Limiting environmental pollution from exhaust emissions from internal combustion engines includes many measures,including encouraging biofuel use because biofuel is environmentally friendly and renewable.A mixture of ...Limiting environmental pollution from exhaust emissions from internal combustion engines includes many measures,including encouraging biofuel use because biofuel is environmentally friendly and renewable.A mixture of diesel fuel and vegetable oil is a form of biofuel.However,some properties of the mixed fuel,such as viscosity and density,are higher than those of traditional diesel fuel,affecting the injection and combustion process and reducing power and non-optimal toxic emissions,especially soot emissions.This study uses Kiva-3V software to simulate the combustion process of a diesel-vegetable oil mixture in the combustion chamber of a fishing vessel diesel engine with changes in fuel injection timing.The results show that when increasing the fuel injection timing of a diesel-vegetable oil mixture about 1–2 degrees of crankshaft rotation angle before the top dead center compared to diesel fuel injection timing,the engine power increases,and soot emissions decrease compared to no adjustment.The above simulation research results will help orient the experiments conveniently and reduce costs in the future experimental research process to quantify the fuel system adjustment of fishing vessels’diesel engines when using biofuels,including diesel-vegetable oil mixtures.Thus,the engine’s economic indicators will improve,and emissions that pollute the environment will be limited.展开更多
The emissions from traditional fossil heavy-duty trucks have become a conspicuous issue worldwide.The electrical road system(ERS)can offer a viable solution for achieving zero CO_(2) emissions and has high energy effi...The emissions from traditional fossil heavy-duty trucks have become a conspicuous issue worldwide.The electrical road system(ERS)can offer a viable solution for achieving zero CO_(2) emissions and has high energy efficiency in long-distance road cargo transport.While many kinds of pantograph structures have been developed for the ERS,their corresponding pantograph-catenary dynamic characteristics under different road conditions have not been investigated.This work performs a numerical study on the dynamics of the pantograph-catenary interaction of an ERS considering different pantograph structures.First,a pantograph-catenary-truck-road model is proposed.The reduced catenary model and reduced-plate model transmission method are used to minimize model scale.Three different types of ERS pantograph structures are considered in the model.After validation,the pantograph-catenary dynamics under the influence of truck-road interactions with complex road roughness and different pantographs are studied and compared.The corresponding vibration transmission mechanism is further focused.The results show that the truck-road interaction has a significant effect on the pantograph-catenary interaction,but the pantograph with only one lower and upper armcan isolate the roll vibrationmotion transmission fromthe truck to the collector head,which has the best dynamic performance and is suggested for use in the ERS.展开更多
Unmanned aerial vehicle(UAV)was introduced to take road segment traffic surveillance.Considering the limited UAV maximum flight distance,UAV route planning problem was studied.First,a multi-objective optimization mode...Unmanned aerial vehicle(UAV)was introduced to take road segment traffic surveillance.Considering the limited UAV maximum flight distance,UAV route planning problem was studied.First,a multi-objective optimization model of planning UAV route for road segment surveillance was proposed,which aimed to minimize UAV cruise distance and minimize the number of UAVs used.Then,an evolutionary algorithm based on Pareto optimality technique was proposed to solve multi-objective UAV route planning problem.At last,a UAV flight experiment was conducted to test UAV route planning effect,and a case with three scenarios was studied to analyze the impact of different road segment lengths on UAV route planning.The case results show that the optimized cruise distance and the number of UAVs used decrease by an average of 38.43% and 33.33%,respectively.Additionally,shortening or extending the length of road segments has different impacts on UAV route planning.展开更多
In this paper,two lifting mechanism models with opposing placements,which use the same hydraulic hoist model and have the same angle of 50°,have been developed.The mechanical and hydraulic simulation models are e...In this paper,two lifting mechanism models with opposing placements,which use the same hydraulic hoist model and have the same angle of 50°,have been developed.The mechanical and hydraulic simulation models are established using MATLAB Simscape to analyze their kinetics and dynamics in the lifting and holding stages.The simulation findings are compared to the analytical calculation results in the steady state,and both methods show good agreement.In the early lifting stage,Model 1 produces greater force and discharges goods in the container faster than Model 2.Meanwhile,Model 2 reaches a higher force and ejects goods from the container cleaner than its counterpart at the end lifting stage.The established simulation models can consider the effects of dynamic loads due to inertial moments and forces generated during the system operation.It is crucial in studying,designing,and optimizing the structure of hydraulic-mechanical systems.展开更多
We use multifractal detrended fluctuation analysis （MF-DFA） method to investigate the multifractal behavior of the interevent time series in a modified Olami-Feder-Christensen （OFC） earthquake model on assortative...We use multifractal detrended fluctuation analysis （MF-DFA） method to investigate the multifractal behavior of the interevent time series in a modified Olami-Feder-Christensen （OFC） earthquake model on assortative scale-free networks. We determine generalized Hurst exponent and singularity spectrum and find that these fluctuations have multifraetal nature. Comparing the MF-DFA results for the original interevent time series with those for shuffled and surrogate series, we conclude that the origin of multifractality is due to both the broadness of probability density function and long-range correlation.展开更多
A gradient nanostructured layer was fabricated on the surface of TA15(Ti-6Al-2Zr-1Mo-1V)alloy(produced by selective laser melting)using severe shot peening(SSP).This study focuses on the evolution of the microstructur...A gradient nanostructured layer was fabricated on the surface of TA15(Ti-6Al-2Zr-1Mo-1V)alloy(produced by selective laser melting)using severe shot peening(SSP).This study focuses on the evolution of the microstructure and the mechanism of grain refinement in TA15 titanium alloy during SSP treatment.Transmission electron microscopyand Rietveld refinement methods were employed.The residual stress and microhardness variations with depth were also characterized.The results show:(1)At the initial stage of deformation,plastic deformation is primarily accommodated through twinning and dislocation slip.(2)As the strain increases,twinning disappears,and dislocations interact to form tangles.Some dislocations annihilate and rearrange into subgrain boundaries,subdividing the original grains into subgrains.(3)With continued dislocation activity,the subgrain size decreases until nanocrystals are formed through the dynamic rotational recrystallization.SSP introduced compressive residual stress(CRS)in the near-surface layer of the material,with the maximum CRS of approximately−1141 MPa observed in the subsurface layer.It also induced work hardening,increasing the surface hardness to approximately 479 HV.However,the surface roughness increases,leading to a slight deterioration in surface quality.展开更多
