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Car parks are essential for every new development, but they often result in inefficient use of space. To save space, basement car parks are adopted. However, their capacity is limited by the building footprint and immovable vertical structural elements. Current research on basement car parks mainly focuses on ventilation under extreme situations such as fires. The impact of specific design factors unique to basement car parks on capacity has not been fully studied yet. Therefore, this paper investigates the impact of basement car park design factors on capacity by studying three design aspects: parking aisle network, building footprint, and column positions. A parking lot placement algorithm is proposed to help model and automate the car park design process. Parking lots are optimized using a genetic algorithm (GA). A series of impact analyses have been conducted for three selected design factors. Four design principles from prior research were used to analyze and compare the impact of design factors on parking lots. It was found that the proposed design optimization method can automate basement car park design and facilitate the impact analysis of design factors. Based on the impact analysis, the increase in parking lots for each layout is not strictly positively correlated with the perimeter of the parking aisle. The long rectangle building footprint can have a larger capacity compared with other shapes. Adjusting column positions can provide more space for parking lots and further optimize car park capacity. This research has practical implications for the field of architectural engineering, benefiting architects, engineers, and designers. Architects and designers working on basement car park projects can utilize the research findings to optimize the layout of parking spaces. By considering nonadjustable column positions and vertical obstructions, they can make more efficient use of available space, thereby increasing car park capacity. The systematic analysis of design factors, including parking aisle networks, building footprints, and column positions, empowers professionals to make informed decisions during the design phase. They can tailor car park designs to meet specific needs, while adhering to structural constraints. The introduction of a novel parking lot placement algorithm with optimization capabilities offers a practical tool for architects and designers. It streamlines the process of positioning parking lots to maximize capacity, particularly in basement car parks with fixed columns and obstacles. The research provides valuable guidance for basement car park design, enabling architects to apply design principles derived from surface and single-use multistory car park design to basement configurations. In practice, these applications result in more efficient and flexible car park designs, addressing the unique challenges of basement car parks, while optimizing their capacity and functionality. [ABSTRACT FROM AUTHOR]

In this document, a new methodology for the optimal allocation of capacitor electric vehicle (EV) charging points on smart grid systems is introduced. To realize a better balance between exploring and exploiting Wing suit Flying Search (WFS), the Chaos Game Optimization (CGO) algorithm is used on WFS performance. Thus, it is named the WFS-CGO approach. Here, along with reactive compensation, allocation of parking and capacitors is presented for congestion management. The proposed approach is used to limit the amount of parking space. The performance of the proposed system is tested on a IEEE 34 bus distribution network. The outcome obtained by the QGDA system is compared to existing procedures like PSO, ALO, ACSO, WFSA, and WFS2ACSO techniques. Under cases 1, 2, and 3, after EV loss, the proposed technique achieves 216.3397, 232.9558, and 265.0174. Additionally, the proposed outcomes portray that an uneven EV charging scenario may cause an important voltage imbalance that goes beyond their allowable limit of 2%. [ABSTRACT FROM AUTHOR]

A new model of energy carriers (micro-refinery output products) in the concept of an energy hub is presented. In addition, in the presented model, the effect of different models of parking lot in an energy hub is analyzed. In this study, the uncertainty of the number of electric vehicles was modeled using the Monte Carlo method, and then considering the same conditions, the uncertainty of the number of electric vehicles was calculated using the Probability-Possibility hybrid method. In addition, the study uses a scenario-based approach to address uncertainties related to multi-carrier energy demand and multi-carrier energy prices. In the present paper, ensuring water demand is of great importance, which is why the proposed energy hub structure of a sea desalination unit and sour water treatment that is extracted from the micro refinery output was analyzed. The optimization model presented in this paper for the energy hub is a complex integer linear programming (MILP) that is solved using a CPLEX solver in the GAMS environment. The results show that the use of the Probability-Possibility method resulted in an 8 % reduction in the final energy supply cost in the energy hub compared to the Monte Carlo method.

