| 1. |
- Kristan, M., et al.
(author)
-
The Eighth Visual Object Tracking VOT2020 Challenge Results
- 2020
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In: Computer Vision. - Cham : Springer International Publishing. - 9783030682378 ; , s. 547-601
-
Conference paper (peer-reviewed)abstract
- The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net ).
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| 2. |
- Yang, Ying, et al.
(author)
-
Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review
- 2024
-
In: Transportation Research Part E: Logistics and Transportation Review. - 1366-5545. ; 183
-
Journal article (peer-reviewed)abstract
- Automatic Identification System (AIS) data holds immense research value in the maritime industry because of its massive scale and the ability to reveal the spatial–temporal variation patterns of vessels. Unfortunately, its potential has long been limited by traditional methodologies. The emergence of machine learning (ML) offers a promising avenue to unlock the full potential of AIS data. In recent years, there has been a growing interest among researchers in leveraging ML to analyze and utilize AIS data. This paper, therefore, provides a comprehensive review of ML applications using AIS data and offers valuable suggestions for future research, such as constructing benchmark AIS datasets, exploring more deep learning (DL) and deep reinforcement learning (DRL) applications on AIS-based studies, and developing large-scale ML models trained by AIS data.
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| 3. |
- Fang, Shan, et al.
(author)
-
A Dynamic Transformation Car-Following Model for the Prediction of the Traffic Flow Oscillation
- 2024
-
In: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 16:1, s. 174-198
-
Journal article (peer-reviewed)abstract
- Car-following (CF) behavior is a fundamental of traffic flow modeling; it can be used for the virtual testing of connected and automated vehicles and the simulation of various types of traffic flow, such as free flow and traffic oscillation. Although existing CF models can replicate the free flow well, they are incapable of simulating complicated traffic oscillation, and it is difficult to strike a balance between accuracy and efficiency. This article investigates the error variation when the traffic oscillation is simulated by the intelligent driver model (IDM). Then, it divides the traffic oscillation into four phases (coasting, deceleration, acceleration, and stationary) by using the space headway of multiple steps. To simulate traffic oscillation between multiple human-driven vehicles, a dynamic transformation CF model is proposed, which includes the long-time prediction submodel [modified sequence-to-sequence (Seq2seq)] model, short-time prediction submodel (Transformer), and their dynamic transformation strategy]. The first submodel is utilized to simulate the coasting and stationary phases, while the second submodel is utilized to simulate the acceleration and deceleration phases. The results of experiments indicated that compared to K-nearest neighbors, IDM, and Seq2seq CF models, the dynamic transformation CF model reduces the trajectory error by 60.79–66.69% in microscopic traffic flow simulations, 7.71–29.91% in mesoscopic traffic flow simulations, and 1.59–18.26% in macroscopic traffic flow simulations. Moreover, the runtime of the dynamic transformation CF model (Inference) decreased by 14.43–66.17% when simulating the large-scale traffic flow.
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| 4. |
- Fedriani, Rubén, 1991, et al.
(author)
-
The SOFIA Massive (SOMA) Star Formation Survey. IV. Isolated Protostars
- 2023
-
In: Astrophysical Journal. - : American Astronomical Society. - 1538-4357 .- 0004-637X. ; 942:1
-
Journal article (peer-reviewed)abstract
- We present similar to 10-40 mu m SOFIA-FORCAST images of 11 isolated protostars as part of the SOFIA Massive (SOMA) Star Formation Survey, with this morphological classification based on 37 mu m imaging. We develop an automated method to define source aperture size using the gradient of its background-subtracted enclosed flux and apply this to build spectral energy distributions (SEDs). We fit the SEDs with radiative transfer models, developed within the framework of turbulent core accretion (TCA) theory, to estimate key protostellar properties. Here, we release the sedcreator python package that carries out these methods. The SEDs are generally well fitted by the TCA models, from which we infer initial core masses M ( c ) ranging from 20-430 M (circle dot), clump mass surface densities sigma(cl) similar to 0.3-1.7 g cm(-2), and current protostellar masses m (*) similar to 3-50 M (circle dot). From a uniform analysis of the 40 sources in the full SOMA survey to date, we find that massive protostars form across a wide range of clump mass surface density environments, placing constraints on theories that predict a minimum threshold sigma(cl) for massive star formation. However, the upper end of the m (*)-sigma(cl) distribution follows trends predicted by models of internal protostellar feedback that find greater star formation efficiency in higher sigma(cl) conditions. We also investigate protostellar far-IR variability by comparison with IRAS data, finding no significant variation over an similar to 40 yr baseline.
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| 5. |
- Fei, Yang, et al.
(author)
-
Critical Roles of Control Engineering in the Development of Intelligent and Connected Vehicles
- 2024
-
In: Journal of Intelligent and Connected Vehicles. - 2399-9802. ; 7:2, s. 79-85
-
Research review (peer-reviewed)abstract
- In recent years, advancements in onboard computing hardware and wireless communication technology have remarkably stimulated the development of intelligent and connected vehicles (ICVs). Specifically, some researchers have investigated the issue of employing various advanced control techniques to optimize the performance of autonomous vehicles in practice (Sun et al., 2023; Zhang et al., 2023a, 2023b). Therefore, this article aims to discuss why and how control engineering plays an essential role in the development of ICVs.
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| 6. |
- Figalova, Nikol, et al.
(author)
-
An extension of the human-factors methodological toolbox for human-AV interaction design research : Deliverable 1.4 in the EC ITN project SHAPE-IT
- 2023
-
Reports (other academic/artistic)abstract
- Early state researchers (ESRs) of the SHAPE-IT project have committed to exploring innovative methods to ensure driving safety during interactions between human and Automated Vehicles (AVs). In this deliverable, insights of ESRs span a broad spectrum of methodologies, from experimental methods, including psychophysiological measures, Virtual Reality/Augmented Reality (VR/AR) applications, and transparency assessments, to human-AV interaction models, with vehicle-pedestrian model and vehicle-cyclist model, and lastly the long-term effects. New types of interactions between humans and AVs need to be evaluated during the systems’ development to ensure that requirements of safety, acceptance, and efficiency are met before they are introduced to the market. Since innovative concepts require great cost and effort for their realization, it is necessary to ascertain whether the expected effects will be achieved. Many of the systems’ ergonomic requirements can be considered using experimental methods based on theoretical knowledge. This proposal outlines different aspects for empirical investigations related to the interaction between human and AV. It is important to mention that different human roles need to be considered inside (passenger or driver) and outside/around (VRU) the AV. The research aspects range from cognitive processes (perception and decision), via motion behavior, to learning and behavioral adaptation. This requires that dedicated methods with clear, consistent definitions be refined or developed. One example is the usage of virtual reality to investigate the complex interaction processes between AVs and VRUs in a safe and controllable setting as an alternative to field trials. Also, different AV communication strategies can be implemented in VR quicker and with reduced effort compared to hardware setups or experimental cars. Further methods are physiological measurements, different types of driving simulation and long-term behavioral study approaches. In their combination the different methods represent a toolbox of methodological approaches to analyze and evaluate different aspects of automated driving realizations. This deliverable presents a collection of recommended experimental approaches that address complex questions using advanced measurement equipment and statistical approaches, and their successful application within the SHAPE-IT project.
