Ground taxiing is the key process of take-off and landing for a tricycle-undercarriage unmanned aerial vehicle( UAV). Nonlinear model of a sample UAV is established based on stiffness and damping model of landing gear...Ground taxiing is the key process of take-off and landing for a tricycle-undercarriage unmanned aerial vehicle( UAV). Nonlinear model of a sample UAV is established based on stiffness and damping model of landing gears and tires taken into account. Then lateral nonlinear model is linearized and state space equations are deduced by using nose wheel and ruder as inputs and lateral states as outputs. Adaptive internal model control( AIMC) is proposed and applied to lateral control based on decoupled and linearized dynamic model during ground taxiing process. Different control strategies are analyzed and compared by simulations,and then a combined control strategy of nose wheel steering with holding and rudder control is given. Hardware in loop simulations( HILS) proves the validity of the controller designed.展开更多
The issue of green aircraft taxiing under various taxi scenarios is studied to improve the efficiency of aircraft surface operations and reduce environmental pollution around the airport from aircraft emissions.A gree...The issue of green aircraft taxiing under various taxi scenarios is studied to improve the efficiency of aircraft surface operations and reduce environmental pollution around the airport from aircraft emissions.A green aircraft taxi programming model based on multi-scenario joint optimization is built according to airport surface network topology modeling by analyzing the characteristics of aircraft operations under three different taxiing scenarios:all-engine taxi,single-engine taxi,and electronic taxi.A genetic algorithm is also used in the model to minimize fuel consumption and pollutant emissions.The Shanghai Pudong International Airport is selected as a typical example to conduct a verification analysis.Compared with actual operational data,the amount of aircraft fuel consumption and gas emissions after optimization are reduced significantly through applying the model.Under an electronic taxiing scenario,fuel consumption can be lowered by 45.3%,and hydrocarbon(HC)and carbon dioxide(CO)emissions are decreased by 80%.The results show that a green aircraft taxiing strategy that integrates taxiway optimization and electronic taxiing can effectively improve the efficiency of airport operations and reduce aircraft pollution levels in an airport′s peripheral environment.展开更多
The objective of this study is to improve the methods of determining unimpeded(nominal) taxiing time,which is the reference time used for estimating taxiing delay,a widely accepted performance indicator of airport s...The objective of this study is to improve the methods of determining unimpeded(nominal) taxiing time,which is the reference time used for estimating taxiing delay,a widely accepted performance indicator of airport surface movement.After reviewing existing methods used widely by different air navigation service providers(ANSP),new methods relying on computer software and statistical tools,and econometrics regression models are proposed.Regression models are highly recommended because they require less detailed data and can serve the needs of general performance analysis of airport surface operations.The proposed econometrics model outperforms existing ones by introducing more explanatory variables,especially taking aircraft passing and over-passing into the considering of queue length calculation and including runway configuration,ground delay program,and weather factors.The length of the aircraft queue in the taxiway system and the interaction between queues are major contributors to long taxi-out times.The proposed method provides a consistent and more accurate method of calculating taxiing delay and it can be used for ATM-related performance analysis and international comparison.展开更多
This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.Th...This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.展开更多
Background and Objective: HIV infection is a major global Public Health threat worldwide, particularly in Sub-Saharan Africa of which Benin. The level of knowledge determines the attitudes and behaviors of the populat...Background and Objective: HIV infection is a major global Public Health threat worldwide, particularly in Sub-Saharan Africa of which Benin. The level of knowledge determines the attitudes and behaviors of the populations towards this infection. The study objective was to assess knowledge, attitudes and practices related to HIV infection among motorbike taxi drivers (MTD) in Parakou in 2021. Methods: This was a descriptive cross-sectional study targeting MTD in Parakou in 2021. Participants were selected by cluster sampling. Pretested Digitized questionnaire using KoboCollect<sup>@</sup> applicationserved as a data collection tool. Knowledge, attitudes and practices variable were treated on a score scale. A knowledge score was considered to reflect a good knowledge of HIV if at least two-thirds of the knowledge statements had been correctly answered provided the subject recognized the sexual route as one of modes of HIV transmission, identified at least one preventive measure and meant the incurability of the disease. Quantitative and qualitative variables were appropriately described using the EPI Info 7.1.3.3 software. The participant was classified at positive attitude/practice for HIV prevention, when it has a score of at least 80% and suggests a good preventive measure face a risk of exposure