Vehicle Parking Research Articles

Blockchain-based fast handover authentication protocol for Internet of Vehicles in small industrial parks

Autonomous vehicles safeguard the security and efficiency of Internet of Vehicles systems in small industrial parks by authenticating and exchanging real-time information with transportation infrastructure. Deploying a multi-server framework reduces the risk of message blocking and privacy information leakage from centralized services. However, in traditional handover authentication protocols, there are still potential security risks such as high-overhead performance issues and single point of failure attacks. Therefore, it is considered challenging to realize efficient authentication while protecting the privacy of vehicles. In this paper, we propose a secure and efficient handover authentication protocol for autonomous vehicles in a small industrial park to address the challenges. The protocol is based on blockchain and Pedersen verifiable secret sharing scheme, which not only ensures lightweight real-time interactions between autonomous vehicles and edge servers in multi-server environments, but also strictly protects the security and privacy of both vehicles and edge servers. We prove the semantic security of the protocol under the Real-Or-Random model and perform a informal analysis of its security attributes to show that it can withstand a wide range of malicious attacks. Performance evaluation shows that the proposed protocol satisfies more security requirements and has better computational efficiency and communication cost than other related protocols.

Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

Research and Design of Intelligent Parking Management System based on the YOLO Algorithm

Given the difficulty in managing parking lots and inefficient utilization of parking spaces at university and college campuses, this paper designs an intelligent campus parking management system with functions such as license plate recognition (LPR), online parking space reservations, campus parking navigation, and mobile app payment through the parking space detection technology based on You Only Look Once (YOLO) algorithm, Internet of Things (IoT) technology, and cloud platform technology. This system obtains information relating to license plates, parking spaces, etc. from sensing nodes with the IoT technology and transmits the collected information through NB-IoT technology to a cloud platform for storage and management to facilitate information exchange between hardware and software. This paper describes the architecture of the intelligent parking management system, parking space detection technology based on the YOLOv4 algorithm, and hardware and software design of the system. The paper proposes an effective solution to parking problems facing colleges and universities. The paper concludes that the intelligent campus parking management system based on the YOLOv4 algorithm can prevent the haphazard parking of vehicles and traffic congestion at open campuses and therefore has important value for application and popularization.

Optimization Method for Allocating Peak-Period Parking Demand in Hub Parking Lot Clusters

With the expansion of urban scale and the popularization of multi-modal transportation, transportation hubs, as the link of multi-modal travel, are becoming increasingly important in urban development and residents’ lives. In situations of high parking demand, the increase in road traffic volume and parking search delays exacerbates the service pressure on hub parking lots and the traffic congestion on surrounding roads. Therefore, reasonable parking demand allocation is one of the key solutions to this problem. Based on the analysis of the vehicle parking search process, this paper constructs a model for estimating parking search delay on roads outside hub parking lots and proposes an optimization model for parking demand allocation aimed at minimizing the total parking search delay of vehicles. Finally, taking a major transportation hub in Nanjing as a case study, data were obtained through field investigations and simulation experiments to identify peak parking demand periods and calibrate the model parameters. The results show that the average vehicle delay was reduced by 4.5%, with a total reduction of 13,860 s in vehicle delay for parking demands at the hub within one hour. Therefore, by optimizing the allocation of parking demand, the average delay for vehicles searching for parking can be reduced to a certain extent.

PERENCANAAN SIRKULASI RUANG PUSAT TERAPI HOLISTIK DI SAMARINDA

Samarinda is one of the most populous cities in East Kalimantan. Living in a densely populated urban area has a greater impact on the risk of mental health disorders. Poor mental health has an impact on physical health. One effort for health care is by conducting holistic health therapy as an alternative to healing overall health, starting from a person's physical, emotional, social, and spiritual health. The purpose of this planning is to produce a holistic therapy center building design in Samarinda with circulation planning that can facilitate building users in their activities in it. Through the analysis method by analyzing the area and building circulation. Analysis of area circulation is carried out by arranging vehicle lanes, parking and building positions. Analysis of building circulation is carried out by determining the pattern of vertical and horizontal space circulation. This planning produces a holistic therapy center in Samarinda that has a circulation space that can increase comfort and facilitate access for building users, so that this therapy center can meet the needs of urban physical and psychological health and become a place for a healthy environmental community