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| 7. |
- Figalova, Nikol, et al.
(author)
-
Methodological Framework for Modelling and Empirical Approaches (Deliverable D1.1 in the H2020 MSCA ITN project SHAPE-IT)
- 2021
-
Reports (other academic/artistic)abstract
- The progress in technology development over the past decades, both with respect to software and hardware, offers the vision of automated vehicles as means of achieving zero fatalities in traffic. However, the promises of this new technology – an increase in road safety, traffic efficiency, and user comfort – can only be realized if this technology is smoothly introduced into the existing traffic system with all its complexities, constraints, and requirements. SHAPE- IT will contribute to this major undertaking by addressing research questions relevant for the development and introduction of automated vehicles in urban traffic scenarios. Previous research has pointed out several research areas that need more attention for a successful implementation and deployment of human-centred vehicle automation in urban environments. In SHAPE-IT, for example, a better understanding of human behaviour and the underlying psychological mechanisms will lead to improved models of human behaviour that can help to predict the effects of automated systems on human behaviour already during system development. Such models can also be integrated into the algorithms of automated vehicles, enabling them to better understand the human interaction partners’ behaviours. Further, the development of vehicle automation is much about technology (software and hardware), but the users will be humans and they will interact with humans both inside and outside of the vehicle. To be successful in the development of automated vehicles functionalities, research must be performed on a variety of aspects. Actually, a highly interdisciplinary team of researchers, bringing together expertise and background from various scientific fields related to traffic safety, human factors, human-machine interaction design and evaluation, automation, computational modelling, and artificial intelligence, is likely needed to consider the human-technology aspects of vehicle automation. Accordingly, SHAPE-IT has recruited fifteen PhD candidates (Early Stage Researchers – ESRs), that work together to facilitate this integration of automated vehicles into complex urban traffic by performing research to support the development of transparent, cooperative, accepted, trustworthy, and safe automated vehicles. With their (and their supervisors’) different scientific background, the candidates bring different theoretical concepts and methodological approaches to the project. This interdisciplinarity of the project team offers the unique possibility for each PhD candidate to address research questions from a broad perspective – including theories and methodological approaches of other interrelated disciplines. This is the main reason why SHAPE-IT has been funded by the European Commission’s Marie Skłodowska-Curie Innovative Training Network (ITN) program that is aimed to train early state researchers in multidisciplinary aspects of research including transferable skills. With the unique scope of SHAPE-IT, including the human-vehicle perspective, considering different road-users (inside and outside of the vehicle), addressing for example trust, transparency, and safety, and including a wide range of methodological approaches, the project members can substantially contribute to the development and deployment of safe and appreciated vehicle automation in the cities of the future. To achieve the goal of interdisciplinary research, it is necessary to provide the individual PhD candidate with a starting point, especially on the different and diverse methodological approaches of the different disciplines. The empirical, user-centred approach for the development and evaluation of innovative automated vehicle concepts is central to SHAPE- IT. This deliverable (D1.1 “Methodological Framework for Modelling and Empirical Approaches”) provides this starting point. That is, this document provides a broad overview of approaches and methodologies used and developed by the SHAPE-IT ESRs during their research. The SHAPE-IT PhD candidates, as well as other researchers and developers outside of SHAPE-IT, can use this document when searching for appropriate methodological approaches, or simply get a brief overview of research methodologies often employed in automated vehicle research. The first chapter of the deliverable shortly describes the major methodological approaches to collect data relevant for investigating road user behaviour. Each subchapter describes one approach, ranging from naturalistic driving studies to controlled experiments in driving simulators, with the goal to provide the unfamiliar reader with a broad overview of the approach, including its scope, the type of data collected, and its limitations. Each subchapter ends with recommendations for further reading – literature that provide much more detail and examples. The second chapter explains four different highly relevant tools for data collection, such as interviews, questionnaires, physiological measures, and as other current tools (the Wizard of Oz paradigm and Augmented and Virtual Reality). As in the first chapter this chapter provides the reader with information about advantages and disadvantages of the different tools and with proposed further readings. The third chapter deals with computational models of human/agent interaction and presents in four subchapters different modelling approaches, ranging from models based on psychological mechanisms, rule-based and artificial intelligence models to simulation models of traffic interaction. The fourth chapter is devoted to Requirements Engineering and the challenge of communicating knowledge (e.g., human factors) to developers of automated vehicles. When forming the SHAPE-IT proposal it was identified that there is a lack of communication of human factors knowledge about the highly technical development of automated vehicles. This is why it is highly important that the SHAPE-IT ESRs get training in requirement engineering. Regardless of the ESRs working in academia or industry after their studies it is important to learn how to communicate and disseminate the findings to engineers. The deliverable ends with the chapter “Method Champions”. Here the expertise and association of the different PhD candidates with the different topics are made explicit to facilitate and encourage networking between PhDs with special expertise and those seeking support, especially with regards to methodological questions.
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| 8. |
- He, Yixu, et al.
(author)
-
Exploring the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms
- 2024
-
In: Transportation Letters. - 1942-7867 .- 1942-7875. ; In Press
-
Journal article (peer-reviewed)abstract
- The application of deep reinforcement learning (DRL) techniques in intelligent transportation systems garners significant attention. In this field, reward function design is a crucial factor for DRL performance. Current research predominantly relies on a trial-and-error approach for designing reward functions, lacking mathematical support and necessitating extensive empirical experimentation. Our research uses vehicle velocity control as a case study, build training and test sets, and develop a DRL framework for speed control. This framework examines both single-objective and multi-objective optimization in reward function designs. In single-objective optimization, we introduce “expected optimal velocity” as an optimization objective and analyze how different reward functions affect performance, providing a mathematical perspective on optimizing reward functions. In multi-objective optimization, we propose a reward function design paradigm and validate its effectiveness. Our findings offer a versatile framework and theoretical guidance for developing and optimizing reward functions in DRL, particularly for intelligent transportation systems.
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| 9. |
- Li, Ying, et al.
