| 1. |
- Fang, Shan, et al.
(författare)
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A Dynamic Transformation Car-Following Model for the Prediction of the Traffic Flow Oscillation
- 2024
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Ingår i: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 16:1, s. 174-198
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Tidskriftsartikel (refereegranskat)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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| 2. |
- He, Yixu, et al.
(författare)
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Exploring the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms
- 2024
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Ingår i: Transportation Letters. - 1942-7867 .- 1942-7875. ; In Press
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Tidskriftsartikel (refereegranskat)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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| 3. |
- Li, Zongbao, et al.
(författare)
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Upconversion Luminescence of Graphene Oxide through Hybrid Waveguide
- 2018
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Ingår i: The Journal of Physical Chemistry C. - : AMER CHEMICAL SOC. - 1932-7447 .- 1932-7455. ; 122:29, s. 16866-16871
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Tidskriftsartikel (refereegranskat)abstract
- Phonon-assisted upconversion is a promising way to generate short-wavelength emissions under excitation of long wavelength based on unique anti-Stokes luminescence properties. Graphene oxide nanosheets (GONs) exhibit excellent optical properties owing to quantum confinement and edge effects, which have driven research into fundamental principles and potential applications. Here, we experimentally demonstrate upconversion emission by exciting an easily fabricated GON hybrid waveguide (GHVV) with enhanced photothermal effects. The results reveal different origins of short-wavelength range and long-wavelength range in the upconversion spectra, whereas the emissive surface defects of GONs and GHW structure play significant roles in the behavior of photoluminescence. Introducing other upconversion materials to promote emission efficiency, the hybrid waveguide system might readily provide the possibility for the construction of upconversion fiber lasers and remote control of the upconversion luminescence.
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| 4. |
- Cao, Danni, et al.
(författare)
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Quantification of the impact of traffic incidents on speed reduction: A causal inference based approach
- 2021
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 157
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Tidskriftsartikel (refereegranskat)abstract
- This paper designs a systemic framework to quantify speed reduction induced by traffic incidents using a causal inference framework. The results can provide a reference to traffic managers for evaluating incident severities, thus take proper control measures after the incident in order not to underestimate or overestimate the negative impact. A two-phase scheme is proposed, including impacted region determination and speed reduction quantification. We first propose a Frame Region (FR) method, based on the shockwave propagation, to determine the spatiotemporal impacted region (SIR) using speed map. It is worth-noting that we design a statistical experiment to prove the rationality of congestion threshold selection. Secondly, we introduce a causal inference method for identifying the matched freeway segments. The traffic condition of finally matched freeway segments can be served as non-incident traffic condition of the incident occurred location, which contributes to quantifying the incident impact on speed reduction. We further demonstrate the proposed method in a case study by taking advantage of an incident record and related real freeway speed data in China. An interesting observation is that, along with the freeway segments away from the incident location, the congestion duration time of different freeway segments firstly rises and then decreases. The case study also illustrates the impact of incident on speed lasts almost 3 h and the congestion caused by the incident spreads 11 km, while the average causal effect of incident on all the impacted freeway segments is 42.3 km/h.
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| 5. |
- Cao, Qi, et al.
(författare)
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Jointly estimating the most likely driving paths and destination locations with incomplete vehicular trajectory data
- 2023
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Ingår i: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 155
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Tidskriftsartikel (refereegranskat)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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| 6. |
- Chen, Peilin, et al.
(författare)
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Fabrication of a silver nanoparticle-coated collagen membrane with anti-bacterial and anti-inflammatory activities for guided bone regeneration
- 2018
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Ingår i: Biomedical materials. - 1748-6041. ; 13:6
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Tidskriftsartikel (refereegranskat)abstract
- Alveolar bone loss is a common problem that affects dental implant placement. A barrier between the bone substitute and gingiva that can prevent fibro-tissue ingrowth, bacterial infection and induce bone formation is a key factor in improving the success of alveolar ridge reconstruction. This study aims to develop a bioactive collagen barrier material for guided bone regeneration, that is coupled with anti-bacterial and anti-inflammatory properties. We have evaluated two silver coating methods and found controllable and precise coating achieved by sonication compared with sputtering. The optimized AgNP-coated collagen membrane exhibited excellent anti-bacterial effects against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa) with limited cellular toxicity. It also displayed effective anti-inflammatory effects by reducing the expression and release of inflammatory cytokines including IL-6 and TNF-alpha. Additionally, AgNP-coated collagen membranes were able to induce osteogenic differentiation of mesenchymal stem cells that guide bone regeneration. These findings demonstrate the potential application of AgNP-coated collagen membranes to prevent infection after bone graft introduction in alveolar ridge reconstruction.
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| 7. |
- Gao, Kun, 1993, et al.
(författare)
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Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior
- 2021
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Ingår i: Transportation Research Part A: Policy and Practice. - : Elsevier BV. - 0965-8564. ; 148, s. 1-21
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Tidskriftsartikel (refereegranskat)abstract
- This study proposes an approach for modeling travel behavior under uncertainty coupling Cumulative Prospect Theory (CPT) with Multi-attribute Decision Making (MADM) theory. CPT is utilized to depict travelers’ evaluations of each attribute, and MADM describes the process of making tradeoffs among multiple conflicting criteria. Divergent perception principles for different attributes are considered in the proposed framework. The proposed approach is utilized for an empirical analysis concerning mode shift behavior for commuting in Shanghai of China, based on data collected by stated preference surveys. Results show that the proposed approach outperforms conventional methods in terms of model performances and behavioral revelations. Empirical results demonstrate that sensitivity to gains and losses in cost and travel time are divergent in mode shift behavior. More importantly, it is found that travelers underestimate the occurrence chances of low-probability travel time and overestimate the occurrence changes of high-probability travel time in mode shift behavior, which is contrary to the findings from economics. Travelers show substantial loss aversion features as well. The heterogeneity in the value functions of CPT is investigated to shed light on differences in the evaluation process among individuals. Results reveal quite different empirical CPT parameters and behavioral mechanisms in mode shift behavior as compared to monetary experiments in economics. It highlights the importance of empirical estimations in various travel choice contexts to essentially understand travel behavior mechanisms, rather than arbitrary usage of findings from economics.
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| 8. |
- Gao, Kun, 1993, et al.