[Objective] The cover development design for urban rail transit parking lot is very complicated, requiring more new column grids. Moreover, after the cover development, the omnidirectional antenna set up on the roof of the complex building cannot cover the ground area of the parking lot. In order to effectively realize the wireless communication coverage of the parking lot, it is necessary to study the related schemes and key technologies. [Method] Based on the systematic engineering design of TETRA (terrestrial trunked radio) communication system for the parking lot of Textile City Station on Xi’an Metro Line 6 and in view of the difficulties of wireless communication coverage engineering, the frequency planning and allocation scheme of wireless communication system is proposed. A wireless communication coverage scheme is proposed for the parking lot, switch area, access sections and roads in the non-cover area of the parking lot. [Result & Conclusion] FA (frequency allocation) frequency group for wireless communication coverage is adopted for the parking lot of Textile City Station, which ensures frequency interval and avoids same frequency interference. The public security wireless communication system is introduced into the above parking lot, and the scheme of signal route coverage combined with the dedicated wireless communication system is adopted. Directional antenna is used as the coverage mode for the covered parking area to replace the omnidirectional antenna on the roof. Antenna correlation is used for the depot, antenna reinforcement for the throat area, and antenna coverage for U-shaped slot to solve the cross-zone switching , and directional antennas are adopted to cover the surrounding roads by utilizing building sidewalls or columns. The proposed wireless system coverage scheme for the cover development parking lot not only reduces the system interference and the impact on the existing operation, but also effectively solves the problem of the wireless communication coverage for the cover development parking lot, saving the project investment.

Electric vehicles (EVs) are becoming more and more popular as they provide significant environmental benefits compared to fossil-fuel vehicles. However, they represent substantial loads on the power grid, and the scheduling of EV charging can be a challenge, especially in large parking lots. This paper presents a metaheuristic-based approach parallelized on multicore processors (CPU) and graphics processing units (GPU) to optimize the scheduling of EV charging in a single smart parking lot. The proposed method uses a particle swarm optimization algorithm that takes as input the arrival time, the departure time, and the power demand of the vehicles and produces an optimized charging schedule for all vehicles in the parking lot, which minimizes the overall charging cost while respecting the chargers' capacity and the parking lot feeder capacity. The algorithm exploits task-level parallelism for the multicore CPU implementation and data-level parallelism for the GPU implementation. The proposed algorithm is tested in simulation on parking lots containing 20 to 500 EVs. The parallel implementation on CPUs provides a speedup of 7.1x, while the implementation on a GPU provides a speedup of up to 247.6x. The parallel implementation on a GPU is able to optimize the charging schedule for a 20-EV parking lot in 0.87 s and a 500-EV lot in just under 30 s. These runtimes allow for real-time computation when a vehicle arrives at the parking lot or when the electricity cost profile changes. [ABSTRACT FROM AUTHOR]

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[Objective] In order to fully utilize the increasingly scarce urban land resources of Suzhou and leverage its geographical advantages, taking Sangtian Island Co-site Parking Lot of Suzhou Rail Transit Line 2 and Line 6 as an example to propose an OOPD (overall overhead property development) scheme of partially operating co-site parking lot. [Method] A necessity and feasibility analysis is conducted on the co-site parking lot OPD scheme. The schemes of parking lot reconstruction general plan, demolition and renovation technology, and operation transition technology are introduced. The economic indicators of Sangtian Island Co-site Parking Lot overhead property reconstruction are analyzed. [Result & Conclusion] The proposed co-site parking lot OOPD scheme includes carrying out the general layout and design of Line 2 Sangtian Island Parking Lot according to the reserved OPD conditions, reserving column space for OPD, demolishing and renovating existing parking lot after the new Line 2 long-term parking lot is built. The proposed co-site parking lot OOPD scheme can effectively improve the co-site parking lot engineering compatibility of the existing line operation during transition period and the new parking lot, minimize the impact on existing line operation, implement the construction process of building followed by demolishing, for the reduction of engineering waste and lower construction costs.

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As an important supporting facility of high-speed railway, EMU servicing depot is an important place for EMU parking, servicing, primary or secondary repair and temporary repair operations, and is a key to ensure the safe, punctual and efficient operation for EMUs. In order to continuously improve the level of the overall layout of the EMU servicing depot, this article studied and summarized the previous general layout of EMU servicing depot, determined the basic principles of the general layout of EMU servicing depot, which included achieving smooth operation process, pedestrian flow, and logistics at a reasonable cost according to the determined scale. A design method was established with the goal of smooth operation process and the layout of parking lots and inspection garages as the main line. The layout of each component of EMU servicing depot site item by item based on the layout of the parking lot and inspection shed was analyzed. Finally, combined with an example, the optimal layout form was demonstrated by determining boundary conditions, relevant facility layout, land use indicators, etc. [ABSTRACT FROM AUTHOR]