Utilizing unmanned aerial vehicle (UAV) photography to timely detect and evaluate potential safety hazards (PSHs) around high-speed rail has great potential to complement and reform the existing manual inspections by ...Utilizing unmanned aerial vehicle (UAV) photography to timely detect and evaluate potential safety hazards (PSHs) around high-speed rail has great potential to complement and reform the existing manual inspections by providing better overhead views and mitigating safety issues. However, UAV inspections based on manual interpretation, which heavily rely on the experience, attention, and judgment of human inspectors, still inevitably suffer from subjectivity and inaccuracy. To address this issue, this study proposes a lightweight hybrid learning algorithm named HDTA (hybrid dual tasks architecture) to automatically and efficiently detect the PSHs of UAV imagery. First, this HDTA architecture seamlessly integrates both detection and segmentation branches within a unified framework. This design enables the model to simultaneously perform PSH detection and railroad parsing, thereby providing comprehensive scene understanding. Such joint learning also lays the foundation for PSH assessment tasks. Second, an innovative lightweight backbone based on the shuffle selective state space model (S^(4)M) is incorporated into HDTA. The state space model approach allows for global contextual information extraction while maintaining linear computational complexity. Furthermore, the incorporation of shuffle operation facilitates more efficient information flow across feature dimensions, enhancing both feature representation and fusion capabilities. Finally, extensive experiments conducted on a railroad environment dataset constructed from UAV imagery demonstrate that the proposed method achieves high detection accuracy while maintaining efficiency and practicality.展开更多
For internal combustion engines,engines installed for transport ships,cargo ships,and fishing vessels are mainly diesel engines.The number of engines is increasing due to the development of the maritime and seafood ex...For internal combustion engines,engines installed for transport ships,cargo ships,and fishing vessels are mainly diesel engines.The number of engines is increasing due to the development of the maritime and seafood exploitation sectors.Therefore,the high demand for petroleum fuels increases environmental pollution due to engine emissions.Reducing environmental pollution from the combustion of petroleum fuels has become a concern worldwide,especially for internal combustion engines.The exhaust gases from the engine contain harmful substances such as soot and nitrogen oxides(NO_(x)).Fuels with higher carbon content generate more soot when burned.In contrast,biofuels have low carbon and sulfur content and supply ample oxygen,which helps to reduce soot formation.For these reasons,biofuels are encouraged as alternative fuels to petroleum.Vegetable oil is one of the primary raw materials for biofuel production.This study presents a mixture of diesel and vegetable oil utilized as fuel for fishing vessels’diesel engines.The results of experimental research on a fishing vessel’s 4CHE Yanmar diesel engine when using diesel fuel mixed with coconut oil(B15,15%coconut oil,and 85%diesel)show that increasing B15 fuel injection pressure by about 10–15%compared with diesel fuel injection pressure reduces the engine’s soot emissions and increases power compared to unadjusted.This solution contributes to reducing environmental pollution from engine emissions.展开更多
Permanent magnet synchronous motors(PMSMs)are typical electromechanical energy-conversion systems,in which the electrical and mechanical subsystems interact and impact each other.However,existing studies have investig...Permanent magnet synchronous motors(PMSMs)are typical electromechanical energy-conversion systems,in which the electrical and mechanical subsystems interact and impact each other.However,existing studies have investigated these two subsystems independently and failed to determine the coupling effect between electrical signals and mechanical vibrations.To address these gaps,a comprehensive electromechanical coupled model is proposed herein.This model integrates the PMSM model based on the winding function and the rotor-bearing dynamics model.The developed model can take into account the variations in inductance and current caused by non-uniform air-gap distribution.The electromechanical dynamic responses of the PMSM under rotor-bearing vibration and rotor eccentricity conditions are systematically analysed using this model.Results demonstrate that the proposed model improved the accuracy of both internal and external excitation representation in PMSMs compared with the conventional models.The dynamic behaviour of the rotor-bearing system is distinctly reflected in the electrical signals,and the variation laws of rotor eccentric distance and eccentric angle on the dynamic characteristics of the PMSM are revealed.The proposed model provides theoretical support for investigating the electromechanical coupled effect in PMSMs and offers an effective approach for state detection and fault diagnosis of motor-driven systems.展开更多
As the demand for bike-sharing has been increasing,the oversupply problem of bike-sharing has occurred,which leads to the waste of resources and disturbance of the urban environment.In order to regulate the supply vol...As the demand for bike-sharing has been increasing,the oversupply problem of bike-sharing has occurred,which leads to the waste of resources and disturbance of the urban environment.In order to regulate the supply volume of bike-sharing reasonably,an estimating model was proposed to quantify the urban carrying capacity(UCC)for bike-sharing through the demand data.In this way,the maximum supply volume of bike-sharing that a city can accommodate can be obtained.The UCC on bike-sharing is reflected in the road network carrying capacity(RNCC)and parking facilities’carrying capacity(PFCC).The space-time consumption method and density-based spatial clustering of application with noise(DBSCAN)algorithm were used to explore the RNCC and PFCC for bike-sharing.Combined with the users’demand,the urban load ratio on bike-sharing can be evaluated to judge whether the UCC can meet users’demand,so that the supply volume of bike-sharing and distribution of the related facilities can be adjusted accordingly.The application of the model was carried out by estimating the UCC and load ratio of each traffic analysis zone in Nanjing,China.Compared with the field survey data,the effect of the proposed algorithm was verified.展开更多
Delay is one of the most crucial factors for both pedestrians and car drivers around pedestrian crossings.Drivers often do not yield to pedestrians,which may result in both delay and impatient pedestrian behaviour.Thi...Delay is one of the most crucial factors for both pedestrians and car drivers around pedestrian crossings.Drivers often do not yield to pedestrians,which may result in both delay and impatient pedestrian behaviour.This tendency may alter after introducing autonomous vehicles as the vehicles will follow the traffic rules in all cases.This study aims to estimate the delay time alteration at a simple zebra crossing using on-site measures and simulation.Roadside video recordings were carried out in Budapest,Hungary,to obtain the crossing decisions of pedestrian groups based on the approaching vehicle distance.We have determined the accepted vehicle distance vales for pedestrian groups that served as input data for microsimulation modelling.The novelty of the study is that the simulation involved autonomous vehicles that hold preset headways from the leading vehicle.The simulation was designed based on the traffic share of autonomous vehicles and the headways they kept.The main findings are that the travel time and stopping time for cars are higher if the modal share of autonomous vehicles is high.For pedestrians,however,we found a slight decrease in both travel times and stopping times.Moreover,we have proposed modifications to the simulation software(Vissim)to handle distancedependent pedestrian decisions and drivers'failure to give priority.The results can be useful for road operators to estimate the road capacity in the era of autonomous vehicles and for software developers to formulate the simulated and real driving mechanism for autonomous vehicles.展开更多
