arXiv subjects

Christian Claudel

Publications and source records attributed to Christian Claudel.

42 records · Page 3Linked to original sources

arXiv2019

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation using Convex Optimization

Monitoring and control of traffic networks represent alternative, inexpensive strategies to minimize traffic congestion. As the number of traffic sensors is naturally constrained by budgetary requirements, real-time estimation of traffic flow in road segments that are not equipped with sensors is of significant importance---thereby providing situational awareness and guiding real-time feedback control strategies. To that end, firstly we build a generalized traffic flow model for stretched highways with arbitrary number of ramp flows based on the Lighthill Whitham Richards (LWR) flow model. Secondly, we characterize the function set corresponding to the nonlinearities present in the LWR model, and use this characterization to design real-time and robust state estimators (SE) for stretched highway segments. Specifically, we show that the nonlinearities from the derived models are locally Lipschitz continuous by providing the analytical Lipschitz constants. Thirdly, the analytical derivation is then incorporated through a robust SE method given a limited number of traffic sensors, under the impact of process and measurement disturbances and unknown inputs. The estimator is based on deriving a convex semidefinite optimization problem. Finally, numerical tests are given showcasing the applicability, scalability, and robustness of the proposed estimator for large systems under high magnitude disturbances, parametric uncertainty, and unknown inputs.

eess.SY↗

arXiv2019

Physics Informed Data Driven model for Flood Prediction: Application of Deep Learning in prediction of urban flood development

Flash floods in urban areas occur with increasing frequency. Detecting these floods would greatlyhelp alleviate human and economic losses. However, current flood prediction methods are eithertoo slow or too simplified to capture the flood development in details. Using Deep Neural Networks,this work aims at boosting the computational speed of a physics-based 2-D urban flood predictionmethod, governed by the Shallow Water Equation (SWE). Convolutional Neural Networks(CNN)and conditional Generative Adversarial Neural Networks(cGANs) are applied to extract the dy-namics of flood from the data simulated by a Partial Differential Equation(PDE) solver. Theperformance of the data-driven model is evaluated in terms of Mean Squared Error(MSE) andPeak Signal to Noise Ratio(PSNR). The deep learning-based, data-driven flood prediction modelis shown to be able to provide precise real-time predictions of flood development

eess.SP↗

arXiv2019

MCRM: Mother Compact Recurrent Memory

LSTMs and GRUs are the most common recurrent neural network architectures used to solve temporal sequence problems. The two architectures have differing data flows dealing with a common component called the cell state (also referred to as the memory). We attempt to enhance the memory by presenting a modification that we call the Mother Compact Recurrent Memory (MCRM). MCRMs are a type of a nested LSTM-GRU architecture where the cell state is the GRU hidden state. The concatenation of the forget gate and input gate interactions from the LSTM are considered an input to the GRU cell. Because MCRMs has this type of nesting, MCRMs have a compact memory pattern consisting of neurons that acts explicitly in both long-term and short-term fashions. For some specific tasks, empirical results show that MCRMs outperform previously used architectures.

cs.NE↗

arXiv2019

IEA: Inner Ensemble Average within a convolutional neural network

Ensemble learning is a method of combining multiple trained models to improve model accuracy. We propose the usage of such methods, specifically ensemble average, inside Convolutional Neural Network (CNN) architectures by replacing the single convolutional layers with Inner Average Ensembles (IEA) of multiple convolutional layers. Empirical results on different benchmarking datasets show that CNN models using IEA outperform those with regular convolutional layers. A visual and a similarity score analysis of the features generated from IEA explains why it boosts the model performance.

cs.LG↗

arXiv2018

A Fast Lax-Hopf formula to solve the Lighthill-Whitham-Richards traffic flow model on networks

Efficient and exact algorithms are important for performing fast and accurate traffic network simulations with macroscopic traffic models. In this paper, we extend the semi-analytical Lax-Hopf algorithm in order to compute link inflows and outflows with the LWR model. Our proposed Fax Lax-Hopf algorithm has a very low computational complexity. We demonstrate that some of the original algorithm's operations (associated with the initial conditions) can be discarded, leading to a faster computation of boundary demand/supplies in network simulation problems, for general concave fundamental diagrams. Moreover, the computational cost can be further reduced for triangular Fundamental Diagrams and specific space-time discretizations. The resulting formulation has a performance comparable to the Link Transmission Model and, since it solves the original LWR model for a wide range of FD shapes, with any initial configuration, it is suitable to solve a broad range of traffic operations problems. As part of the analysis, we compare the performance of the proposed scheme to other well-known computational methods.

math.AP↗

arXiv2016

Short range networks of wearables for safer mobility in smart cities

Ensuring safe and efficient mobility is a critical issue for smart city operators. Increasing safety not only reduces the likelihood of road injuries and fatalities, but also reduces traffic congestion and disruptions caused by accidents, increasing efficiency. While new vehicles are increasingly equipped with semi-automation, the added costs will initially limit the penetration rate of these systems. An inexpensive way to replace or augment these systems is to create networks of wearables (smart glasses, watches) that exchange positional and path data at a very fast rate between all users, identify collision risks and feedback collision resolution information to all users in an intuitive way through their smart glasses.

cs.CY↗