Transport in deformed centrosymmetric networks

Centrosymmetry often mediates Perfect State Transfer (PST) in various complex systems ranging from quantum wires to photosynthetic networks. We introduce the Deformed Centrosymmetric Ensemble (DCE) of random matrices, $H(\lambda) \equiv H_+ + \lambda H_-$, where $H_+$ is centrosymmetric while $H_-$ is skew-centrosymmetric. The relative strength of the $H_\pm$ prompts the system size scaling of the control parameter as $\lambda = N^{-\frac{\gamma}{2}}$. We propose two quantities, $\mathcal{P}$ and $\mathcal{C}$, quantifying centro- and skewcentro-symmetry, respectively, exhibiting second order phase transitions at $\gamma_\text{P}\equiv 1$ and $\gamma_\text{C}\equiv -1$. In addition, DCE posses an ergodic transition at $\gamma_\text{E} \equiv 0$. Thus equipped with a precise control of the extent of centrosymmetry in DCE, we study the manifestation of $\gamma$ on the transport properties of complex networks. We propose that such random networks can be constructed using the eigenvectors of $H(\lambda)$ and establish that the maximum transfer fidelity, $F_T$, is equivalent to the degree of centrosymmetry, $\mathcal{P}$.Comment: 13 pages, 5 figure

Hybrid hidden Markov LSTM for short-term traffic flow prediction

Deep learning (DL) methods have outperformed parametric models such as historical average, ARIMA and variants in predicting traffic variables into short and near-short future, that are critical for traffic management. Specifically, recurrent neural network (RNN) and its variants (e.g. long short-term memory) are designed to retain long-term temporal correlations and therefore are suitable for modeling sequences. However, multi-regime models assume the traffic system to evolve through multiple states (say, free-flow, congestion in traffic) with distinct characteristics, and hence, separate models are trained to characterize the traffic dynamics within each regime. For instance, Markov-switching models with a hidden Markov model (HMM) for regime identification is capable of capturing complex dynamic patterns and non-stationarity. Interestingly, both HMM and LSTM can be used for modeling an observation sequence from a set of latent or, hidden state variables. In LSTM, the latent variable is computed in a deterministic manner from the current observation and the previous latent variable, while, in HMM, the set of latent variables is a Markov chain. Inspired by research in natural language processing, a hybrid hidden Markov-LSTM model that is capable of learning complementary features in traffic data is proposed for traffic flow prediction. Results indicate significant performance gains in using hybrid architecture compared to conventional methods such as Markov switching ARIMA and LSTM

Mitigating Reflective Cracking Through the Use of a Ductile Concrete Interlayer

Reflective cracking is considered one of the most important issues that causes premature deterioration of composite pavements. Many types of mitigation methods have been studied in the past. However, they are either not effective in delaying the reflective cracking, or they only extend the service life by a few years. To address this critical issue and significantly extend the service life of the composite pavement, in this research, a ductile interlayer made of engineered cementitious composites (ECC) was proposed. It was hypothesized that by adding a thin layer of highly ductile ECC material between the existing pavement and overlay, reflective cracking could be arrested by the ductile interlayer. This study experimentally evaluated the effectiveness of ECC as an interlayer system. A laboratory test protocol was designed to simulate repeated traffic loads to measure the fatigue performance of ECC interlayer system. The strain field and reflective cracking were monitored using digital image correlation (DIC) technique. It was found that the composite pavement specimens with ECC interlayer provided significantly higher fatigue life as compared to the control specimens without an interlayer. The failure mode also changed from single reflective crack to multiple cracks in overlaid HMA mixtures. The results indicated that ECC could be used as a potential effective interlayer system to retard or mitigate reflective cracking

A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models

Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates, it is difficult to place any level of trust to the model predictions, and operational strategies relying on overconfident predictions can lead to worsening traffic conditions. In this study, we propose a Bayesian recurrent neural network framework for uncertainty quantification in traffic prediction with higher generalizability by introducing spectral normalization to its hidden layers. In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting to the training data. This, in turn, helps improve the generalization performance of the model on out-of-distribution datasets. Results demonstrate that spectral normalization improves uncertainty estimates and significantly outperforms both the layer normalization and model without normalization in single-step prediction horizons. This improved performance can be attributed to the ability of spectral normalization to better localize the feature space of the data under perturbations. Our findings are especially relevant to traffic management applications, where predicting traffic conditions across multiple locations is the goal, but the availability of training data from multiple locations is limited. Spectral normalization, therefore, provides a more generalizable approach that can effectively capture the underlying patterns in traffic data without requiring location-specific models

Mitigating Reflective Cracking Through the Use of a Ductile Concrete Interlayer [Supporting Dataset]

69A3551747106National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT's Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2022-11-11. If, in the future, you have trouble accessing this dataset at the host repository, please email [email protected] describing your problem. NTL staff will do its best to assist you at that time.Reflective cracking is considered one of the most important issues that causes premature deterioration of composite pavements. Many types of mitigation methods have been studied in the past. However, they are either not effective in delaying the reflective cracking, or they only extend the service life by a few years. To address this critical issue and significantly extend the service life of the composite pavement, in this research, a ductile interlayer made of engineered cementitious composites (ECC) was proposed. It was hypothesized that by adding a thin layer of highly ductile ECC material between the existing pavement and overlay, reflective cracking could be arrested by the ductile interlayer. This study experimentally evaluated the effectiveness of ECC as an interlayer system. A laboratory test protocol was designed to simulate repeated traffic loads to measure the fatigue performance of ECC interlayer system. The strain field and reflective cracking were monitored using digital image correlation (DIC) technique. It was found that the composite pavement specimens with ECC interlayer provided significantly higher fatigue life as compared to the control specimens without an interlayer. The failure mode also changed from single reflective crack to multiple cracks in overlaid HMA mixtures. The results indicated that ECC could be used as a potential effective interlayer system to retard or mitigate reflective cracking. The total size of the described zip file is 375 KB. Files with the .xlsx extension are Microsoft Excel spreadsheet files. These can be opened in Excel or open-source spreadsheet programs. Docx files are document files created in Microsoft Word. These files can be opened using Microsoft Word or with an open source text viewer such as Apache OpenOffice