An optimal parking space search model based on fuzzy multiple attribute decision making

A parking guidance and information system for the Shinjuku area of Tokyo is described as Japan's first parking guidance and information system (PGIS) to have a full-fledged guidance function. The concept of the Shinjuku area parking guidance and information system is outlined. The surveys conducted before and after the introduction of the Shinjuku area PGIS confirmed its effectiveness in: (1) improving the convenience of users; (2) reducing on-street parking; (3) enhancing the order of traffic in the Shinjuku area; (4) absorbing into off-street parking lots those cars parked on-street for a short time; and (5) equalizing the number of cars parked between available parking lots. The measures to be taken for further improving the Shinjuku area PGIS are presented at the end.

This paper proposes a complete set of Urban Parking Guidance and Information System, which includes Parking Subsystem and City Parking Subsystem. The system adopts wireless sense network based on ZigBee technology for the communication network. The parking information terminals that set up in the parking areas automatically identify the parking vehicles and upload the parking information. After processing the data, the Central Control Module would distribute the parking information by LCD screens for the drivers. In terms of parking supply and demand status, the system sends the separately parking information to the corresponding phone number. The system achieves barrier-free intelligent parking management by realizing functions such as three ways of releasing the information of parking lots, inquiry of parking location and convenient register operation.

To draw the simulation diagram of the unmanned vehicle driving from the current position to the optimal parking space, the parking space with the shortest distance from the starting point to the control point is defined as the optimal point in this paper. We simplify the driving route into a straight line and establish the coordinate axes. Depending on the driving direction, the driving trajectory can be divided into different cases. In each case, the order of the parking spaces passed by the vehicle is fixed. Therefore, if the parking space is occupied, its value is assigned as 0; otherwise, it is 1. The initial value is assigned as 0, and we accumulate the weighted value in each case. When the total weight reaches 1, accumulation stops, and the optimal parking space in this case is recorded. The optimal parking space is in which the vehicle travels the shortest distance among all the cases. Based on the A* algorithm, a raster map is constructed. The obstacle area is expanded by narrowing the feasible area range of control points in the parking lot so that the path of control points can meet the kinematic and obstacle avoidance constraints of vehicles. Finally, a simulation diagram of the parking trajectory is drawn.

With the development and application of intelligent transportation systems (ITS), parking guidance and information system (PGIS) has become a hot topic for research and applications. Parking variable message sign (parking VMS) is the most common form of PGIS currently in use, particularly in providing the space availability information to en-route drivers. The aim of this study is to model drivers' responses to the parking VMSs under various parking information in a dynamical way. To ensure the guiding efficiency and assist drivers to identify parking lots with vacant spaces, the display strategies of parking VMS were studied. A discriminated release strategy with a space occupancy indicator (SOI) was then proposed to determine the display content on the parking VMS, in which an agent-based simulation was introduced to incorporate both parking choice model and traffic assignment algorithm dynamically. Guiding reliability and display strategies were further analyzed to attain a discriminated release strategy with SOI effectively. The results indicate that the guiding reliability was improved by reducing the SOI value of the corresponding parking lot, and the relatively uniform guiding reliabilities of all parking lots were attained by modifying SOI values iteratively. Additionally, the performance by incorporating the waiting time into the parking choice model was investigated and recommended. The discriminated release strategy presented in this study will assist in designing and operating urban PGIS.

Operators of parking guidance and information (PGI) systems often have difficulty providing the best car park availability information to drivers in periods of high demand. A new PGI configuration model based on the optimized combination method is proposed by analyzing parking choice behavior. This paper firstly describes a parking choice behavioral model incorporating drivers perceptions of waiting times at car parks based on PGI signs. Then relationships are developed for estimating the arrival rates at car parks based on driver characteristics, car park attributes and car park availability information displayed on PGI signs. A mathematical program is formulated to determine the optimal display PGI sign configuration to minimize total travel time. A genetic algorithm is used to identify solutions that reduce queue lengths and total travel time. These procedures were applied to a PGI system operating in Deqing Town near Shanghai, producing significant reductions in total travel time of parking vehicles.

Reservation berth is one of most important part of parking guidance. This paper studies a reservation strategy model of finite berth. It develops reservation strategy of finite berth. Based on the every aspect of life, the strategy can obtain the last reservation berth by game- playing algorithm considering the controlling to berth of guidance system and park. This paper develops a game-playing algorithm of reservation berth by analyzing the four possible strategies: reserve strategy, lag reserve, advance breach of faith, and berth occupying. Simulation experiments show this reservation strategy can reserve berth having no use for accurate reserve berth and using parameters, which is more near to real life. It is of use value.