to HIV. Results: A total of 374 subjects were recruited into the study. The mean age was 31.51 ± 7.76 years. Most participants (86.06%) had good knowledge of condom use as an HIV prevention method. The sources of information mentioned were mainly the media (77.07%), relatives or friends (63.38%), and field-workers from non-governmental organizations (37.26%). Routine HIV testing was 50.53%. Among participants, 76.10% reported at least two different sexual partners. Condom use was 59.18 % during the casual sexual intercourse. Within the client-provider relationship with female sex workers, 33.17% had had sexual intercourse with them. The sexual route was the most cited (92.99%), and 90.23% stated that HIV infection can be stabilized by medication in a health structure. Conclusion: The level of knowledge of motorbike taxi drivers in Parakou does not match their behavior with regard to HIV prevention. Appropriate strategies are needed to develop prevention skills in this population. To effectively comb at HIV, it will be necessary to strengthen the targeted HIV preventive interventions at key and bridge populations including motorbike taxi drivers in Benin.展开更多
Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications...Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.展开更多
The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with ...The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with traditional probability techniques based on the linear chain of events. However, it could not comprehensively analyze system safety problems, especially in operating environment, interaction of subsystems, and human factors. Thus,we consider system safety as a control problem based on the system-theoretic accident model, the processes(STAMP) model and the system theoretic process analysis(STPA) technique to compensate the deficiency of traditional techniques. Meanwhile,system safety simulation is considered as system control simulation, and Monte Carlo methods are used which consider the range of uncertain parameters and operation deviation to quantitatively study system safety influence factors in control simulation. Firstly,we construct the STAMP model and STPA feedback control loop of the wheel brake system based on the system functional requirement. Then four unsafe control actions are identified, and causes of them are analyzed. Finally, we construct the Monte Carlo simulation model to analyze different scenarios under disturbance. The results provide a basis for choosing corresponding process model variables in constructing the context table and show that appropriate brake strategies could prevent hazards in aircraft landing taxiing.展开更多
Aircraft ground movement plays a key role in improving airport efficiency,as it acts as a link to all other ground operations.Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport i...Aircraft ground movement plays a key role in improving airport efficiency,as it acts as a link to all other ground operations.Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations.Moreover,autonomous taxiing is envisioned as a key component in future digitalized airports.However,state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases.The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions.This paper proposes a new approach for generating efficient four-dimensional trajectories(4DTs)on the basis of a high-fidelity aircraft model and gainscheduling control strategy.Working in conjunction with a routing and scheduling algorithm that determines the taxi route,waypoints,and time deadlines,the proposed approach generates fuel-efficient 4DTs in real time,while respecting operational constraints.The proposed approach can be used in two contexts:①as a reactive decision support tool to generate new trajectories that can resolve unprecedented events;and②as an autopilot system for both partial and fully autonomous taxiing.The proposed methodology is realistic and simple to implement.Moreover,simulation studies show that the proposed approach is capable of providing an up to 11%reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.展开更多
The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained ...The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained from one domain(e.g.taxi data)applies badly to a different domain(e.g.Uber data).To achieve accurate analyses on a new domain,substantial amounts of data must be available,which limits practical applications.To remedy this,we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task:Selectively choosing a small amount of datapoints from a new domain while achieving comparable performances to using all the datapoints.We choose the New York City(NYC)transportation data of taxi and Uber as our dataset,simulating different domains with 90%as the source data domain for training and the remaining 10%as the target data domain for evaluation.We propose semi-supervised and active learning strategies and apply it to the source domain for selecting datapoints.Experimental results show that our adaptation achieves a comparable performance of using all datapoints while using only a fraction of them,substantially reducing the amount of data required.Our approach has two major advantages:It can make accurate analytics and predictions when big datasets are not available,and even if big datasets are available,our approach chooses the most informative datapoints out of the dataset,making the process much more efficient without having to process huge amounts of data.展开更多
