Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach

Huaiwu Zhang, Yutong Xia, Siru Zhong, Kun Wang, Zekun Tong, Qingsong Wen, Roger Zimmermann, Yuxuan Liang·May 29, 2024

Summary

The study addresses traffic congestion in Singapore by introducing DeepPA, a deep-learning framework for real-time parking availability (PA) prediction across 1,687 parking lots. The SINPA dataset, a year-long PA data source enriched with spatial and temporal factors, is used. DeepPA combines Graph Cosine Operator for spatial dependencies and causal Multi-head Self-Attention for temporal sequences, reducing 3-hour forecast errors by 9.2% compared to existing models. The model's efficiency and adaptability make it suitable for urban planning, with a web platform for practical applications. The dataset and code are publicly available for further research and smart city applications. The work highlights the importance of capturing spatial and temporal patterns in large-scale PA prediction and compares favorably with previous studies, demonstrating the effectiveness of DeepPA in reducing traffic congestion and supporting data-driven urban planning.

Key findings

5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of predicting real-time Parking Availability (PA) in Singapore to help alleviate traffic congestion and related social issues in densely populated cities like Singapore . This is not a new problem, but the paper introduces a novel deep-learning framework called DeepPA to collectively and efficiently predict future PA across thousands of parking lots in Singapore, demonstrating a 9.2% reduction in prediction error compared to existing advanced models .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to predicting parking availability by introducing a new dataset called SINPA and developing a data-driven approach named DeepPA to collectively forecast future Parking Availability (PA) across Singapore using complex factors from various domains . The study focuses on addressing the pressing issue of efficiently managing parking space to mitigate traffic congestion and related social problems in densely populated cities like Singapore . The contributions of the paper include introducing the SINPA dataset enriched with spatial and temporal factors, presenting the DeepPA deep-learning framework for predicting PA across thousands of parking lots, and demonstrating a 9.2% reduction in prediction error compared to existing advanced models . The paper also implements DeepPA in a practical web-based platform to provide real-time PA predictions for drivers and urban planning authorities in Singapore .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach" introduces several novel ideas, methods, and models to predict future Parking Availability (PA) across Singapore efficiently . Here are the key contributions outlined in the paper:

  1. New Dataset (SINPA): The paper introduces the SINPA dataset, which contains a year's worth of PA data from 1,687 parking lots in Singapore. This dataset is enriched with various spatial and temporal factors to enhance the prediction accuracy .

  2. Data-Driven Approach (DeepPA): The paper presents DeepPA, a novel deep-learning framework designed to collectively and efficiently predict future PA across thousands of parking lots in Singapore. DeepPA aims to mitigate traffic congestion and related social issues by providing real-time PA predictions .

  3. Extensive Experiments and Deployment: DeepPA demonstrates a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models. The framework is implemented in a practical web-based platform to offer real-time PA predictions for drivers and support urban planning in Singapore .

  4. DeepPA Framework: DeepPA consists of multiple DeepPA blocks that interact to capture spatial-temporal dependencies within the data. Each DeepPA block includes sub-blocks like the Spatial Learning Block (SLBlock) to capture intricate spatial relationships between parking lots using a Graph Cosine Operator .

  5. Innovative Techniques: The paper leverages attention mechanisms, neural ordinary differential equations, and causal tools to address complex spatial correlations and computational efficiency challenges in PA forecasting. Techniques like GMAN, STTN, STGODE, and MixRNN are employed to capture dynamic spatial-temporal dependencies effectively .

By introducing the SINPA dataset, DeepPA framework, and utilizing advanced techniques, the paper offers a comprehensive approach to predicting PA in Singapore, contributing significantly to the field of urban traffic management and smart city development . The paper "Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach" introduces the DeepPA framework, which offers several characteristics and advantages compared to previous methods, as detailed in the paper .

Characteristics and Advantages of DeepPA Framework:

  1. Efficiency and Accuracy: DeepPA efficiently predicts future Parking Availability (PA) by capturing complex spatial correlations and ensuring computational efficiency. It outperforms existing models by reducing the prediction error by 9.2% for up to 3-hour forecasts .

