utilizing
fundamental theory to studying the mechanism of population mobility and solving
practical problems with engineering techniques, such as computational models.
Specifically, regional economics, urban geography, and sociophysics are adept
at employing theoretical research methods to explore the underlying mechanisms
of OD flows. They have developed three influential theoretical models: the
gravity model, the intervening opportunities model, and the radiation model.
These models specifically focus on examining the fundamental influences of
distance, opportunities, and population on OD flows, respectively. In the
meantime, fields such as transportation, urban planning, and computer science
primarily focus on addressing four practical problems: OD prediction, OD
construction, OD estimation, and OD forecasting. Advanced computational models,
such as deep learning models, have gradually been introduced to address these
problems more effectively. Finally, based on the existing research, this survey
summarizes current challenges and outlines future directions for this topic.
Through this survey, we aim to break down the barriers between disciplines in
OD flow-related research, fostering interdisciplinary perspectives and modes of
thinking.Comment: 49 pages, 6 figure
Collocation of Sensing, Computing, and Actuation in Low-Power Wireless Nodes for Smart Structure Applications in Civil and Mechanical Systems.
Effective management of civil structures requires both data and a physical means to
mitigate the negative consequences of the effects of extreme loading events. This thesis
presents a smart structure framework characterized by low-cost wireless nodes with
collocated sensing, computing, and actuation capabilities. These nodes are intended to
function as an automated first line of defense during extreme loading events, providing
both rapid assessment of structural condition (i.e., health) and automated response (i.e.,
control). Low-cost wireless sensing and actuation nodes promote dense instrumentations
that can provide great insight into the dynamic behavior and condition of structures.
However, wireless networks should not be viewed merely as one-to-one replacements for
traditional tethered systems. Rather, the goal of this thesis is to demonstrate the
embedment of computationally expedient approaches for traditional smart structure tasks
(i.e., load estimation, structural health monitoring, and structural control) implemented
within wireless sensor and actuation networks. The distributed nature of these computing
resources, coupled with limitations on power and communication bandwidth, require
unique decentralized data processing algorithms that can operate effectively within the
decentralized wireless smart structure environment. To accomplish this goal, this thesis
first presents the development and validation of a novel wireless sensing and actuation
platform necessary to meet the specific requirements of this thesis work. Then, using this
wireless system, a method for estimating wind loading from measured wind turbine tower
response is experimentally validated. This method can generate reference loading data
that may be used to improve the design economy of future turbines. In addition, a
wireless structural health monitoring method based on a physical parameterization of
time-series model coefficients is presented for damage detection in post-earthquake
scenarios. This method employs a physics-based method of evaluating and integrating
damage indications derived from individual sensors within the network. Finally, a
partially-decentralized method for wireless structural control is presented in which the
wireless network dynamically trades bandwidth for performance of actuators engaged in
feedback control. This method provides a means to allocate scarce bandwidth resources
while still allowing the wireless controllers to improve performance by identifying and
broadcasting only the most valuable feedback data over the network.Ph.D.Civil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75812/1/asgard_1.pd
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