Multi-objective robust optimization for optimal operation of gas-electricity networks with the penetration of integrated P2G and electric vehicle parking lots

Energy is considered one of the most important human needs. Therefore, various energy resources are exploited to meet human needs. Among all available resources, electricity and natural gas have accounted for the largest share in meeting these needs. Accordingly, management of these resources is one of the most important issues in different societies. This paper presents a multi-objective optimization model for optimal management and operation of the power distribution network and natural gas network as well as reducing carbon emissions. In this modeling, the natural gas network and distribution power network are connected through two electric vehicle parking lots (EVPLs) and two gas-fired units (GFUs), and these two EVPLs and GFUs exchange energy with each other. The EVPLs are equipped with various facilities including power-to-gas (P2G) systems and natural gas storage that facilitate energy exchange between the two networks. The modeling presented in this article is based on mixed integer linear programming, so the nonlinear equations including power flow equations, the Weymouth equation and the cost functions of GFUs are also linearized using various methods. It should be noted that the price of purchased electricity poses uncertainty which is modeled using robust optimization techniques. The proposed model is tested on the 33-bus power distribution network and the 20-node natural gas network. The results show that the presence of EVPLs significantly reduces the operational costs of the natural gas network and power distribution network and carbon emissions.

The Influence of Vehicles Parking on the Roadside as Reduction of Lane Width on Traffic Performance

The city of Poso is experiencing rapid economic growth, attracting many newcomers and developing various commercial facilities along its streets. This study analyzes traffic performance on three road sections in Poso City: Jl. Pulau Irian Jaya (District Road), Jl. Pulau Sumatera (National Road), and Jl. Kasintuwu (Provincial Road), focusing on the impact of on-street parking as a side friction and lane width reduction on road capacity and degree of saturation (DS). The methods used are MKJI 1997 and PKJI 2023. The study results indicate that road capacity decreases and DS increases significantly when using the PKJI 2023 method compared to MKJI 1997. Jl. Pulau Irian Jaya experienced an increase in DS from 0.372 to 0.410 (side friction) and from 0.874 to 0.94 (lane width reduction). Jl. Pulau Sumatera showed an increase in DS from 0.716 to 0.776 (side friction) and from 0.75 to 0.84 (lane width reduction). Meanwhile, Jl. Kasintuwu showed an increase in DS from 0.114 to 0.118 (side friction) and from 0.20 to 0.21 (lane width reduction). These results suggest that the PKJI 2023 method provides a more conservative estimate, indicating a more significant decline in traffic performance compared to MKJI 1997.

Day-ahead electric vehicle charging behavior forecasting and schedulable capacity calculation for electric vehicle parking lot

The Electric Vehicle Parking Lot (EVPL) aims to address the growing demand for EV charging infrastructure. EVs connected to EVPLs have extended access times compared to those at public charging stations. Consequently, EVPL owners can aggregate the schedulable capacity of connected EVs to participate in day-ahead and ancillary service markets, thereby gaining economic benefits. However, achieving this objective is hindered by the lack of accurate day-ahead forecasts of EV charging behavior. To address this issue, this paper introduces a novel day-ahead forecasting method for EV charging behavior at EVPLs, alongside a strategy for calculating EV schedulable capacity based on these forecasts. Unlike existing methods, this paper presents a day-ahead time-of-use clustering forecasting strategy, which provides more detailed and accurate predictions of EV charging behavior, eliminating the need for numerous assumptions during schedulable capacity calculations. The study demonstrates that the proposed method, validated using actual historical data, enables precise forecasting of EV charging behavior. Furthermore, the proposed day-ahead schedulable capacity calculation strategy is shown to be both effective and practical.