One of the common transportation systems in Korea is calling taxis through online applications,which is more convenient for passengers and drivers in the modern area.However,the driver’s passenger taxi request can be...One of the common transportation systems in Korea is calling taxis through online applications,which is more convenient for passengers and drivers in the modern area.However,the driver’s passenger taxi request can be rejected based on the driver’s location and distance.Therefore,there is a need to specify driver’s acceptance and rejection of the received request.The security of this systemis anothermain core to save the transaction information and safety of passengers and drivers.In this study,the origin and destination of the Jeju island SouthKorea were captured from T-map and processed based on machine learning decision tree and XGBoost techniques.The blockchain framework is implemented in the Hyperledger Fabric platform.The experimental results represent the features of socio-economic.The cross-validation was accomplished.Distance is another factor for the taxi trip,which in total trip in midnight is quite shorter.This process presents the successful matching of ride-hailing taxi services with the specialty of distance,the trip request,and safety based on the total city measurement.展开更多
Through analysis of trap catches and fruit decay rate of different pear varieties,it was found that there were significant differences in parasitization of Grapholitha molesta( Busck) among different pear varieties....Through analysis of trap catches and fruit decay rate of different pear varieties,it was found that there were significant differences in parasitization of Grapholitha molesta( Busck) among different pear varieties. The trap catches of Huangguan pear was the highest; the fruit decay rate of Xueqing pear was extremely higher than those of other varieties; the trap catches of early red comice were significantly lower than those of other varieties,and no injured fruits were found.展开更多
[Objective]The paper was to preliminarily determine the susceptible varieties of Grapholita molesta(Busck)by analyzing trap catches of peach trees at different ripening stages,new shoot damage rate and fruit damage ra...[Objective]The paper was to preliminarily determine the susceptible varieties of Grapholita molesta(Busck)by analyzing trap catches of peach trees at different ripening stages,new shoot damage rate and fruit damage rate at different development stages.[Method]Three traps were hanged in peach orchard of the same variety,with the interval of 30 m.The traps were fixed on ventilated and shaded branches for regular and fixed-point monitoring.Three trees were randomly selected from each variety to investigate the damage fruit rate and new shoot damage rate at east,south,west and north directions of each tree,and the number of infected fruits and damaged new shoots was recorded.[Result]There were obvious differences in taxis selection of G.molesta to different peach varieties,and the trap catches in the same variety varied among different periods.The damage caused by G.molesta in the same variety over the same period was different among different organs.The catch peak of late ripening peach 03-46-78 appeared on August 11.In summer shoot period,the trap catches of late ripening peach 03-46-78 were extremely higher than those of other varieties,and the damage rate of late ripening peach Jinyuan was extremely higher than that of other varieties.In autumn shoot period,the average trap catches of late ripening peach 03-46-78 were significantly higher than those of other varieties,and the damage rate of late ripening peach Jinyuan was significantly higher than that of other varieties.The investigation and analysis of different ripening stages of peach fruits showed that the damage rate of late ripening variety Ruiguang 39 was significantly higher than that of other varieties in the ripening stage.The trap catches of late ripening peach 03-46-78 were significantly higher than those of other varieties in the young fruit stage.The trap catches of late ripening peach 03-46-78 had significant difference with those of other varieties during fruit expansion stage.The trap catches of late ripening peach 03-46-78 had significant difference with that of other varieties during fruit ripening stage.[Conclusion]With the development of new shoots and the ripening of fruits,G.molesta appears different dynamic regularities in different organs of various varieties.The time point that G.molesta transfers with the development of fruit tree organs should be grasped to deeply analyze the physical and chemical properties of fruit trees during the growth period,so as to find out reasonable and effective prevention and control techniques for fruit farmers.展开更多
基金Sponsored by the Knowledge Innovation Project of Chinese Academy of Sciences(Grant No.YYJ-1122)
文摘Ground taxiing is the key process of take-off and landing for a tricycle-undercarriage unmanned aerial vehicle( UAV). Nonlinear model of a sample UAV is established based on stiffness and damping model of landing gears and tires taken into account. Then lateral nonlinear model is linearized and state space equations are deduced by using nose wheel and ruder as inputs and lateral states as outputs. Adaptive internal model control( AIMC) is proposed and applied to lateral control based on decoupled and linearized dynamic model during ground taxiing process. Different control strategies are analyzed and compared by simulations,and then a combined control strategy of nose wheel steering with holding and rudder control is given. Hardware in loop simulations( HILS) proves the validity of the controller designed.
