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
With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerba...With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerbated these challenges. This pressing issue underscores the critical necessity for a structured approach to managing university entries and overseeing parking at the gates. The proposed smart parking management system aims to address these concerns by introducing a design concept that restricts unauthorized access and provides exclusive parking privileges to authorized users. Through image processing, the system identifies available parking spaces, relaying real-time information to users via a mobile application. This comprehensive solution also generates detailed reports (daily, weekly, and monthly), aiding university safety authorities in future gate management decisions.展开更多
Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adja...Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.展开更多
Parking difficulties have become a social issue that people have to solve.Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking acc...Parking difficulties have become a social issue that people have to solve.Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents.The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method(RF-LNMPC)in order to improve the overall path tracking accuracy in parking conditions.Firstly,A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively.Secondly,the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization,of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution.Thirdly,the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented.The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically.Finally,the proposed RF-LNMPC is implemented on a selfdriving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions.The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.展开更多
The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle parti...The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle participation.However,instead of being an isolated module,the incentive mechanism usually interacts with other modules.Based on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real time.Specifically,we utilize an LSTM method to predict parking areas roughly for recommendations accurately.Its incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network effects.In order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking decision-making.Then its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is Pareto-optimal.Finally,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.展开更多
Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck s...Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck stops are the only places for the trucks to stop legally and safely. In locations without sufficient parking areas, trucks often park on interstate ramps, which create safety risks for other interstate motorists. Historically, agencies have employed costly and time intensive manual counting methods, camera surveillance, and driver surveys to assess truck parking. Connected truck data, available in near real-time, offers an efficient alternative to practitioners to assess truck parking patterns and identify areas where there may be insufficient safe parking spaces. This paper presents a case study of interstate I-70 in east central Indiana and documents the observed spatiotemporal impacts of a rest area closure on truck parking on nearby interstate ramps. Results showed that there was a 28% increase in parking on ramps during the rest area closure. Analysis also found that ramps closest to the rest area were most impacted by the closure, seeing a rise in truck parking sessions as high as 2.7 times. Parking duration on the ramps during rest area closure also increased drastically. Although it was expected that this would result in increased parking by trucks on adjacent ramps, this before, during, after scenario provided an ideal scenario to evaluate the robustness of these techniques to assess changing parking characteristics of long-haul commercial trucks. The data analytics and visualization tools presented in this study are scalable nationwide and will aid stakeholders in informed data-driven decision making when allocating resources towards improving the nations commercial vehicle parking infrastructure.展开更多
Despite cities being recognized as being potential sources of microplastic pollution to the wider environment, most surveys of COVID-19 plastic-based litter have been undertaken through linear transects of marine beac...Despite cities being recognized as being potential sources of microplastic pollution to the wider environment, most surveys of COVID-19 plastic-based litter have been undertaken through linear transects of marine beaches. For the far fewer number of studies conducted on inland and urban locations, the site-specific focus has primarily been surveys along the length of streets. The present study is the first to specifically assess the standing stock (i.e., moment-in-time) of littered face masks for the entire surface area of urban parking lots. The density of face masks in 50 parking lots in a Canadian coastal town (0.00054 m2 ± 0.00051 m2) was found to be significantly greater than the background level of littering of town streets. Face mask density was significantly related to visitation “usage” of parking lots as gauged by the areal size of the lots and of their onsite buildings, as well as the number of vehicles present. Neither parking lot typology nor estimates of inferred export (various measures of wind exposure) and entrapment (various metrics of obstruction) of face masks had a significant influence on the extent of whole-lot littering. In consequence, modelling of the potential input of mask-derived microplastics to the marine environment from coastal communities can use the areal density of face masks found here in association with the total surface area of lots for individual municipalities as determined through GIS analysis.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