Investigation of the Effects of Sprinkler System in Automated Vehicle Parking Structures Using Performance-Based Fire Assessment

Investigation of the Effects of Sprinkler System in Automated Vehicle Parking Structures Using Performance-Based Fire Assessment

ANALISIS KEBUTUHAN RUANG PARKIR PADA OBJEK WISATA PANTAI SANUR

The parking area available at Sanur Beach currently utilizes the road body, not the special parking lot that the beach has, thus creating a significant challenge for managers and visitors. Along with the increasing popularity of Sanur Beach as a tourist destination, the road to the beach has become congested due to the number of vehicles passing by, both motorcycles and cars. This causes visitors to often park their vehicles carelessly and irregularly, which has the potential to cause safety problems and inconvenience for other road users. Many visitors park outside the beach area, thus interfering with activities on the streets around the tourist attraction, which in turn can affect the image of Sanur Beach as a friendly and organized tourist destination. This research focuses on the need for parking spaces in Sanur Beach tourist attractions, with the hope of providing an effective solution to this problem. The data used in planning the needs of parking spaces is primary data, namely the results of surveys conducted on parking managers and vehicle traffic data. The survey results show that the parking area for motorcycles is most used on Sunday afternoon, reaching almost 228 plots, while for cars, the peak of use occurs on Sunday morning, reaching 34 plots. These findings show the importance of developing better and planned parking infrastructure at Sanur Beach, to improve the visitor experience and maintain a smooth flow of traffic. Thus, careful planning in the provision of parking spaces will be a crucial step to support the sustainability of tourism at Sanur Beach.

Trajectory Planning Design for Parallel Parking of Autonomous Ground Vehicles with Improved Safe Travel Corridor

In this paper, the concept of symmetry is utilized to design the trajectory planning for parallel parking of autonomous ground vehicles—that is, the construction and the solution of the optimization-based trajectory planning approach are symmetrical. Parking is the main factor that troubles most drivers for their daily driving travel, and it can even lead to traffic congestion in severe cases. With the rise of new intelligent and autonomous vehicles, automatic parking seems to have become a trend. Traditional geometric planning methods are less adaptable to parking scenarios, while the parking paths planned by graph search methods may only achieve local optimality. Additionally, significant computational time is often required by numerical optimization methods to find a parking path when a good initial solution is not available. This paper presents a hierarchical trajectory planning approach for high-quality parallel parking of autonomous ground vehicles. The approach begins with a graph search layer to roughly generate an initial solution, which is refined by a numerical optimization layer to produce a high-quality parallel parking trajectory. Considering the high dimensionality and difficulty of finding an optimal solution for the path planning optimization problem, this paper proposes an improved safe travel corridor (I-STC) with the construction of collision constraints isolated from surrounding environmental obstacles. By constructing collision constraints of the I-STC based on the initial solution, the proposed method avoids the complexities and non-differentiability of traditional obstacle avoidance constraints, and simplifies the problem modeling the subsequent numerical optimization process. The simulation results demonstrate that the I-STC is capable of generating parallel parking trajectories with both comfort and safety.

An Orderly Charging and Discharging Strategy of Electric Vehicles Based on Space–Time Distributed Load Forecasting

Given the widespread adoption of electric vehicles, their charging load is influenced not only by vehicle numbers but also by driving and parking behaviors. This paper proposes a method for forecasting electric vehicle charging load based on these behaviors, considering both spatial and temporal distribution. Initially, the parking generation rate model predicts parking demand, establishing the spatial and temporal distribution model for electric vehicle parking needs across various vehicle types and destinations. Subsequently, analyzing daily mileage and parking demand distributions of electric vehicles informs charging demand assessment. Using the Monte Carlo simulation method, large-scale electric vehicle behaviors in different spatial and temporal contexts—parking, driving, and charging—are simulated to predict charging load distributions. Optimization of electric vehicle charging and discharging enhances grid stability, cost management, charging efficiency, and user experience, supporting smart grid development. Furthermore, charging load forecasting examples under diverse scenarios validate the model’s feasibility and effectiveness.

Multi-objective economic operation of smart distribution network with renewable-flexible virtual power plants considering voltage security index

This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual unit with electric vehicles and price-based demand response. This is performed to promote economic, operational, and network security objectives. According to numerical results, the approach with optimal power management of renewable virtual units is capable of boosting the economic, operation, and voltage security status of the network by approximately 43%, 47–62%, and 26.9%, respectively, to power flow studies. Only price-based demand response can improve the voltage security, operation, and economic states of the network by about 19.5%, 35–47%, and 44%, respectively, compared to the power flow model.