To reduce the amount of CO 2 produced from the personal cars in the world, the parking lot guidance systems are considered to be the solution in shopping malls or department stores in many countries. However, most of the current parking lot systems are located in the parking area of each shopping mall and they cannot show the parking lots information for the driver that are driving on the road. So, The drivers can see the parking lot information if and only if they arrive at the shopping mall area. This is the fact that the CO 2 are still produced to the world although we use the parking lot guidance system. From the reason as mentioned, we propose the parking lots guidance software to share a parking lot information to a large number of clients in real-time based on MQTT Protocol. The proposed software can share the real-time parking lot information to the mobile devices of the drivers in any location or when they are driving on the road. The results show that the proposed software can share the parking lot information in real-time for at least 1,000 sessions simultaneously with the high average score of usability, design and the benefits.

This paper aims to effectively tackle decision-making problems on interval-valued Fermatean fuzzy sets; the current research suggested an integrated approach based on the WASPAS method. The criteria weights were determined by combining the objective weights obtained by the similarity measure approach with the subjective weights provided by decision-makers. This combination made achieving more realistic weights possible. Interval-valued Fermatean fuzzy sets were subjected to improved scoring functions and novel similarity measures concerning objective and subjective weights. The application of green supply chain management is discussed to show that the created approach fully applies to multi-criteria decision-making issues in the actual world. Green supply chain management was examined using critical success factors to control and audit enterprises’ processes as a numerical example of the developed method. In the examinations, it was seen that the companies implementing the selected GSC applications achieved close results and thus acted appropriately to the situation. To validate the stability of the developed technique, this study also includes a sensitivity analysis utilizing different weights of criterion and different values of the method’s parameters. According to the investigation, merging subjective and objective weights enhanced the method’s stability created with different criteria weights. The outcomes of the approach developed here were compared with those of other approaches previously put forth in the literature to assess its performance accurately.

Weight aggregation is the key process to solve a multiple-attribute group decision-making (MAGDM) problem. This paper is trying to propose a possible approach to objectivize subjective information and to aggregate information from attribute values themselves and decision-makers’ judgment. An MAGDM problem without information about decision-makers’ and attributes’ weight is considered. In order to define decision-makers’ subjective preference, their utility function is introduced. The attributes value matrix is converted into a subjective attributes value matrix based on their subjective judgment on attribute values. By utilizing the entropy weighting technique, decision-maker’s subjective weight on attributes and objective weight on attributes are determined individually based on the subjective attributes value matrix and attributes value matrix. Based on the principle of minimum cross-entropy, all decision-makers’ subjective weights are integrated into a single weight vector that is closest to all decision-makers’ judgment without any extra information added. Then, by applying the principle of minimum cross-entropy again, a weight aggregation method is proposed to combine the subjective and objective weight of attributes. Finally, an MAGDM example of project choosing is presented to illustrate the procedure of the proposed method.

Developing a fuzzy TOPSIS approach based on subjective weights and objective weights

As an important part of Urban Intelligent Traffic System, parking guidance is one of the most effective measures to solve the difficult parking problems. How to plan and design the Parking Guidance System (PGS) effectively is an issue in this paper. Through the design of the simulation, we can realize the main functions of PGS. In this article, we put forward a framework of parking guidance system, establish the 3D Dynamic Simulation System, and focus on the construction of the Microscopic Guidance Simulation Model of parking lots. Meanwhile, we also combine the PGS outside the parking lots with PGS inside to display the real-time response status while parking. With the theory of Car Following Model and Vehicle GenerationModel, we can use the priority rules and the parking paths in VISSIM to set the dynamic simulation of the traffic behavior.

In view of the subjectivity of subjective weighting method in multi-attribute decision making and the fact that objective weighting method does not take into account the intention of decision makers, this paper proposes a method of weighting based on the combination of subjective and objective information—subjective and objective combination weighting method. In this method, we carried out weighted synthesis to the subjective weight and the objective weight, and used the combination of the two optimization goal programming model—the minimization of deviation square sum between subjective and objective weights, and the maximization of the comprehensive evaluation value of the decision schemes, to obtain weighted coefficients. Finally, we made an example analysis and compared this method with the existing weighting method. The contrast results show that the sorting scheme by this method can reflect both subjective and objective information, which shows that this method is practical and effective.