文摘The issue of green aircraft taxiing under various taxi scenarios is studied to improve the efficiency of aircraft surface operations and reduce environmental pollution around the airport from aircraft emissions.A green aircraft taxi programming model based on multi-scenario joint optimization is built according to airport surface network topology modeling by analyzing the characteristics of aircraft operations under three different taxiing scenarios:all-engine taxi,single-engine taxi,and electronic taxi.A genetic algorithm is also used in the model to minimize fuel consumption and pollutant emissions.The Shanghai Pudong International Airport is selected as a typical example to conduct a verification analysis.Compared with actual operational data,the amount of aircraft fuel consumption and gas emissions after optimization are reduced significantly through applying the model.Under an electronic taxiing scenario,fuel consumption can be lowered by 45.3%,and hydrocarbon(HC)and carbon dioxide(CO)emissions are decreased by 80%.The results show that a green aircraft taxiing strategy that integrates taxiway optimization and electronic taxiing can effectively improve the efficiency of airport operations and reduce aircraft pollution levels in an airport′s peripheral environment.
基金supported by FAA ATO-G under contract DTFAWA-09-P-00245
文摘The objective of this study is to improve the methods of determining unimpeded(nominal) taxiing time,which is the reference time used for estimating taxiing delay,a widely accepted performance indicator of airport surface movement.After reviewing existing methods used widely by different air navigation service providers(ANSP),new methods relying on computer software and statistical tools,and econometrics regression models are proposed.Regression models are highly recommended because they require less detailed data and can serve the needs of general performance analysis of airport surface operations.The proposed econometrics model outperforms existing ones by introducing more explanatory variables,especially taking aircraft passing and over-passing into the considering of queue length calculation and including runway configuration,ground delay program,and weather factors.The length of the aircraft queue in the taxiway system and the interaction between queues are major contributors to long taxi-out times.The proposed method provides a consistent and more accurate method of calculating taxiing delay and it can be used for ATM-related performance analysis and international comparison.
基金supported by the Surface Project of the National Natural Science Foundation of China(No.71273024)the Fundamental Research Funds for the Central Universities of China(2021YJS080).
文摘This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.