(author)
-
Deep knowledge distillation: A self-mutual learning framework for traffic prediction
- 2024
-
In: Expert Systems with Applications. - 0957-4174. ; 252
-
Journal article (peer-reviewed)abstract
- Traffic flow prediction in spatio-temporal networks is a crucial aspect of Intelligent Transportation Systems (ITS). Existing traffic flow forecasting methods, particularly those utilizing graph neural networks, encounter limitations. When processing large-scale graph data, the depth of these models can restrict their ability to effectively capture complex relationships and patterns. Additionally, these methods often focus mainly on local neighborhood information, which can limit their capability to recognize and analyze global relationships and patterns within the graph data. Therefore, we proposed a deep knowledge distillation model, tailored to effectively capture spatio-temporal patterns in traffic flow prediction. This model incorporates a bidirectional random walk process on a directed graph, enabling it to effectively capture both spatial and temporal dependencies. Utilizing a blend of mutual learning and self-distillation, our approach enhances the detection of spatio-temporal relationships within traffic data and improves the feature perception ability at both local and global levels. We tested our model on two real-world datasets, achieving notable improvements in prediction accuracy, especially for predictions within a one-hour timeframe. In comparison to the baseline model, our proposed model achieved accuracy improvements of 0.19 and 0.18 on the respective datasets. These results highlight the success of using mutual learning and self-distillation to transfer knowledge effectively within and between models and to improve the model's capability in identifying and extracting features.
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| 10. |
- Liu, Yang, 1991, et al.
(author)
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DeepTSP: Deep traffic state prediction model based on large-scale empirical data
- 2021
-
In: Communications in Transportation Research. - : Elsevier BV. - 2772-4247. ; 1
-
Journal article (peer-reviewed)abstract
- Real-time traffic state (e.g., speed) prediction is an essential component for traffic control and management in an urban road network. How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem. This study focuses on the construction of an effective solution designed for spatio-temporal data to predict the traffic state of large-scale traffic systems. In this study, we first summarize the three challenges faced by large-scale traffic state prediction, i.e., scale, granularity, and sparsity. Based on the domain knowledge of traffic engineering, the propagation of traffic states along the road network is theoretically analyzed, which are elaborated in aspects of the temporal and spatial propagation of traffic state, traffic state experience replay, and multi-source data fusion. A deep learning architecture, termed as Deep Traffic State Prediction (DeepTSP), is therefore proposed to address the current challenges in traffic state prediction. Experiments demonstrate that the proposed DeepTSP model can effectively predict large-scale traffic states.
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| 11. |
- Wu, Pan, et al.
(author)
-
A Low-Rank Bayesian Temporal Matrix Factorization for the Transfer Time Prediction Between Metro and Bus Systems
- 2024
-
In: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 25:7, s. 7206-7222
-
Journal article (peer-reviewed)abstract
- Accurate transfer time prediction and future transfer time information are important for both public transport operators and passengers. However, existing studies cannot effectively manage high-dimensional transfer time data, capture the complex nonlinearity of transfer time, or provide accurate transfer time information. This study provides a reliable prediction model called low-rank Bayesian temporal matrix factorization (LBTMF) to address these challenges. First, on the basis of a high-dimensional spatiotemporal matrix of transfer time data, we develop a low-rank temporal-regularized matrix factorization-based imputation module to capture spatial and temporal characteristics to replace missing transfer time data. Second, to further predict the transfer time with the imputation of missing data, we propose the spatiotemporal-based Bayesian temporal matrix factorization prediction module to recover hourly and daily regular characteristics to predict the transfer time at different metro stations during various periods. Finally, the comprehensive experimental findings suggest that the LBTMF model outperforms other excellent approaches in terms of imputation efficiency, prediction accuracy, and robustness.
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| 12. |
- Yang, Ying, et al.
(author)
-
An overview of solutions to the bus bunching problem in urban bus systems
- 2024
-
In: Frontiers of Engineering Management. - 2096-0255 .- 2095-7513. ; In Press
-
Research review (peer-reviewed)abstract
- Bus bunching has been a persistent issue in urban bus system since it first appeared, and it remains a challenge not fully resolved. This phenomenon may reduce the operational efficiency of the urban bus system, which is detrimental to the operation of fast-paced public transport in cities. Fortunately, extensive research has been undertaken in the long development and optimization of the urban bus system, and many solutions have emerged so far. The purpose of this paper is to summarize the existing solutions and serve as a guide for subsequent research in this area. Upon careful examination of current findings, it is found that, based on the different optimization objects, existing solutions to the bus bunching problem can be divided into five directions, i.e., operational strategy improvement, traffic control improvement, driver driving rules improvement, passenger habit improvement, and others. While numerous solutions to bus bunching are available, there remains a gap in research exploring the integrated application of methods from diverse directions. Furthermore, with the development of autonomous driving, it is expected that the use of modular autonomous vehicles could be the most potential solution to the issue of bus bunching in the future.
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| 13. |
- Zhang, Yuan, et al.
(author)
-
Full-scale spatio-temporal traffic flow estimation for city-wide networks: a transfer learning based approach
- 2023
-
In: Transportmetrica B. - : Informa UK Limited. - 2168-0582 .- 2168-0566. ; 11:1, s. 869-895
-
Journal article (peer-reviewed)abstract
- The full-scale spatio-temporal traffic flow estimation/prediction has always been a hot spot in transportation engineering. The low coverage rate of detectors in transport networks brings difficulties to the city-wide traffic flow estimation/prediction. Moreover, it is difficult for traditional analytical traffic flow models to deal with the traffic flow estimation/prediction problem over urban transport networks in a complex environment. Current data-driven methods mainly focus on road segments with detectors. An instance-based transfer learning method is proposed to estimate network-wide traffic flows including road segments without detectors. Case studies based on simulation data and empirical data collected from the open-source PeMS database are conducted to verify its effectiveness. For the traffic flow estimation of segments without detectors, the mean absolute percentage error (MAPE) is approximately 11% for both datasets, which is superior to the existing methods in the literature and reduces MAPE by two percentage points.
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| 14. |
- Zhao, Ming, et al.
(author)
-
A hierarchical reconstruction for DG/FV method with low dispersion: Basic formulation and applications
- 2021
-
In: Computers and Fluids. - : Elsevier BV. - 0045-7930. ; 231
-
Journal article (peer-reviewed)abstract
- In this paper, the numerical dispersion of a hierarchical reconstruction strategy for DG/FV method has been optimized. For the hierarchical strategy, the cell averages and the first derivatives are reconstructed based on the Hermite WENO method; the second derivatives are reconstructed via Green-Gauss integration and WENO reconstruction. Then, the numerical dispersion has been optimized by minimizing error with bandwidth optimization technique. Furthermore, to adjust the loss of compactness due to the reconstructed second derivatives, two modifications were also proposed with optimal weights. Eventually, from the implementations in scalar and compressible Euler equations, the superiority of numerical dispersion and accuracy of present methods could be validated. The shock capturing capacity has also been validated in 1D and 2D cases.
