To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs,a mixed parking demand assignment method based on regression modeling is proposed.First,an optimal model aiming to mi...To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs,a mixed parking demand assignment method based on regression modeling is proposed.First,an optimal model aiming to minimize total time expenditure is constructed.It incorporates parking search time,walking time,and departure time,focusing on short-term parking features.Then,the information dimensions that the parking lot can obtain are evaluated,and three assignment strategies based on three types of regression models-linear regression(LR),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP)-are proposed.A parking process simulation model is built using the traffic simulation package SUMO to facilitate data collection,model training,and case studies.Finally,the performance of the three strategies is com-pared,revealing that the XGBoost-based strategy performs the best in case parking lots,which reduces time expendi-ture by 29.3%and 37.2%,respectively,compared with the MLP-based strategy and LR-based strategy.This method offers diverse options for practical parking manage-ment.展开更多
Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, curr...Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, current interdependence, and future potential through the lens of environmental, social, and economic sustainability. Historically, parking systems evolved from manual designs to automated processes yet remained focused on convenience rather than sustainability. Presently, advancements in smart infrastructure and vehicle-to-infrastructure (V2I) communication have enabled AVs and APS to operate as a cohesive system, optimizing space, energy, and transportation efficiency. Looking ahead, the seamless integration of AVs and APS into broader smart city ecosystems promises to redefine urban landscapes by repurposing traditional parking infrastructure into multifunctional spaces and supporting renewable energy initiatives. These technologies align with global sustainability goals by mitigating emissions, reducing urban sprawl, and fostering adaptive land uses. This reflection highlights the need for collaborative efforts among stakeholders to address regulatory and technological challenges, ensuring the equitable and efficient deployment of AVs and APS for smarter, greener cities.展开更多
Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban citi...Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban cities with heavy traffic flow,these challenges can result in traffic disruptions,rear-end collisions,sideswipes,and congestion as drivers struggle to make decisions.We propose a real-time detection system for on-street parking spaces using YOLO models and recommend the most suitable space based on KD-tree search.Lightweight versions of YOLOv5,YOLOv7-tiny,and YOLOv8 with different architectures are trained.Among the models,YOLOv5s with SPPF at the backbone achieved an F1-score of 0.89,which was selected for validation using k-fold cross-validation on our dataset.The Low variance and standard deviation recorded across folds indicate the model’s generalizability,reliability,and stability.Inference with KD-tree using predictions from the YOLO models recorded FPS of 37.9 for YOLOv5,67.2 for YOLOv7-tiny,and 67.0 for YOLOv8.The models successfully detect both marked and unmarked empty parking spaces on test data with varying inference speeds and FPS.These models can be efficiently deployed for real-time applications due to their high FPS,inference speed,and lightweight nature.In comparison with other state-of-the-art models,our models outperform them,further demonstrating their effectiveness.展开更多
Automated valet parking systems based on parking automated guided vehicles(P-AGVs)are effective for improving parking convenience and increasing parking density.The ability of P-AGVs to move towards any position and a...Automated valet parking systems based on parking automated guided vehicles(P-AGVs)are effective for improving parking convenience and increasing parking density.The ability of P-AGVs to move towards any position and attain any orientation simultaneously due to their mecanum wheels makes it convenient to transport vehicles in a parking lot.In this study,a nonlinear disturbance observer-based sliding mode controller for the trajectory tracking problem of a P-AGV is proposed.The kinematic and dynamic models for a P-AGV tracking trajectory are first analyzed in sequence and the influences of disturbing forces considered.Subsequently,a nonlinear disturbance observer(NDO)is designed to estimate the disturbing forces and torques generated by the caster wheels.Based on the designed NDO,a robust nonsingular terminal sliding-mode(NTSM)controller is used to track reference trajectories.The stabilities of the NDO and NDO-NTSM control systems are theoretically verified using their Lyapunov functions.Finally,simulations and experiments are performed to verify the effectiveness of the proposed control scheme.The experimental results show that the proposed NDO-NTSM controller can improve the trajectory tracking stability by 42-68%compared to a traditional NTSM controller.The NDO-based sliding mode controller for trajectory tracking proposed in this study can effectively reduce the impact of disturbances on trajectory tracking accuracy.展开更多