Traffic accident severity prediction is essential for dynamic traffic safety management.To explore the factors influencing the severity of traffic accidents on mountain freeways and to predict the severity of traffic ...Traffic accident severity prediction is essential for dynamic traffic safety management.To explore the factors influencing the severity of traffic accidents on mountain freeways and to predict the severity of traffic accidents,four models based on machine learning algorithms are constructed using support vector machine(SVM),decision tree classifier(DTC),Ada_SVM and Ada_DTC.In addition,random forest(RF)is used to calculate the importance degree of variables and the accident severity influences with high importance levels form the RF dataset.The results show that rainfall intensity,collision type,number of vehicles involved in the accident and toad section type are important variables influencing accident severity.The RF feature selection method improves the classification performance of four machine leaming algorithms,resulting in a 9.3%,5.5%,7.2% and 3.6% improvement in prediction accuracy for SVM,DTC,Ada_SVM and Ada_DTC,respectively.The combination of the Ada_SVM integrated algorithm and RF feature selection method has the best prediction performance,and it achieves 78.9% and 88.4% prediction precision and accuracy,respectively.展开更多
This paper presents studies of spray characteristics and controlling mechanism of fuel containing CO2. Using diesel fuel containing CO2 gas, experiments were conducted on diesel hole-type nozzles and simple nozzles. T...This paper presents studies of spray characteristics and controlling mechanism of fuel containing CO2. Using diesel fuel containing CO2 gas, experiments were conducted on diesel hole-type nozzles and simple nozzles. The steady spray and transient spray characteristics were observed and measured by instantaneous shadowgraphy, high-speed photography, phase Doppler anemometry （PDA） and LDSA respectively. The effects of CO2 concentration in the fuel, the injection pressure, the nozzle L/D ratio, surrounding gas pressure and temperature on the atomization behavior and spray pattern were evaluated. The results show that the injection of fuel containing CO2 can greatly improve the atomization and produce a parabolic-shaped spray; and the CO2 gas concentration, surrounding gas pressure, temperature and nozzle config- uration have dominant influences on spray characteristics of the fuel containing CO2. New insight into the controlling mechanism of atomization of the fuel containing CO2 was provided.展开更多
Driving for a long time can lead to fatigue,which can affect information processing and decision-making,potentially threatening driving safety.By exploring the generation and variation patterns of driving fatigue and ...Driving for a long time can lead to fatigue,which can affect information processing and decision-making,potentially threatening driving safety.By exploring the generation and variation patterns of driving fatigue and clarifying the application status of its detection and warning technology,traffic safety can be improved.In order to understand the research progress of driving fatigue,based on the Web of Science core database,this paper obtained 2127 English literature published from 1998 to 2023(as of May 12,2023),covering a total of 5963 authors and 6019 keywords,and the bibliometric tool VOSviewer and R-Bibliometrix are used to analyze the literatures.Firstly,the literature statistical analysis is carried out from annual distribution,country distribution,source journals,and core authors.Secondly,the research hotspots and trends are analyzed from keywords co-occurrence,high-impact literature,theme evolution,and development trends.Finally,the development trend of driving fatigue research is discussed.The result shows that the hotspots in the field of driving fatigue include driving fatigue detection,fatigue driving risk and fatigue research of professional drivers,driver alertness and coping strategies for driver fatigue,and driver fatigue under automatic driving.And the improvement of the accuracy and reliability of driving fatigue detection will be the difficult point to break through in the future.It is necessary to build a multi-disciplinary and meticulously quantified driving fatigue and its risk assessment system,and strengthen the control of fatigue risks.Research on intervention measures and education training for professional drivers needs to be deepened,while exploring the acceptance and compliance of intervention measures.Moreover,the evolution law and prevention strategies of driver fatigue under automatic conditions should be the focus of future research.展开更多
Smart mobility solutions are trending in the mobility domain realized through pilot activities and commercial solutions,but there is a lack of a broad framework defining the readiness to introduce such mobility soluti...Smart mobility solutions are trending in the mobility domain realized through pilot activities and commercial solutions,but there is a lack of a broad framework defining the readiness to introduce such mobility solutions in a specific area.In this research,smart mobility solutions are examined in the perspective of the Mobility-as-a-Service(MaaS)scheme that is an adequate representation of the maturity of a region regarding smart mobility solutions including technology,business,and coopetition aspects.These three aspects define the feature selection,whereas surveys are used to collect input data from local experts(LEs).For weighting the features,the analytic hierarchy process(AHP)is used with a modified interpretive structural modeling(ISM).With this modification,an expert-friendly process is developed without affecting the results.The elaborated MaaS readiness index(MRI)is applied to six regions with different types of mobility related pilot activities to demonstrate the MRI as a comparison tool between regions and between ex-ante and ex-post pilot activities.The developed interpretive structural modeling with Graph(ISM-G)methodology requites remarkably less work from the evaluators compared to the ISM,while no important difference appeared in the results.The MRI can support smart mobility related pilot evaluations,whereas the ISM-G can be used widely in decision-making.展开更多