Analisa Pengetahuan Pecalang sebagai Pengatur Lalu Lintas

As a traditional security officer in Balinese society, Pecalang has a very important role, especially in keeping traditional and religious activities running. Along with the progress of the times, apart from being a security officer for traditional activities, Pecalang also has a duty as police intelligence in providing information about the areas it guards. The existence of this transformation of pecalang tasks requires pecalang to be more active in updating information and knowledge about the tasks that are being or will be carried out. In this study, the knowledge of pecalang was extracted on the task of pecalang as a traffic controller, both in carrying out the duties as the person in charge of diverting traffic flow and conditioning parking vehicles. The method used in this study is a method of collecting data with a questionnaire or questionnaire in which the questions asked are divided into several topics related to the task of pecalang as traffic controllers. Based on the analysis of the collected questionnaires, it was found that the knowledge of Pecalang in Pakraman Cemagi Village related to their duties as traffic controllers as follows: Pecalang already understands or knows about traffic and the duties of Pecalang, about traffic signs, about traffic procedures, and about parking. However, pecalangs do not understand or know less about traffic laws and regulations, about road markings and intersections, as well as about diversion, closure and classification of road divisions.

Security constrained unit commitment in smart energy systems: A flexibility-driven approach considering false data injection attacks in electric vehicle parking lots

In this paper, a new structure, the so-called FBSCUCFDIP-P/EV is proposed as flexibility-based security constrained unit commitment (SCUC) in the presence of false data injection (FDI) attack into the communication infrastructure of electric vehicle parking lot (EVPL). Herein, the uncertain EVPLs are integrated into the SCUC problem with the aim of reducing the operation cost. It is notable that growing integration of unspecified EVPLs can introduce novel challenges to the power system, significantly impacting its flexibility. In this study, electric vehicles are leveraged as a means to enhance system flexibility. Meanwhile, the FDI attacks in EVPLs can distort the system’s flexibility and lead to inaccurate assessments of the power system’s ability to adapt to changing conditions. In order to model the FDI attack, a bi-level optimization problem based on mixed integer linear programming is formulated. At the upper level, the impact of EVPLs on the flexibility indices of the SCUC is evaluated, and the false data injected into the EVPL is calculated at the lower level. Since both levels of the proposed FBSCUCFDIP-P/EV include discrete variables, a reformulation and decomposition technique is utilized to achieve the optimal solution. Instead, an extreme gradient boosting (XGBoost)-based machine learning method is considered to detection and correction of FDI attack. The proposed approach is tested on the IEEE 24-bus system. The simulation results initially indicate the improvement of the flexibility of the power system in proposed structure. Further, injecting false data into all available EVPLs causes to increase the system operation cost. Besides, false data leads to distorted charging and discharging scheduling of EVPLs; likewise, scheduling and commitment of power generation units also changes. Subsequently, the application of the XGBoost algorithm effectively mitigates the impact of FDI attacks, achieving a maximum accuracy of 85.41%.

Brake Response Time and the Effects of Parked and Incurring Vehicles on Driver Behavior

A commonly investigated parameter in driving research is perception-response time (PRT), which refers to the duration required for a driver to respond to a roadway hazard. This study used a driving simulator to reproduce a commonly cited test-track response-time study by Mazzae et al., where responses to a laterally incurring vehicle were investigated. The primary goal of the current study was to evaluate how different factors in the configuration of the Mazzae et al. study, namely presence of parked roadside vehicles and incurring vehicles, affected response times. We enrolled 88 subjects and had them operate a driving simulator that replicated the prior study’s test track with and without the parked and incurring vehicles. A delay in accelerator pedal release was found when the incursion vehicles were present during three prior non-event passes through the critical intersection. The presence of parked vehicles did not affect response times. Accelerator-release times and brake response times from our simulator study align well with the original test-track response-time data.