The issues of slow traffic connection around Pingguoyuan Metro Station were studied,such as the disorderly parking of non-motorized vehicles in the entrances and exits and the difficulty in maintaining order over the ...The issues of slow traffic connection around Pingguoyuan Metro Station were studied,such as the disorderly parking of non-motorized vehicles in the entrances and exits and the difficulty in maintaining order over the long term.Through policy review,on-site observation,and analysis of public feedback,the underlying reasons such as extensive parking space configuration,the lack of coordination mechanisms,and the spatial tension caused by the overlapping of peak traffic flows were revealed.Besides,comprehensive response paths were proposed from aspects of facility optimization,the reconfiguration of the responsibility system,and the improvement of the public participation mechanism,including the construction of intelligent parking spaces,the establishment of a joint management model between the local area and the operating unit,and the transformation of the handling of public demands from passive response to proactive governance.The research results can provide a reference for the coordination of traffic organization and grassroots governance around urban railway stations.展开更多
The lift-and-transverse type parking equipment, with its core advantages such as high space utilization, modular and flexible layout, and intelligent operation, has become an efficient solution to alleviate the urban ...The lift-and-transverse type parking equipment, with its core advantages such as high space utilization, modular and flexible layout, and intelligent operation, has become an efficient solution to alleviate the urban parking problem. However, existing research still lacks a systematic evaluation of its structural performance, particularly in areas such as the fatigue characteristics of steel frame materials, stress distribution under dynamic loads, and resonance risk analysis. The stress amplitude (S) and fatigue life (N) relationship curve of Q235 steel, the material used in the steel frame of the lift-and-transverse type parking equipment, was obtained through fatigue testing methods. The finite element simulation model of the steel frame structure of the lift-and-transverse type parking equipment was established using ABAQUS simulation software. Through static simulation analysis, the deformation and stress distribution under various operating conditions, including full load, partial load, and no load, was obtained. The fatigue life under full load and partial load was determined through FE-Safe fatigue analysis. Modal analysis was conducted to get the natural frequencies of the steel frame structure at various modes, which were compared with the operating frequency of the equipment to avoid resonance. This paper examines the evaluation methods for the structural performance of lift-and-transverse type parking equipment, providing a reliable research basis for the design and fatigue life analysis of this specialized structure.展开更多
Accurate prediction of parking spaces plays a crucial role in maximizing the efficiency of parking resources and optimizing traffic conditions.However,the majority of earlier research has used models based on past par...Accurate prediction of parking spaces plays a crucial role in maximizing the efficiency of parking resources and optimizing traffic conditions.However,the majority of earlier research has used models based on past parking data or the plethora of variables that influence parking prediction,which not only makes the data more complicated and costs more time to run but can also lead to poor model fits.To solve this problem,a hybrid parking prediction model combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and gated recurrent unit(GRU)model is proposed to predict the number of parking spaces.In this model,CEEMDAN has the ability to gradually break down time series fluctuations or trends at various scales,producing a sequence of intrinsic mode functions(IMF)with various characteristic scales.Then,by keeping the majority of the original data’s content,removing superfluous information,and enhancing predicted response time,principal component analysis(PCA)decreases the dimensionality of the IMF series.Subsequently,the high-level abstract characteristics are entered into the GRU network,and the network is built,tested,and predicted based on the deep learning framework Keras.The validity of the presented model is verified by making use of real parking datasets from two three-dimensional parking lots.The test results reveal that the model outperforms the baseline model’s predictive accuracy,i.e.,a lower testing error.The real parking time series are most closely modeled by the CEEMDAN-PCA-GRU model.As a result,the method is superior to existing models for parking prediction.展开更多
In the pursuit of carbon peaking and neutrality goals,multi-energy parks,as major energy consumers and carbon emitters,urgently require low-carbon operational strategies.This paper proposes an electricity-carbon syner...In the pursuit of carbon peaking and neutrality goals,multi-energy parks,as major energy consumers and carbon emitters,urgently require low-carbon operational strategies.This paper proposes an electricity-carbon synergy-driven optimization method for the low-carbon operation ofmulti-energy parks.Themethod integratesmultienergy complementary scheduling with a tiered carbon trading mechanism to balance operational security,economic efficiency,and environmental objectives.A mixed-integer linear programming model is developed to characterize the coupling relationships and dynamic behaviors of key equipment,including photovoltaic systems,ground-source heat pumps,thermal storage electric boilers,combined heat and power units,and electrical energy storage systems.Furthermore,a tiered carbon