Article
Ergonomics
Zhenjie Zheng, Xin Qi, Zhengli Wang, Bin Ran
Summary: The study introduces a new approach that analyzes the spatiotemporal impact of traffic incidents incorporating multiple congestion levels, which results in more accurate and comprehensive estimation compared to existing research.
ACCIDENT ANALYSIS AND PREVENTION
(2021)
Article
Environmental Sciences
Max Harleman, Lena Harris, Mary D. Willis, Beate Ritz, Perry Hystad, Elaine L. Hill
Summary: We find that widening roads increases congestion during construction, but intersection projects have no effect on congestion. Over the first three years post-construction, widening reduces congestion by 33%, while intersection projects reduce congestion by 52%. Furthermore, both widening and intersection projects have negative impacts on NO2 levels within 500m.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Economics
Jiaohong Xie, Zhenyu Yang, Xiongfei Lai, Yang Liu, Xiao Bo Yang, Teck-Hou Teng, Chen-Khong Tham
Summary: This study focuses on the optimal dynamical information dissemination problem in a transportation network affected by traffic incidents. The proposed decision tool based on double deep Q-learning aims to improve system performance by dynamically generating and disseminating information to road users. The performance of the tool is evaluated using a microscopic simulation model and it demonstrates good performance in improving congestion and other performance metrics.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Environmental Studies
Albert Saiz, Luyao Wang
Summary: Traffic congestion is a significant environmental and social issue caused by various factors, such as urban sprawl, imbalanced distribution of home and job locations, increased car ownership, and lack of public transportation. This study focuses on the understudied factor of geographic barriers and finds that they not only directly affect travel delays but also contribute to worsening downtown congestion. The research also shows that areas near geographic obstacles are at a higher risk of congestion due to lower traffic diffusion ability.
ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
(2023)
Article
Physics, Multidisciplinary
Jiaxin Wu, Xubing Zhou, Yi Peng, Xiaojun Zhao
Summary: The study utilizes multi-scale theory and recurrence analysis to analyze urban traffic systems, finding that low-frequency components are important for long-term traffic state prediction, with differences between weekday and weekend traffic states and increased complexity on Fridays.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Sachin Ranjan, Yeong-Chan Kim, Navin Ranjan, Sovit Bhandari, Hoon Kim
Summary: Traffic congestion is a significant problem worldwide, and a hybrid deep neural network algorithm based on HRNet and ConvLSTM has been proposed for traffic congestion prediction. The model outperforms four other state-of-the-art architectures in terms of accuracy, precision, and recall.
APPLIED SCIENCES-BASEL
(2023)
Article
Green & Sustainable Science & Technology
Sura Mahmood Abdullah, Muthusamy Periyasamy, Nafees Ahmed Kamaludeen, S. K. Towfek, Raja Marappan, Sekar Kidambi Raju, Amal H. H. Alharbi, Doaa Sami Khafaga
Summary: Recently, researchers have been using deep learning techniques to detect and reduce traffic congestion. This research proposes a bidirectional recurrent neural network (BRNN) with Gated Recurrent Units (GRUs) to classify and predict traffic congestion. The results show that the proposed model outperforms existing state-of-the-art methods.
Article
Transportation
JuYeong Lee, JiIn Kwak, YongKyung Oh, Sungil Kim
Summary: Traffic incidents have a common impact on urban traffic networks, but predicting their effects is challenging due to network complexity and the dynamic characteristics of traffic data. In this study, we developed a novel method to quantify the impacts of traffic incidents and identify influential features that affect individual incidents.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Chemistry, Physical
Krzysztof Ciecielag, Krzysztof Kecik, Agnieszka Skoczylas, Jakub Matuszak, Izabela Korzec, Radoslaw Zaleski
Summary: This paper presents the results of ultrasonic non-destructive testing on carbon fibre-reinforced plastics (CFRPs) and glass-fibre reinforced plastics (GFRPs). The study used ultrasonic C-scan analysis to detect defects within the composite materials and recurrence methods to analyze the drilling process. The results confirmed the effectiveness of recurrence methods in detecting defects formed during the machining of composite materials.
Article
Clinical Neurology
Philippa C. Lavallee, Hugo Charles, Gregory W. Albers, Louis R. Caplan, Geoffrey A. Donnan, Jose M. Ferro, Michael G. Hennerici, Julien Labreuche, Carlos Molina, Peter M. Rothwell, Philippe Gabriel Steg, Pierre-Jean Touboul, Shinichiro Uchiyama, Eric Vicaut, Lawrence K. S. Wong, Pierre Amarenco
Summary: This study aimed to assess the coexistence of underlying causes and the 5-year risk for major vascular events in patients with TIA and minor ischemic stroke, using the ASCOD grading system.
Article
Clinical Neurology
Simon Hellwig, Thomas Ihl, Ramanan Ganeshan, Inga Laumeier, Michael Ahmadi, Maureen Steinicke, Joachim E. Weber, Matthias Endres, Heinrich J. Audebert, Jan F. Scheitz
Summary: This study found an association between high-sensitivity cardiac troponin T (hs-cTnT) and major adverse cardiovascular events (MACE) in patients with minor stroke or transient ischemic attack (TIA), especially after risk stratification based on the ABCD(2) score. In the lower risk category, patients with high hs-cTnT levels had a higher risk of MACE occurrence.