Aiming to improve thermal environment for outdoor public spaces, the author had been developing a unit‐type green parklet for parking lots that both introduce greenery into cities and produce walkable urban spaces. In this study, the author examined and created a full‐size unit‐type green parklet, installed it in a parking lot adjacent to sidewalk space next to a bus stop in the traditional urban area, Kyomachi district, of Kumamoto City, and investigated the improvement to summer thermal environment from a field measurement and its usage by people from user survey in summer (from August 30 to September 5, 2022). The results showed that although the parklet shaded with the parasol and greening units, air temperature dropped by only a maximum of 1°C in daytime. Given the characteristics of parking lot as a location, it was confirmed that not only the wind and airflow conditions was blocked and worsened, but reflected solar radiation from the surroundings and generated heat from parked cars degraded air temperature and MRT from the field measurement. Furthermore, it was confirmed to be ensured the visibility of the surroundings when sitting on the parklet bench for the users waiting for a bus from user survey. [ABSTRACT FROM AUTHOR]

E-mobility represents an important part of the EU's green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector's development it is necessary to invest in energy infrastructure and to assure favorable conditions in terms of competitive electricity prices to make the technology even more attractive. Large peak consumption of parking lots which use different variants of uncoordinated charging strategies increases grid problems and increases electricity supply costs. On the other hand, as observed lately in energy markets, different, mostly uncontrollable, factors can drive electricity prices to extreme levels, making the use of electric vehicles very expensive. In order to reduce exposure to these extreme conditions, it is essential to identify the optimal way to supply parking lots in the long term and to apply an adequate charging strategy that can help to reduce costs for end consumers and bring higher profit for parking lot owners. The significant decline in photovoltaic (PV) and battery storage technology costs makes them an ideal complement for the future supply of parking lots if they are used in an optimal manner in coordination with an adequate charging strategy. This paper addresses the optimal power supply investment problem related to parking lot electricity supply coupled with the application of an optimal EV charging strategy. The proposed optimization model determines optimal investment decisions related to grid supply and contracted peak power, PV plant capacity, battery storage capacity, and operation while optimizing EV charging. The model uses realistic data of EV charging patterns (arrival, departure, energy requirements, etc.) which are derived from commercial platforms. The model is applied using the data and prices from the Croatian market. [ABSTRACT FROM AUTHOR]

Abstract Aiming to improve thermal environment for outdoor public spaces, the author had been developing a unit‐type green parklet for parking lots that both introduce greenery into cities and produce walkable urban spaces. In this study, the author examined and created a full‐size unit‐type green parklet, installed it in a parking lot adjacent to sidewalk space next to a bus stop in the traditional urban area, Kyomachi district, of Kumamoto City, and investigated the improvement to summer thermal environment from a field measurement and its usage by people from user survey in summer (from August 30 to September 5, 2022). The results showed that although the parklet shaded with the parasol and greening units, air temperature dropped by only a maximum of 1°C in daytime. Given the characteristics of parking lot as a location, it was confirmed that not only the wind and airflow conditions was blocked and worsened, but reflected solar radiation from the surroundings and generated heat from parked cars degraded air temperature and MRT from the field measurement. Furthermore, it was confirmed to be ensured the visibility of the surroundings when sitting on the parklet bench for the users waiting for a bus from user survey.

Vehicular ad hoc networks (VANETs) have the potential to reduce car accidents by facilitating connectivity and warning message exchange between vehicles, both on roads and in parking lots. This research endeavored to accomplish three primary goals: conducting a field measurement in the parking lot of a large shopping mall in Auckland, developing an OPNET-based simulation model to analyze and validate the system performance, and analyzing the compatibility between five selected radio propagation models (Free-space, Shadowing Path-loss, Egli, Hata, and COST231). These models were selected based on their popularity and relevance to our study. We found that the "Free Space" model outperforms in the scenario in which measurements were conducted from the Level-1 car park to the Roadside. The received signal strengths in the parking lot ranged from −45 dBm to −92 dBm. This research also examines the coverage distance for the successful transmission of warning messages, achieving up to 57 m, 17.5 m, 9.4 m, and 68 m at parking levels 1, 2, 3, and the roadside, respectively. Research findings reveal that a low-cost Wi-Fi-based VANET system can be utilized to prevent car accidents in parking lots. Finally, we provide guidelines for network planners to deploy Wi-Fi-based VANET systems in parking lots. [ABSTRACT FROM AUTHOR]