  2. Spatial-Temporal Dependencies: DeepPA effectively captures spatial-temporal dependencies within the data through DeepPA blocks that interact to model intricate relationships between parking lots. The Spatial Learning Block (SLBlock) within DeepPA utilizes a Graph Cosine Operator to efficiently capture non-Euclidean spatial relationships .

  3. Real-Time Forecasting: The DeepPA framework is deployed in a practical web-based platform to provide real-time PA predictions for drivers and support urban planning in Singapore. The system offers accurate predictions, as demonstrated by the correlation between predicted and actual PA readings .

  4. Innovative Techniques: DeepPA leverages attention mechanisms, neural ordinary differential equations, and causal tools to address challenges in PA forecasting. It introduces novel methods like the Graph Cosine Operator (GCO) to improve efficiency while maintaining performance .

  5. Model Comparison: DeepPA is compared to existing models like DCRNN, STGCN, GWNET, and MTGNN in terms of parameters, training time, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). DeepPA demonstrates competitive performance in terms of accuracy and efficiency .

By incorporating these characteristics and advantages, the DeepPA framework stands out as an innovative and effective approach for predicting Parking Availability in Singapore, offering improved efficiency, accuracy, and real-time forecasting capabilities compared to previous methods .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of predicting parking availability, with notable researchers contributing to this area. Some of the noteworthy researchers mentioned in the context are Yuxuan Liang, Yutong Xia, Kun Wang, Zhengyang Zhou, and Roger Zimmermann . These researchers have worked on developing innovative approaches and frameworks for predicting future parking availability in urban areas like Singapore.

The key solution mentioned in the paper is the development of DeepPA, a deep learning framework designed to efficiently predict future Parking Availability (PA) across thousands of parking lots in Singapore. DeepPA utilizes complex spatial and temporal factors, such as weather and geo-location, to enhance the accuracy of PA forecasting. The framework involves transforming historical PA data along with spatial and temporal features into a latent feature space using encoders and Multi-Layer Perceptrons (MLPs), followed by interactions within DeepPA blocks to capture spatial-temporal dependencies . This approach has shown a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing models, demonstrating its effectiveness in improving PA predictions and aiding urban planning efforts .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of the DeepPA model for predicting parking availability in Singapore. The experiments aimed to showcase the accuracy, efficiency, and adaptability of the model . The study involved extensive experiments and deployment of DeepPA, which demonstrated a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models . The model was implemented in a practical web-based platform to provide real-time parking availability predictions to aid drivers and inform urban planning in Singapore . The experiments focused on predicting future parking availability across thousands of parking lots in Singapore by leveraging a new dataset called SINPA, enriched with various spatial and temporal factors . The DeepPA model utilized a deep-learning framework to collectively and efficiently predict future parking availability by incorporating complex factors from various domains .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the SINPA dataset, which contains a year's worth of Parking Availability (PA) data from 1,687 parking lots in Singapore, enriched with various spatial and temporal factors . The dataset is publicly accessible and the source code is open source, available at https://github.com/yoshall/SINPA .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study aimed to predict future Parking Availability (PA) in Singapore using a new dataset and a data-driven approach . The contributions included introducing the SINPA dataset with PA data from 1,687 parking lots enriched with various spatial and temporal factors, presenting the DeepPA deep-learning framework for predicting future PA, and demonstrating a 9.2% reduction in prediction error compared to existing models .

The experiments addressed key research questions such as comparing the performance of DeepPA to existing PA forecasting approaches, analyzing the contribution of each module within DeepPA to overall model performance, assessing the efficiency gains achieved through the implementation of the GCO module, and evaluating the effectiveness of DeepPA for real-time online prediction . These experiments provided valuable insights into the effectiveness and efficiency of the proposed DeepPA model in predicting PA across thousands of parking lots in Singapore.

Furthermore, the study utilized a dataset containing over three years of real-time PA data from 1,921 parking lots in Singapore, which was resampled into 15-minute intervals for experimentation . The dataset was enriched with external attributes such as meteorological data, panning areas, utilization type, and road networks data, providing a comprehensive basis for the analysis and prediction of PA in urban settings like Singapore.