(författare)
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Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility
- 2023
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Ingår i: Journal of Transport Geography. - 0966-6923. ; 110
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the usage demand of shared mobility systems in different areas of a city and its determinants is crucial for planning, operation and management of the systems. This study leverages an unbiased data-driven approach called accumulated effect analysis for examining the complex (nonlinear and interactive) effects of correlated built environment factors on the usage of shared mobility. Special research emphasis is given to unraveling the complex effects using an unbiased and data-driven approach that can overcome the impacts of correlations among built environment factors. Based on empirical analysis of synthetic data and a field dataset about dockless bike sharing systems (DLBS), results demonstrate that the method of partial dependency analysis prevalent in the relevant literature, will result in biases when investigating the effects of correlated built environment factors. In comparison, accumulated local effect analysis can appropriately interpret the effects of correlated built environment factors. The main effects of many built environment factors on the usage of DLBS present nonlinear and threshold patterns, quantitively revealed by accumulated local analysis. The approach can reveal complex interaction effects between different built environment factors (e.g., commercial service and education facility, and metro station coverage and living facility) on the usage of DLBS as well. The interactions among two built environment factors could even change with the values of the factors rather than invariant. The outcomes offer a new approach for revealing complex influences of different built environment factors with correlations as well as in-depth empirical understandings regarding the usage of DLBS.
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| 9. |
- Gao, Kun, 1993, et al.
(författare)
-
Diverging effects of subjective prospect values of uncertain time and money
- 2021
-
Ingår i: Communications in Transportation Research. - : Elsevier BV. - 2772-4247. ; 1
-
Tidskriftsartikel (refereegranskat)abstract
- Studies from behavioral economics show that the subjective prospect value of money has diminishing sensitivity to losses/gains, represented by an S-shape, and this has been applied in representing the subjective prospect value of time in many transportation studies such as travel behavior modeling and network equilibrium. In this study, we demonstrate that the prospect value of time has an increasing sensitivity to losses/gains and can be represented by an Ϩ-shape, which contrasts that of money. We further explain the rationality of this surprising finding based on psychological and behavioral theories and discuss extensive practical implications. The correlations between sensitivities to gains and losses in terms of magnitude are revealed as well to shed light on potential underlying correlated behavioral principles. Substantial loss-aversion features are observed in the empirical analysis, supporting endowment effects. Implications of the findings on decision-making and other areas that utilize time as a key indicator have been discussed. The findings may revolutionize many research areas that utilize time as a key indicator such as transportation engineering.
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| 10. |
- Gao, Kun, 1993, et al.
(författare)
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Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction
- 2021
-
Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 97
-
Tidskriftsartikel (refereegranskat)abstract
- Improving passengers’ satisfaction is crucial for airline industry and requires in-depth understandings regarding the complex effects of various factors. This study investigates the importance, complex nonlinear effects and interaction effects of various factors (including passenger characteristics and service attributes) on airline travel satisfaction in data-driven manners leveraging machine-learning (ML) approaches. The results show that ML algorithms such as Random Forest have superiority in modeling airline travel satisfaction as compared to conventional logistic regressions. The quantitative importance of various factors is estimated and compared to reveal key determinants of passengers’ satisfaction using permutation-based importance and accumulated local effect analysis. More importantly, results suggest that the main effects of service attributes present piecewise nonlinear patterns. There are piecewise interaction effects between passenger characteristics and service attributes and among service attributes on airline travel satisfaction. Practical implications on efficient and cost-effective measures of promoting satisfaction are derived and discussed based on the findings.
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| 11. |
- Gao, Kun, 1993, et al.
(författare)
-
Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory
- 2021
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Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 218
-
Tidskriftsartikel (refereegranskat)abstract
- Modeling individuals’ travel decision making in terms of choosing transport modes, route and departure time for daily activities is an indispensable component for transport system optimization and management. Conventional approaches of modeling travel decision making suffer from presumed model structures and parametric specifications. Emerging machine learning algorithms offer data-driven and non-parametric solutions for modeling travel decision making but encounter extrapolation issues (i.e., disability to predict scenarios beyond training samples) due to neglecting behavioral mechanisms in the framework. This study proposes an extrapolation-enhanced approach for modeling travel decision making, leveraging the complementary merits of ensemble machine learning algorithms (Random Forest in our study) and knowledge-based decision-making theory to enhance both predictive accuracy and model extrapolation. The proposed approach is examined using three datasets about travel decision making, including one estimation dataset (for cross-validation) and two test datasets (for model extrapolation tests). Especially, we use two test datasets containing extrapolated choice scenarios with features that exceed the ranges of training samples, to examine the predictive ability of proposed models in extrapolated choice scenarios, which have hardly been investigated by relevant literature. The results show that both proposed models and the direct application of Random Forest (RF) can give quite good predictive accuracy (around 80%) in the estimation dataset. However, RF has a deficient predictive ability in two test datasets with extrapolated choice scenarios. In contrast, the proposed models provide substantially superior predictive performances in the two test datasets, indicating much stronger extrapolation capacity. The model based on the proposed framework could improve the precision score by 274.93% than the direct application of RF in the first test dataset and by 21.9% in the second test dataset. The results indicate the merits of the proposed approach in terms of prediction power and extrapolation ability as compared to existing methods.
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| 12. |
- Gao, Kun, 1993, et al.
(författare)
-
Revealing psychological inertia in mode shift behavior and its quantitative influences on commuting trips
- 2020
-
Ingår i: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 71, s. 272-287
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Tidskriftsartikel (refereegranskat)abstract
- The inertia effects stemmed from repeated past behavior have been investigated by both psychology and transportation studies because of its bearing on explaining human mobility and forecasting travel demand. However, the existing literature from psychology does not strictly control potential endogeneity due to ignorance of detailed level-of-service (LOS) variables of alternatives and rational preference in the analysis. Quantitative transportation studies are insufficient in providing explicit behavior mechanisms. This paper aims to fill the gaps by empirically examining the effects of irrational psychological inertia in mode shift behavior with controlling potential endogeneity. A specific-designed comparison experiment is conducted to demonstrate the existence of psychological inertia in mode shift behavior. The effects of dominance in LOS variables and rational preference towards a certain transport mode are controlled to eliminate potential endogeneity in the analysis. The results demonstrate that after controlling the above-mentioned endogeneity, both car and metro users show significantly and substantially larger predilections to previously used transport mode in mode shift scenarios without overturning travel contexts than those in new context mode choice scenarios with noticeable changes in travel contexts. The results support that psychological inertia plays a significant role in mode shift behavior after controlling potential endogeneity. Moreover, this study utilizes hybrid choice modeling to quantitatively measure the effect of psychological inertia. The relationships between travelers’ characteristics and strength of psychological inertia are analyzed as well to shed light on heterogeneity in the strength of psychological inertia. The findings provide solid evidence of psychological inertia in mode shift behavior by a novel method and provide an approach to measure the quantitative effects of psychological inertia along with empirical studies.
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| 13. |
- Gao, Kun, 1993, et al.