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| 15. |
- Abe, K., et al.
(author)
-
J-PARC Neutrino Beamline Upgrade Technical Design Report
- 2019
-
Reports (peer-reviewed)abstract
- In this document, technical details of the upgrade plan of the J-PARC neutrino beamline for the extension of the T2K experiment are described. T2K has proposed to accumulate data corresponding to 2×1022 protons-on-target in the next decade, aiming at an initial observation of CP violation with 3σ or higher significance in the case of maximal CP violation. Methods to increase the neutrino beam intensity, which are necessary to achieve the proposed data increase, are described.
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| 16. |
- Cao, Qi, et al.
(author)
-
Jointly estimating the most likely driving paths and destination locations with incomplete vehicular trajectory data
- 2023
-
In: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 155
-
Journal article (peer-reviewed)abstract
- With an ever-increasing deployment density of probe and fixed sensors, massive vehicular trajectory data is available and show a promising foundation to improve the observability of dynamic traffic demand pattern. However, due to technical and privacy issues, the raw trajectories are not always complete and the paths and destinations between discontinuous trajectory nodes are usually missing. This paper proposes a probabilistic method to jointly reconstruct the missing driving path and destination location of vehicles with incomplete trajectory data. One problem-specific HMM-structured model incorporating spatial and temporal analysis (ST-HMM) is constructed to define the matching probability between observed data and possible movement. Two algorithms, namely candidate set generation and best-match search algorithms, are developed to seek the most possible one as matching result. It can implement end-to-end processing from incomplete trajectory data to complete and connective paths and destinations for the target vehicle. The proposed method is tested based on field-test data and city-wide road network. Compared with two benchmark methods, the proposed method improved the matching accuracy in terms of both path identification and destination inference. Additionally, sensitivity analyses on the size of training dataset and candidate set were performed. We believe that experiment results of these sensitivity analyses can help to provide guidance on data sensing and candidate generation.
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| 17. |
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| 18. |
- Fan, Qunping, 1989, et al.
(author)
-
A Non-Conjugated Polymer Acceptor for Efficient and Thermally Stable All-Polymer Solar Cells
- 2020
-
In: Angewandte Chemie - International Edition. - : Wiley. - 1433-7851 .- 1521-3773. ; 59:45, s. 19835-19840
-
Journal article (peer-reviewed)abstract
- A non-conjugated polymer acceptor PF1-TS4 was firstly synthesized by embedding a thioalkyl segment in the mainchain, which shows excellent photophysical properties on par with a fully conjugated polymer, with a low optical band gap of 1.58 eV and a high absorption coefficient >105 cm−1, a high LUMO level of −3.89 eV, and suitable crystallinity. Matched with the polymer donor PM6, the PF1-TS4-based all-PSC achieved a power conversion efficiency (PCE) of 8.63 %, which is ≈45 % higher than that of a device based on the small molecule acceptor counterpart IDIC16. Moreover, the PF1-TS4-based all-PSC has good thermal stability with ≈70 % of its initial PCE retained after being stored at 85 °C for 180 h, while the IDIC16-based device only retained ≈50 % of its initial PCE when stored at 85 °C for only 18 h. Our work provides a new strategy to develop efficient polymer acceptor materials by linkage of conjugated units with non-conjugated thioalkyl segments.
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| 19. |
- Gao, Kun, 1993, et al.
(author)
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Unraveling the mode substitution of dockless bike-sharing systems and its determinants: A trip level data-driven interpretation
- 2023
-
In: Sustainable Cities and Society. - 2210-6707. ; 98
-
Journal article (peer-reviewed)abstract
- Understanding the mode substitution of shared micro-mobility systems is essential for assessing their societal and environmental impact and developing improvement planning instruments. This study carries out a fine-grained analysis of the mode substitution of dockless bike sharing (DLBS) in relation to other transport modes at the trip level, leveraging multi-modal route planning techniques, transaction data of bike-sharing, and travel behavior modeling. More importantly, the study leverages interpretable machine learning to reveal the complex effects of built environment factors on the mode substitution patterns of DLBS based on multiple data sources. The results indicate that the probabilities of DLBS replacing other transport modes present large heterogeneity among different trips and in different urban contexts, which can be successfully quantified by the proposed approach at the trip level. The average substitution rates of bike-sharing to bus, metro, walking and ride-hailing in Shanghai are estimated to be 0.356, 0.116, 0.347 and 0.181, respectively. Built environment factors such as presence of transit systems can explain the variations in the substitution rates of DLBS to a certain transport mode in different urban contexts. Especially, the effects of some built environment factors show complex nonlinear and threshold patterns revealed by the data-driven method. The effects of key built environment factors are quantitatively interpreted and their practical implications discussed.
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| 20. |
- Ge, Chenjie, 1991, et al.
(author)
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A spiking neural network model for obstacle avoidance in simulated prosthetic vision
- 2017
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In: Information Sciences. - : Elsevier BV. - 0020-0255. ; 399:August 2017, s. 30-42
-
Journal article (peer-reviewed)abstract
- Limited by visual percepts elicited by existing visual prosthesis, it’s necessary to enhance its functionality to fulfill some challenging tasks for the blind such as obstacle avoidance. This paper argues that spiking neural networks (SNN) are effective techniques for object recognition and introduces for the first time a SNN model for obstacle recognition to as- sist blind people wearing prosthetic vision devices by modelling and classifying spatio- temporal (ST) video data. The proposed methodology is based on a novel spiking neural network architecture, called NeuCube as a general framework for video data modelling in simulated prosthetic vision. As an integrated environment including spiking trains en- coding, input variable mapping, unsupervised reservoir training and supervised classifier training, the NeuCube consists of a spiking neural network reservoir (SNNr) and a dy- namic evolving spiking neural network classifier (deSNN). First, input data is captured by visual prosthesis, then ST feature extraction is utilized in the low-resolution prosthetic vi- sion generated by prostheses. Finally such ST features are fed to the NeuCube to output classification result of obstacle analysis for an early warning system to be activated. Ex- periments on collected video data and comparison with other computational intelligence methods indicate promising results. This makes it possible to directly utilize available neu- romorphic hardware chips, embedded in visual prostheses, to enhance significantly their functionality. The proposed NeuCube-based obstacle avoidance methodology provides use- ful guidance to the blind, thus offering a significant improvement of current prostheses and potentially benefiting future prosthesis wearers.
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| 21. |
- Ge, Chenjie, 1991, et al.
