Du-Parking

Abstract

Realtime parking availability information is of great importance to help drivers to find a parking space faster and thus to reduce parking search traffic. While there are limited realtime parking availability systems in a city due to the expensive cost of sensor device and maintaining realtime parking information. In this paper, we estimate the realtime parking availability throughout a city using historical parking availability data reported by a limited number of existing sensors of parking lots and a variety of datasets we observed in the city, such as meteorology, events, map mobility trace data and navigation data from Baidu map, and POIs. We propose a deep-learning-based approach, called Du-Parking, which consists of three major components modeling temporal closeness, period and current general influence, respectively. More specifically, we employ long short-term memory (LSTM) to model the temporal closeness and period, and meanwhile using two fully-connected layers to model the current general factors. Our approach learns to dynamically aggregate the output of the three components, to estimate the final parking availability of given parking lot. Using the proposed approach, we have provided the realtime parking availability information in Baidu map app, in nine cities in China. We evaluated our approach in Beijing and Shenzhen. The results show the advantages of our method over two categories of baselines, including linear interpolations, and the well-known classification model like GBDT.

Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning

“…Combining the characteristics and skills of the neural network with the features and challenges of the parking perdition problem, several customized neural networks are designed to complete the forecasting task. Generally, current research mainly uses existing machine learning models, such as, Convolution Neural Network (CNN), 53,57 Recurrent Neural Network (RNN), 23,54,58,59 and then adopts them to address the real parking challenges. These models always include several features extraction parts: spatial features extraction, temporal features extraction, and attribute information.…”

“…Generally, parking prediction are often affected by three types of information, spatial and temporal features, attributes features and environmental features. Current cutting‐edge parking prediction methods are mainly used in the urban area, which they pay more attention to integrating the spatial‐temporal features 53,58 . Also, at present, without the attributes pattern integration, parking prediction methods overemphasize short‐term feature extraction (i.e.…”

Electric Vehicle Charging Process and Parking Guidance App

“…Just like a normal parking space in an open parking lot or on the street-park, the parking spaces with electric charging properties can be influenced by weather factors. Temperature, rain and wind intensity can affect the parking occupancy [8], as bad weather conditions could lead to lower traffic flow than expected [17], meaning that people may be less susceptible to driving. The period of the day and time of year are also important [18], like holidays, weekdays and hour of the day could have a direct impact on park occupancy, in this case in the CS occupancy.…”

“…To build the predictive model [17] used methods like Gradient Boosting Decisions Trees, as it is very effective in training and on scoring. Algorithms used on model training for the [20] were Decision Trees, Support Vector Machine, Multilayer Perceptrons and Gradient Boosted Trees, concluding that Extreme Gradient Boosting has had the best accuracy result of all of them.…”

The Prediction of Parking Space Availability

“…Many different types of models are listed for IPS in the literature, e.g., the neuron networks and deep learning (Camero et al 2019;Rong et al 2018); the fuzzy approach (Sun et al 2018); the models of the game theory (Li et al 2014;Mamandi et al 2015); the stochastic algorithms, multi logit models, and simulation (Schlote et al 2014;Liang et al 2017); the geometric programming (Balzano, Vitale 2017); MADM models (Li et al 2017).…”