In recent years, parking lot management systems have garnered significant research attention, particularly concerning the application of deep learning techniques. Numerous approaches have emerged for tackling parking lot occupancy challenges using deep learning models. This study contributes to the field by addressing a critical aspect of parking lot management systems: accurate vehicle occupancy determination in specific parking spaces. We propose an advanced solution by harnessing an optimized MobileNetV3 model with custom architectural enhancements, trained on the CNRPark-EXT and PKLOT datasets. The model processes individual parking space patches from real-time video feeds, providing occupancy classification for each patch, identifying occupied or available spaces. Our architectural modifications include the integration of a convolutional block attention mechanism in place of the native attention module and the adoption of blueprint separable convolutions instead of the traditional depth-wise separable convolutions. In terms of performance, our proposed model exhibits superior results when benchmarked against state-of-the-art methods. Achieving an exceptional area under the ROC curve (AUC) value of 0.99 for most experiments with the PKLot dataset, our enhanced MobileNetV3 showcases its exceptional discriminatory power in binary classification. Benchmarked against the CarNet and mAlexNet models, representative of previous state-of-the-art solutions, our proposed model showcases exceptional performance. During evaluations using the combined CNRPark-EXT and PKLot datasets, the proposed model attains an impressive average accuracy of 98.01%, while CarNet achieves 97.03%. Beyond achieving high accuracy and precision comparable to previous models, the proposed model exhibits promise for real-time applications. This work contributes to the advancement of parking lot occupancy detection by offering a robust and efficient solution with implications for urban mobility enhancement and resource optimization. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and clean energy play a crucial role in addressing the pressing challenges of climate change, environmental degradation, and energy security. However, the increasing adoption of EVs brings with it several challenges related to the charging load. This paper proposes a new stochastic approach for coordinated charging/discharging of EVs in a parking lot powered with photovoltaic (PV) energy systems. The main aim of the strategy is to schedule the charging/discharging of EVs to satisfy their energy needs while minimizing the operating cost of the parking lot. In this study, the coordinated grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operating modes are compared against the first-come/first-serve (FCFS) charging mode. To obtain practical results, different uncertainties associated with EVs and PV generation are considered using stochastic optimization. The uncertainties are considered using the sample average approximation method by applying the Monte Carlo Simulation. Hence, a mixed integer linear programming problem is formulated and is solved using CPLEX to minimize the expected day-ahead operating cost of the parking lot. Several Scenario studies are simulated to validate the effectiveness of the proposed approach, which achieved up to 24% reduction in operating costs on weekdays.

Car-dominated university campuses allocate large areas of land for parking lots, which are major hubs for users to start and end their daily walking trips. However, studies on the walkability of large parking lots are limited, and there is a study gap in the assessment of existing constructed walking infrastructures and their usage and effectiveness in facilitating walking. In this study, a method is developed that can assess both the walking infrastructure and its usage, then applied in five large campus parking lots based on observational strategies. The results indicate that the orientation of a walking path perpendicular to a destination (which provides short-distance walks), availability of proper access to walking paths and designated crossing areas that connect between walking paths, proper implementation of effective traffic calming strategies, and presence of a single entrance and exit for vehicles all play important roles in promoting the use of existing walking infrastructure and creating pedestrian-friendly parking lots. Additionally, this study method extends the walkability assessment of built environments, particularly in large parking lots. This study promotes the creation of sustainable university campuses, thereby enhancing the quality of life of students and staff who use the facilities. [ABSTRACT FROM AUTHOR]