(författare)
-
Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai
- 2021
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Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 94
-
Tidskriftsartikel (refereegranskat)abstract
- Distance decay is a vital aspect for modeling spatial interactions of human movements and an indispensable input for land use planning and travel demand prediction models. Although many studies have investigated the usage demand of bike-sharing systems in an area, research investigating the distance decay patterns of using dockless bike-sharing systems (DLBS) from a spatially heterogeneous perspective based on large-scale datasets is lacking. This study firstly utilizes massive transaction record data from DLBS in Shanghai of China and online map navigator Application Programming Interface to empirically estimate the distance decay patterns of using DLBS and reveal the spatial heterogeneity in distance decay of using DLBS across different urban contexts. Afterward, this study examines the mechanism of spatial heterogeneity in distance decay, leveraging multiple data resources including Point of Interest (POI) data, demographic data, and road network data. The associations among the distance decay of using DLBS with built environment factors are investigated by multiple linear regression. Results indicate that factors such as population density, land use entropy, branch road density, and metro station density are significantly related to larger distance decay of using DLBS, while factors such as commercial land use ratio, industrial land use ratio, and motorway density are significantly linked to smaller distance decay in Shanghai. Lastly, we further employ an adaptative geographically weighted regression to investigate the spatial divergences of the influences of built environment factors on distance decay. Results reveal notably distinct and even inverse influences of a built environment factor on the distance decay of using DLBS in different urban contexts. The findings provide insights into the distance decay patterns of using DLBS in different urban contexts and their interactions with the built environment, which can support accurate planning and management of sustainable DLBS as per specific urban characteristics.
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| 14. |
- Guo, Zhiming, et al.
(författare)
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Detection of Heavy Metals in Food and Agricultural Products by Surface-enhanced Raman Spectroscopy
- 2023
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Ingår i: Food reviews international (Print). - : Taylor & Francis Group. - 8755-9129 .- 1525-6103. ; 39:3, s. 1440-1461
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Forskningsöversikt (refereegranskat)abstract
- Heavy metals accumulating in the human body produce physiological toxicity by interfering with the transport of human proteins and enzymes. Heavy metals detection is significant for food safety assurance. This review focuses on recent advances of heavy metals detection of food and agricultural products by surface-enhanced Raman spectroscopy (SERS). The article covers the SERS basic principles and advances in heavy metals detection, including mercury, arsenic, cadmium, lead, chromium among others. Insights in the potential of combining chemometrics and multivariate analysis with SERS and the exploration of novel SERS substrate platforms from both macro and micro scale are discussed. Finally, future application of SERS in heavy metal detection are prospected. SERS is a powerful and promising technique offering the advantages of simple sampling, rapid data collection and non-invasiveness. The findings of this study can allow better understanding of the heavy metals' occurrence and the possibility of its detection using SERS.
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| 15. |
- Jin, Yunzhe, et al.
(författare)
-
Effects of in-situ stress on heat transfer in fracture networks
- 2024
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Ingår i: Geomechanics for Energy and the Environment. - : Elsevier BV. - 2352-3808. ; 37
-
Tidskriftsartikel (refereegranskat)abstract
- Stress-induced fracture deformation is the principal cause for permeability change in geothermal systems. This study focuses on the influence of the nonlinear deformation and dilation effect of fractures on the geothermal system under the action of in-situ stress. By adopting a nonlinear constitutive model of rock fractures and embedding discrete fracture networks, numerical studies are first conducted to investigate the effects of different in-situ stress schemes on fracture aperture evolution using a rigid-body spring method. Based on the anisotropic aperture field of the fracture network caused by the in-situ stress, a finite element method is then used to study the flow and heat transfer process. The effects of different stress schemes on the heat flow transfer process are analyzed. Numerical simulation results show that when the ratio of horizontal to vertical stresses is not sufficient to cause shear dilation effects, the nonlinear normal deformation is the main factor affecting flow and heat transfer. In this case, the heat extraction efficiency is reduced. As the stress ratio increases, the shear dilation gradually becomes the dominant mechanism, and the heat extraction performance is improved. The obtained results provide a practical guide for geothermal site siting and optimizing heat extraction efficiency in geothermal reservoirs.
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| 16. |
- Jin, Yunzhe, et al.
(författare)
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Experimental and numerical simulation study on the evolution of mechanical properties of granite after thermal treatment
- 2024
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Ingår i: Computers and geotechnics. - : Elsevier BV. - 0266-352X .- 1873-7633. ; 172
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Tidskriftsartikel (refereegranskat)abstract
- High temperature significantly influences the mechanical properties of granite, which is relevant to various engineering applications, including geothermal energy extraction. The objective of this study is to investigate the meso-mechanics of granite, specifically focusing on the formation of thermal cracks and the temperature-dependent mechanical properties in heterogeneous rock. Firstly, we heat the granite to 25–1000 ℃ by muffle furnace. Following this, we conduct triaxial compression tests with 0–20 MPa confining pressures on the heated-specimens cooled by cold water. Subsequently, we combine the grain-based model (GBM) and the finite-discrete element method (FDEM) to simulate the heat treatment process and the triaxial experiments. We calibrate the micromechanical parameters of granite by experimental results. Results show that the mechanism behind the formation of thermal cracks in granite subjected to high-temperature is the differential thermal expansion coefficients of mineral particles in granites, leading to the degradation of mechanical properties in thermal-treated granite. The temperature threshold for the formation of thermal cracks is between 500 °C and 550 °C. Particularly, the stress-strain curve of granite exhibits ideal elastic-plastic characteristics under temperature is 1000 °C. These results can help to demonstrate the temperature-dependent evolution of mechanical properties of crystalline rocks, providing a theoretical basis for the utilization of engineering applications.
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| 17. |
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| 18. |
- Kuang, Yan, et al.
(författare)
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Novel Crash Surrogate Measure for Freeways
- 2020
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Ingår i: Journal of Transportation Engineering Part A: Systems. - 2473-2893 .- 2473-2907. ; 146:8
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a disturbance-based methodology was proposed to represent the safety level of a car-following scenario. According to the probabilistic causal model, the following vehicles always take evasive actions to avoid a collision in the crash mechanism. In this study, the probabilistic model is modified to evaluate the maximum disturbance a car-following scenario can accommodate corresponding to the maximum evasive action taken by the following vehicle. This paper aims to investigate the safety level of a car-following scenario by estimating its capability index on accommodating disturbance. The surrogate measure, Disturbance Accommodate Index (DAI), is thus proposed to represent the stability of a car-following scenario by measuring its maximum capability on accommodating disturbance. Further, a case study is conducted to evaluate the performance of DAI by using the traffic and crash data provided by the Department of Transport and Main Roads in Queensland. The results show that the DAI outperforms the other crash surrogate measures (e.g., Aggregated Crash Index, Time to Collision, and Proportion of Stopping Distance) in representing rear-end crash risk, followed by the discussion.
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| 19. |
- Li, Guofa, et al.
(författare)
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Driver Behavior in Intelligent Transportation Systems
- 2022
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Ingår i: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 14:3, s. 7-9
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Drivers are the center of road/air/sea transportation systems, and they can be either human beings or artificial beings. Inconsistency between human driver behavior and artificial driver behavior will lead to accidents and congestion in intelligent transportation systems (ITSs) [1] , [2] . To make future ITSs trustworthy for traffic safety and acceptable for travel efficiency, developing industrial ITS applications based on drivers’ reliable behavioral and cognitive intelligence is essential [3] . However, there are many challenges to be addressed, including real-time behavior prediction, reliable decision making, safe interaction among human and artificial drivers, and so on.