In recent years,disorderly parking and difficult charging of electric bicycles have been challenges in urban management.The rapid growth of electric bicycles is in contradiction with the lack of dedicated parking spac...In recent years,disorderly parking and difficult charging of electric bicycles have been challenges in urban management.The rapid growth of electric bicycles is in contradiction with the lack of dedicated parking spaces and charging service facilities in towns and villages.To solve the issue of parking and charging electric bicycles in limited urban and rural spaces,prefabricated building technology is applied to the design of a multi-story electric bicycle parking lot.The multi-story prefabricated electric bicycle parking lot is utilized in urban and rural planning and design to upgrade parking facilities in old urban areas,land-constrained commercial areas,as well as counties,towns,and rural areas with inadequate municipal facilities.Multi-story prefabricated electric bicycle parking lots are the application exploration of industrial buildings,and promote the high-quality development planning and construction of towns and counties and villages.Compared with the single-story metal charging station,the multi-story assembled electric bicycle parking lot has the characteristics of integrating parking and charging,being more durable and safer in structure,accommodating a large number of vehicles,and improving the space utilization rate.展开更多
Aiming at solving pressing parking issues in the urban environment, a residential parking spaces sharing model was proposed in this study. In this model, firstly, the residential community pattern, the status of idle ...Aiming at solving pressing parking issues in the urban environment, a residential parking spaces sharing model was proposed in this study. In this model, firstly, the residential community pattern, the status of idle parking spaces, and the temporal and spatial characteristics of sharing parking had been analyzed. Next,in the convenience of modeling,medical institutions that have the most prominent parking problems were selected as the subject of study. Based on the K-S statistical analysis results and the actual parking sharing situation,it was observed that the residential parking sharing time satisfied the shifted negative exponential distribution( SNED). Finally,a probability model of shared service capacity based on the SNED and critical time condition was established. By applying the statistical analysis method,the time of vehicles passing in and out of parking spaces, the idle time of parking spaces, the shifted distribution parameters, and other important model parameters had been calibrated,which was leading to the algorithm of model. In addition,considering the feasibility of model without sufficient data,the vehicle travel probability,the stagnation rate of parking space,and the status of parking spaces were defined and the reference data were also provided. The results of case studies indicate that it is very promising to solve urban parking issues if the residential community shares its rich parking resources with adjacent commercial buildings.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
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
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
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
Curb parking lot is a major part of city parking facility with lots of problems, especially in CCA (citycenter area and it has a lot of advantages and has much effect on dynamic traffic as well. It is therefore necess...Curb parking lot is a major part of city parking facility with lots of problems, especially in CCA (citycenter area and it has a lot of advantages and has much effect on dynamic traffic as well. It is therefore necessaryto control the scale of curb parking. Basing the whole benefits of the traffic system and considering the minimumsynthetical cost on curb parking, a optimization model is brought forward of cur. b parking planning in CCAbased on minimum generalized cost. Based on this model, the scale of curb parking can be defined reasonablyto make the whole benefits of traffic system optimum in CCA.展开更多
The searching of parking burns a lot of barrels of the world’s oil every day. Car parking problem is a major contributor in congestion of traffic and has been, still a major problem with increasing vehicle size in th...The searching of parking burns a lot of barrels of the world’s oil every day. Car parking problem is a major contributor in congestion of traffic and has been, still a major problem with increasing vehicle size in the luxurious segment and also confines parking spaces in urban cities. The rapid growth in the number of vehicles worldwide is intensifying the problem of the lack of parking space. As the global population continues to urbanize, without a well-planned, convenience-driven retreat from the car, these problems will worsen in many countries. The current unmanaged car parks and transportation facilities make it difficult to accommodate the increasing number of vehicles in a proper, convenient manner so it is necessary to have an efficient and smart parking system. Smart parking management systems are capable of providing extreme level of convenience to the drivers. In this paper, a proposed web App system, named “Park Easy” is based on the usage of smart phones, sensors monitoring techniques with a camera which is used as a sensor to take photos to show the occupancy of cars parks. By implementing this system, the utilization of parking spaces will increase. It allocates available parking space to a given driver to park their vehicle, renew the availability of the parking space when the car leaves and compute the charges due. Smart parking App, “Park Easy”, will also enable most important techniques to provide all the possible shortage route for parking from any area of the city mainly, it helps to predict accurately and sense spot/vehicle occupancy in real-time.展开更多