Rapid advancements in automated vehicles(AVs)technology have transformed the measurability,controllability and unpredictability of transportation systems.Evaluating driving risk and effectively managing driving behavi...Rapid advancements in automated vehicles(AVs)technology have transformed the measurability,controllability and unpredictability of transportation systems.Evaluating driving risk and effectively managing driving behaviours is critical.AVs should be enabled to identify,analyse,evaluate and devise effective countermeasures for driving risk by autonomously learning from human driving experiences.This learning will enhance the interactive decision-making capabilities and achieve driving behaviours that reflect human-like logic.The primary challenge lies in integrating human-like logic and driving risk constraints based on behavioural decision-making outcomes.This integration is crucial to align the AVs’cognitive levels-movement,comprehension,memory and inference-more closely with human driving necessities,habits and styles.Such alignment holds the potential to improve the prediction and planning of future actions and facilitate the development of motion planning schemes geared towards minimizing driving risk.We performed a comprehensive review of AVs’behavioural decision-making and intelligent motion-planning research from 2000 to 2023 from four key perspectives-driving risk,human-like logic,behavioural decision making and motion planning.Based on the Web of Science and China National Knowledge Internet database,the results of our review indicate that significant progress has been made in AV behavioural decision making and intelligent motion planning over time.When AVs and human-driven vehicles coexist,greater incorporation of human-like logic is required.Guided by these findings,we delineate future development directions and propose a research paradigm for human-like logic and a research framework for human-like logic-driven behavioural decision making and intelligent motion planning of AVs.展开更多
The carbon neutrality of existing internal combustion engines can be significantly enhanced through the use of sustainable e-fuels;thus,their price has to be reduced.Artificial intelligence(AI)offers a promising pathw...The carbon neutrality of existing internal combustion engines can be significantly enhanced through the use of sustainable e-fuels;thus,their price has to be reduced.Artificial intelligence(AI)offers a promising pathway to streamline and accelerate fuel development by enabling faster and more efficient model creation compared to conventional physicochemical simulations.Despite the apparent advantages,state-of-the-art research typically limits the application of AI to basic predictions within narrow operating ranges.This study introduces a novel AIbased fuel design tool capable of accurately predicting detailed engine performance across a broad range of operating conditions,using comprehensive physicochemical fuel properties as input.The proposed approach provides greater detail and precision than existing state-of-the-art methods.Building on a cost-efficient AI development strategy established in our previous work,the tool was constructed using 17 single-output multi-layer perceptron networks.The tool was validated using engine dynamometer measurements with various test fuels,and then it was applied to a fuel optimization task to demonstrate its effectiveness.The results indicate that the tool’s predictions closely match actual engine performance.Specifically,10 out of the 17 models achieved a mean absolute percentage error of<3%.In the optimization scenario,the optimized fuel had a predicted engine operating score of 40.51%,while the actual score was 41.3%,demonstrating the tool’s potential for accurate fuel design.Thus,this novel approach can support the development of low-cost e-fuels,enabling economically viable,carbon-neutral mobility across a wide range of transport applications.展开更多
The yellow light dilemma zone is widely known as an area on the high-speed intersection ap- proach, where vehicles neither safely stop before the stop line nor proceed through the intersection dur- ing amber interval....The yellow light dilemma zone is widely known as an area on the high-speed intersection ap- proach, where vehicles neither safely stop before the stop line nor proceed through the intersection dur- ing amber interval. Within such an area, a vehicle might be involved in a right-angle crash or rear-end collision. This issue has been extensively discussed over five decades in traffic engineering field, cov- eting from theory to practice. However, few comprehensive review literatures on the amber signal di- lemma zone problems can be found. The objective of this paper is to summarize the evolution of yellow light dilemma zone researches. Basic definition and boundary of dilemma zone followed by driver be- havior and dilemma zone hazard measurement are depicted. At last, the future directions of yellow light dilemma zone research are discussed.展开更多
Freeway logistics plays a pivotal role in economic development.Although the rapid development in big data and artificial inteligence motivates long+haul freeway logistics towards informatization and intellectualizatio...Freeway logistics plays a pivotal role in economic development.Although the rapid development in big data and artificial inteligence motivates long+haul freeway logistics towards informatization and intellectualization,the transportation of bulk commodities still faces serious challenges arisen from dispersed freight demands and the lack of co-ordination among different operators.The present study thereby proposed inteligent algorithms for truck dispatching for freeway logistics.Specifically,our contributions include the establishment of mathematical models for full-truckload(FTL)and less-than-truckload(LTL)transportation modes,respectively,and the introduction of reinforcement learning with deep Q-networks tailored for each transportation mode to improve the decision-making in order acceptance and truck repositioning.Simulation experiments based on the realworld freeway logistics data collected in Guiyang,China show that our algorithms improved operational profitability substantially with a 76%and 30%revenue increase for FTL and LTL modes,respectively,compared with single-stage optimization.These results demonstrate the potential of reinforcement learning in revolutionizing freeway logistics and should lay a foundation for future research in intelligent logistics systems.展开更多
Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,exclusively.However,the n...Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,exclusively.However,the number of vehicles participating in the traffic-in other words,the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management-is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network level.To tackle this gap,an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment procedure.By optimally scaling the Origin-Destination matrices of the sample fleet,an appropriate model can be approximated to provide traffic flow data beside average speeds.The iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic model.The method has been tested through two different real-world traffic networks,justifying the viability of the proposed method.Overall,the contribution of the study is a practical solution based on commonly available fleet traffic data,suggested for practitioners in traffic planning and management.展开更多
文摘Limiting environmental pollution from exhaust emissions from internal combustion engines includes many measures,including encouraging biofuel use because biofuel is environmentally friendly and renewable.A mixture of diesel fuel and vegetable oil is a form of biofuel.However,some properties of the mixed fuel,such as viscosity and density,are higher than those of traditional diesel fuel,affecting the injection and combustion process and reducing power and non-optimal toxic emissions,especially soot emissions.This study uses Kiva-3V software to simulate the combustion process of a diesel-vegetable oil mixture in the combustion chamber of a fishing vessel diesel engine with changes in fuel injection timing.The results show that when increasing the fuel injection timing of a diesel-vegetable oil mixture about 1–2 degrees of crankshaft rotation angle before the top dead center compared to diesel fuel injection timing,the engine power increases,and soot emissions decrease compared to no adjustment.The above simulation research results will help orient the experiments conveniently and reduce costs in the future experimental research process to quantify the fuel system adjustment of fishing vessels’diesel engines when using biofuels,including diesel-vegetable oil mixtures.Thus,the engine’s economic indicators will improve,and emissions that pollute the environment will be limited.