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| 20. |
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| 21. |
- Li, Zongbao, et al.
(författare)
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High throughput trapping and arrangement of biological cells using self-assembled optical tweezer
- 2018
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Ingår i: Optics Express. - : OPTICAL SOC AMER. - 1094-4087. ; 26:26, s. 34665-34674
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Tidskriftsartikel (refereegranskat)abstract
- Lately, a fiber-based optical tweezer that traps and arranges the micro/nanoparticles is crucial in practical applications, because such a device can trap the biological samples and drive them to the designated position in a microfluidic system or vessel without harming them. Here, we report a new type of fiber optical tweezer, which can trap and arrange erythrocytes. It is prepared by coating graphene on the cross section of a microfiber. Our results demonstrate that thermal-gradient-induced natural convection flow and thermophoresis can trap the erythrocytes under low incident power, and the optical scattering force can arrange them precisely under higher incident power. The proposed optical tweezer has high flexibility, easy fabrication, and high integration with lab-on-a-chip, and shows considerable potential for application in various fields, such as biophysics, biochemistry, and life sciences.
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| 22. |
- Lin, Hongyi, et al.
(författare)
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Deep Demand Prediction: An Enhanced Conformer Model With Cold-Start Adaptation for Origin–Destination Ride-Hailing Demand Prediction
- 2024
-
Ingår i: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 16:3, s. 111-124
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Tidskriftsartikel (refereegranskat)abstract
- In intelligent transportation systems, one key challenge for managing ride-hailing services is the balancing of traffic supply and demand while meeting passenger needs within vehicle availability constraints. Accurate origin–destination (OD) demand predictions can empower platforms to execute timely reallocation of cruising vehicles and improve ride-sharing services. Nonetheless, the complexity of OD-based demand prediction arises from intricate spatiotemporal dependencies and a higher need for precision compared to zone-based predictions, which leads to many unprecedented OD pairs. To tackle this issue, we design a comprehensive set of 102 features, including travel demand, passenger count, travel volume, liveliness, weather, and cross features. We also introduce an enhanced conformer model, which is composed of a single conformer block that integrates feedforward layers, multihead self-attention mechanisms, and depth-wise separable convolution layers. To address the cold-start problem and manage large values, we design a specific algorithm for OD pairs lacking training data and apply a technique to handle larger values. Our approach demonstrates a marked improvement in prediction performance, with an 18% decrease in the total travel demand error and up to a 47% reduction for certain larger values in some cases. Through extensive experiments on a dataset collected from a city, provided by a ride-hailing platform, our proposed methods significantly outperform the most advanced models.
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| 23. |
- Lin, Hongyi, et al.
(författare)
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Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models
- 2024
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Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; In Press
-
Tidskriftsartikel (refereegranskat)abstract
- With the growing prevalence of autonomous vehicles and the integration of intelligent and connected technologies, the demand for effective and reliable vehicle speed control algorithms has become increasingly critical. Traditional car-following models, which primarily focus on individual vehicle pairs, exhibit limitations in complex traffic environments. To this end, this paper proposes an enhanced state representation for the application of multi-agent reinforcement learning (MARL) in platoon-following scenarios. Specifically, the proposed representation, influenced by feature engineering techniques in time series prediction tasks, thoroughly accounts for the intricate relative relationships between different vehicles within a platoon and can offer a distinctive perspective on traffic conditions to help improve the performance of MARL models. Experimental results show that the proposed method demonstrates superior performance in platoon-following scenarios across key metrics such as the time gap, distance gap, and speed, even reducing the time gap by 63%, compared with traditional state representation methods. These enhancements represent a significant step forward in ensuring the safety, efficiency, and reliability of platoon-following models within the context of autonomous vehicles.
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| 24. |
- Lin, Hongyi, et al.
(författare)
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How generative adversarial networks promote the development of intelligent transportation systems: A survey
- 2023
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Ingår i: IEEE/CAA Journal of Automatica Sinica. - 2329-9274 .- 2329-9266. ; 10:9, s. 1781-1796
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Tidskriftsartikel (refereegranskat)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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| 25. |
- Lin, Xiongwei, et al.
(författare)
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Noise reduction optimization of sound sensor based on a Conditional Generation Adversarial Network
- 2021
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Ingår i: Journal of Physics: Conference Series. - : IOP Publishing. - 1742-6588 .- 1742-6596. ; 1873:1
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Konferensbidrag (refereegranskat)abstract
- To address the problems in the traditional speech signal noise elimination methods, such as the residual noise, poor real-time performance and narrow applications a new method is proposed to eliminate network voice noise based on deep learning of conditional generation adversarial network. In terms of the perceptual evaluation of speech quality (PESQ) and shorttime objective intelligibility measure (STOI) functions used as the loss function in the neural network, which were used as the loss function in the neural network, the flexibility of the whole network was optimized, and the training process of the model simplified. The experimental results indicate that, under the noisy environment, especially in a restaurant, the proposed noise reduction scheme improves the STOI score by 26.23% and PESQ score by 17.18%, respectively, compared with the traditional Wiener noise reduction algorithm. Therefore, the sound sensor's noise reduction scheme through our approach has achieved a remarkable noise reduction effect, more useful information transmission, and stronger practicability.
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| 26. |
- Liu, Yang, 1991, et al.
(författare)
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Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform
- 2022
-
Ingår i: Transportation Research Part E: Logistics and Transportation Review. - : Elsevier BV. - 1366-5545. ; 161
-
Tidskriftsartikel (refereegranskat)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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| 27. |
- Liu, Yang, 1991, et al.
(författare)
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DeepTSP: Deep traffic state prediction model based on large-scale empirical data
- 2021
-
Ingår i: Communications in Transportation Research. - : Elsevier BV. - 2772-4247. ; 1
-
Tidskriftsartikel (refereegranskat)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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| 28. |
- Liu, Yang, 1991, et al.
(författare)
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How machine learning informs ride-hailing services: A survey
- 2022
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Ingår i: Communications in Transportation Research. - : Elsevier BV. - 2772-4247. ; 2
-
Forskningsöversikt (refereegranskat)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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| 29. |
- Lyu, Cheng, et al.
(författare)
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Personalized Modeling of Travel Behaviors and Traffic Dynamics
- 2022
-
Ingår i: Journal of Transportation Engineering Part A: Systems. - 2473-2893 .- 2473-2907. ; 148:10
-
Tidskriftsartikel (refereegranskat)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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| 30. |
- Pajecki, Robert, et al.