In light of growing urban traffic,car parking becomes increasingly critical for cities to manage.As a result,the prediction of parking occupancy has sparked significant research interest in recent years.While many ext...In light of growing urban traffic,car parking becomes increasingly critical for cities to manage.As a result,the prediction of parking occupancy has sparked significant research interest in recent years.While many external data sources have been considered in the prediction models,the underlying geographic context has mostly been ignored.Thus,in order to study the contribution of geospatial information to parking occupancy prediction models,road network centrality,land use,and Point of Interest(POI)data were incorporated in Random Forest(RF)and Artificial Neural Network(ANN,specifically Feedforward Neural Network FFNN)prediction models in this work.Model performances were compared to a baseline,which only considers historical and temporal input data.Moreover,the influence of the amount of training data,the prediction horizon,and the spatial variation of the prediction were explored.The results show that the inclusion of geospatial information led to a performance improvement of up to 25%compared to the baseline.Besides,as the prediction horizon expanded,predictions became less reliable,while the relevance of geospatial data increased.In general,land use and POI data proved to be more beneficial than road network centrality.The amount of training data did not have a significant influence on the performance of the RF model.The ANN model,conversely,achieved optimal results on a training input of 5 days.Likely attributable to varying occupancy patterns,prediction performance disparities could be identified for different parking districts and street segments.Generally,the RF model outperformed the ANN model on all predictions.展开更多
In order to reduce the controlling difficulty caused by trajectory meandering and improve the adaptability to parking into regular lots,a versatile optimal planner(OP)is proposed.Taking advantage of the low speed spec...In order to reduce the controlling difficulty caused by trajectory meandering and improve the adaptability to parking into regular lots,a versatile optimal planner(OP)is proposed.Taking advantage of the low speed specificity of parking vehicle,the OP algorithm was modeled the planning problem as a convex optimization problem.Collision-free constraints were formalized into the shortest distance between convex sets by describing obstacles and autonomous vehicle as affine set.Since employing Lagrange dual function and combining KKT conditions,the collision-free constraints translated into convex functions.Taking the national standard into account,5 kinds of regular parking scenario,which contain 0°,30°,45°,60°and 90°parking lots,were designed to verify the OP algorithm.The results illustrate that it is benefit from the continuous and smooth trajectory generated by the OP method to track,keep vehicle's stability and improve ride comfort,compared with A*and hybrid A*algorithms.Moreover,the OP method has strong generality since it can ensure the success rate no less than 82%when parking planning is carried out at the start node of 369 different locations.Both of evaluation criteria,as the pear error and RMSE in x direction,y axis and Euclidean distance d,and heading deviation 6,are stable and feasible in real tests,which illustrates that the OP planner can satisfy the requirements of regular parking scenarios.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.
文摘With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerbated these challenges. This pressing issue underscores the critical necessity for a structured approach to managing university entries and overseeing parking at the gates. The proposed smart parking management system aims to address these concerns by introducing a design concept that restricts unauthorized access and provides exclusive parking privileges to authorized users. Through image processing, the system identifies available parking spaces, relaying real-time information to users via a mobile application. This comprehensive solution also generates detailed reports (daily, weekly, and monthly), aiding university safety authorities in future gate management decisions.
基金supported by National Natural Science Foundation of China(52222215, 52272420, 52072051)。
文摘Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.
基金Supported by National Key R&D Program of China (Grant No.2021YFB2501800)National Natural Science Foundation of China (Grant No.52172384)+1 种基金Science and Technology Innovation Program of Hunan Province of China (Grant No.2021RC3048)State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle of China (Grant No.72275004)。
文摘Parking difficulties have become a social issue that people have to solve.Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents.The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method(RF-LNMPC)in order to improve the overall path tracking accuracy in parking conditions.Firstly,A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively.Secondly,the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization,of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution.Thirdly,the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented.The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically.Finally,the proposed RF-LNMPC is implemented on a selfdriving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions.The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.
基金supported in part by the Natural Science Foundation of Shandong Province of China(ZR202103040180)the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-004the Fundamental Research Funds for the Central Universities under Grant 20CX05019A.
文摘The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle participation.However,instead of being an isolated module,the incentive mechanism usually interacts with other modules.Based on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real time.Specifically,we utilize an LSTM method to predict parking areas roughly for recommendations accurately.Its incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network effects.In order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking decision-making.Then its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is Pareto-optimal.Finally,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.