ANNALS OF NEUROLOGY
(2021)
Article
Biochemistry & Molecular Biology
Michaela Zeiner, Iva Juranovic Cindric
Summary: The investigation in Vienna on heavy metal accumulation in pine needles revealed significant differences in elemental contents based on traffic volume, soil elemental levels, and translocation processes. One-year-old needles contained higher amounts of elements compared to fresh shoots, showing statistically significant variations in many cases.
Article
Transportation
Fahem Mouhous, Djamil Aissani, Nadir Farhi
Summary: Based on a modelling approach of continuous-time piecewise-deterministic Markov processes, a stochastic risk model for incident occurrences and duration in road networks is proposed. The model allows the evaluation of quantities and thresholds related to traffic incidents, providing practical implications for road operators in managing interventions and evaluating the severity of incident frequency and duration.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Article
Computer Science, Hardware & Architecture
Yijing Huang, Wanyue Wei, Yang He, Qihong Wu, Kaiming Xu
Summary: Prior research on traffic event detection has encountered two problems: limited sample numbers and unbalanced datasets. To solve these issues, this study proposes a Hybrid Deep Learning-based Automated Incident Detection and Management (HDL-AIDM) system, which uses Temporal and Spatial Stacked Autoencoder (TSSAE) and generative adversarial network (GAN) to collect temporal and spatial associations of traffic conditions and improve sample numbers and dataset balance. The suggested method achieves higher incident detection rates with high accuracy.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Green & Sustainable Science & Technology
Suleiman Hassan Otuoze, Dexter V. L. Hunt, Ian Jefferson
Summary: This study employed a neural network to investigate traffic vulnerability and resilience in Nigerian cities, using an adaptive AI application to predict congestion. The LM algorithm performed well in fitting and validating models for both Lagos and Kano, offering a modern approach for urban traffic simulation and congestion prediction.
Article
Mathematics, Applied
Zahra Shahriari, Shannon D. Algar, David M. Walker, Michael Small
Summary: We propose a robust algorithm for constructing first return maps of dynamical systems from time series without embedding. Our method is based on ordinal partitions of the time series, and the first return map is constructed from successive intersections with specific ordinal sequences. We define entropy-based measures to guide our selection of the ordinal sequence for a good first return map and show that this method can robustly be applied to time series from classical chaotic systems.
Review
Mathematics, Applied
Eugene Tan, Shannon Algar, Debora Correa, Michael Small, Thomas Stemler, David Walker
Summary: Delay embedding methods are important tools in time series analysis and prediction. The selection of embedding parameters can greatly impact the analysis, leading researchers to develop various methods for optimization. This paper provides a comprehensive overview of embedding theory, outlining existing methods for selecting embedding lag in both uniform and non-uniform cases. The proposed method, SToPS, combines dynamical and topological arguments to select embedding lags, and performs similarly to existing methods for non-uniform embedding. It also outperforms other methods when predicting fast-slow time series.
Article
Computer Science, Information Systems
Tongfeng Weng, Xiaolu Chen, Zhuoming Ren, Huijie Yang, Jie Zhang, Michael Small
Summary: We adopt reservoir computing, a machine learning technique, to study synchronization phenomena in complex networks. By constructing a coupled configuration, we demonstrate that coupled reservoir oscillators exhibit synchrony with the learned dynamical system. Through this synchronization scheme, we recover the observed system's bifurcation behavior solely based on its chaotic dynamics. Our work provides an alternative framework for studying synchronization phenomena in nature when only observed data are available.
INFORMATION SCIENCES
(2023)
Article
Physics, Multidisciplinary
Siyang Jiang, Jin Zhou, Michael Small, Jun-an Lu, Yanqi Zhang
Summary: Searching for key nodes and edges in a network has been a longstanding problem. Recently, there has been increased attention on the cycle structure in networks. This study proposes a ranking algorithm for cycle importance by identifying key cycles that contribute significantly to the network's dynamics. The researchers provide a concrete definition of importance using the Fiedler value and present a neat index for ranking cycles based on the sensitivity of the Fiedler value to different cycles. Numerical examples demonstrate the effectiveness of this method.
