scholarly journals Neural-Network-Based Dynamic Distribution Model of Parking Space Under Sharing and Non-Sharing Modes

2020 ◽  
Vol 12 (12) ◽  
pp. 4864
Author(s):  
Ziyao Zhao ◽  
Yi Zhang ◽  
Yi Zhang ◽  
Kaifeng Ji ◽  
He Qi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Walking Distance ◽  
Distribution Model ◽  
City Management ◽  
Parking Space ◽  
Parking Lot ◽  
Utilisation Rate ◽  
Urban Parking ◽  
Parking Demand

In recent years, with the rapid development of China’s automobile industry, the number of vehicles in China has been increasing steadily. Vehicles represent a convenient mode of travel, but the growth rate of the number of urban motor vehicles far exceeds the construction rate of parking facilities. The continuous improvement of parking allocation methods has always been key for ensuring sustainable city management. Thus, developing an efficient and dynamic parking distribution algorithm will be an important breakthrough to alleviate the urban parking shortage problem. However, the existing parking distribution models do not adequately consider the influence of real-time changes in parking demand and supply on parking space assignment. Therefore, this study proposed a method for dynamic parking allocation using parking demand predictions and a predictive control method. A neural-network-based dynamic parking distribution model was developed considering seven influencing factors: driving duration, walking distance, parking fee, traffic congestion, possibility of finding a parking space in the target parking lot and adjacent parking lot, and parking satisfaction degree. Considering whether the parking spaces in the targeted parking lots are shared or not, two allocation modes—sharing mode and non-sharing mode—were proposed and embedded into the model. At the experimental stage, a simulation case and a real-time case were performed to evaluate the developed models. The experimental results show that the dynamic parking distribution model based on neural networks can not only allocate parking spaces in real time but also improve the utilisation rate of different types of parking spaces. The performance score of the dynamic parking distribution model for a time interval of 2–20 min was maintained above 80%. In addition, the distribution performance of the sharing mode was better than that of the non-sharing mode and contributed to a better overall effectiveness. This model can effectively improve the utilisation rate of resources and the uniformity of distribution and can reduce the failure rate of parking; thus, it significantly contributes to more smart and sustainable urban parking management.

scholarly journals Real Time Short-term Forecasting Method of Remaining Parking Space in Urban Parking Guidance Systems

2018 ◽  
Vol 30 (2) ◽  
pp. 173-185 ◽  
Author(s):  
Xiaobo Zhu ◽  
Jianhua Guo ◽  
Wei Huang ◽  
Fengquan Yu ◽  
Byungkyu Brian Park
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Real Time ◽  
Parking Space ◽  
Short Term ◽  
Markov Chain Method ◽  
Proposed Model ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Urban Parking

Short-term forecasting of the remaining parking space is important for urban parking guidance systems (PGS). The previous methods like polynomial equations and neural network methods are difficult to be applied in practice because of low accuracy or lengthy initial training time which is unfavourable if real-time training is carried out on adapting to changing traffic conditions. To forecast the remaining parking space in real-time with higher accuracy and improve the performances of PGS, this study develops an online forecasting model based on a time series method. By analysing the characteristics of data collected in Nanjing, China, an autoregressive integrated moving average (ARIMA) model has been established and a real-time forecasting procedure developed. The performance of this proposed model has been further analysed and compared with the performances of a neural network method and the Markov chain method. The results indicate that the mean error of the proposed model is about 2 vehicles per 15 minutes, which can meet the requirements for general PGS. Furthermore, this method outperforms the neural network model and the Markov chain method both in individual and collective error analysis. In summary, the proposed online forecasting method appears to be promising for forecasting the remaining parking space in supporting the PGS.

scholarly journals THE PREDICTION OF PARKING SPACE AVAILABILITY

Transport ◽  
2020 ◽  
Vol 35 (5) ◽  
pp. 462-473
Author(s):  
Helena Brožová ◽  
Miroslav Růžička
Keyword(s):  
Real Time ◽  
Personal Communication ◽  
Navigation Systems ◽  
Parking Space ◽  
Transition Matrices ◽  
Communication Services ◽  
Proposed Model ◽  
Harmful Emissions ◽  
Parking Lot ◽  
Car Park

Intelligent Parking Systems (IPS) allow customers to select a car park according to their preferences, rapidly park their vehicle without searching for the available parking space (place) or even book their place in advance avoiding queues. IPS provides the possibility to reduce the wastage of fuel (energy) while finding a parking place and consequently reduce harmful emissions. Some systems interact with in-vehicle navigation systems and provide users with information in real-time such as free places available at a given parking lot (car park), the location and parking fees. Few of these systems, however, provide information on the forecasted utilisation at specific time. This paper describes results of a traffic survey carried out at the parking lot of supermarket and the proposal of the model predicting real-time parking space availability based on these surveyed data. The proposed model is formulated as the non-homogenous Markov chains that are used as a tool for the forecasting of parking space availability. The transition matrices are calculated for different time periods, which allow for and include different drivers’ behaviour and expectations. The proposed forecasting model is adequate for potential use by IPS with the support of different communication means such as the internet, navigation systems (GPS, Galileo etc.) and personal communication services (mobile-phones).