文摘Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck stops are the only places for the trucks to stop legally and safely. In locations without sufficient parking areas, trucks often park on interstate ramps, which create safety risks for other interstate motorists. Historically, agencies have employed costly and time intensive manual counting methods, camera surveillance, and driver surveys to assess truck parking. Connected truck data, available in near real-time, offers an efficient alternative to practitioners to assess truck parking patterns and identify areas where there may be insufficient safe parking spaces. This paper presents a case study of interstate I-70 in east central Indiana and documents the observed spatiotemporal impacts of a rest area closure on truck parking on nearby interstate ramps. Results showed that there was a 28% increase in parking on ramps during the rest area closure. Analysis also found that ramps closest to the rest area were most impacted by the closure, seeing a rise in truck parking sessions as high as 2.7 times. Parking duration on the ramps during rest area closure also increased drastically. Although it was expected that this would result in increased parking by trucks on adjacent ramps, this before, during, after scenario provided an ideal scenario to evaluate the robustness of these techniques to assess changing parking characteristics of long-haul commercial trucks. The data analytics and visualization tools presented in this study are scalable nationwide and will aid stakeholders in informed data-driven decision making when allocating resources towards improving the nations commercial vehicle parking infrastructure.
文摘Despite cities being recognized as being potential sources of microplastic pollution to the wider environment, most surveys of COVID-19 plastic-based litter have been undertaken through linear transects of marine beaches. For the far fewer number of studies conducted on inland and urban locations, the site-specific focus has primarily been surveys along the length of streets. The present study is the first to specifically assess the standing stock (i.e., moment-in-time) of littered face masks for the entire surface area of urban parking lots. The density of face masks in 50 parking lots in a Canadian coastal town (0.00054 m2 ± 0.00051 m2) was found to be significantly greater than the background level of littering of town streets. Face mask density was significantly related to visitation “usage” of parking lots as gauged by the areal size of the lots and of their onsite buildings, as well as the number of vehicles present. Neither parking lot typology nor estimates of inferred export (various measures of wind exposure) and entrapment (various metrics of obstruction) of face masks had a significant influence on the extent of whole-lot littering. In consequence, modelling of the potential input of mask-derived microplastics to the marine environment from coastal communities can use the areal density of face masks found here in association with the total surface area of lots for individual municipalities as determined through GIS analysis.
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.
文摘In recent years,disorderly parking and difficult charging of electric bicycles have been challenges in urban management.The rapid growth of electric bicycles is in contradiction with the lack of dedicated parking spaces and charging service facilities in towns and villages.To solve the issue of parking and charging electric bicycles in limited urban and rural spaces,prefabricated building technology is applied to the design of a multi-story electric bicycle parking lot.The multi-story prefabricated electric bicycle parking lot is utilized in urban and rural planning and design to upgrade parking facilities in old urban areas,land-constrained commercial areas,as well as counties,towns,and rural areas with inadequate municipal facilities.Multi-story prefabricated electric bicycle parking lots are the application exploration of industrial buildings,and promote the high-quality development planning and construction of towns and counties and villages.Compared with the single-story metal charging station,the multi-story assembled electric bicycle parking lot has the characteristics of integrating parking and charging,being more durable and safer in structure,accommodating a large number of vehicles,and improving the space utilization rate.
基金National High Technology Research and Development Plan Project,China(No.2014BAG03B03)National Natural Science Fundation,China(No.51378171)
文摘Aiming at solving pressing parking issues in the urban environment, a residential parking spaces sharing model was proposed in this study. In this model, firstly, the residential community pattern, the status of idle parking spaces, and the temporal and spatial characteristics of sharing parking had been analyzed. Next,in the convenience of modeling,medical institutions that have the most prominent parking problems were selected as the subject of study. Based on the K-S statistical analysis results and the actual parking sharing situation,it was observed that the residential parking sharing time satisfied the shifted negative exponential distribution( SNED). Finally,a probability model of shared service capacity based on the SNED and critical time condition was established. By applying the statistical analysis method,the time of vehicles passing in and out of parking spaces, the idle time of parking spaces, the shifted distribution parameters, and other important model parameters had been calibrated,which was leading to the algorithm of model. In addition,considering the feasibility of model without sufficient data,the vehicle travel probability,the stagnation rate of parking space,and the status of parking spaces were defined and the reference data were also provided. The results of case studies indicate that it is very promising to solve urban parking issues if the residential community shares its rich parking resources with adjacent commercial buildings.