基金supported by the National Natural Science Foundation of China(grant number 12302048,received by author Yan Xu)Yunnan fundamental research projects(grant No.202501AT070321,received by author Yan Xu).
文摘The emissions from traditional fossil heavy-duty trucks have become a conspicuous issue worldwide.The electrical road system(ERS)can offer a viable solution for achieving zero CO_(2) emissions and has high energy efficiency in long-distance road cargo transport.While many kinds of pantograph structures have been developed for the ERS,their corresponding pantograph-catenary dynamic characteristics under different road conditions have not been investigated.This work performs a numerical study on the dynamics of the pantograph-catenary interaction of an ERS considering different pantograph structures.First,a pantograph-catenary-truck-road model is proposed.The reduced catenary model and reduced-plate model transmission method are used to minimize model scale.Three different types of ERS pantograph structures are considered in the model.After validation,the pantograph-catenary dynamics under the influence of truck-road interactions with complex road roughness and different pantographs are studied and compared.The corresponding vibration transmission mechanism is further focused.The results show that the truck-road interaction has a significant effect on the pantograph-catenary interaction,but the pantograph with only one lower and upper armcan isolate the roll vibrationmotion transmission fromthe truck to the collector head,which has the best dynamic performance and is suggested for use in the ERS.
基金Project(2009AA11Z220)supported by National High Technology Research and Development Program of ChinaProjects(61070112,61070116)supported by the National Natural Science Foundation of China+1 种基金Project(2012LLYJTJSJ077)supported by the Ministry of Public Security of ChinaProject(KYQD14003)supported by Tianjin University of Technology and Education,China
文摘Unmanned aerial vehicle(UAV)was introduced to take road segment traffic surveillance.Considering the limited UAV maximum flight distance,UAV route planning problem was studied.First,a multi-objective optimization model of planning UAV route for road segment surveillance was proposed,which aimed to minimize UAV cruise distance and minimize the number of UAVs used.Then,an evolutionary algorithm based on Pareto optimality technique was proposed to solve multi-objective UAV route planning problem.At last,a UAV flight experiment was conducted to test UAV route planning effect,and a case with three scenarios was studied to analyze the impact of different road segment lengths on UAV route planning.The case results show that the optimized cruise distance and the number of UAVs used decrease by an average of 38.43% and 33.33%,respectively.Additionally,shortening or extending the length of road segments has different impacts on UAV route planning.
基金Ho Chi Minh City University of Technology(HCMUT)Vietnam National University Ho Chi Minh City(VNU-HCM)for supporting this study。
文摘In this paper,two lifting mechanism models with opposing placements,which use the same hydraulic hoist model and have the same angle of 50°,have been developed.The mechanical and hydraulic simulation models are established using MATLAB Simscape to analyze their kinetics and dynamics in the lifting and holding stages.The simulation findings are compared to the analytical calculation results in the steady state,and both methods show good agreement.In the early lifting stage,Model 1 produces greater force and discharges goods in the container faster than Model 2.Meanwhile,Model 2 reaches a higher force and ejects goods from the container cleaner than its counterpart at the end lifting stage.The established simulation models can consider the effects of dynamic loads due to inertial moments and forces generated during the system operation.It is crucial in studying,designing,and optimizing the structure of hydraulic-mechanical systems.
基金Supported by Foundation for Outstanding Young and Middle-aged Scientists in Shandong Province under Grant No.BS2011HZ019State Key Laboratory of Data Analysis and Applications,State Oceanic Administration under Grant No.LDAA-2011-02the Fundamental Research Funds for the Central Universities under Grant No.201113006
文摘We use multifractal detrended fluctuation analysis （MF-DFA） method to investigate the multifractal behavior of the interevent time series in a modified Olami-Feder-Christensen （OFC） earthquake model on assortative scale-free networks. We determine generalized Hurst exponent and singularity spectrum and find that these fluctuations have multifraetal nature. Comparing the MF-DFA results for the original interevent time series with those for shuffled and surrogate series, we conclude that the origin of multifractality is due to both the broadness of probability density function and long-range correlation.
基金financially supported by the National Natural Science Foundation of China(No.12262014).
文摘A gradient nanostructured layer was fabricated on the surface of TA15(Ti-6Al-2Zr-1Mo-1V)alloy(produced by selective laser melting)using severe shot peening(SSP).This study focuses on the evolution of the microstructure and the mechanism of grain refinement in TA15 titanium alloy during SSP treatment.Transmission electron microscopyand Rietveld refinement methods were employed.The residual stress and microhardness variations with depth were also characterized.The results show:(1)At the initial stage of deformation,plastic deformation is primarily accommodated through twinning and dislocation slip.(2)As the strain increases,twinning disappears,and dislocations interact to form tangles.Some dislocations annihilate and rearrange into subgrain boundaries,subdividing the original grains into subgrains.(3)With continued dislocation activity,the subgrain size decreases until nanocrystals are formed through the dynamic rotational recrystallization.SSP introduced compressive residual stress(CRS)in the near-surface layer of the material,with the maximum CRS of approximately−1141 MPa observed in the subsurface layer.It also induced work hardening,increasing the surface hardness to approximately 479 HV.However,the surface roughness increases,leading to a slight deterioration in surface quality.