In the line-of-sight multiple-input multiple-output (LOS-MIMO) system, the optimal spacing of antennas is obtained to maximize the capacity. However, since the optimal spacing depends on the distance between a transmitter and receiver or the number of antennas, the maximum capacity is not achievable for any distance or number of antennas. Therefore, we propose a multiple-stream transmission for LOS environments that uses multiple circular polarizations radiated by a set of vertical and horizontal polarized antennas without using the optimal antenna spacing. By theoretical and numerical analyses, it is shown that the proposed method enables the cancellation of the inter-stream interferences using only two antennas without requiring the optimal antenna spacing. It is also found that when the transmission distance is far from the design point of antenna spacing, the proposed method can achieve higher spectral efficiency than using conventional LOS-MIMO, so using multiple circular polarizations is a breakthrough to improve the spectral efficiency in the LOS environment.

Building an integrated energy system (IES) is an excellent solution to environmental pollution and energy consumption currently. However, there are usually various IES configurations with different performances, and it is usually difficult for the decision-makers to select the most sustainable IES solution among multiple choices. In this paper, we attempt to design an evaluation method that considers the renewable energy and energy system model for evaluating the benefit of an IES. Firstly, this paper constructs an evaluation index system including four dimensions of technology, economy, environment, and society, based on the physical architecture of IES and its benefit influencing factors. It focuses on multiple energy sources or systems, and satisfies the overall evaluation of IES, resulting in accurate benefit evaluation results. Secondly, the subjective weight is determined by combining the triangular fuzzy number (TFN) and the analytic hierarchy process (AHP). The objective weight is determined by the entropy method. The combined weight is obtained by combining the subjective and objective weights, which solves the problem of the weight calculation error caused by the mutual influence between the indexes. Finally, the fuzzy comprehensive evaluation method is used to conduct the benefit evaluation of the IES, which solves the problem that the qualitative index cannot be calculated. This paper takes an IES project in a city in northern China as a case study. Through sensitivity analysis and comparative analysis, the reliability and feasibility of the evaluation results and proposed method are verified. The analysis results show that the model evaluation results are intuitive and consistent with reality, and the data information utilization rate is high.

The surge in roadway truck freight has intensified the difficulties faced by truck drivers in locating safe and legal parking spaces. Parking shortage on the highway system not only creates logistical challenges, but also has substantial economic implications. To help drivers make safer, more efficient parking decisions and better utilize existing truck parking facilities, Truck Parking Information Management Systems (TPIMS) have been deployed in several states to provide real-time parking information to truck drivers through various communication channels. This dissertation first presents an anomaly detection method to identify sensor failures by tracking structural changes in time series parking data. In addition, a data dashboard is developed to evaluate and visualize the performance of the TPIMS through multidimensional aggregation of parking flow data. It can be observed from the dashboard that after the implementation of TPIMS, the utilization among parking facilities along I80 is more evenly distributed. Such intuitive tools can help agencies evaluate the system performance and make informed decisions. Despite the availability of real-time utilization information provided by truck parking information and management systems, there remains a significant requirement for truck drivers to know the anticipated availability of parking spaces at their intended arrival time. The second part of this dissertation develops predictive models that aid truck drivers in making informed decisions regarding trip planning, specifically focusing on two scenarios: pre-trip planning and en-route decision-making. Several tree-based ensembled machine learning models and a recurrent neural network based architecture is developed to forecast the utilization of parking sites and the results show that the prediction can be achieved with small errors. Models with and without other input attributes (e.g., weather, truck volume data) are discussed and compared at different prediction horizon, which enables various levels of forecasting. The integration of long-term and short-term prediction models aligns more closely with drivers' travel planning tendencies and this will help build a more efficient planning and decision-making process, thus optimizing the transportation industry's overall performance. Finally, to address the issue that the reliability and accuracy will hinder the future adoption of the system, a novel approach to enhance TPIMS is discussed by utilizing image processing techniques. In particular, the state-of-the-art You Only Look Once (YOLO) v5 algorithm is used to detect the occupancy of truck parking sites. By comparing the image processing results with the sensor reported data, sensor fault can be detected. Accordingly, a real time reporting system is proposed to communicate sensor fault result to the system operators in a timely fashion. The experimental results demonstrate the effectiveness and efficiency of the proposed system, and it also provides insights for the development and improvement of truck parking information and management systems.