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(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.
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.
文摘Curb parking lot is a major part of city parking facility with lots of problems, especially in CCA (citycenter area and it has a lot of advantages and has much effect on dynamic traffic as well. It is therefore necessaryto control the scale of curb parking. Basing the whole benefits of the traffic system and considering the minimumsynthetical cost on curb parking, a optimization model is brought forward of cur. b parking planning in CCAbased on minimum generalized cost. Based on this model, the scale of curb parking can be defined reasonablyto make the whole benefits of traffic system optimum in CCA.
文摘The searching of parking burns a lot of barrels of the world’s oil every day. Car parking problem is a major contributor in congestion of traffic and has been, still a major problem with increasing vehicle size in the luxurious segment and also confines parking spaces in urban cities. The rapid growth in the number of vehicles worldwide is intensifying the problem of the lack of parking space. As the global population continues to urbanize, without a well-planned, convenience-driven retreat from the car, these problems will worsen in many countries. The current unmanaged car parks and transportation facilities make it difficult to accommodate the increasing number of vehicles in a proper, convenient manner so it is necessary to have an efficient and smart parking system. Smart parking management systems are capable of providing extreme level of convenience to the drivers. In this paper, a proposed web App system, named “Park Easy” is based on the usage of smart phones, sensors monitoring techniques with a camera which is used as a sensor to take photos to show the occupancy of cars parks. By implementing this system, the utilization of parking spaces will increase. It allocates available parking space to a given driver to park their vehicle, renew the availability of the parking space when the car leaves and compute the charges due. Smart parking App, “Park Easy”, will also enable most important techniques to provide all the possible shortage route for parking from any area of the city mainly, it helps to predict accurately and sense spot/vehicle occupancy in real-time.
文摘In light of growing urban traffic,car parking becomes increasingly critical for cities to manage.As a result,the prediction of parking occupancy has sparked significant research interest in recent years.While many external data sources have been considered in the prediction models,the underlying geographic context has mostly been ignored.Thus,in order to study the contribution of geospatial information to parking occupancy prediction models,road network centrality,land use,and Point of Interest(POI)data were incorporated in Random Forest(RF)and Artificial Neural Network(ANN,specifically Feedforward Neural Network FFNN)prediction models in this work.Model performances were compared to a baseline,which only considers historical and temporal input data.Moreover,the influence of the amount of training data,the prediction horizon,and the spatial variation of the prediction were explored.The results show that the inclusion of geospatial information led to a performance improvement of up to 25%compared to the baseline.Besides,as the prediction horizon expanded,predictions became less reliable,while the relevance of geospatial data increased.In general,land use and POI data proved to be more beneficial than road network centrality.The amount of training data did not have a significant influence on the performance of the RF model.The ANN model,conversely,achieved optimal results on a training input of 5 days.Likely attributable to varying occupancy patterns,prediction performance disparities could be identified for different parking districts and street segments.Generally,the RF model outperformed the ANN model on all predictions.
文摘In order to reduce the controlling difficulty caused by trajectory meandering and improve the adaptability to parking into regular lots,a versatile optimal planner(OP)is proposed.Taking advantage of the low speed specificity of parking vehicle,the OP algorithm was modeled the planning problem as a convex optimization problem.Collision-free constraints were formalized into the shortest distance between convex sets by describing obstacles and autonomous vehicle as affine set.Since employing Lagrange dual function and combining KKT conditions,the collision-free constraints translated into convex functions.Taking the national standard into account,5 kinds of regular parking scenario,which contain 0°,30°,45°,60°and 90°parking lots,were designed to verify the OP algorithm.The results illustrate that it is benefit from the continuous and smooth trajectory generated by the OP method to track,keep vehicle's stability and improve ride comfort,compared with A*and hybrid A*algorithms.Moreover,the OP method has strong generality since it can ensure the success rate no less than 82%when parking planning is carried out at the start node of 369 different locations.Both of evaluation criteria,as the pear error and RMSE in x direction,y axis and Euclidean distance d,and heading deviation 6,are stable and feasible in real tests,which illustrates that the OP planner can satisfy the requirements of regular parking scenarios.