基金supported in part by the National Natural Science Foundation of China(grantNo.52362048)in part by Yunnan Fundamental Research Projects(grantNo.202301BE070001-042 and grant No.202401AT070409).
文摘Utilizing unmanned aerial vehicle (UAV) photography to timely detect and evaluate potential safety hazards (PSHs) around high-speed rail has great potential to complement and reform the existing manual inspections by providing better overhead views and mitigating safety issues. However, UAV inspections based on manual interpretation, which heavily rely on the experience, attention, and judgment of human inspectors, still inevitably suffer from subjectivity and inaccuracy. To address this issue, this study proposes a lightweight hybrid learning algorithm named HDTA (hybrid dual tasks architecture) to automatically and efficiently detect the PSHs of UAV imagery. First, this HDTA architecture seamlessly integrates both detection and segmentation branches within a unified framework. This design enables the model to simultaneously perform PSH detection and railroad parsing, thereby providing comprehensive scene understanding. Such joint learning also lays the foundation for PSH assessment tasks. Second, an innovative lightweight backbone based on the shuffle selective state space model (S^(4)M) is incorporated into HDTA. The state space model approach allows for global contextual information extraction while maintaining linear computational complexity. Furthermore, the incorporation of shuffle operation facilitates more efficient information flow across feature dimensions, enhancing both feature representation and fusion capabilities. Finally, extensive experiments conducted on a railroad environment dataset constructed from UAV imagery demonstrate that the proposed method achieves high detection accuracy while maintaining efficiency and practicality.
文摘For internal combustion engines,engines installed for transport ships,cargo ships,and fishing vessels are mainly diesel engines.The number of engines is increasing due to the development of the maritime and seafood exploitation sectors.Therefore,the high demand for petroleum fuels increases environmental pollution due to engine emissions.Reducing environmental pollution from the combustion of petroleum fuels has become a concern worldwide,especially for internal combustion engines.The exhaust gases from the engine contain harmful substances such as soot and nitrogen oxides(NO_(x)).Fuels with higher carbon content generate more soot when burned.In contrast,biofuels have low carbon and sulfur content and supply ample oxygen,which helps to reduce soot formation.For these reasons,biofuels are encouraged as alternative fuels to petroleum.Vegetable oil is one of the primary raw materials for biofuel production.This study presents a mixture of diesel and vegetable oil utilized as fuel for fishing vessels’diesel engines.The results of experimental research on a fishing vessel’s 4CHE Yanmar diesel engine when using diesel fuel mixed with coconut oil(B15,15%coconut oil,and 85%diesel)show that increasing B15 fuel injection pressure by about 10–15%compared with diesel fuel injection pressure reduces the engine’s soot emissions and increases power compared to unadjusted.This solution contributes to reducing environmental pollution from engine emissions.
基金supported by the National Natural Science Foundation of China(Grant Nos.52275132,52388102)the National Key R&D Program of China(Grant No.2022YFB3402100)the Sichuan Science and Technology Program(Grant No.2024NSFTD0011)。
文摘Permanent magnet synchronous motors(PMSMs)are typical electromechanical energy-conversion systems,in which the electrical and mechanical subsystems interact and impact each other.However,existing studies have investigated these two subsystems independently and failed to determine the coupling effect between electrical signals and mechanical vibrations.To address these gaps,a comprehensive electromechanical coupled model is proposed herein.This model integrates the PMSM model based on the winding function and the rotor-bearing dynamics model.The developed model can take into account the variations in inductance and current caused by non-uniform air-gap distribution.The electromechanical dynamic responses of the PMSM under rotor-bearing vibration and rotor eccentricity conditions are systematically analysed using this model.Results demonstrate that the proposed model improved the accuracy of both internal and external excitation representation in PMSMs compared with the conventional models.The dynamic behaviour of the rotor-bearing system is distinctly reflected in the electrical signals,and the variation laws of rotor eccentric distance and eccentric angle on the dynamic characteristics of the PMSM are revealed.The proposed model provides theoretical support for investigating the electromechanical coupled effect in PMSMs and offers an effective approach for state detection and fault diagnosis of motor-driven systems.
基金Project(2018YFE0120100)supported by the National Key R&D Program of ChinaProject(YBPY2040)supported by the Scientific Research Foundation of Graduate School of Southeast University,China。
文摘As the demand for bike-sharing has been increasing,the oversupply problem of bike-sharing has occurred,which leads to the waste of resources and disturbance of the urban environment.In order to regulate the supply volume of bike-sharing reasonably,an estimating model was proposed to quantify the urban carrying capacity(UCC)for bike-sharing through the demand data.In this way,the maximum supply volume of bike-sharing that a city can accommodate can be obtained.The UCC on bike-sharing is reflected in the road network carrying capacity(RNCC)and parking facilities’carrying capacity(PFCC).The space-time consumption method and density-based spatial clustering of application with noise(DBSCAN)algorithm were used to explore the RNCC and PFCC for bike-sharing.Combined with the users’demand,the urban load ratio on bike-sharing can be evaluated to judge whether the UCC can meet users’demand,so that the supply volume of bike-sharing and distribution of the related facilities can be adjusted accordingly.The application of the model was carried out by estimating the UCC and load ratio of each traffic analysis zone in Nanjing,China.Compared with the field survey data,the effect of the proposed algorithm was verified.