trading model is established that incorporates carbon quota allocation and tiered carbon pricing to internalize carbon costs and discourage high-emission practices.Multi-scenario comparative analyses demonstrate that the electricity-carbon synergy scenario achieves a 42.64%reduction in carbon emissions compared to economy-oriented operation,while limiting the increase in operational costs to 20.85%.The carbon-prioritized scenario further reduces emissions by 9.7%,underscoring the inhibitory effect of the tiered carbon pricing mechanism on highcarbon activities.Sensitivity analyses confirm the model’s robustness against fluctuations in energy load,uncertainty in renewable generation,and variations in carbon price.This optimization method provides theoretical support for multi-energy coordinated scheduling and carbon responsibility allocation in industrial parks,offering valuable insights for promoting green transformation initiatives.展开更多
In an era of profound changes to global trade,food security,and industrial organization,the ChinaIndonesia“Two Countries,Twin Parks”initiative has emerged as a strategic experiment with farreaching implications.Anch...In an era of profound changes to global trade,food security,and industrial organization,the ChinaIndonesia“Two Countries,Twin Parks”initiative has emerged as a strategic experiment with farreaching implications.Anchored in marine protein processing and advanced food manufacturing,this collaboration reflects not only a convergence of economic interests but also a deliberate rethinking of how China engages with global resources,technology,and partners.展开更多
In order to carry out comprehensive decision-making of multi-class shared parking measures within a region, a bilevel model assisting decision-making is proposed. The upper level selects parkers＇ average satisfaction...In order to carry out comprehensive decision-making of multi-class shared parking measures within a region, a bilevel model assisting decision-making is proposed. The upper level selects parkers＇ average satisfaction and the violation rate during peak hours as indices in object function, and sets probability distribution models describing dynamic parking demand of each site, the feasibility of shared parking scenarios and occupancy requirements during peak hours of each parking lot as restrictions. The simulation model in the lower level sets up rules to assign each parker in the random parking demand series to the proper parking lot. An iterative method is proposed to confirm the state of each parking lot at the start of formal simulations. Besides, two patterns linking initialization and formal simulation are presented to acquire multiple solutions. The results of the numerical examples indicate the effectiveness of the model and solution methods.展开更多
In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and...In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and the choice behavior utility function is established combining trip time, search time, waiting time, access time and parking fee. By the utility function, a probit-based parking choice behavior model is constructed. On the basis of these, the curb parking pricing model is deduced by considering the constrained conditions, and an incremental assignment algorithm of the model is also designed. Finally, the model is applied to the parking planning of Tongling city. It is pointed out that the average parking time of curb parking decreases 34%, and the average turnover rate increases 67% under the computed parking price system. The results show that the model can optimize the utilization of static traffic facilities.展开更多
Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-secti...Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-sections are periodical and self-similar, and the fluctuation of the APSO increases with the decrease in time-sections. Taking the short-time change behavior into account, an APSO forecasting model combined wavelet analysis and a weighted Markov chain is presented. In this model, an original APSO time series is first decomposed by wavelet analysis, and the results include low frequency signals representing the basic trends of APSO and several high frequency signals representing disturbances of the APSO. Then different Markov models are used to forecast the changes of low and high frequency signals, respectively. Finally, integrating the predicted results induces the final forecasted APSO. A case study verifies the applicability of the proposed model. The comparisons between measured and forecasted results show that the model is a competent model and its accuracy relies on real-time update of the APSO database.展开更多
This paper introduces a parking management system based on a wireless sensor network developed by our group. The system consists of a large amount of parking space monitoring nodes, a few parking guiding nodes, a sink...This paper introduces a parking management system based on a wireless sensor network developed by our group. The system consists of a large amount of parking space monitoring nodes, a few parking guiding nodes, a sink node and a management station. All the nodes exchange information with each other through wireless communication. The prototype of the parking management system has been implemented and the preliminary test results show that the performance of the system can satisfy the requirements of the application.展开更多