scholarly journals Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 277 ◽  
Author(s):  
Sherzod Nurullayev ◽  
Sang-Woong Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Traffic Congestion ◽  
Parking Space ◽  
Urban Centers ◽  
Detection Systems ◽  
Perspective Distortion ◽  
Occupancy Detection ◽  
Parking Lot ◽  
Parking Space Detection

The importance of vacant parking space detection systems is increasing dramatically as the avoidance of traffic congestion and the time-consuming process of searching an empty parking space is a crucial problem for drivers in urban centers. However, the existing parking space occupancy detection systems are either hardware expensive or not well-generalized for varying images captured from different camera views. As a solution, we take advantage of an affordable visual detection method that is made possible by the fact that camera monitoring is already available in the majority of parking areas. However, the current problem is a challenging vision task because of outdoor lighting variation, perspective distortion, occlusions, different camera viewpoints, and the changes due to the various seasons of the year. To overcome these obstacles, we propose an approach based on Dilated Convolutional Neural Network specifically designed for detecting parking space occupancy in a parking lot, given only an image of a single parking spot as input. To evaluate our method and allow its comparison with previous strategies, we trained and tested it on well-known publicly available datasets, PKLot and CNRPark + EXT. In these datasets, the parking lot images are already labeled, and therefore, we did not need to label them manually. The proposed method shows more reliability than prior works especially when we test it on a completely different subset of images. Considering that in previous studies the performance of the methods was compared with well-known architecture—AlexNet, which shows a highly promising achievement, we also assessed our model in comparison with AlexNet. Our investigations showed that, in comparison with previous approaches, for the task of classifying given parking spaces as vacant or occupied, the proposed approach is more robust, stable, and well-generalized for unseen images captured from completely different camera viewpoints, which has strong indications that it would generalize effectively to other parking lots.

Research on Dynamic Parking Space Allocation Model in Closed Parking Lot

2015 ◽  
Vol 734 ◽  
pp. 435-439
Author(s):  
Qing Gang Wang
Keyword(s):  
Optimization Model ◽  
Utilization Efficiency ◽  
Random Allocation ◽  
Parking Space ◽  
Allocation Model ◽  
Space Allocation ◽  
Parking Lot ◽  
On Line ◽  
Reservation System ◽  
Parking Demand

This paper investigated Dynamic Parking Space Allocation Model (DPSAM), which is one of functional modules of On-line Parking Space Reservation System (OPSRS). In the model, the parking spaces were regarded as a two-dimensional parking resource pool of temporal and spatial components. An integer programming formulation was presented with the objective function of minimizing the discontinuous parking resources. The experiment was executed to compare the allocation solutions of optimization model and random allocation with the indicators of parking resource vacancy rate, parking resource sunk rate and parking demand satisfaction rate. The result shows that optimization model can improve the utilization efficiency of parking lot and is valuable for the application of OPSRS.

scholarly journals A Routing Algorithm Based on Zigbee Technology

2018 ◽  
Vol 14 (11) ◽  
pp. 90 ◽  
Author(s):  
Xieji Gang
Keyword(s):  
Routing Algorithm ◽  
Route Planning ◽  
Utilization Rate ◽  
Parking Space ◽  
Filtering Algorithm ◽  
Zigbee Technology ◽  
Parking System ◽  
Parking Lot ◽  
The Status ◽  
Urban Parking

To realize the exploration of A routing algorithm for ZigBee technology, a kind of intelligent parking system based on ZigBee wireless sensor network is designed, and the parking space is managed by online and offline interaction. First of all, the status of parking at home and abroad is studied, and the demand for parking is analyzed. Secondly, the intelligent scheme of parking system is studied, including parking guidance technology and parking space intelligent recommendation technology. The former is based on the A routing algorithm to search the shortest path, and uses Unity for route planning simulation, and the latter is based on collaboration filtering algorithm to discuss the recommendation method of similar parking spaces. The result shows that the prototype of an intelligent parking system based on ZigBee is eventually realized, and the functions of online viewing, online parking reservation, online path planning and parking guidance are realized. As a result, the purpose of managing offline parking spaces through online is achieved, the utilization rate of resources in the existing parking lot is improved and the use rate of parking space is promoted. At last, it effectively alleviates the urban parking chaos and has certain application value.