基金the Hungarian Academy of Science for awarding him the Bolyai János Research Scholarship(BO/00393/22)。
文摘Delay is one of the most crucial factors for both pedestrians and car drivers around pedestrian crossings.Drivers often do not yield to pedestrians,which may result in both delay and impatient pedestrian behaviour.This tendency may alter after introducing autonomous vehicles as the vehicles will follow the traffic rules in all cases.This study aims to estimate the delay time alteration at a simple zebra crossing using on-site measures and simulation.Roadside video recordings were carried out in Budapest,Hungary,to obtain the crossing decisions of pedestrian groups based on the approaching vehicle distance.We have determined the accepted vehicle distance vales for pedestrian groups that served as input data for microsimulation modelling.The novelty of the study is that the simulation involved autonomous vehicles that hold preset headways from the leading vehicle.The simulation was designed based on the traffic share of autonomous vehicles and the headways they kept.The main findings are that the travel time and stopping time for cars are higher if the modal share of autonomous vehicles is high.For pedestrians,however,we found a slight decrease in both travel times and stopping times.Moreover,we have proposed modifications to the simulation software(Vissim)to handle distancedependent pedestrian decisions and drivers'failure to give priority.The results can be useful for road operators to estimate the road capacity in the era of autonomous vehicles and for software developers to formulate the simulated and real driving mechanism for autonomous vehicles.
基金supported by the Science and Technology Innovation programme of the Department of Transportation,Yunnan Province,China(Grants No.2019303 and[2020]75)the general programme of key science and technology in transportation,the Ministry of Transport,China(Grants No.2018-MS4-102 and 2021-TG-005)the research fund of the Nanjing Joint Institute for Atmospheric Sciences(Grant No.BJG202101).
文摘Traffic accident severity prediction is essential for dynamic traffic safety management.To explore the factors influencing the severity of traffic accidents on mountain freeways and to predict the severity of traffic accidents,four models based on machine learning algorithms are constructed using support vector machine(SVM),decision tree classifier(DTC),Ada_SVM and Ada_DTC.In addition,random forest(RF)is used to calculate the importance degree of variables and the accident severity influences with high importance levels form the RF dataset.The results show that rainfall intensity,collision type,number of vehicles involved in the accident and toad section type are important variables influencing accident severity.The RF feature selection method improves the classification performance of four machine leaming algorithms,resulting in a 9.3%,5.5%,7.2% and 3.6% improvement in prediction accuracy for SVM,DTC,Ada_SVM and Ada_DTC,respectively.The combination of the Ada_SVM integrated algorithm and RF feature selection method has the best prediction performance,and it achieves 78.9% and 88.4% prediction precision and accuracy,respectively.
文摘This paper presents studies of spray characteristics and controlling mechanism of fuel containing CO2. Using diesel fuel containing CO2 gas, experiments were conducted on diesel hole-type nozzles and simple nozzles. The steady spray and transient spray characteristics were observed and measured by instantaneous shadowgraphy, high-speed photography, phase Doppler anemometry （PDA） and LDSA respectively. The effects of CO2 concentration in the fuel, the injection pressure, the nozzle L/D ratio, surrounding gas pressure and temperature on the atomization behavior and spray pattern were evaluated. The results show that the injection of fuel containing CO2 can greatly improve the atomization and produce a parabolic-shaped spray; and the CO2 gas concentration, surrounding gas pressure, temperature and nozzle config- uration have dominant influences on spray characteristics of the fuel containing CO2. New insight into the controlling mechanism of atomization of the fuel containing CO2 was provided.
基金supported by the National Natural Science Foundation of China(Grant No.71961012)the Yunnan Provincial Department of Education Science Research Fund Project(Grant No.2024Y132)the Analysis and Testing Foundation of Kunming University of Science and Technology(Grant No.2022M20202106029).
文摘Driving for a long time can lead to fatigue,which can affect information processing and decision-making,potentially threatening driving safety.By exploring the generation and variation patterns of driving fatigue and clarifying the application status of its detection and warning technology,traffic safety can be improved.In order to understand the research progress of driving fatigue,based on the Web of Science core database,this paper obtained 2127 English literature published from 1998 to 2023(as of May 12,2023),covering a total of 5963 authors and 6019 keywords,and the bibliometric tool VOSviewer and R-Bibliometrix are used to analyze the literatures.Firstly,the literature statistical analysis is carried out from annual distribution,country distribution,source journals,and core authors.Secondly,the research hotspots and trends are analyzed from keywords co-occurrence,high-impact literature,theme evolution,and development trends.Finally,the development trend of driving fatigue research is discussed.The result shows that the hotspots in the field of driving fatigue include driving fatigue detection,fatigue driving risk and fatigue research of professional drivers,driver alertness and coping strategies for driver fatigue,and driver fatigue under automatic driving.And the improvement of the accuracy and reliability of driving fatigue detection will be the difficult point to break through in the future.It is necessary to build a multi-disciplinary and meticulously quantified driving fatigue and its risk assessment system,and strengthen the control of fatigue risks.Research on intervention measures and education training for professional drivers needs to be deepened,while exploring the acceptance and compliance of intervention measures.Moreover,the evolution law and prevention strategies of driver fatigue under automatic conditions should be the focus of future research.
基金European Union under the INTERREG Central Europe Programme.Project No.TKP2021-NVA-02 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research,Development and Innovation Fund,financed under the TKP2021-NVA funding scheme.
文摘Smart mobility solutions are trending in the mobility domain realized through pilot activities and commercial solutions,but there is a lack of a broad framework defining the readiness to introduce such mobility solutions in a specific area.In this research,smart mobility solutions are examined in the perspective of the Mobility-as-a-Service(MaaS)scheme that is an adequate representation of the maturity of a region regarding smart mobility solutions including technology,business,and coopetition aspects.These three aspects define the feature selection,whereas surveys are used to collect input data from local experts(LEs).For weighting the features,the analytic hierarchy process(AHP)is used with a modified interpretive structural modeling(ISM).With this modification,an expert-friendly process is developed without affecting the results.The elaborated MaaS readiness index(MRI)is applied to six regions with different types of mobility related pilot activities to demonstrate the MRI as a comparison tool between regions and between ex-ante and ex-post pilot activities.The developed interpretive structural modeling with Graph(ISM-G)methodology requites remarkably less work from the evaluators compared to the ISM,while no important difference appeared in the results.The MRI can support smart mobility related pilot evaluations,whereas the ISM-G can be used widely in decision-making.
基金support of the National Natural Science Foundation of China(Grant No.72261021)the Science and Technology R&D Project of Yunnan Communications Investment&Construction Group CO,LTD.(Grant No.YCIC-YF-2021-05)the Research Project on Key Technologies for Road Safety and Disaster Mitigation and Prevention,Department of Transport of Yunnan Province,China(Grant No.SZKM202231022).