Analyzes the spatial structure of parking behavior and establishes a basic parking behavior model to represent the parking problem in downtown, and establishes a parking pricing model to analyze the parking equilibriu...Analyzes the spatial structure of parking behavior and establishes a basic parking behavior model to represent the parking problem in downtown, and establishes a parking pricing model to analyze the parking equilibrium with a positive parking fee and uses a paired combinatorial logit model to analyze the effect of trip integrative cost on parking behavior and concludes from empirical results that the parking behavior model performs well.展开更多
A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of avail...A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.展开更多
Parking into small berths remains difficult for unskilled drivers. Researchers had proposed different automatic parking systems to solve this problem. The first kind of strategies(called parking trajectory planning) d...Parking into small berths remains difficult for unskilled drivers. Researchers had proposed different automatic parking systems to solve this problem. The first kind of strategies(called parking trajectory planning) designs a detailed reference trajectory that links the start and ending points of a special parking task and let the vehicle track this reference trajectory so as to park into the berth. The second kind of strategies(called guidance control) just characterizes several regimes of driving actions as well as the important switching points in certain rule style and let the vehicle follows the pre-selected series of actions so as to park into the berth. Parking guidance control is simpler than parking trajectory planning. However, no studies thoroughly validated parking guidance control before. In this paper, a new automatic parking method is presented, which could characterize the desired control actions directly. Then the feasibility is examined carefully. Tests show that a simple parking guidance control strategy can work in most parallel parking tasks, if the available parking berth is not too small. This finding helps to build more concise automatic parking systems that can efficiently guide human drivers.展开更多
In order to solve the problem that the drivers can't find the optimal parking lot timely,a reservation based optimal parking lot recommendation model in Internet of Vehicle(IoV) environment is designed.Based on th...In order to solve the problem that the drivers can't find the optimal parking lot timely,a reservation based optimal parking lot recommendation model in Internet of Vehicle(IoV) environment is designed.Based on the users oriented parking information recommendation system,the model considers subjective demands of drivers comprehensively,makes a deeply analysis of the evaluation indicators.This recommendation model uses a phased selection method to calculate the optimal objective parking lot.The first stage is screening which based on the users' subjective parking demands;the second stage is processing the candidate parking lots through multiple attribute decision making.Simulation experiments show that this model can effectively solve the problems encountered in the process of finding optimal parking lot,save the driver's parking time and parking costs and also improve the overall utilization of parking facilities to ease the traffic congestion caused by vehicles parked patrol.展开更多
基金The National Natural Science Foundation of China(No.52302388)the Natural Science Foundation of Jiangsu Province(No.BK20230853).
文摘To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs,a mixed parking demand assignment method based on regression modeling is proposed.First,an optimal model aiming to minimize total time expenditure is constructed.It incorporates parking search time,walking time,and departure time,focusing on short-term parking features.Then,the information dimensions that the parking lot can obtain are evaluated,and three assignment strategies based on three types of regression models-linear regression(LR),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP)-are proposed.A parking process simulation model is built using the traffic simulation package SUMO to facilitate data collection,model training,and case studies.Finally,the performance of the three strategies is com-pared,revealing that the XGBoost-based strategy performs the best in case parking lots,which reduces time expendi-ture by 29.3%and 37.2%,respectively,compared with the MLP-based strategy and LR-based strategy.This method offers diverse options for practical parking manage-ment.
文摘Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, current interdependence, and future potential through the lens of environmental, social, and economic sustainability. Historically, parking systems evolved from manual designs to automated processes yet remained focused on convenience rather than sustainability. Presently, advancements in smart infrastructure and vehicle-to-infrastructure (V2I) communication have enabled AVs and APS to operate as a cohesive system, optimizing space, energy, and transportation efficiency. Looking ahead, the seamless integration of AVs and APS into broader smart city ecosystems promises to redefine urban landscapes by repurposing traditional parking infrastructure into multifunctional spaces and supporting renewable energy initiatives. These technologies align with global sustainability goals by mitigating emissions, reducing urban sprawl, and fostering adaptive land uses. This reflection highlights the need for collaborative efforts among stakeholders to address regulatory and technological challenges, ensuring the equitable and efficient deployment of AVs and APS for smarter, greener cities.
基金supports this paper.Project Nos.NSTC-112-2221-E-324-003 MY3,NSTC-111-2622-E-324-002 and NSTC-112-2221-E-324-011-MY2.
