MePark: Using Meters as Sensors for Citywide On-Street Parking Availability Prediction

Zhang, Dong; Ju, Chen; Zhu, Guanzhou; Jing, Ning; Luo, Dan; Stankovic, John A.; Ma, Huadóng

doi:10.1109/tits.2021.3067675

Cited by 18 publications

(14 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhao et al [182] developed a system for real-time city-wide parking availability prediction based on parking transaction data and contextual information. To this end, they integrated the inflow and duration prediction models in order to achieve the outflow information for different time slots.…”

Section: E Parking Managementmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.

show abstract

Section: E Parking Managementmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…During the last decade, models of deep neural networks have gained huge popularity in the area of traffic modeling and prediction, and network structure optimization is emphasized to make full use of spatial and temporal data [15,16,30]. As illustrated by MGCN-LSTM [31] which extracts discriminative features from the multi-source data, and combines the multiple-graph convolutional neural network (MGCN) and the long shortterm memory (LSTM) network for capturing complex spatio-temporal correlations. Even though these models can handle fused data with high performance, they have a heavy dependence on the availability of other data sources.…”

Section: Related Solutionsmentioning

confidence: 99%

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

Liu

et al. 2022

Mathematics

View full text Add to dashboard Cite

Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine learning models. To tackle the challenges in small-sample POP, we propose an approach named Adaptation and Learning to Learn (ALL) by adopting the capability of advanced deep learning and federated learning. ALL integrates two novel ideas: (1) Adaptation: by leveraging the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning technique, an auto-selector module is implemented, which can group and select data-scarce parks automatically as supporting sources to enable the knowledge adaptation in model training; and (2) Learning to learn: by applying federated meta-learning on selected supporting sources, a meta-learner module is designed, which can train a high-performance local prediction model in a collaborative and privacy-preserving manner. Results of an evaluation with 42 parking lots in two Chinese cities (Shenzhen and Guangzhou) show that, compared to state-of-the-art baselines: (1) the auto-selector can reduce the model variance by about 17.8%; (2) the meta-learner can train a converged model 102x faster; and (3) finally, ALL can boost the forecasting performance by about 29.8%. Through the integration of advanced machine learning methods, i.e., reinforcement learning, meta-learning, and federated learning, the proposed approach ALL represents a significant step forward in solving small-sample issues in parking occupancy prediction.

show abstract

“…Some existing methods use not only historical parking data, but also other data sources for predicting parking lot availability. For example, data sources include historical data generated by mobile phones (e.g., [16]), data extracted from vehicles equipped with GPS receivers (e.g., [17]), and information from web maps (e.g., [1,31,36]). In this regard, our model could be improved if we include these extra data.…”

Section: Related Workmentioning

confidence: 99%

Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander

Sasaki¹,

Takayama²,

Santana³

et al. 2022

Preprint

View full text Add to dashboard Cite

Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world. SmartSantander is a smart city project in Santander, Spain, which has relied on wireless sensor network technologies to deploy heterogeneous sensors within the city to measure multiple parameters, including outdoor parking information. In this paper, we study the prediction of parking lot availability using historical data from more than 300 outdoor parking sensors with SmartSantander. We design a graph-to-sequence model to capture the periodical fluctuation and geographical proximity of parking lots. For developing and evaluating our model, we use a 3-year dataset of parking lot availability in the city of Santander. Our model achieves a high accuracy compared with existing sequence-to-sequence models, which is accurate enough to provide a parking information service in the city. We apply our model to a smartphone application to be widely used by citizens and tourists.

show abstract

MePark: Using Meters as Sensors for Citywide On-Street Parking Availability Prediction

Cited by 18 publications

References 48 publications

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander

Contact Info

Product

Resources

About