(author)
-
Co-saliency detection via inter and intra saliency propagation
- 2016
-
In: Signal Processing: Image Communication. - 0923-5965. ; 44, s. 69-83
-
Journal article (peer-reviewed)abstract
- The goal of salient object detection from an image is to extract the regions which capture the attention of the human visual system more than other regions of the image. In this paper a novel method is presented for detecting salient objects from a set of images, known as co-saliency detection. We treat co-saliency detection as a two-stage saliency propagation problem. The first inter-saliency propagation stage utilizes the similarity between a pair of images to discover common properties of the images with the help of a single image saliency map. With the pairwise co-salient foreground cue maps obtained, the second intra-saliency propagation stage refines pairwise saliency detection using a graph-based method combining both foreground and background cues. A new fusion strategy is then used to obtain the co-saliency detection results. Finally an integrated multi-scale scheme is employed to obtain pixel-level co-saliency maps. The proposed method makes use of existing saliency detection models for co-saliency detection and is not overly sensitive to the initial saliency model selected. Extensive experiments on three benchmark databases show the superiority of the proposed co-saliency model against the state-of-the-art methods both subjectively and objectively.
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| 22. |
- Huang, Qi, et al.
(author)
-
Effective photocatalytic sterilization based on composites of Ag/InVO4/BiOBr : Factors, mechanism and application
- 2023
-
In: Separation and Purification Technology. - 1383-5866 .- 1873-3794. ; 327
-
Journal article (peer-reviewed)abstract
- We hypothesized that photocatalysts with a low band gap could be useful in the sterilization of ceramic tiles in the natural environments of toilets using natural light in those settings. Certain photocatalysts can produce reactive oxygen species (ROS) under light illumination, which in turn are bactericidal. The properties of the BiOBr-containing photocatalysts were tuned by creating junctions and heterostructures with Ag and InVO4 and studied with respect to their bactericidal effect in dispersion. The bactericidal mechanism was studied through experiments in which active species were captured and via electron paramagnetic resonance (EPR) spectroscopy. At an optimal dosage of 0.5 g/L, the Ag/InVO4/BiOBr composite had a sterilization efficacy of 99.9999 % in 30 min under visible light illumination of 1000 W. It retained a sterilization efficacy of 99.999 % after four cycles. Anions such as Cl−, SO42−, and NO3− were shown to have no negative impact on sterilization efficacy. It was shown that the holes in the composite photocatalyst and hydroxyl radicals (·OH) were mechanistically critical for the sterilization. The photocatalysts were also studied in the field in the natural environment of a restroom, where they were loaded on ceramic tiles. Samples were collected from the surface of the ceramic tiles and analyzed for bacterial cultures and microbial diversity. The results were compared in the scope of the sterilization ability of various agents at the microbial level. The ceramic tiles loaded with Ag/InVO4/BiOBr showed the least amount of bacteria on their surfaces, and the microbial community richness was also the lowest.
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| 23. |
- Jia, Ruo, 1993, et al.
(author)
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A spatio-temporal deep learning model for short-term bike-sharing demand prediction
- 2023
-
In: Electronic Research Archive. - : American Institute of Mathematical Sciences (AIMS). - 2688-1594. ; 31:2, s. 1031-1047
-
Journal article (peer-reviewed)abstract
- Bike-sharing systems are widely operated in many cities as green transportation means to solve the last mile problem and reduce traffic congestion. One of the critical challenges in operating high-quality bike-sharing systems is rebalancing bike stations from being full or empty. However, the complex characteristics of spatiotemporal dependency on usage demand may lead to difficulties for traditional statistical models in dealing with this complex relationship. To address this issue, we propose a graph-based neural network model to learn the representation of bike-sharing demand spatial-temporal graph. The model has the ability to use graph-structured data and takes both spatial -and temporal aspects into consideration. A case study about bike-sharing systems in Nanjing, a large city in China, is conducted based on the proposed method. The results show that the algorithm can predict short-term bike demand with relatively high accuracy and low computing time. The predicted errors for the hourly station level usage demand prediction are often within 20 bikes. The results provide helpful tools for short-term usage demand prediction of bike-sharing systems and other similar shared mobility systems.
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| 24. |
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| 25. |
- Lin, Hongyi, et al.
(author)
-
How generative adversarial networks promote the development of intelligent transportation systems: A survey
- 2023
-
In: IEEE/CAA Journal of Automatica Sinica. - 2329-9274 .- 2329-9266. ; 10:9, s. 1781-1796
-
Journal article (peer-reviewed)abstract
- In current years, the improvement of deep learning has brought about tremendous changes: As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) have been widely employed in various fields including transportation. This paper reviews the development of GANs and their applications in the transportation domain. Specifically, many adopted GAN variants for autonomous driving are classified and demonstrated according to data generation, video trajectory prediction, and security of detection. To introduce GANs to traffic research, this review summarizes the related techniques for spatio-temporal, sparse data completion, and time-series data evaluation. GAN-based traffic anomaly inspections such as infrastructure detection and status monitoring are also assessed. Moreover, to promote further development of GANs in intelligent transportation systems (ITSs), challenges and noteworthy research directions on this topic are provided. In general, this survey summarizes 130 GAN-related references and provides comprehensive knowledge for scholars who desire to adopt GANs in their scientific works, especially transportation-related tasks.
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| 26. |
- Lin, Hongyi, et al.
(author)
-
Insights into Travel Pattern Analysis and Demand Prediction: A Data-Driven Approach in Bike-Sharing Systems
- 2024
-
In: Journal of Transportation Engineering Part A: Systems. - 2473-2893 .- 2473-2907. ; 150:2
-
Journal article (peer-reviewed)abstract
- With the advent of the Internet of Things, bike-sharing systems have seen widespread adoption globally, whereas they often grapple with an uneven spatiotemporal distribution of vehicles. This issue is particularly acute in the wake of electronic fences, with some areas often faced with the predicament of inadequate supply. To tackle this challenge, accurate prediction of borrowing and returning demands at different parking spots and varying times is necessary. In this study, we used a comprehensive data set from Yancheng, Jiangsu, China, covering shared bicycle usage across 394 parking spots. These data enabled us to delve deep into urban travel patterns and discern the various factors influencing these behaviors. To enhance the prediction accuracy, we propose the time-series weighted regression (TSWR) model, a long-term multistep forecasting method, which adeptly addresses issues associated with sparse statistical data and long-term prediction inaccuracies, outperforming other machine learning models in our experiments. Further recognizing the considerable impact of geographical location and weather conditions on shared bicycle demand, we incorporated the rule-based adjustment optimization (RAO) method into our approach, which refines nonlinear components by accounting for various factors. The implementation of RAO resulted in a 10.34% increase in accuracy compared to TSWR alone and an improvement of over 35% in comparison to other approaches. Overall, this study illuminates the underlying influences on urban travel patterns and offers valuable suggestions for bike dispatching to those enterprises, contributing significantly to the research in this field.
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| 27. |
- Lin, Peiqun, et al.