(författare)
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Estimating passenger car equivalent of heavy vehicles at roundabout entry using micro-traffic simulation
- 2019
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Ingår i: Frontiers in Built Environment. - : Frontiers Media SA. - 2297-3362. ; 5
-
Tidskriftsartikel (refereegranskat)abstract
- Passenger Car Equivalent (PCE) is a unit used to represent the impact of a large vehicle on a road by expressing it as the number of equivalent passenger vehicles. This paper focuses on estimating the PCE of various sized heavy vehicles in roundabouts with respect to different entry flow rates. A single-lane roundabout was tested under predefined mixed traffic and demand scenarios in VISSIM micro-simulation environments. The individual and group behavior of four separate heavy-vehicle types were tested: single-unit trucks, buses, small semitrailers, and large semitrailers. The obtained PCE values were found to be on average lower than those suggested in the United States guidelines for roundabouts. The estimated PCE values for heavy vehicles in mixed traffic conditions are 1.30 for single unit trucks, 1.40 for small semitrailers, 1.60 for buses, and 1.70 for large semitrailers. Additional factors such as varying inflow (balanced, unbalanced, and congested traffic) show direct influences on the PCE values. The PCE value under these conditions ranged from 1.25 to 1.75 for smaller vehicles (single-unit trucks, buses, and small semitrailers) and 1.45–2.10 for larger heavy vehicles (large semitrailers). A general equation was developed based on the data to relate vehicle proportions and heavy-vehicle reduction factors that would be useful for professionals to analyze the operational performance of roundabouts with better accuracy.
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| 31. |
- Qi, Weiwei, et al.
(författare)
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Modeling drivers’ scrambling behavior in China: An application of theory of planned behavior
- 2021
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Ingår i: Travel Behaviour and Society. - : Elsevier BV. - 2214-367X. ; 24, s. 164-171
-
Tidskriftsartikel (refereegranskat)abstract
- Scrambling behavior is one of the main causes of road traffic accidents in China. This study aimed to investigate the characteristics of drivers’ scrambling behavior and its influencing factors based on the theory of planned behavior. A total of 388 drivers answered the questionnaire and 359 provided valid data. The structure equation model of scrambling behavior showed that positive attitudes towards scrambling behavior, subjective norms and perceived behavior control increased the intention of scrambling behavior. Furthermore, the path coefficient of the structural equation model for the scrambling behavior revealed that attitude was the most important factor influencing scrambling behavior. Thus, to prevent drivers from scrambling, traffic regulators should focus on improving drivers’ attitude towards this behavior, while auxiliary measures should be enacted to regulate drivers' subjective norms and perceptual behavior control. Implications on intervention strategy and policy to reduce scrambling behavior are discussed.
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| 32. |
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| 33. |
- Qu, Xiaobo, 1983, et al.
(författare)
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Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach
- 2020
-
Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 257
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Tidskriftsartikel (refereegranskat)abstract
- It has been well recognized that human driver's limits, heterogeneity, and selfishness substantially compromise the performance of our urban transport systems. In recent years, in order to deal with these deficiencies, our urban transport systems have been transforming with the blossom of key vehicle technology innovations, most notably, connected and automated vehicles. In this paper, we develop a car following model for electric, connected and automated vehicles based on reinforcement learning with the aim to dampen traffic oscillations (stop-and-go traffic waves) caused by human drivers and improve electric energy consumption. Compared to classical modelling approaches, the proposed reinforcement learning based model significantly reduces the modelling constraints and has the capability of self-learning and self-correction. Experiment results demonstrate that the proposed model is able to improve travel efficiency by reducing the negative impact of traffic oscillations, and it can also reduce the average electric energy consumption.
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| 34. |
- Ren, Weicheng, et al.
(författare)
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Genetic and transcriptomic analyses of diffuse large B-cell lymphoma patients with poor outcomes within two years of diagnosis
- 2024
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Ingår i: Leukemia. - : Springer Nature. - 0887-6924 .- 1476-5551. ; 38:2, s. 438-441
-
Tidskriftsartikel (refereegranskat)abstract
- Despite the improvements in clinical outcomes for DLBCL, a significant proportion of patients still face challenges with refractory/relapsed (R/R) disease after receiving first-line R-CHOP treatment. To further elucidate the underlying mechanism of R/R disease and to develop methods for identifying patients at risk of early disease progression, we integrated clinical, genetic and transcriptomic data derived from 2805 R-CHOP-treated patients from seven independent cohorts. Among these, 887 patients exhibited R/R disease within two years (poor outcome), and 1918 patients remained in remission at two years (good outcome). Our analysis identified four preferentially mutated genes (TP53, MYD88, SPEN, MYC) in the untreated (diagnostic) tumor samples from patients with poor outcomes. Furthermore, transcriptomic analysis revealed a distinct gene expression pattern linked to poor outcomes, affecting pathways involved in cell adhesion/migration, T-cell activation/regulation, PI3K, and NF-kappa B signaling. Moreover, we developed and validated a 24-gene expression score as an independent prognostic predictor for treatment outcomes. This score also demonstrated efficacy in further stratifying high-risk patients when integrated with existing genetic or cell-of-origin subtypes, including the unclassified cases in these models. Finally, based on these findings, we developed an online analysis tool (https://lymphprog.serve.scilifelab.se/app/lymphprog) that can be used for prognostic prediction for DLBCL patients.
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| 35. |
- Shi, Peng, et al.
(författare)
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Age- and gender-specific trends in respiratory outpatient visits and diagnoses at a tertiary pediatric hospital in China : a 10-year retrospective study
- 2020
-
Ingår i: BMC Pediatrics. - : BioMed Central. - 1471-2431. ; 20:1
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Respiratory infections are one of three leading causes of childhood mortality, and worldwide increase and recent plateau in childhood asthma has been reported. However, data on trends of respiratory diseases over long period of time is limited. This study aimed to determine the trends of respiratory disease outpatient visits (ROVs) and diagnoses (RODs) in one of the largest children's teaching hospitals in China between 2009 and 2018.METHODS: A retrospective study based on routine administrative data was designed and implemented according to the RECORD statement. Demographic details and diagnoses of the outpatients < 18 years visiting the respiratory department of the hospital were extracted from the Hospital Information System. Age- and gender-specific trends were illustrated by calculating average annual growth rate (AAGR) for ROVs and comparing change of proportion for different RODs over time.RESULTS: There were 698,054 ROVs from 285,574 children (40.4% female). AAGR of ROVs was 15.2%. Children aged 4 to < 7 years had a faster increase than other age groups. Bronchitis (27.6%), pneumonia (18.5%), pneumonia affecting other systems (18.4%), asthma and status asthmaticus (10.7%), and vasomotor and allergic rhinitis (9.2%) accounted for 84.4% of all RODs. The proportion of bronchitis decreased across years, with the concomitant increasing trend in the proportion of pneumonia. Age-specific trend in diagnoses showed greater proportion of asthma in all visits for the children aged 7 to < 18 years than younger children. Gender-specific trend in diagnoses showed the proportion of asthma was greater for males but the AAGR was greater for females.CONCLUSION: The persistent upward trend in ROVs was observed among children at different ages and a gender difference was also seen. In contrast to what has been reported, burden of asthma and allergies diseases continues to increase locally.