Along with society's development, transportation has become a key factor in human daily life, increasing the number of vehicles on the streets. Consequently, the task of finding free parking slots in metropolitan areas can be dramatically challenging, increasing the chance of getting involved in an accident and the carbon footprint, and negatively affecting the driver's health. Therefore, technological resources to deal with parking management and real-time monitoring have become key players in this scenario to speed up the parking process in urban areas. This work proposes a new computer-vision-based system that detects vacant parking spaces in challenging situations using color imagery processed by a novel deep-learning algorithm. This is based on a multi-branch output neural network that maximizes the contextual image information to infer the occupancy of every parking space. Every output infers the occupancy of a specific parking slot using all the input image information, unlike existing approaches, which only use a neighborhood around every slot. This allows it to be very robust to changing illumination conditions, different camera perspectives, and mutual occlusions between parked cars. An extensive evaluation has been performed using several public datasets, proving that the proposed system outperforms existing approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid optimization system depending on optimal location for electric vehicles parking lot (PL) and capacitors on distribution system to maintain voltage profile with electricity loss. The proposed system is the consolidation of Seagull optimization algorithm (SGO) and salp swarm algorithm (SSA). The migration and attacking behaviors of seagull is empowered through SSA method. By this manner, the proposed hybrid optimization scheme is known as SGOS2A method. Here, parking zone allocation with capacitor is considered to congestion management in conjunction through the compensation of reactive energy. So, one can optimally decide the size of automobile parking space, SGOS2A method is followed. Moreover, parking lot with capacitor allocation is considered to congestion control at the side of reactive power compensation. By this proper manipulate, the capacitors exact location, automobile parking space of electric vehicles on the grid, lessening of active with reactive power loss, voltage profile conversion is selected optimally. Besides, the proposed SGOS2A scheme is activated on MATLAB/Simulink site, then the efficiency is examined with different techniques. The mean, median and standard deviation of the proposed approach achieves 1.0593, 1.0915 and 0.1050. [ABSTRACT FROM AUTHOR]

The thoracic fraction of road dust (PM10) was measured for the first time in Portugal in parking areas, both outdoors and indoors, with the aim of completing existing studies carried out in active lanes of various roads. An in situ resuspension chamber was used to collect a total of 23 samples in three parking areas of Aveiro, whilst the laboratory procedures included determination of carbonaceous content (OC and EC) by a thermo-optical technique, elemental composition by ICP-MS and ICP-OES after acid digestion, and the Aliivribrio fisherii bioluminescent bacteria ecotoxicity bioassay. Dust loadings (DL10) obtained were 18.5 ± 9.8 mg PM10 m−2, in outdoor parking, and 1.8–23.7 mg PM10 m−2 for indoor parking, corresponding to emission factors of 476 and 75–589 mg veh−1 km−1, respectively. OC represented 9–30 % of PM10 for the indoor parking areas. However, for the outdoor samples, the high iron oxide content jeopardised the OC-EC separation. In those samples, carbonates accounted for 10.0 ± 3.3% of the PM10 mass. The analysis of elemental components focused on major elements (Al, Ca, Fe, K, and Mg) as well as minor elements. The total mass fraction of element oxides accounted for 27.1% (outdoor) and 23.6–34.3% (indoor). ΣPAH calculated for all parking areas accounted for 8.38–36.9 μg g−1 PM10. The ecotoxicological bioassay showed that all aqueous solutions were toxic to bioluminescent bacteria, whereas no clear correlations could be made with specific component groups, with the exception of ΣPAH and EC50. [ABSTRACT FROM AUTHOR]

The issue related to parking lots has a multidimensional nature. For the purposes of the article, the analysis covered 113 parking lots located directly next to national roads in the Mazovian Voivodeship (Poland), which is similar in terms of economic development and conditions of the linear infrastructure to other countries in Central Europe. The conducted research included analyzing the correlation of parameters in individual parking lots and those related to nearby roads (traffic volume, technical quality) and the type of area in which they occurred (population density). In addition, the presented results describe the capacity of parking in the analyzed region in terms of the number of available parking slots for heavy goods vehicles, the size of the additional operating area, and the presence of security-related elements (fencing, lighting, physical protection, monitoring). The analysis demonstrated a correlation between some parameters (type of nearby road and security rs = 0.35, area size and number of slots rs = 0.35) and significant differences in results between private and public parking lots (average number of slots 18 versus 34, percent of most secured parkings 45% versus 69%). The obtained innovative results can be used by public institutions and entities providing parking services to reorganize the space at parking places and to conduct more effective analyses from the point of view of future investments in infrastructure accompanying the public road network. [ABSTRACT FROM AUTHOR]