文摘Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban cities with heavy traffic flow,these challenges can result in traffic disruptions,rear-end collisions,sideswipes,and congestion as drivers struggle to make decisions.We propose a real-time detection system for on-street parking spaces using YOLO models and recommend the most suitable space based on KD-tree search.Lightweight versions of YOLOv5,YOLOv7-tiny,and YOLOv8 with different architectures are trained.Among the models,YOLOv5s with SPPF at the backbone achieved an F1-score of 0.89,which was selected for validation using k-fold cross-validation on our dataset.The Low variance and standard deviation recorded across folds indicate the model’s generalizability,reliability,and stability.Inference with KD-tree using predictions from the YOLO models recorded FPS of 37.9 for YOLOv5,67.2 for YOLOv7-tiny,and 67.0 for YOLOv8.The models successfully detect both marked and unmarked empty parking spaces on test data with varying inference speeds and FPS.These models can be efficiently deployed for real-time applications due to their high FPS,inference speed,and lightweight nature.In comparison with other state-of-the-art models,our models outperform them,further demonstrating their effectiveness.
基金Supported by National Key R&D Program of China(Grant No.2018YFB0105102)Anhui Provincial Natural Science Foundation(Grant No.2208085QE153).
文摘Automated valet parking systems based on parking automated guided vehicles(P-AGVs)are effective for improving parking convenience and increasing parking density.The ability of P-AGVs to move towards any position and attain any orientation simultaneously due to their mecanum wheels makes it convenient to transport vehicles in a parking lot.In this study,a nonlinear disturbance observer-based sliding mode controller for the trajectory tracking problem of a P-AGV is proposed.The kinematic and dynamic models for a P-AGV tracking trajectory are first analyzed in sequence and the influences of disturbing forces considered.Subsequently,a nonlinear disturbance observer(NDO)is designed to estimate the disturbing forces and torques generated by the caster wheels.Based on the designed NDO,a robust nonsingular terminal sliding-mode(NTSM)controller is used to track reference trajectories.The stabilities of the NDO and NDO-NTSM control systems are theoretically verified using their Lyapunov functions.Finally,simulations and experiments are performed to verify the effectiveness of the proposed control scheme.The experimental results show that the proposed NDO-NTSM controller can improve the trajectory tracking stability by 42-68%compared to a traditional NTSM controller.The NDO-based sliding mode controller for trajectory tracking proposed in this study can effectively reduce the impact of disturbances on trajectory tracking accuracy.
基金Sponsored by the Innovation and Entrepreneurship Training Planning Project for University Students in 2025:High-quality Undergraduate Teaching Plan of Beijing Universities(108051360024XN094).
文摘The issues of slow traffic connection around Pingguoyuan Metro Station were studied,such as the disorderly parking of non-motorized vehicles in the entrances and exits and the difficulty in maintaining order over the long term.Through policy review,on-site observation,and analysis of public feedback,the underlying reasons such as extensive parking space configuration,the lack of coordination mechanisms,and the spatial tension caused by the overlapping of peak traffic flows were revealed.Besides,comprehensive response paths were proposed from aspects of facility optimization,the reconfiguration of the responsibility system,and the improvement of the public participation mechanism,including the construction of intelligent parking spaces,the establishment of a joint management model between the local area and the operating unit,and the transformation of the handling of public demands from passive response to proactive governance.The research results can provide a reference for the coordination of traffic organization and grassroots governance around urban railway stations.
文摘The lift-and-transverse type parking equipment, with its core advantages such as high space utilization, modular and flexible layout, and intelligent operation, has become an efficient solution to alleviate the urban parking problem. However, existing research still lacks a systematic evaluation of its structural performance, particularly in areas such as the fatigue characteristics of steel frame materials, stress distribution under dynamic loads, and resonance risk analysis. The stress amplitude (S) and fatigue life (N) relationship curve of Q235 steel, the material used in the steel frame of the lift-and-transverse type parking equipment, was obtained through fatigue testing methods. The finite element simulation model of the steel frame structure of the lift-and-transverse type parking equipment was established using ABAQUS simulation software. Through static simulation analysis, the deformation and stress distribution under various operating conditions, including full load, partial load, and no load, was obtained. The fatigue life under full load and partial load was determined through FE-Safe fatigue analysis. Modal analysis was conducted to get the natural frequencies of the steel frame structure at various modes, which were compared with the operating frequency of the equipment to avoid resonance. This paper examines the evaluation methods for the structural performance of lift-and-transverse type parking equipment, providing a reliable research basis for the design and fatigue life analysis of this specialized structure.