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| 36. |
- Wang, Guanqun, et al.
(författare)
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Predictability of Vehicle Fuel Consumption Using LSTM: Findings from Field Experiments
- 2023
-
Ingår i: Journal of Transportation Engineering Part A: Systems. - 2473-2893 .- 2473-2907. ; 149:5
-
Tidskriftsartikel (refereegranskat)abstract
- It has been well-recognized that driving behaviors significantly impact the fuel consumption of vehicles. To explore how well deep learning methods can predict fuel consumption precisely and efficiently and then guide drivers to go in an energy-saving way, we propose a fuel consumption prediction model, namely FuelNet, based on long short-term memory (LSTM) neural networks in this study. First, we develop the proposed FuelNet model with numerous vehicle kinematics data and corresponding fuel consumption data collected in the test field and real-world scenarios. And we analyze the relationship between the prediction accuracy and different combinations of input variables, training set size, and the sampling interval of the raw data. Second, we conduct intensive field tests to demonstrate the applicability of our model to fuel consumption prediction for different speed conditions and vehicle types. Furthermore, the superior prediction performance of FuelNet is shown by comparing it with five other types of models, such as the physical model, statistical and regression model, conventional neural networks model, and other deep learning models. Finally, we apply it to three real case studies, which verify that FuelNet can precisely predict fuel consumption for different driving trajectories in many scenarios such as signalized intersection (average value of RE is 0.049), campus environments (RE is 0.030), urban roads (RE is 0.077), and highways (RE is 0.097), as well as can contribute to detecting abnormal fuel consumption.
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| 37. |
- Wang, Kai, et al.
(författare)
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A Two-Stage Teaching Philosophy for Postgraduate Students
- 2023
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Ingår i: Smart Innovation, Systems and Technologies. - 2190-3026 .- 2190-3018. ; 356, s. 211-220
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Konferensbidrag (refereegranskat)abstract
- In this study, we develop a two-stage structured teaching philosophy that is able to cater to the needs of postgraduate students with different expectations about the learning outcomes. At the postgraduate level, different students have distinct expectations about their future careers. Some may want to pursue their career as an engineer, with little interest in research and development. Others may have different views and would like to pursue their career as a researcher or an academic in the future. Therefore, it is necessary to take into account the distinctions among students’ expectations about courses. The proposed teaching philosophy divides the course sessions into two stages. In the first stage, fundamental and common knowledge bases for the courses are delivered to all students, which ensures the students receive the necessary and basic knowledge that is required for both industrial and academic pathways. Afterward, the second stage leverages the flipped classroom model to let the students choose their learning and course materials with different emphases as per their own expectations and interests. Customized learning and teaching materials are prepared for students who prefer the industrial pathway and students who show more predilections for the industrial pathway. We will use a master course about transportation engineering to empirically test this teaching philosophy and evaluate its performance, including a comparison with the conventional teaching process. The results demonstrate that the new structure is well received by students and is much beneficial for improving students’ subjective evaluations of the courses and performances in learning.
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| 38. |
- Wen, Wanqing, et al.
(författare)
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Genome-wide association studies in East Asians identify new loci for waist-hip ratio and waist circumference
- 2016
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6
-
Tidskriftsartikel (refereegranskat)abstract
- Sixty genetic loci associated with abdominal obesity, measured by waist circumference (WC) and waist-hip ratio (WHR), have been previously identified, primarily from studies conducted in Europeanancestry populations. We conducted a meta-analysis of associations of abdominal obesity with approximately 2.5 million single nucleotide polymorphisms (SNPs) among 53,052 (for WC) and 48,312 (for WHR) individuals of Asian descent, and replicated 33 selected SNPs among 3,762 to 17,110 additional individuals. We identified four novel loci near the EFEMP1, ADAMTSL3, CNPY2, and GNAS genes that were associated with WC after adjustment for body mass index (BMI); two loci near the NID2 and HLA-DRB5 genes associated with WHR after adjustment for BMI, and three loci near the CEP120, TSC22D2, and SLC22A2 genes associated with WC without adjustment for BMI. Functional enrichment analyses revealed enrichment of corticotropin-releasing hormone signaling, GNRH signaling, and/or CDK5 signaling pathways for those newly-identified loci. Our study provides additional insight on genetic contribution to abdominal obesity.
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| 39. |
- Wu, Jiaming, 1989, et al.
(författare)
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The cooperative sorting strategy for connected and automated vehicle platoons
- 2021
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Ingår i: Transportation Research, Part C: Emerging Technologies. - : Elsevier BV. - 0968-090X. ; 123
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a "cooperative vehicle sorting" strategy that seeks to optimally sort connected and automated vehicles (CAVs) in a multi-lane platoon to reach an ideally organized platoon. In the proposed method, a CAV platoon is firstly discretized into a grid system, where a CAV moves from one cell to another in discrete time-space domain. Then, the cooperative sorting problem is modeled as a path-finding problem in the graphic domain. The problem is solved by the deterministic A* algorithm with a stepwise strategy, where only one vehicle can move within a movement step. The resultant shortest path is further optimized with an integer linear programming algorithm to minimize the sorting time by allowing multiple movements within a step. To improve the algorithm running time and address multiple shortest paths, a distributed stochastic A* algorithm (DSA*) is developed by introducing random disturbances to the edge costs to break uniform paths (with equal path cost). Numerical experiments are conducted to demonstrate the effectiveness of the proposed DSA* method. The results report shorter sorting time and significantly improved algorithm running time due to the use of DSA*. In addition, we find that the optimization performance can be further improved by increasing the number of processes in the distributed computing system.
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| 40. |
- Wu, Jiaming, 1989, et al.
(författare)
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Why is your paper rejected? Lessons learned from over 5000 rejected transportation papers
- 2024
-
Ingår i: Communications in Transportation Research. - 2772-4247. ; 4
-
Tidskriftsartikel (refereegranskat)abstract
- Academic papers are the cornerstone of knowledge dissemination and crucial for researchers’ career development. This is particularly true for rapidly evolving research domains such as transportation, as evidenced by the surge of journals and papers in the past decade. While abundant literature offers guidance on successful publication strategies, insights into the reasons for rejection are rare. This study fills in this gap by examining why papers are rejected in the area of transportation. We present concrete evidence based on data from over 5,000 rejected transport papers. Quantitative analyses are conducted to reveal the impacts of similarity rate, duplication submission rate, and topic on desk rejections. Additionally, we shed light on the distinct focus reviewers have when serving different journals. We hope the results could equip transport researchers with a deeper comprehension of publication criteria and a better awareness of common but avoidable mistakes.
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| 41. |
- Wu, Xinchen, et al.