(author)
-
Bus timetable optimization model in response to the diverse and uncertain requirements of passengers for travel comfort
- 2023
-
In: Electronic Research Archive. - : American Institute of Mathematical Sciences (AIMS). - 2688-1594. ; 31:4, s. 2315-2336
-
Journal article (peer-reviewed)abstract
- Most existing public transit systems have a fixed dispatching and service mode, which cannot effectively allocate resources from the perspective of the interests of all participants, resulting in resource waste and dissatisfaction. Low passenger satisfaction leads to a considerable loss of bus passengers and further reduces the income of bus operators. This study develops an optimization model for bus schedules that considers vehicle types and offers two service levels based on heterogeneous passenger demands. In this process, passenger satisfaction, bus company income, and government subsidies are considered. A bilevel model is proposed with a lower-level passenger ride simulation model and an upper-level multiobjective optimization model to maximize the interests of bus companies, passengers, and the government. To verify the effectiveness of the proposed methodology, a real-world case from Guangzhou is presented and analyzed using the nondominated sorting genetic algorithm-II (NSGA-II), and the related Pareto front is obtained. The results show that the proposed bus operation system can effectively increase the benefits for bus companies, passengers, and the government
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| 28. |
- Liu, Chunxin, 1991-, et al.
(author)
-
Fabrication of a widely tunable fiber Bragg grating filter using fused deposition modeling 3D printing
- 2019
-
In: Optical Materials Express. - 2159-3930 .- 2159-3930. ; 9:11, s. 4409-
-
Journal article (peer-reviewed)abstract
- The use of 3D-printing for designing a simple wavelength tunable device based on fiber Bragg gratings is demonstrated. Using fused deposition modeling (FDM), the fiber Bragg grating is embedded into a beam of polyethylene terephthalate glycol (PETG). Through bending, resulting in compression or tension of the optical fiber, the Bragg wavelength could be continuously tuned over a range of 60 nm, with maintained reflectivity and 3-dB linewidth.
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| 29. |
- Liu, Chunxin, 1991-, et al.
(author)
-
Widely tunable Er:Yb fiber laser using a fiber Bragg grating embedded in a 3D printed beam
- 2020
-
In: Optical Materials Express. - 2159-3930. ; 10:12, s. 3353-3358
-
Journal article (peer-reviewed)abstract
- A narrow linewidth (Δλ < 0.07 nm), low noise, widely tunable Er:Yb ring fiber laser is demonstrated using a fiber Bragg grating mirror embedded in a 3D printed polymer beam. By bending the polymer beam, continuous tuning of the laser was achieved over 30 nm, from 1543 nm to 1574 nm, with power variation below 1 dB, showing high temporal and spectral stability and a signal-to-background value exceeding 50 dB. These results present a versatile and simple method for tailoring tunable narrow-linewidth lasers.
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| 30. |
- Liu, Mengyao, et al.
(author)
-
The SOFIA Massive (SOMA) Star Formation Survey. III. From Intermediate- to High-mass Protostars
- 2020
-
In: Astrophysical Journal. - : American Astronomical Society. - 1538-4357 .- 0004-637X. ; 904:1
-
Journal article (peer-reviewed)abstract
- We present similar to 10-40 mm SOFIA-FORCAST images of 14 intermediate-mass protostar candidates as part of the SOFIA Massive (SOMA) Star Formation Survey. We build spectral energy distributions, also using archival Spitzer, Herschel, and IRAS data. We then fit the spectral energy distributions with radiative transfer models of Zhang & Tan, based on turbulent core accretion theory, to estimate key protostellar properties. With the addition of these intermediate-mass sources, based on average properties derived from SED fitting, SOMA protostars span luminosities from similar to 10(2) to 10(6) L-circle dot, current protostellar masses from similar to 0.5 to 35 M-circle dot, and ambient clump mass surface densities, Scl, from 0.1 to g cm(-2). A wide range of evolutionary states of the individual protostars and of the protocluster environments is also probed. We have also considered about 50 protostars identified in infrared dark clouds that are expected to be at the earliest stages of their evolution. With this global sample, most of the evolutionary stages of high- and intermediate-mass protostars are probed. The best-fitting models show no evidence that a threshold value of the protocluster clump mass surface density is required to form protostars up to similar to 25 M.. However, to form more massive protostars, there is tentative evidence that Sigma(cl) needs to be greater than or similar to 1 g cm(-2). We discuss how this is consistent with expectations from core accretion models that include internal feedback from the forming massive star.
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| 31. |
- Liu, Yang, 1991, et al.
(author)
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Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform
- 2022
-
In: Transportation Research Part E: Logistics and Transportation Review. - : Elsevier BV. - 1366-5545. ; 161
-
Journal article (peer-reviewed)abstract
- The vehicle dispatching system is one of the most critical problems in online ride-hailing platforms, which requires adapting the operation and management strategy to the dynamics of demand and supply. In this paper, we propose a single-agent deep reinforcement learning approach for the vehicle dispatching problem called deep dispatching, by reallocating vacant vehicles to regions with a large demand gap in advance. The simulator and the vehicle dispatching algorithm are designed based on industrial-scale real-world data and the workflow of online ride-hailing platforms, ensuring the practical value of our approach. Besides, the vehicle dispatching problem is translated in analogy with the load balancing problem in computer networks. Inspired by the recommendation system, the problem of high concurrency of dispatching requests is addressed by sorting the actions as a recommendation list, whereby matching action with requests. Experiments demonstrate that the proposed approach is superior to existing benchmarks. It is also worth noting that the proposed approach won first place in the vehicle dispatching task of KDD Cup 2020.
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| 32. |
- Liu, Yang, 1991, et al.
(author)
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How machine learning informs ride-hailing services: A survey
- 2022
-
In: Communications in Transportation Research. - : Elsevier BV. - 2772-4247. ; 2
-
Research review (peer-reviewed)abstract
- In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed.
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| 33. |
- Liu, Yang, 1991, et al.
(author)
-
The role of intelligent technology in the development of urban air mobility systems: A technical perspective
- 2024
-
In: Fundamental Research. - 2667-3258. ; In Press
-
Journal article (peer-reviewed)abstract
- Urban Air Mobility (UAM) is an emerging transportation system that aims at revolutionizing urban mobility through the deployment of small electric vertical takeoff and landing (eVTOL) aircraft. The development of UAM is largely driven by advances in Intelligent Technology (IT). This review article provides an overview of the UAM system and discusses the application of IT in UAM. Major challenges facing UAM are also identified, and an outlook on the future of this promising transportation system is presented. Our main conclusions suggest that IT is a fundamental driver of UAM, enabling a range of applications such as air traffic management and autonomous drone control. However, the UAM system is facing a number of challenges, including eVTOL technology, system integration issues, and noise pollution. Despite these challenges, the future of UAM appears promising; as a disruptive transportation mode, UAM is expected to play an important role in addressing the growing demand of urban transportation in the coming decades.