PHYSICAL REVIEW LETTERS
(2023)
Article
Mathematics, Applied
Lixiang Liu, Shanshan Chen, Michael Small, Jack Murdoch Moore, Keke Shang
Summary: This paper presents a novel SIRS model on scale-free networks that considers behavioral memory and time delay to depict an adaptive behavioral feedback mechanism in the spread of epidemics. The study includes a rigorous analysis of the dynamics of the model, determines the basic reproduction number R0, uniform persistence, and global asymptotic stability of equilibria. The model exhibits a sharp threshold property, and optimal control strategies for effective vaccination and treatment are demonstrated. Stochastic network simulations validate the findings and indicate that time delay does not affect R0, but behavioral memory does.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Editorial Material
Biology
Shannon D. Algar, Jennifer Rodger, Michael Small
PHYSICS OF LIFE REVIEWS
(2023)
Article
Chemistry, Analytical
Dimitrios Angelis, Filippos Sofos, Konstantinos Papastamatiou, Theodoros E. Karakasidis
Summary: In this paper, an alternative approach is proposed to calculate the transport coefficients of fluids and the slip length inside nano-conduits. The method utilizes genetic programming-based symbolic regression to derive interpretable mathematical expressions based on molecular dynamics simulation data. The resulting equations have reduced complexity and increased accuracy, adhering to existing domain knowledge and offering the potential for time-saving fluid property interpolation and extrapolation.
Article
Green & Sustainable Science & Technology
Konstantinos Kokkinos, Eftihia Nathanail
Summary: The CO2 reduction promise needs to be widely adopted to decrease future emissions and change the trajectory of urban mobility. However, the long-term strategic vision of CO2 mitigation is influenced by uncertainty and volatility. This study aims to analyze major PESTEL factors that impact the dynamics of urban mobility in a rapidly changing environment.
Proceedings Paper
Green & Sustainable Science & Technology
Theonymphi Xydianou, Eftihia Nathanail
Summary: This research aims to explore the use of drones for last mile deliveries in order to achieve more environmentally friendly city logistics. Data from a transport operator were collected and two scenarios reflecting the current and future situations were synthesized. Two integer mathematical programming models were formulated and implemented with a case study. The results showed that the use of drones reduced carbon dioxide emissions, average delivery time per package, and distribution costs in urban distribution. The lack of a legal framework was identified as the most important obstacle towards the use of drones for parcel distribution in urban environments, based on the literature review and stakeholder analysis.
SMART ENERGY FOR SMART TRANSPORT, CSUM2022
(2023)
Proceedings Paper
Green & Sustainable Science & Technology
Konstantinos Kokkinos, Eftihia Nathanail
Summary: Numerous urban mobility projects aim to reduce greenhouse effects by implementing near zero-CO2 emission practices. This study utilizes a fuzzy semi-quantitative methodology to assess the impact of these initiatives on urban mobility and explores sustainable decision-making in the context of urbanization and the rise of urban actors. The suggested decision support system employs analytics and optimization algorithms to guide responsible authorities and decision-makers towards sustainable urban mobility and decarbonization.
SMART ENERGY FOR SMART TRANSPORT, CSUM2022
(2023)
Article
Mathematics, Interdisciplinary Applications
David M. Walker, Debora C. Correa
Summary: Compression networks transform univariate time series to complex network representation using a compression algorithm. They can capture relationships among multivariate time series and track dynamical change through thresholding of the compression edge weights. These networks can also identify partially synchronized states in the dynamics of networked oscillators and classify musical compositions.
JOURNAL OF COMPLEX NETWORKS
(2023)
Article
Physics, Fluids & Plasmas
Jack Murdoch Moore, Haiying Wang, Michael Small, Gang Yan, Huijie Yang, Changgui Gu
Summary: The network correlation dimension controls the distribution of network distance in terms of a power-law model and has significant impacts on both structural properties and dynamical processes. We have developed new maximum likelihood methods that can robustly and objectively identify network correlation dimension as well as a bounded interval of distances where the model accurately represents the structure. We have also compared the traditional practice of estimating correlation dimension with a proposed alternative method using the fraction of nodes at a distance modeled as a power law.
Article
Physics, Multidisciplinary
Eugene Tan, Shannon D. Algar, Debora Correa, Thomas Stemler, Michael Small
Summary: A method of constructing a discretised network representation of a system's attractor is proposed and its applicability in identifying dynamical change points in different systems is demonstrated.
COMMUNICATIONS PHYSICS
(2023)
Article
Materials Science, Multidisciplinary
E. Tsoutsoumanos, T. Karakasidis, N. Laskaris, P. G. Konstantinidis, G. S. Polymeris, G. Kitis
Summary: This study investigates the correlation between nanocrystal dimensions and thermoluminescence signal magnitude through simulations conducted in Python. Two mathematical models, OTOR and IMTS, were used to derive theoretical luminescence signals. The obtained results were compared with experimental data and a thorough comparative discussion was conducted.
MATERIALS RESEARCH BULLETIN
(2024)
Article
Mathematics, Interdisciplinary Applications
Tongfeng Weng, Xiaolu Chen, Zhuoming Ren, Huijie Yang, Jie Zhang, Michael Small
Summary: This study investigates the collective behavior of multiply moving reservoir computing oscillators. These oscillators gradually exhibit coherent rhythmic behavior when their number is large enough, showing excellent agreement with their learned dynamical system. Furthermore, the oscillators can exhibit significantly distinct collective behaviors resembling bifurcation phenomenon when changing a critical reservoir parameter. Intermittent synchronization emerges among the oscillators when studying a continuous chaotic system.
CHAOS SOLITONS & FRACTALS
(2023)