文摘Background and Objective: HIV infection is a major global Public Health threat worldwide, particularly in Sub-Saharan Africa of which Benin. The level of knowledge determines the attitudes and behaviors of the populations towards this infection. The study objective was to assess knowledge, attitudes and practices related to HIV infection among motorbike taxi drivers (MTD) in Parakou in 2021. Methods: This was a descriptive cross-sectional study targeting MTD in Parakou in 2021. Participants were selected by cluster sampling. Pretested Digitized questionnaire using KoboCollect<sup>@</sup> applicationserved as a data collection tool. Knowledge, attitudes and practices variable were treated on a score scale. A knowledge score was considered to reflect a good knowledge of HIV if at least two-thirds of the knowledge statements had been correctly answered provided the subject recognized the sexual route as one of modes of HIV transmission, identified at least one preventive measure and meant the incurability of the disease. Quantitative and qualitative variables were appropriately described using the EPI Info 7.1.3.3 software. The participant was classified at positive attitude/practice for HIV prevention, when it has a score of at least 80% and suggests a good preventive measure face a risk of exposure to HIV. Results: A total of 374 subjects were recruited into the study. The mean age was 31.51 ± 7.76 years. Most participants (86.06%) had good knowledge of condom use as an HIV prevention method. The sources of information mentioned were mainly the media (77.07%), relatives or friends (63.38%), and field-workers from non-governmental organizations (37.26%). Routine HIV testing was 50.53%. Among participants, 76.10% reported at least two different sexual partners. Condom use was 59.18 % during the casual sexual intercourse. Within the client-provider relationship with female sex workers, 33.17% had had sexual intercourse with them. The sexual route was the most cited (92.99%), and 90.23% stated that HIV infection can be stabilized by medication in a health structure. Conclusion: The level of knowledge of motorbike taxi drivers in Parakou does not match their behavior with regard to HIV prevention. Appropriate strategies are needed to develop prevention skills in this population. To effectively comb at HIV, it will be necessary to strengthen the targeted HIV preventive interventions at key and bridge populations including motorbike taxi drivers in Benin.
基金supported by 2022 Shenyang Philosophy and Social Science Planning under grant SY202201Z,Liaoning Provincial Department of Education Project under grant LJKZ0588.
文摘Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.
文摘The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with traditional probability techniques based on the linear chain of events. However, it could not comprehensively analyze system safety problems, especially in operating environment, interaction of subsystems, and human factors. Thus,we consider system safety as a control problem based on the system-theoretic accident model, the processes(STAMP) model and the system theoretic process analysis(STPA) technique to compensate the deficiency of traditional techniques. Meanwhile,system safety simulation is considered as system control simulation, and Monte Carlo methods are used which consider the range of uncertain parameters and operation deviation to quantitatively study system safety influence factors in control simulation. Firstly,we construct the STAMP model and STPA feedback control loop of the wheel brake system based on the system functional requirement. Then four unsafe control actions are identified, and causes of them are analyzed. Finally, we construct the Monte Carlo simulation model to analyze different scenarios under disturbance. The results provide a basis for choosing corresponding process model variables in constructing the context table and show that appropriate brake strategies could prevent hazards in aircraft landing taxiing.
基金This work was funded by the UK Engineering and Physical Sciences Research Council(EP/N029496/1,EP/N029496/2,EP/N029356/1,EP/N029577/1,and EP/N029577/2).
文摘Aircraft ground movement plays a key role in improving airport efficiency,as it acts as a link to all other ground operations.Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations.Moreover,autonomous taxiing is envisioned as a key component in future digitalized airports.However,state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases.The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions.This paper proposes a new approach for generating efficient four-dimensional trajectories(4DTs)on the basis of a high-fidelity aircraft model and gainscheduling control strategy.Working in conjunction with a routing and scheduling algorithm that determines the taxi route,waypoints,and time deadlines,the proposed approach generates fuel-efficient 4DTs in real time,while respecting operational constraints.The proposed approach can be used in two contexts:①as a reactive decision support tool to generate new trajectories that can resolve unprecedented events;and②as an autopilot system for both partial and fully autonomous taxiing.The proposed methodology is realistic and simple to implement.Moreover,simulation studies show that the proposed approach is capable of providing an up to 11%reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.
文摘The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained from one domain(e.g.taxi data)applies badly to a different domain(e.g.Uber data).To achieve accurate analyses on a new domain,substantial amounts of data must be available,which limits practical applications.To remedy this,we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task:Selectively choosing a small amount of datapoints from a new domain while achieving comparable performances to using all the datapoints.We choose the New York City(NYC)transportation data of taxi and Uber as our dataset,simulating different domains with 90%as the source data domain for training and the remaining 10%as the target data domain for evaluation.We propose semi-supervised and active learning strategies and apply it to the source domain for selecting datapoints.Experimental results show that our adaptation achieves a comparable performance of using all datapoints while using only a fraction of them,substantially reducing the amount of data required.Our approach has two major advantages:It can make accurate analytics and predictions when big datasets are not available,and even if big datasets are available,our approach chooses the most informative datapoints out of the dataset,making the process much more efficient without having to process huge amounts of data.