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| 34. |
- Lyu, Cheng, et al.
(author)
-
Personalized Modeling of Travel Behaviors and Traffic Dynamics
- 2022
-
In: Journal of Transportation Engineering Part A: Systems. - 2473-2893 .- 2473-2907. ; 148:10
-
Journal article (peer-reviewed)abstract
- Emerging mobile Internet applications have become valuable data sources for fine-grained transportation analysis, which allows the introduction of the concept of Personalization in both microscopic and macroscopic modeling of travel behaviors and traffic dynamics. Inspired by personalized recommendation systems, the personalized transportation models emphasize the importance of individual and local information. Two representative cases are presented in this study and two architectures, namely the travel behavior modeling architecture and the geoinformation modeling architecture, are proposed to address the problems of bike-sharing destination prediction and ensemble of ride-hailing demand predictors, respectively. Their performance has been verified by two case studies using the Mobike bike-sharing data and the DiDi ride-hailing demand data.
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| 35. |
- Murari, A., et al.
(author)
-
A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors
- 2024
-
In: Nature Communications. - 2041-1723 .- 2041-1723. ; 15:1
-
Journal article (peer-reviewed)abstract
- The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices.
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| 36. |
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| 37. |
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| 38. |
- Qu, Zhiguo, et al.
(author)
-
DTQFL : A Digital Twin-Assisted Quantum Federated Learning Algorithm for Intelligent Diagnosis in 5G Mobile Network
- 2023
-
In: IEEE journal of biomedical and health informatics. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; , s. 1-10
-
Journal article (peer-reviewed)abstract
- Smart healthcare aims to revolutionize med-ical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quantum federated learning algorithm (DTQFL). By leveraging the 5G mobile network, digital twins (DT) of patients can be created instantly using data from various Internet of Medical Things (IoMT) devices and simultane-ously reduce communication time in federated learning (FL) at the same time. DTQFL generates DT for patients with specific diseases, allowing for synchronous training and updating of the variational quantum neural network (VQNN) without disrupting the VQNN in the real world. This study utilized DTQFL to train its own personalized VQNN for each hospital, considering privacy security and training speed. Simultaneously, the personalized VQNN of each hospital was obtained through further local iterations of the final global parameters. The results indicate that DTQFL can train a good VQNN without collecting local data while achieving accuracy comparable to that of data-centralized algorithms. In addition, after personalized train-ing, the VQNN can achieve higher accuracy than that with-out personalized training.
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| 39. |
- Shen, Zichao, et al.
(author)
-
Analysis of Driving Behavior in Unprotected Left Turns for Autonomous Vehicles using Ensemble Deep Clustering
- 2023
-
In: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; In Press
-
Journal article (peer-reviewed)abstract
- The advent of autonomous driving technology offers transformative potential in mitigating traffic congestion and enhancing road safety. A particularly challenging aspect of traffic dynamics is the unprotected left turn-a scenario at an intersection where the vehicle intending to turn left does not have a dedicated traffic signal, posing a risk to traffic safety and efficiency. This study investigates the dynamics of unprotected left turns by employing data-driven techniques that analyze multi-vehicle data and trajectory patterns to decode complex interactions and behaviors that occur during this maneuver. Our research targets the subtleties of driver behavior in these situations, employing a novel Ensemble Deep Clustering algorithm that innovatively categorizes driving behaviors based on a combination of learned representations and clustering advancements. The deep clustering component involves an iterative process that refines behavioral categorization, while the ensemble technique enhances the precision of these determinations. Using the INTERACTION Dataset, the proposed model is trained and evaluated to offer a better understanding of the intricate driving behaviors in unprotected left turns at intersections. Through the quantitative analysis and comparison with the baseline, we show the superiority of the algorithm, and the results are also interpretable. This methodology can be utilized to improve the decision-making of autonomous vehicles in such scenarios, thus improving the safety of autonomous vehicles, traffic efficiency, and realizing human-robot interaction between autonomous vehicles and drivers.
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| 40. |
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| 41. |
- Wang, Shuli, 1996, et al.
(author)
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Probabilistic Prediction of Longitudinal Trajectory Considering Driving Heterogeneity With Interpretability
- 2024
-
In: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; In Press
-
Journal article (peer-reviewed)abstract
- To promise a high degree of safety in complex mixed-traffic scenarios alongside human-driven vehicles, accurately predicting the maneuvers of surrounding vehicles and their future positions is a critical task and attracts much attention. However, most existing studies focus on reasoning about positional information based on objective historical trajectories without fully considering the heterogeneity of driving behaviors. Besides, previous works have focused more on improving models’ accuracy than investigating their interpretability to explore the extent to which a cause and effect can be observed within a system. Therefore, this article proposes a personalized trajectory prediction framework that integrates driving behavior feature representation to account for driver heterogeneity. Specifically, based on a certain length of historical trajectory data, the situation-specific driving preferences of each driver are identified, where key driving behavior feature vectors are extracted to characterize heterogeneity in driving behavior among different drivers. The proposed LSTMMD-DBV (long short-term memory and mixture density networks with driving behavior vectors) framework integrates driving behavior feature representations into a long short-term memory encoder–decoder network to investigate its feasibility and validate its effectiveness in enhancing predictive model performance. Finally, the Shapley Additive Explanations method interprets the trained model for predictions. After experimental analysis, the results indicate that the proposed model can generate probabilistic future trajectories with remarkably improved predictions compared to existing benchmark models. Moreover, the results confirm that the additional input of driving behavior feature vectors representing the heterogeneity of driving behavior could provide more information and, thus, contribute to improving prediction accuracy.
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| 42. |
- Wang, Yang, 1991-, et al.
(author)
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Interface-induced contraction of core–shell Prussian blue analogues toward hollow Ni-Co-Fe phosphide nanoboxes for efficient oxygen evolution electrocatalysis
- 2023
-
In: Chemical Engineering Journal. - : Elsevier BV. - 1385-8947 .- 1873-3212. ; 451, part 1
-
Journal article (peer-reviewed)abstract
- Hollow metal phosphides play an important role in electrocatalytic oxygen evolution reaction (OER), but directly phosphatizing solid precursors into well-defined hollow structures and further regulating electronic structures of their derived (oxy)hydroxides for boosting OER performance remain great challenges. Herein, we report hollow Ni-Co-Fe phosphide (Ni-Co-Fe-P) nanoboxes with well-defined interior spaces via a core–shell Prussian blue analogue (PBA)-dependent conversion. Starting from Ni-Co PBA nanocubes, a chemical reduction and decomposition process is utilized to fabricate core–shell Ni-Co@Fe PBA nanocubes based on the lattice matching principle, which are then phosphatized into hollow Ni-Co-Fe-P nanoboxes through an interface-induced contraction process. Thanks to well-designed hollow structures, polymetallic compositions, and doping of carbon, hollow Ni-Co-Fe-P nanobox catalyst shows a remarkable OER activity with a small overpotential of 277 mV at 20 mA cm−2. Notably, this catalyst is also highly stable with only 5 % activity decay over 24 h for the OER. Mechanism studies reveal that the electronic regulation of precatalyst-derived (oxy)hydroxides is the key to boost their OER activity. This work demonstrates a novel and effective protocol for designing polymetallic phosphides with well-defined hollow structures towards various targeted applications.