基金the National Natural Science Foundation of China(No.52062027)the Key Research and Development Project of Gansu Province(No.22YF7GA142)+3 种基金the Soft Science Special Project of Gansu Basic Research Plan(No.22JR4ZA035)the Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project(Nos.22ZD6GA010 and 21ZD3GA002)the Natural Science Foundation of Gansu Province(No.22JR5RA343)the Gansu Provincial Education Technology Innovation Project(No.2023CXZX-582)。
文摘Accurate prediction of parking spaces plays a crucial role in maximizing the efficiency of parking resources and optimizing traffic conditions.However,the majority of earlier research has used models based on past parking data or the plethora of variables that influence parking prediction,which not only makes the data more complicated and costs more time to run but can also lead to poor model fits.To solve this problem,a hybrid parking prediction model combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and gated recurrent unit(GRU)model is proposed to predict the number of parking spaces.In this model,CEEMDAN has the ability to gradually break down time series fluctuations or trends at various scales,producing a sequence of intrinsic mode functions(IMF)with various characteristic scales.Then,by keeping the majority of the original data’s content,removing superfluous information,and enhancing predicted response time,principal component analysis(PCA)decreases the dimensionality of the IMF series.Subsequently,the high-level abstract characteristics are entered into the GRU network,and the network is built,tested,and predicted based on the deep learning framework Keras.The validity of the presented model is verified by making use of real parking datasets from two three-dimensional parking lots.The test results reveal that the model outperforms the baseline model’s predictive accuracy,i.e.,a lower testing error.The real parking time series are most closely modeled by the CEEMDAN-PCA-GRU model.As a result,the method is superior to existing models for parking prediction.
基金supported by Technology Project of State Grid Tianjin Electric Power Company(2024-06)“Research on hierarchical partition dynamic calculation and panoramic monitoring technology of electric power carbon emission and its application”.
文摘In the pursuit of carbon peaking and neutrality goals,multi-energy parks,as major energy consumers and carbon emitters,urgently require low-carbon operational strategies.This paper proposes an electricity-carbon synergy-driven optimization method for the low-carbon operation ofmulti-energy parks.Themethod integratesmultienergy complementary scheduling with a tiered carbon trading mechanism to balance operational security,economic efficiency,and environmental objectives.A mixed-integer linear programming model is developed to characterize the coupling relationships and dynamic behaviors of key equipment,including photovoltaic systems,ground-source heat pumps,thermal storage electric boilers,combined heat and power units,and electrical energy storage systems.Furthermore,a tiered carbon trading model is established that incorporates carbon quota allocation and tiered carbon pricing to internalize carbon costs and discourage high-emission practices.Multi-scenario comparative analyses demonstrate that the electricity-carbon synergy scenario achieves a 42.64%reduction in carbon emissions compared to economy-oriented operation,while limiting the increase in operational costs to 20.85%.The carbon-prioritized scenario further reduces emissions by 9.7%,underscoring the inhibitory effect of the tiered carbon pricing mechanism on highcarbon activities.Sensitivity analyses confirm the model’s robustness against fluctuations in energy load,uncertainty in renewable generation,and variations in carbon price.This optimization method provides theoretical support for multi-energy coordinated scheduling and carbon responsibility allocation in industrial parks,offering valuable insights for promoting green transformation initiatives.
文摘In an era of profound changes to global trade,food security,and industrial organization,the ChinaIndonesia“Two Countries,Twin Parks”initiative has emerged as a strategic experiment with farreaching implications.Anchored in marine protein processing and advanced food manufacturing,this collaboration reflects not only a convergence of economic interests but also a deliberate rethinking of how China engages with global resources,technology,and partners.
基金The Planning Program of Science and Technology of Ministry of Housing and Urban-Rural Development of China (No. 2010-K5-16)
文摘In order to carry out comprehensive decision-making of multi-class shared parking measures within a region, a bilevel model assisting decision-making is proposed. The upper level selects parkers＇ average satisfaction and the violation rate during peak hours as indices in object function, and sets probability distribution models describing dynamic parking demand of each site, the feasibility of shared parking scenarios and occupancy requirements during peak hours of each parking lot as restrictions. The simulation model in the lower level sets up rules to assign each parker in the random parking demand series to the proper parking lot. An iterative method is proposed to confirm the state of each parking lot at the start of formal simulations. Besides, two patterns linking initialization and formal simulation are presented to acquire multiple solutions. The results of the numerical examples indicate the effectiveness of the model and solution methods.