E-mobility represents an important part of the EU’s green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector’s development it is necessary to invest in energy infrastructure and to assure favorable conditions in terms of competitive electricity prices to make the technology even more attractive. Large peak consumption of parking lots which use different variants of uncoordinated charging strategies increases grid problems and increases electricity supply costs. On the other hand, as observed lately in energy markets, different, mostly uncontrollable, factors can drive electricity prices to extreme levels, making the use of electric vehicles very expensive. In order to reduce exposure to these extreme conditions, it is essential to identify the optimal way to supply parking lots in the long term and to apply an adequate charging strategy that can help to reduce costs for end consumers and bring higher profit for parking lot owners. The significant decline in photovoltaic (PV) and battery storage technology costs makes them an ideal complement for the future supply of parking lots if they are used in an optimal manner in coordination with an adequate charging strategy. This paper addresses the optimal power supply investment problem related to parking lot electricity supply coupled with the application of an optimal EV charging strategy. The proposed optimization model determines optimal investment decisions related to grid supply and contracted peak power, PV plant capacity, battery storage capacity, and operation while optimizing EV charging. The model uses realistic data of EV charging patterns (arrival, departure, energy requirements, etc.) which are derived from commercial platforms. The model is applied using the data and prices from the Croatian market.

In recent years, parking lot management systems have garnered significant research attention, particularly concerning the application of deep learning techniques. Numerous approaches have emerged for tackling parking lot occupancy challenges using deep learning models. This study contributes to the field by addressing a critical aspect of parking lot management systems: accurate vehicle occupancy determination in specific parking spaces. We propose an advanced solution by harnessing an optimized MobileNetV3 model with custom architectural enhancements, trained on the CNRPark-EXT and PKLOT datasets. The model processes individual parking space patches from real-time video feeds, providing occupancy classification for each patch, identifying occupied or available spaces. Our architectural modifications include the integration of a convolutional block attention mechanism in place of the native attention module and the adoption of blueprint separable convolutions instead of the traditional depth-wise separable convolutions. In terms of performance, our proposed model exhibits superior results when benchmarked against state-of-the-art methods. Achieving an exceptional area under the ROC curve (AUC) value of 0.99 for most experiments with the PKLot dataset, our enhanced MobileNetV3 showcases its exceptional discriminatory power in binary classification. Benchmarked against the CarNet and mAlexNet models, representative of previous state-of-the-art solutions, our proposed model showcases exceptional performance. During evaluations using the combined CNRPark-EXT and PKLot datasets, the proposed model attains an impressive average accuracy of 98.01%, while CarNet achieves 97.03%. Beyond achieving high accuracy and precision comparable to previous models, the proposed model exhibits promise for real-time applications. This work contributes to the advancement of parking lot occupancy detection by offering a robust and efficient solution with implications for urban mobility enhancement and resource optimization.

With the rapid increase of electric vehicles (EVs), the uncoordinated charging of large-scale EVs will inevitably form a new peak of power consumption, which will put forward new requirements for the adjustment of EV charging behavior and the power supply capacity of the distribution network. However, upgrading the infrastructure of the distribution network is very expensive, so it is of great value to research on how to realize the optimal integration of EVs without infrastructure upgrading. In this paper, an intelligent grouping method is proposed considering the coupling relationship among EV travel information, battery status and other characteristics, and a charging/discharging priority model is established based on the contribution index of charging process. Finally, a smart real-time energy management strategy is formed in order to maximize the penetration level of EVs under current conditions. With the strategy proposed, the maximum penetration level of EVs is increased from 20% to 60% in the simulation. The strategy has good adaptability to the gradually growth of the base load, which can effectively delay infrastructure upgrading of the distribution network and reduce the overall operation cost of the station. It could furtherly provide a reference for the operation and upgrading of the parking lot charging station.

The random decisions of electric vehicle (EV) drivers, together with the vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) energy transfer modes, make scheduling for an intelligent parking lot (IPL) more complex; thus, they have not been considered simultaneously during IPL planning in other studies. To fill this gap, this paper presents a complete optimal schedule for an IPL in which all the above-mentioned items are considered simultaneously. Additionally, using a complete objective function—including charging/discharging rates and prices, together with penalties, discounts, and reward sets—increases the profits of IPL and EV owners. In addition, during peak times, the demand for energy from the distribution system is decreased. The performance of the proposed schedule is validated by comparing three different scenarios during numerical simulations. The results confirm that the proposed algorithm can improve the IPL's benefits up to USD 1000 and USD 2500 compared to the cases that do not consider the V2V and V2G energy transfer modes, respectively. [ABSTRACT FROM AUTHOR]