(författare)
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Core-satellite nanoassembly system with aptamer-conjugated Au@Ag nanoparticles for SERS detection of patulin in apples
- 2024
-
Ingår i: Food Control. - : Elsevier. - 0956-7135 .- 1873-7129. ; 159
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Tidskriftsartikel (refereegranskat)abstract
- Patulin (PAT), a major contaminant in apples, poses a huge threat to human health as well as the economic sector. There is an urgent need to develop a sensitive, selective, and fast-responsive method to detect PAT in apples. However, one of the main challenges is overcoming the interferences of complex food matrices. In this study, we developed a highly sensitive competitive SERS sensor based on plasmonic nanoparticles modified by aptamers. The study utilized the formation of nanocomposites through aptamer-modified Au@Ag nanoparticles (NPs) and gold nano-stars (AuNSs) to induce high-intensity Raman signals from the SERS tag. Subsequently, in the presence of PAT, the nanocomposites underwent decomposition, evident through the significant decrease in SERS intensity. According to the standard curve established in this study, the detection limit was 0.0281 ng/mL. The competitive sensor was applied to spiked apple fruit and juice samples, indicating a recovery rate ranging from 91.98% to 102.94%. The excellent analytical performances and high sensitivity observed suggest the potential of the plasmonic nanocomposite sensing strategy in detecting PAT in real matrices.
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| 42. |
- Xue, Shanshan, et al.
(författare)
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A film-like SERS aptasensor for sensitive detection of patulin based on GO@Au nanosheets
- 2024
-
Ingår i: Food Chemistry. - : Elsevier. - 0308-8146 .- 1873-7072. ; 441
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Tidskriftsartikel (refereegranskat)abstract
- Patulin (PAT) commonly contaminates fruits, posing a significant risk to human health. Therefore, a highly effective and sensitive approach in identifying PAT is warranted. Herein, a SERS aptasensor was constructed based on a two-dimensional film -like structure. GO@Au nanosheets modified with SH-cDNA were employed as capture probes, while core -shell Au@Ag nanoparticles modified with 4 -MBA and SH-Apt were utilized as signal probes. Through the interaction between capture probes and signal probes, adjustable hotspots were formed, yielding a significant Raman signal. During sensing, the GO@Au-cDNA competitively attached to Au@AgNPs@MBA-Apt, resulting in an inverse relationship between PAT levels and SERS intensity. The acquired results exhibited linear responses to PAT within the range of 1-70 ng/mL, with a calculated limit of detection of 0.46 ng/mL. In addition, the SERS aptasensor exhibited satisfactory recoveries in apple samples, which aligned closely with HPLC. With high sensitivity and specificity, this method holds significant potential for PAT detection.
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| 43. |
- Yang, Xiaoxue, et al.
(författare)
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Improved deep reinforcement learning for car-following decision-making
- 2023
-
Ingår i: Physica A. - : Elsevier B.V.. - 0378-4371 .- 1873-2119. ; 624
-
Tidskriftsartikel (refereegranskat)abstract
- Accuracy improvement of Car-following (CF) model has attracted much attention in recent years. Although a few studies incorporate deep reinforcement learning (DRL) to describe CF behaviors, proper design of reward function is still an intractable problem. This study improves the deep deterministic policy gradient (DDPG) car-following model with stacked denoising autoencoders (SDAE), and proposes a data-driven reward representation function, which quantifies the implicit interaction between ego vehicle and preceding vehicle in car-following process. The experimental results demonstrate that DDPG-SDAE model has superior ability of imitating driving behavior: (1) validating effectiveness of the reward representation method with low deviation of trajectory; (2) demonstrating generalization ability on two different trajectory datasets (HighD and SPMD); (3) adapting to three traffic scenarios clustered by a dynamic time warping distance based k-medoids method. Compared with Recurrent Neural Networks (RNN) and intelligent driver model (IDM), DDPG-SDAE model shows better performance on the deviation of speed and relative distance. This study demonstrates superiority of a novel reward extraction method fusing SDAE into DDPG algorithm and provides inspiration for developing driving decision-making model. © 2023 Elsevier B.V.
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| 44. |
- Yu, Yang, et al.
(författare)
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A Modified Full Velocity Difference Model with Acceleration and Deceleration Confinement: Calibrations, Validations, and Scenario Analyses
- 2021
-
Ingår i: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 13:2, s. 222-235
-
Tidskriftsartikel (refereegranskat)abstract
- The Full Velocity Difference (FVD) model is a well-recognized and widely-used time continuous car following model. Although the model has very good simulation performances in most cases, it is not applicable to some specific traffic scenarios, where it can generate very large or even overshooting accelerations or decelerations that are totally unnecessary and might be far beyond the acceleration/deceleration limits of real vehicles. In this paper, we explore the reason and attempt to correct it by proposing a confined Full Velocity Difference (c-FVD) model in which we limit the accelerations or decelerations generated by the existing FVD models to a reasonable level. The performances of both models are compared from both microscopic and macroscopic perspectives. The ability of the modified model to generate strong but reasonable decelerations to avoid accidents in urgent traffic scenarios is also validated. According to the comparative analyses, both models will have same performances in most cases while the c-FVD model will outperform the existing FVD model in certain scenarios where very large or overshooting accelerations or decelerations are involved.
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| 45. |
- Yu, Yang, et al.
(författare)
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Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach
- 2022
-
Ingår i: International Journal of Transportation Science and Technology. - : Elsevier BV. - 2046-0449 .- 2046-0430. ; 11:2, s. 268-282
-
Tidskriftsartikel (refereegranskat)abstract
- The existing conventional traffic flow models aims to simulate human-driven following vehicles in real world. In this era of emerging transport solutions, controlling or intervening traffic flow to achieve high fuel efficiency along with good driving safety and travel efficiency becomes a reality. As such, it is worth exploring the possibility of developing eco-driving models to optimise vehicle movements for fuel consumption minimisation, while maintaining safety and efficiency. In this study, we propose a modified genetic algorithm (GA) based calibration method that enables the calibrated parametric traffic flow (car following) models to simulate or control vehicles in an eco-driving manner. By developing a novel objective function for the GA method based on the widely-used VT-Micro fuel consumption model, the proposed method can calibrate model parameters towards improving fuel efficiency. Besides, by subtly using heavy fuel consumptions as a surrogate index to represent low travel efficiency or dangerous driving strategies, the modified GA method with the novel objective function can guide the calibrated model towards achieving complete eco-driving requirements. Experimental simulation results further indicate that traffic flow models calibrated by the modified GA-based method can also alleviate traffic disturbances and oscillations in a more effective manner.
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| 46. |
- Yu, Yang, et al.