Multi-collaborative Regression Convolutional Neural Network for Vehicle Angle Detection

2020 ◽  
Vol 13 (8) ◽  
pp. 1227-1241
Author(s):  
Guoqiang Chen ◽  
Mengchao Liu ◽  
Hongpeng Zhou ◽  
Bingxin Bai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Training Sample ◽  
Detection Accuracy ◽  
Detection Error ◽  
Detection Failure ◽  
Pose Detection ◽  
Parking Lot

Background: The vehicle pose detection plays an important role in monitoring vehicle behavior and the parking situation. The real-time detection of vehicle pose with high accuracy is of great importance. Objective: The goal of the work is to construct a new network to detect the vehicle angle based on the regression Convolutional Neural Network (CNN). The main contribution is that several traditional regression CNNs are combined as the Multi-Collaborative Regression CNN (MCR-CNN), which greatly enhances the vehicle angle detection precision and eliminates the abnormal detection error. Methods: Two challenges with respect to the traditional regression CNN have been revealed in detecting the vehicle pose angle. The first challenge is the detection failure resulting from the conversion of the periodic angle to the linear angle, while the second is the big detection error if the training sample value is very small. An MCR-CNN is proposed to solve the first challenge. And a 2- stage method is proposed to solve the second challenge. The architecture of the MCR-CNN is designed in detail. After the training and testing data sets are constructed, the MCR-CNN is trained and tested for vehicle angle detection. Results: The experimental results show that the testing samples with the error below 4° account for 95% of the total testing samples based on the proposed MCR-CNN. The MCR-CNN has significant advantages over the traditional vehicle pose detection method. Conclusion: The proposed MCR-CNN cannot only detect the vehicle angle in real-time, but also has a very high detection accuracy and robustness. The proposed approach can be used for autonomous vehicles and monitoring of the parking lot.

scholarly journals Optimization design and evaluation of parking route based on automatic assignment mechanism of parking lot

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401771241 ◽  
Author(s):  
Yan Han ◽  
Jiawen Shan ◽  
Meng Wang ◽  
Guang Yang
Keyword(s):  
Optimization Design ◽  
Causal Chain ◽  
Walking Distance ◽  
Parking Space ◽  
Dijkstra Algorithm ◽  
Automatic Assignment ◽  
Traffic Conflicts ◽  
Parking Lot ◽  
Individual Demand ◽  
The Individual

The congestion causes of parking lot were analyzed with causal chain method and a questionnaire about individual demand of parking users was carried out. The results show that more than 90% of the users hope parking spaces can be automatically assigned to them when it’s difficult to find a parking space. The layout principles of WiFi in parking lot were provided. The automatic assignment mechanism of parking lot was given considering the individual demand of parking users and the avoidance of traffic conflicts. Some attribute decision factors such as lane occupancy conditions, travel distance, walking distance, and the occupancy situation of parking space on both sides were selected and optimal parking lot assignment model was established. Optimal paths were calculated through Dijkstra algorithm, and the information about the assignment location and path was sent to the drivers’ cell phones. The driver’s compliance was evaluated by comparing the driver’s parking trajectory with system recommended path. Finally, a large parking lot in Beijing was taken as an example. The results can offer constructive suggestions on parking route design and parking assignment mechanism, which can make the use of the limited parking resources more effectively.

A BACK PROPAGATION BASED REAL-TIME LICENSE PLATE RECOGNITION SYSTEM

2008 ◽  
Vol 22 (02) ◽  
pp. 233-251 ◽  
Author(s):  
YO-PING HUANG ◽  
TSUN-WEI CHANG ◽  
YEN-REN CHEN ◽  
FRODE EIKA SANDNES
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
License Plate ◽  
License Plate Recognition ◽  
Video Frames ◽  
Parking Lot

License plate recognition systems have been used extensively for many applications including parking lot management, tollgate monitoring, and for the investigation of stolen vehicles. Most researches focus on static systems, which require a clear and level image to be taken of the license plate. However, the acquisition of images that can be successfully analyzed relies on both the location and movement of the target vehicle and the clarity of the environment. Moreover, only few studies have addressed the problems associated with instant car image processing. In view of these problems, a real-time license plate recognition system is proposed that recognizes the video frames taken from existing surveillance cameras. The proposed system finds the location of the license plate using projection analysis, and the characters are identified using a back propagation neural network. The strategy achieves a recognition rate of 85.8% and almost 100% after the neural network has been retrained using the erroneously recognized characters, respectively.

Sensitivity analysis of the influencing factors of parking lot selection based on BP neural network

2021 ◽  
pp. 1-9
Author(s):  
Jingjing Yin ◽  
Qiang Sun ◽  
Juan Zhou
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Selection Process ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Walking Distance ◽  
Management Measures ◽  
Parking Lot ◽  
Parking Management ◽  
Selection Of

The driver’s selection process of parking lot will consider a variety of influencing factors, and consider different influencing factors for different travel purposes. In this paper, the driver’s travel purposes were divided into three categories according to the degree of emergency: emergency, routine and leisure. Four influencing factors of parking lot selection including walking distance, charge, parking index and parking convenience were selected, and ranked according to their sensitivity, and their sensitivity was analyzed by using the BP (back propagation) neural network, which provides a basis for the development of differentiated parking guidance and parking management measures to avoid the uneven parking due to random selection of parking lot and realize the maximum utilization of parking resources.

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