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| 43. |
- Wu, Fanyou, et al.
(author)
-
A personalized recommendation system for multi-modal transportation systems
- 2022
-
In: Multimodal Transportation. - : Elsevier BV. - 2772-5863. ; 1:2
-
Journal article (peer-reviewed)abstract
- Recommendation system has recently experienced widespread applications in fields like advertising and streaming platforms. Its ability of extracting valuable information from complex data makes it a promising tool for multi-modal transportation system. In this paper, we propose a conceptual framework for proactive travel mode recommendation combining recommendation system and transportation engineering. The proposed framework works by learning from historical user behavioral preferences and ranking the candidate travel modes. In this framework, an incremental scanning method with multiple time windows is designed to acquire multi-scale features from user behaviors. In addition, to alleviate the computational burden brought by the large data size, a hierarchical behavior structure is developed. To further allow for social benefits, the proposed framework proposes to adjust the candidate modes according to real-time traffic states, which is potential in promoting the use of public transport, alleviating traffic congestion, and reducing environmental pollution.
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| 44. |
- Yin, Jie, et al.
(author)
-
Editorial for the Special Issue on Laser Additive Manufacturing: Design, Processes, Materials and Applications
- 2022
-
In: Micromachines. - : MDPI AG. - 2072-666X. ; 13:12
-
Journal article (other academic/artistic)abstract
- Laser-based additive manufacturing (LAM) is a revolutionary advanced digital manufacturing technology developed in recent decades, which is also a key strategic technology for technological innovation and industrial sustainability. This technology unlocks the design and constraints of traditional manufacturing and meets the needs of complex geometry fabrication and high-performance part fabrication. A deeper understanding of the design, materials, processes, structures, properties and applications is desired to produce novel functional devices, as well as defect-free structurally sound and reliable LAM parts. The topics in this Special Issue include macro- and micro-scale additive manufacturing with lasers, such as structure/material design, fabrication, modeling and simulation, in situ characterization of additive manufacturing processes and ex situ materials characterization and performance, with an overview that covers various applications in aerospace, biomedicine, optics and energy.
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| 45. |
- Zhang, Wensong, et al.
(author)
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Traffic flow prediction under multiple adverse weather based on self-attention mechanism and deep learning models
- 2023
-
In: Physica A: Statistical Mechanics and its Applications. - 0378-4371. ; 625
-
Journal article (peer-reviewed)abstract
- To improve the accuracy of traffic flow prediction under adverse weather, a deep hybrid attention (DHA) model including the traffic and weather blocks is proposed. The traffic block introduces the convolutional neural network (CNN) and the gated recurrent unit (GRU) neural network to capture the spatio-temporal rules of the traffic flow data. To consider the impacts of adverse weather on traffic flow, the weather block is introduced. The weather block utilizes the convolutional long short-term memory (ConvLSTM) neural network to extract the relationship between the weather and traffic flow data. The self-attention mechanism is embedded in the two blocks. Four cases involving rainy, foggy and windy weather are used to verify the DHA model. The experiments reveal that: the DHA model shows the excellent performance under multiple adverse weather; different adverse weather has various impacts on the rules of traffic volume and speed; the prediction accuracy of each model reduces with the increase of the severe degree of each type of adverse weather.
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| 46. |
- Zheng, Chunlei, et al.
(author)
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Prospects of eVTOL and Modular Flying Cars in China Urban Settings
- 2023
-
In: Journal of Intelligent and Connected Vehicles. - 2399-9802. ; 6:4, s. 187-189
-
Journal article (other academic/artistic)abstract
- Throughout much of human history, the vast majority of people lived in small communities. However, in the last few centuries, and particularly in recent decades, there has been a dramatic shift. A massive migration has moved populations from rural to urban areas. United Nations reports state that over 4.3 billion individuals now inhabit urban regions, which accounts for more than half (55% as of 2017) of the global population. In most high-income nations, including Western Europe, the Americas, Australia, Japan, and the Middle East, over 80% of people live in urban areas. This figure ranges from 50% to 80% in upper-middle-income countries like Eastern Europe, East Asia, North Africa, South Africa, and South America (United Nations, Department of Economic and Social Affairs, Population Division, 2019). The urban population is anticipated to rise across all countries in the coming decades, albeit at different rates. By 2050, the global population is expected to reach approximately 9.8 billion, with about 6.7 billion residing in cities and 3.1 billion in rural areas. Despite this rapid urbanization, only around 1% of the Earth's land is allocated for urban and infrastructure development. While urbanization has spurred socio-economic growth, it has also led to significant challenges such as traffic congestion and air pollution. In China, the swift growth of cities has notably expanded urban areas and extended the commuting times of residents. The “2022 Commuting Monitoring Report of Major Chinese Cities” reveals that in 2022, over 14 million people in 44 major Chinese cities experienced extreme commuting, with upwards of 13% spending over an hour in transit (Baidu Maps, 2023). Beijing recorded the highest rate, where 26% of commuters faced this issue.
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| 47. |
- Zheng, Jian, et al.
(author)
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Dynamic vessel schedule recovery strategy of liner shipping with uncertainties: An event-triggered model predictive control solution
- 2024
-
In: Computers and Industrial Engineering. - 0360-8352. ; 193
-
Journal article (peer-reviewed)abstract
- Considering uncertain disturbances in the vessel schedule recovery problem (VSRP) of liner shipping, a schedule recovery model based on a receding horizon optimization mechanism is proposed. We build a hybrid automaton in mixed logical dynamical (MLD) approach and an event-triggered model predictive control (e-MPC) controller based on a dynamic strategy to perform online optimization and to handle with uncertainties. We compare the performances of the traditional static strategy, the single-speed management strategy, and the proposed dynamic strategy; the results of the experiments validate the effectiveness and superiority of the dynamic strategy. We further explore the impact of the magnitude of disturbance, stakeholder preferences, and collaborative agreements between liner companies and terminal operators through sensitivity analysis and finally derive recommendations for liner company operations from the analysis of the experimental results.
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