(författare)
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On the Impact of Prior Experiences in Car-Following Models: Model Development, Computational Efficiency, Comparative Analyses, and Extensive Applications
- 2023
-
Ingår i: IEEE Transactions on Cybernetics. - 2168-2275 .- 2168-2267. ; 53:3, s. 1405-1418
-
Tidskriftsartikel (refereegranskat)abstract
- A major shortcoming of the conventional car-following models is that these models only consider the current spacing and speeds of the target vehicle and its immediate leading vehicle, without taking into account prior driving actions, even for those from the same driver. In other words, the numerous prior experiences have no influence in predicting vehicular movements for the next time step. In this research, we propose a machine-learning-based data-driven methodology that is able to take advantage of the high-resolution historical traffic data in the current data-rich era, to predict vehicular movements in an accurate manner with high computational efficiency. The proposed car-following model has a simple model structure based on a fixed-radius near neighbors (FRNN) search algorithm and it can be applied to high-resolution, real-time vehicle movement prediction, modeling, and control. A comprehensive performance comparison is also conducted among the proposed car-following model, another similar data-driven model, and two conventional formula-based models. The results indicate that the FRNN algorithm-based car-following model is superior to all other three models in terms of prediction accuracy and is more computationally efficient compared to its data-driven-based counterpart. Some extensive applications of the proposed car-following model are also discussed at the end of this article.
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| 47. |
- Yu, Yang, et al.
(författare)
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To Investigate the Hidden Gap between Traffic Flow Fundamental Diagrams and the Derived Microscopic Car Following Models: A Theoretical Analysis
- 2020
-
Ingår i: Smart Innovation, Systems and Technologies. - Singapore : Springer Singapore. - 2190-3026 .- 2190-3018. ; 185, s. 89-98
-
Konferensbidrag (refereegranskat)abstract
- Traffic flow fundamental diagram, or simply speed–density relationship and/or flow–density relationship, is the basis of traffic flow theories and road performance studies since it depicts the mathematical relationship among three traffic flow fundamental parameters—density, speed, and traffic flow. In this paper, through mathematical analyses and simulations, we find that for all existing fundamental diagram models, their derived microscopic car following models do not perform well and cannot reproduce the status of the stable flow described by the corresponding fundamental diagrams. The results indicate that there seems to exist a hidden gap between existing traffic flow fundamental diagrams and the corresponding microscopic car following models. We further discuss about the fundamental causes behind such gap and propose a simple yet incomplete solution at the end of this paper.
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| 48. |
- Yu, Yang, et al.
(författare)
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Towards Eliminating Overreacted Vehicular Maneuvers: Part I Model Development and Calibration
- 2019
-
Ingår i: Smart Innovation, Systems and Technologies. - Singapore : Springer Singapore. - 2190-3026 .- 2190-3018. ; 149, s. 135-143
-
Konferensbidrag (refereegranskat)abstract
- Microscopic car following models are of great importance to traffic flow studies and vehicular dynamics reproducing. The Full Velocity Difference (FVD) model is a well-known example with satisfactory simulation performances in most times. However, by analyzing the structure of the model formulas, we find that it can sometimes generate overreacted vehicular maneuvers such as unrealistically strong (overshooting for short) accelerations or decelerations that conflict with normal driver habits or even beyond the actual vehicular acceleration/deceleration performance, especially when the target vehicle encounter a leader cut-in or move out (leader lane change for short). As Part I of the entire research, this paper corrects the above deficiency of the FVD model by proposing a capped-Full Velocity Difference (capped-FVD) model in which we limit any potential overshooting accelerations or decelerations generated to a reasonable range. Then, all model parameters are also calibrated using field data. Performance comparative analyses to validate the performance improvement of the capped-FVD model are included in the other companion paper serving as Part II of this research.
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| 49. |
- Yu, Yang, et al.
(författare)
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Towards Eliminating Overreacted Vehicular Maneuvers: Part II Comparative Analyses
- 2019
-
Ingår i: Smart Innovation, Systems and Technologies. - Singapore : Springer Singapore. - 2190-3026 .- 2190-3018. ; 149, s. 145-154
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Konferensbidrag (refereegranskat)abstract
- © 2019, Springer Nature Singapore Pte Ltd. Microscopic car following models are of great importance to traffic flow studies and vehicular dynamics reproducing. The Full Velocity Difference (FVD) model is a well-known example with satisfactory simulation performances in most times. However, by analyzing the structure of the model formula, we find that it can sometimes generate overreacted vehicular maneuvers such as unrealistically strong (overshooting for short) accelerations or decelerations that conflict with normal driver habits or even beyond the actual vehicular acceleration/deceleration performance, especially when the target vehicle encounter a leader cut-in or move-out (leader lane change for short). As Part II of the entire research, this paper conducts performance comparative analyses between the existing FVD model and the capped Full Velocity Difference (capped-FVD) model introduced in Part I of the research (the other companion paper) to address the above deficiency, and the results indicate that both models are equivalent in most times but the capped-FVD model will outperform the existing FVD model in aforementioned traffic scenarios since overreacted vehicular maneuvers (overshooting accelerations or decelerations) are totally eliminated. In other words, the aforementioned deficiency of the existing FVD model is totally corrected by the capped-FVD model and the capped-FVD model is a better choice for simulating vehicle movements in multi-lane roadways.
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- Yun, Hanbo, et al.
(författare)
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Warming and Increased Respiration Have Transformed an Alpine Steppe Ecosystem on the Tibetan Plateau From a Carbon Dioxide Sink Into a Source
- 2022
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Ingår i: Journal of Geophysical Research: Biogeosciences. - 2169-8953 .- 2169-8961. ; 127:1
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Tidskriftsartikel (refereegranskat)abstract
- Cold region ecosystems store vast amounts of soil organic carbon (C), which upon warming and decomposition can affect the C balance and potentially change these ecosystems from C sinks to carbon dioxide (CO2) sources. We quantified the decadal year-round CO2 flux from an alpine steppe-ecosystem on the Tibetan Plateau using eddy covariance and automatic chamber approaches during a period of significant warming (0.13°C per 10 years; and 0.18°C in the non-growing season alone: 1st October to next 30th April). The results showed that ongoing climate change, mainly warming within the topsoil layers, is the main reason for the site’s change from a sink for to a source of CO2 in the atmosphere. Non-growing-season ecosystem respiration accounted for 51% of the annual ecosystem respiration and has increased significantly. The growing seasons (1st May to 30th September) were consistent CO2 sink periods without significant changes over the study period. Observations revealed high-emission events from the end of the non-growing season to early in the growing season (1st March to fifteenth May), which significantly (p < 0.01) increased at a rate of 22.6 g C m−2 decade−1, ranging from 14.6 ± 10.7 g C m−2 yr−1 in 2012 to 35.3 ± 12.1 g C m−2 yr−1 in 2017. Structural equation modeling suggested that active layer warming was the key factor in explaining changes in ecosystem respiration, leading to significant changes in net ecosystem exchange over the period 2011–2020 and indicated that these changes have already transformed the ecosystem from a CO2 sink into a source. These results can be used to improve our understanding of the sensitivity of ecosystem respiration to increased warming during the non-growing period.
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