基金The National Natural Science Foundation of China(No50308005), the National Basic Research Program of China (973Program) (No2006CB705500)
文摘In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and the choice behavior utility function is established combining trip time, search time, waiting time, access time and parking fee. By the utility function, a probit-based parking choice behavior model is constructed. On the basis of these, the curb parking pricing model is deduced by considering the constrained conditions, and an incremental assignment algorithm of the model is also designed. Finally, the model is applied to the parking planning of Tongling city. It is pointed out that the average parking time of curb parking decreases 34%, and the average turnover rate increases 67% under the computed parking price system. The results show that the model can optimize the utilization of static traffic facilities.
基金The National Natural Science Foundation of China(No50738001)the National Basic Research Program of China (973Program) (No2006CB705501)
文摘Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-sections are periodical and self-similar, and the fluctuation of the APSO increases with the decrease in time-sections. Taking the short-time change behavior into account, an APSO forecasting model combined wavelet analysis and a weighted Markov chain is presented. In this model, an original APSO time series is first decomposed by wavelet analysis, and the results include low frequency signals representing the basic trends of APSO and several high frequency signals representing disturbances of the APSO. Then different Markov models are used to forecast the changes of low and high frequency signals, respectively. Finally, integrating the predicted results induces the final forecasted APSO. A case study verifies the applicability of the proposed model. The comparisons between measured and forecasted results show that the model is a competent model and its accuracy relies on real-time update of the APSO database.
基金Supported by National Natural Science Foundation of P. R. China (60373049) National Basic Research Program of P.R.China (2006CB 3030000)
文摘This paper introduces a parking management system based on a wireless sensor network developed by our group. The system consists of a large amount of parking space monitoring nodes, a few parking guiding nodes, a sink node and a management station. All the nodes exchange information with each other through wireless communication. The prototype of the parking management system has been implemented and the preliminary test results show that the performance of the system can satisfy the requirements of the application.
文摘Analyzes the spatial structure of parking behavior and establishes a basic parking behavior model to represent the parking problem in downtown, and establishes a parking pricing model to analyze the parking equilibrium with a positive parking fee and uses a paired combinatorial logit model to analyze the effect of trip integrative cost on parking behavior and concludes from empirical results that the parking behavior model performs well.
基金Project(51561135003)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(51338003)supported by the Key Project of National Natural Science Foundation of China
文摘A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.
基金supported in part by the National Key Research and Development Program of China(2018AAA0101400)the National Natural Science Foundation of China(61603005,61790565)the Joint Laboratory for Future Transport and Urban Computing of Amap
文摘Parking into small berths remains difficult for unskilled drivers. Researchers had proposed different automatic parking systems to solve this problem. The first kind of strategies(called parking trajectory planning) designs a detailed reference trajectory that links the start and ending points of a special parking task and let the vehicle track this reference trajectory so as to park into the berth. The second kind of strategies(called guidance control) just characterizes several regimes of driving actions as well as the important switching points in certain rule style and let the vehicle follows the pre-selected series of actions so as to park into the berth. Parking guidance control is simpler than parking trajectory planning. However, no studies thoroughly validated parking guidance control before. In this paper, a new automatic parking method is presented, which could characterize the desired control actions directly. Then the feasibility is examined carefully. Tests show that a simple parking guidance control strategy can work in most parallel parking tasks, if the available parking berth is not too small. This finding helps to build more concise automatic parking systems that can efficiently guide human drivers.
基金partially supported by the National Natural Science Foundation of China under Grants No.60903176the Provincial Natural Science Foundation of Shandong under Grants No.ZR2012FM010,No.ZR2010FQ028+1 种基金the Program for Youth science and technology starfund of Jinan No.TNK1108the Sub-Project of the National Key Technology R&D Program No.2012BAF12B07-3
文摘In order to solve the problem that the drivers can't find the optimal parking lot timely,a reservation based optimal parking lot recommendation model in Internet of Vehicle(IoV) environment is designed.Based on the users oriented parking information recommendation system,the model considers subjective demands of drivers comprehensively,makes a deeply analysis of the evaluation indicators.This recommendation model uses a phased selection method to calculate the optimal objective parking lot.The first stage is screening which based on the users' subjective parking demands;the second stage is processing the candidate parking lots through multiple attribute decision making.Simulation experiments show that this model can effectively solve the problems encountered in the process of finding optimal parking lot,save the driver's parking time and parking costs and also improve the overall utilization of parking facilities to ease the traffic congestion caused by vehicles parked patrol.