A day‐ahead energy management strategy for electric vehicles in parking lots considering multi‐scenario simulations and hydrogen storage system

AbstractThis article investigated the charge and discharge management structure of electric vehicles (EVs) in intelligent parking lots (IPLs). It seems that with the expansion of renewable energy sources (RESs) as clean energy and investigation of the effects of EVs on the operation and planning of future distribution networks around the way EVs exchange energy with each other and the upstream network operator, RESs such as solar and wind sources, along with hydrogen storage are essential. Therefore, a new stochastic multi‐scenario approach for charge/discharge management of EVs parked in IPL was proposed, in which a newly developed model for the IPL with a hydrogen storage system (HSS) consisting of a fuel cell, an electrolyzer, and a hydrogen storage tank was presented. In the proposed model, the constraints of upstream network and power balance constraints, with the operating constraints related to the IPL, including EVs, renewable energy sources, and the hydrogen storage system, were formulated as the most important objectives of the optimization problem. The optimization algorithm, Competitive Swarm Optimizer (CSO), was formulated for implementation, and its results were compared with those of the PSO and GWO algorithms. The CSO must handle a variety of practical, large‐scale optimization problems. Based on the obtained results, the excess energy purchased from the upstream network or renewable sources was used as hydrogen storage for consumption during peak hours. As expected, the technical constraints and financial goals of the system were met, and the proposed system was evaluated on a 33‐bus network. As the expected benefits will be the most beneficial with the presence of renewable sources, the final profit will be reduced by taking into account uncertainty for charge/discharge management in EVs such as the uncertainty of electric load, market price, wind turbine, and photovoltaic cell sources. Nonetheless, the profit obtained from renewable resources is preferable to losses resulting from the uncertainty of the system, and according to expectation, the performance of the system for managing the optimal charging and discharging of EVs in the IPL will be acceptable with the maximum profit for the grid.

Energy management algorithm development for smart car parks including charging stations, storage, and renewable energy sources

In this study, a photovoltaic system and stationary energy storage unit integrated vehicle charging station energy management algorithm were developed using a long-short term based prediction model (LSTM). The aim of the proposed system is to develop intelligent car parks with controlled charging stations aggregated by centralized charging stations instead of individual or dispersed uncontrolled charging stations to eliminate the imbalance in the demands of charging. The system has four energy sources: grid, vehicle batteries, PV system, and the stationary battery group. By calculating the power demand for vehicles in the car park, a dynamic energy management algorithm has been developed that provides efficient use of energy resources by considering the power demand density. Moreover, the forecasting model has been created by LSTM) not only to adjust charging timing and effectively use energy sources. The model was performed by forecasting not to take energy from the grid at critical times (overloaded times). The grid load is analyzed by the energy management algorithm and run for different times of the year, creating a load profile for 16 vehicles. The results of this study show that energy demand during the critical time interval can be reduced by 8–20 % solely through load shifting before and after the critical time interval without any additional resource support. Moreover, while the integration of only the PV system supports the grid by 15–20 %, the optimal utilization of other energy sources during the relevant time frame supports the grid by 65–75 %. In addition, by collecting charging stations at a central point, energy storage capacity is increased and effective energy management is achieved. In conclusion, it is proposed that the infrastructure of such a parking system should be set up before the use of electric vehicles increases significantly.

A novel risk-averse optimal scheduling strategy for active distribution networks equipped with power-to-X technologies

This paper proposes a novel risk-averse approach for scheduling of an active electrical distribution network (AEDN). The network is connected to an electric vehicle parking lot (PL) and gas and hydrogen fueling stations (GHFSs). In addition, the AEDN operator is able to participate in both day-ahead and real-time markets to supply energy demands. In order to address the uncertainties of the studied network, a two-stage stochastic programming incorporating conditional value-at-risk (CVaR) is employed to model uncertainties of wind turbine and photovoltaic output, electric vehicles behavior in PL, and GHFSs energy consumption, while a robust optimization method is used to handle the uncertainty of real-time market price. According to the numerical results, the AEDN operator can decrease the risk level by up to 1.2% while the operational cost rises by 7.5%. Furthermore, the optimal use of multiple energy storage technologies and price-sensitive shiftable electric loads (PSELs) reduces the operational cost by 7.4%.

Effect of Haulage Vehicle Activities on Ambient Air Pollutants at a Large Haulage Vehicle Park in Nigeria

Effect of Haulage Vehicle Activities on Ambient Air Pollutants at a Large Haulage Vehicle Park in Nigeria