文摘Rapid advancements in automated vehicles(AVs)technology have transformed the measurability,controllability and unpredictability of transportation systems.Evaluating driving risk and effectively managing driving behaviours is critical.AVs should be enabled to identify,analyse,evaluate and devise effective countermeasures for driving risk by autonomously learning from human driving experiences.This learning will enhance the interactive decision-making capabilities and achieve driving behaviours that reflect human-like logic.The primary challenge lies in integrating human-like logic and driving risk constraints based on behavioural decision-making outcomes.This integration is crucial to align the AVs’cognitive levels-movement,comprehension,memory and inference-more closely with human driving necessities,habits and styles.Such alignment holds the potential to improve the prediction and planning of future actions and facilitate the development of motion planning schemes geared towards minimizing driving risk.We performed a comprehensive review of AVs’behavioural decision-making and intelligent motion-planning research from 2000 to 2023 from four key perspectives-driving risk,human-like logic,behavioural decision making and motion planning.Based on the Web of Science and China National Knowledge Internet database,the results of our review indicate that significant progress has been made in AV behavioural decision making and intelligent motion planning over time.When AVs and human-driven vehicles coexist,greater incorporation of human-like logic is required.Guided by these findings,we delineate future development directions and propose a research paradigm for human-like logic and a research framework for human-like logic-driven behavioural decision making and intelligent motion planning of AVs.
基金supported by the European Union within the framework of the National Laboratory for Autonomous Systems(RRF-2.3.1-21-2022-00002)supported by AVL Hungary Kft.
文摘The carbon neutrality of existing internal combustion engines can be significantly enhanced through the use of sustainable e-fuels;thus,their price has to be reduced.Artificial intelligence(AI)offers a promising pathway to streamline and accelerate fuel development by enabling faster and more efficient model creation compared to conventional physicochemical simulations.Despite the apparent advantages,state-of-the-art research typically limits the application of AI to basic predictions within narrow operating ranges.This study introduces a novel AIbased fuel design tool capable of accurately predicting detailed engine performance across a broad range of operating conditions,using comprehensive physicochemical fuel properties as input.The proposed approach provides greater detail and precision than existing state-of-the-art methods.Building on a cost-efficient AI development strategy established in our previous work,the tool was constructed using 17 single-output multi-layer perceptron networks.The tool was validated using engine dynamometer measurements with various test fuels,and then it was applied to a fuel optimization task to demonstrate its effectiveness.The results indicate that the tool’s predictions closely match actual engine performance.Specifically,10 out of the 17 models achieved a mean absolute percentage error of<3%.In the optimization scenario,the optimized fuel had a predicted engine operating score of 40.51%,while the actual score was 41.3%,demonstrating the tool’s potential for accurate fuel design.Thus,this novel approach can support the development of low-cost e-fuels,enabling economically viable,carbon-neutral mobility across a wide range of transport applications.
基金jointly supported by the National Natural Science Foundation of China ( No . 51208238 )the Science & Technology Plan Program of Yunnan Province , China ( No . 2013CA025 )
文摘The yellow light dilemma zone is widely known as an area on the high-speed intersection ap- proach, where vehicles neither safely stop before the stop line nor proceed through the intersection dur- ing amber interval. Within such an area, a vehicle might be involved in a right-angle crash or rear-end collision. This issue has been extensively discussed over five decades in traffic engineering field, cov- eting from theory to practice. However, few comprehensive review literatures on the amber signal di- lemma zone problems can be found. The objective of this paper is to summarize the evolution of yellow light dilemma zone researches. Basic definition and boundary of dilemma zone followed by driver be- havior and dilemma zone hazard measurement are depicted. At last, the future directions of yellow light dilemma zone research are discussed.
基金supported by the National Key Research and Development Program of China(No.2021YFC3001500)Natural Science Foundation of China(No.52302433)+3 种基金Natural Science Foundation of Guangdong Province,China(No.2023A1515012404)Science and Technology Projects in Guangzhou(No.2024A04J3838)Fundamental Research Funds for the Central Universities(No.2022ZYGXZR052)China Postdoctoral Science Foundation(No.2021M701898).
文摘Freeway logistics plays a pivotal role in economic development.Although the rapid development in big data and artificial inteligence motivates long+haul freeway logistics towards informatization and intellectualization,the transportation of bulk commodities still faces serious challenges arisen from dispersed freight demands and the lack of co-ordination among different operators.The present study thereby proposed inteligent algorithms for truck dispatching for freeway logistics.Specifically,our contributions include the establishment of mathematical models for full-truckload(FTL)and less-than-truckload(LTL)transportation modes,respectively,and the introduction of reinforcement learning with deep Q-networks tailored for each transportation mode to improve the decision-making in order acceptance and truck repositioning.Simulation experiments based on the realworld freeway logistics data collected in Guiyang,China show that our algorithms improved operational profitability substantially with a 76%and 30%revenue increase for FTL and LTL modes,respectively,compared with single-stage optimization.These results demonstrate the potential of reinforcement learning in revolutionizing freeway logistics and should lay a foundation for future research in intelligent logistics systems.
基金Project No.TKP2021-NVA-02.Project No.TKP2021-NVA-02 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research,Development and Innovation Fund,financed under the TKP2021-NVA funding schemeThe research was also supported by Project No.2022-2.1.1-NL-2022-00012,which has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research,Development and Innovation Fund,financed under the National Laboratories funding scheme.
文摘Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,exclusively.However,the number of vehicles participating in the traffic-in other words,the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management-is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network level.To tackle this gap,an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment procedure.By optimally scaling the Origin-Destination matrices of the sample fleet,an appropriate model can be approximated to provide traffic flow data beside average speeds.The iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic model.The method has been tested through two different real-world traffic networks,justifying the viability of the proposed method.Overall,the contribution of the study is a practical solution based on commonly available fleet traffic data,suggested for practitioners in traffic planning and management.