Parking scenarios are spatially dense and have a lot of interactions, making predicting vehicles' search behavior crucial and challenging for autonomous driving. Existing data-driven prediction methods struggle to determine vehicles' intents and consider the surrounding environment accurately. This study proposes a novel two-stage framework for parking search behavior prediction, involving parking intent and vehicle trajectory predictions based on imitation learning and deep learning. First, we develop an adversarial inverse reinforcement learning model for parking search intent (PSI-AIRL) learning from measured trajectory data in an actual parking lot. Then, we design an integrated convolutional neural network (CNN) and transformer model to forecast vehicle trajectory using historical observations and the predicted parking search intents. This two-stage framework achieves parking search intent prediction by applying global information about the parking lot while improving vehicle trajectory prediction's accuracy and robustness. Finally, the experiments are conducted on the Dragon Lake Parking (DLP) dataset to compare our framework with state-of-the-art models. The results show that our model outperforms other baseline models in accuracy for parking intent and vehicle trajectory predictions. Moreover, our model shows exceptional accuracy and robustness in predicting across diverse parking scenarios. • A novel two-stage framework for parking search behavior prediction is proposed. • An AIRL model is developed for the first stage of parking intention learning. • Trajectory forecasting is then based on an integrated CNN and transformer model. • The experiments prove the superiority of our model via actual parking datasets. [ABSTRACT FROM AUTHOR]

The Metro Crenshaw/LAX rail-transit line is currently under construction and planned for completion in 2019. The rail-transit line will connect the Metro Expo Line to the Los Angeles Airport. Installation of the project will require road surface space at some segments and as a result, it will run at-grade in the Park Mesa Heights Neighborhood of South Los Angeles. Construction on the commercial Crenshaw Boulevard from 48th Street to Slauson Boulevard requires the removal of over 300 parking spaces. Business owners along the corridor fear that the loss of parking will create barriers for employees and customers to access their establishments. This report examines shared parking as a parking management treatment that can increase the supply of parking without building more of it. The report assesses the technical and political feasibility of a shared parking program.

Puzzle-based storage systems are a new type of automated storage systems that allow storage of unit loads (e.g. cars, pallets, boxes) in a rack on a very small footprint with individual accessibility of all loads. They resemble the famous 15-sliding tile puzzle. Current models for such systems study retrieving loads one at a time. However, much time can be saved by considering multiple retrieval loads simultaneously. We develop an optimal method to do this for two loads and heuristics for three or more loads. Optimal retrieval paths are constructed for multiple load retrieval, which consists of moving multiple loads first to an intermediary 'joining location'. We find that, compared to individual retrieval, optimal dual load retrieval saves on average 17% move time, and savings from the heuristic is almost the same. For three loads, savings are 23% on average. A limitation of our method is that it is valid only for systems with a very high space utilisation, i.e. only one empty location is available. Future research should investigate retrieving multiple loads for systems with multiple empty slots. [ABSTRACT FROM AUTHOR]

Along with society’s development, transportation has become a key factor in human daily life, increasing the number of vehicles on the streets. Consequently, the task of finding free parking slots in metropolitan areas can be dramatically challenging, increasing the chance of getting involved in an accident and the carbon footprint, and negatively affecting the driver’s health. Therefore, technological resources to deal with parking management and real-time monitoring have become key players in this scenario to speed up the parking process in urban areas. This work proposes a new computer-vision-based system that detects vacant parking spaces in challenging situations using color imagery processed by a novel deep-learning algorithm. This is based on a multi-branch output neural network that maximizes the contextual image information to infer the occupancy of every parking space. Every output infers the occupancy of a specific parking slot using all the input image information, unlike existing approaches, which only use a neighborhood around every slot. This allows it to be very robust to changing illumination conditions, different camera perspectives, and mutual occlusions between parked cars. An extensive evaluation has been performed using several public datasets, proving that the proposed system outperforms existing approaches.