基金This research was financially supported by the Ministry of Small and Mediumsized Enterprises(SMEs)and Startups(MSS),Korea,under the“Regional Specialized Industry Development Program(R&D,S3091627)”supervised by Korea Institute for Advancement of Technology(KIAT).
文摘One of the common transportation systems in Korea is calling taxis through online applications,which is more convenient for passengers and drivers in the modern area.However,the driver’s passenger taxi request can be rejected based on the driver’s location and distance.Therefore,there is a need to specify driver’s acceptance and rejection of the received request.The security of this systemis anothermain core to save the transaction information and safety of passengers and drivers.In this study,the origin and destination of the Jeju island SouthKorea were captured from T-map and processed based on machine learning decision tree and XGBoost techniques.The blockchain framework is implemented in the Hyperledger Fabric platform.The experimental results represent the features of socio-economic.The cross-validation was accomplished.Distance is another factor for the taxi trip,which in total trip in midnight is quite shorter.This process presents the successful matching of ride-hailing taxi services with the specialty of distance,the trip request,and safety based on the total city measurement.
基金Supported by Youth Fund of Hebei Academy of Agriculture and Forestry(A2015020106)
文摘Through analysis of trap catches and fruit decay rate of different pear varieties,it was found that there were significant differences in parasitization of Grapholitha molesta( Busck) among different pear varieties. The trap catches of Huangguan pear was the highest; the fruit decay rate of Xueqing pear was extremely higher than those of other varieties; the trap catches of early red comice were significantly lower than those of other varieties,and no injured fruits were found.
文摘[Objective]The paper was to preliminarily determine the susceptible varieties of Grapholita molesta(Busck)by analyzing trap catches of peach trees at different ripening stages,new shoot damage rate and fruit damage rate at different development stages.[Method]Three traps were hanged in peach orchard of the same variety,with the interval of 30 m.The traps were fixed on ventilated and shaded branches for regular and fixed-point monitoring.Three trees were randomly selected from each variety to investigate the damage fruit rate and new shoot damage rate at east,south,west and north directions of each tree,and the number of infected fruits and damaged new shoots was recorded.[Result]There were obvious differences in taxis selection of G.molesta to different peach varieties,and the trap catches in the same variety varied among different periods.The damage caused by G.molesta in the same variety over the same period was different among different organs.The catch peak of late ripening peach 03-46-78 appeared on August 11.In summer shoot period,the trap catches of late ripening peach 03-46-78 were extremely higher than those of other varieties,and the damage rate of late ripening peach Jinyuan was extremely higher than that of other varieties.In autumn shoot period,the average trap catches of late ripening peach 03-46-78 were significantly higher than those of other varieties,and the damage rate of late ripening peach Jinyuan was significantly higher than that of other varieties.The investigation and analysis of different ripening stages of peach fruits showed that the damage rate of late ripening variety Ruiguang 39 was significantly higher than that of other varieties in the ripening stage.The trap catches of late ripening peach 03-46-78 were significantly higher than those of other varieties in the young fruit stage.The trap catches of late ripening peach 03-46-78 had significant difference with those of other varieties during fruit expansion stage.The trap catches of late ripening peach 03-46-78 had significant difference with that of other varieties during fruit ripening stage.[Conclusion]With the development of new shoots and the ripening of fruits,G.molesta appears different dynamic regularities in different organs of various varieties.The time point that G.molesta transfers with the development of fruit tree organs should be grasped to deeply analyze the physical and chemical properties of fruit trees during the growth period,so as to find out reasonable and effective prevention and control techniques for fruit farmers.