Unorganized city growth causes traffic congestion, and, in return, this forces governments to invest in costly buildings such as public parking lots. The demand for parking spaces has further increased due to rapid population growth, especially in metropolitan areas. One solution to this problem is to locate new public parking lots within city centers so that car owners can use these facilities instead of on-street parking. In this study, a facility location and allocation scheme was engaged to determine the optimal number of and locations for public parking lots. A multiobjective, multiperiod, decision-dependent, two-stage, stochastic mathematical model was developed to fulfill the decision makers' aims via different objectives. In the model, three objectives were considered: minimizing total facility establishment and operational cost, minimizing average unsatisfied demand, and minimizing CO2 emissions. The proposed framework is the first in parking lot literature to address the multiobjectivity of decision-dependent demand within an uncertain environment. The weighted sum method was used to develop the multiobjective model, and two-stage stochastic programming was applied to manage the uncertainty. Hence, the proposed model satisfies critical gaps, predominantly in relation to the problem of parking lot location, and most importantly in creating sustainable cities. The results provide meaningful outcomes for decision makers in terms of optimizing unsatisfied demand, tackling CO2 emissions, and determining the optimal number and location of public parking lots. Furthermore, the robustness of the model was tested through sensitivity analyses. [ABSTRACT FROM AUTHOR]

This article proposes an optimization technique to regulate the best site for electric vehicle parking lot (EVPL) in a grid to monitor the voltage profile with frequency by using discharging method of operation. The proposed hybrid scheme is the joint execution of both the balancing composite motion optimization (BCMO) and the political optimizer (PO); hence, it is called the BCMPO technique. The balancing composite motion properties are enhanced by political optimizer algorithm. Here, allocation of the parking lot (PL) including capacitor is presented for congestion management (CM) along with reactive power compensation. BCMPO technique is adopted to optimally determine the size of the PL. The propsoed approach is used to reduce the active and reactive power loss and voltage fluctuation by optimally siting the EVPL. The proposed BCMPO model is performed in MATLAB/Simulink platform, and then the performance is compared with other models. The simulation time for the corresponding iterations 100, 250, 500, and 1000 becomes 14.8, 29.2, 69.9 and 77.1 second. [ABSTRACT FROM AUTHOR]

The thoracic fraction of road dust (PM10) was measured for the first time in Portugal in parking areas, both outdoors and indoors, with the aim of completing existing studies carried out in active lanes of various roads. An in situ resuspension chamber was used to collect a total of 23 samples in three parking areas of Aveiro, whilst the laboratory procedures included determination of carbonaceous content (OC and EC) by a thermo-optical technique, elemental composition by ICP-MS and ICP-OES after acid digestion, and the Aliivribrio fisherii bioluminescent bacteria ecotoxicity bioassay. Dust loadings (DL10) obtained were 18.5 ± 9.8 mg PM10 m−2, in outdoor parking, and 1.8–23.7 mg PM10 m−2 for indoor parking, corresponding to emission factors of 476 and 75–589 mg veh−1 km−1, respectively. OC represented 9–30 % of PM10 for the indoor parking areas. However, for the outdoor samples, the high iron oxide content jeopardised the OC-EC separation. In those samples, carbonates accounted for 10.0 ± 3.3% of the PM10 mass. The analysis of elemental components focused on major elements (Al, Ca, Fe, K, and Mg) as well as minor elements. The total mass fraction of element oxides accounted for 27.1% (outdoor) and 23.6–34.3% (indoor). ΣPAH calculated for all parking areas accounted for 8.38–36.9 μg g−1 PM10. The ecotoxicological bioassay showed that all aqueous solutions were toxic to bioluminescent bacteria, whereas no clear correlations could be made with specific component groups, with the exception of ΣPAH and EC50.

A new model of energy carriers (micro-refinery output products) in the concept of an energy hub is presented. In addition, in the presented model, the effect of different models of parking lot in an energy hub is analyzed. In this study, the uncertainty of the number of electric vehicles was modeled using the Monte Carlo method, and then considering the same conditions, the uncertainty of the number of electric vehicles was calculated using the Probability-Possibility hybrid method. In addition, the study uses a scenario-based approach to address uncertainties related to multi-carrier energy demand and multi-carrier energy prices. In the present paper, ensuring water demand is of great importance, which is why the proposed energy hub structure of a sea desalination unit and sour water treatment that is extracted from the micro refinery output was analyzed. The optimization model presented in this paper for the energy hub is a complex integer linear programming (MILP) that is solved using a CPLEX solver in the GAMS environment. The results show that the use of the Probability-Possibility method resulted in an 8 % reduction in the final energy supply cost in the energy hub compared to the Monte Carlo method., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)