Overall, the experiments conducted in the study, along with the results obtained, effectively validated the scientific hypotheses put forth in the research. The DeepPA model's performance improvements, the analysis of module contributions, and the practical applicability of the model for real-time PA prediction all contribute to the robustness and credibility of the study's findings .


What are the contributions of this paper?

The contributions of the paper "Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach" are as follows:

  • Introduction of a New Dataset: The paper introduces the SINPA dataset, which contains a year's worth of Parking Availability (PA) data from 1,687 parking lots in Singapore, enriched with various spatial and temporal factors .
  • Development of a Data-Driven Approach: The paper presents DeepPA, a novel deep-learning framework designed to collectively and efficiently predict future PA across thousands of parking lots by leveraging complex factors from various domains .
  • Extensive Experiments and Deployment: DeepPA demonstrates a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models. Additionally, the model is implemented in a practical web-based platform to provide real-time PA predictions for drivers and aid urban planning in Singapore .
  • Public Release of Dataset and Source Code: The authors have made the SINPA dataset and the source code available at https://github.com/yoshall/SINPA .
  • Future Research Direction: The paper hints at exploring reinforcement learning to enhance parking recommendation services in the future .

What work can be continued in depth?

To delve deeper into the research on predicting parking availability in Singapore with cross-domain data, several avenues for further exploration can be pursued:

  1. Reinforcement Learning Enhancement: Future research could focus on integrating reinforcement learning techniques to enhance parking recommendation services, thereby improving the overall efficiency and effectiveness of the predictive models .

  2. Exploration of Causal-Based Methods: Investigating causality-based methods like CaST, which address distribution shift problems through causal tools, could provide insights into refining the predictive accuracy of parking availability forecasts .

  3. Incorporation of External Factors: Further studies could delve into the impact and integration of external factors such as meteorological data, panning areas, utilization type, and road networks data from various sources like Data.gov.sg, the Urban Redevelopment Authority (URA), and the Land Transport Authority (LTA) website to enhance the predictive capabilities of the models .

By delving into these areas, researchers can advance the field of predicting parking availability by refining model accuracy, efficiency, and adaptability to real-world scenarios in Singapore.

Tables

2

Introduction
Background
Overview of traffic congestion in Singapore
Importance of real-time parking availability prediction
Objective
To develop DeepPA: a deep-learning model for PA prediction
To improve accuracy and efficiency in PA forecasting
To contribute to smart city planning and urban management
Method
Data Collection
Source: SINPA dataset - 1-year parking availability data
Spatial and temporal factors included
Data Preprocessing
Data cleaning and normalization
Feature extraction for spatial and temporal dependencies
DeepPA Architecture
Graph Cosine Operator (GCO) for spatial dependencies
Causal Multi-head Self-Attention for temporal sequences
Model Development
Model design and implementation
Performance comparison with existing models
Evaluation
3-hour forecast error reduction (9.2% improvement)
Model efficiency and adaptability analysis
Results and Applications
Model Performance
Accuracy and precision of DeepPA predictions
Real-world impact on traffic congestion reduction
Web Platform
Development of a user-friendly web platform
Practical applications for urban planning and smart parking
Public Availability
Release of the SINPA dataset and code
Encouragement for further research and innovation
Conclusion
Significance of capturing spatial and temporal patterns in PA prediction
DeepPA's contribution to smart city initiatives
Future directions and potential for scalability
Basic info
papers
artificial intelligence
Advanced features
Insights
What method does DeepPA use for real-time parking availability prediction?
What is the significance of the SINPA dataset in the study?
What is the primary focus of the study described in the user input?
How does DeepPA improve upon existing models in terms of forecast errors?

Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach

Huaiwu Zhang, Yutong Xia, Siru Zhong, Kun Wang, Zekun Tong, Qingsong Wen, Roger Zimmermann, Yuxuan Liang·May 29, 2024

Summary

The study addresses traffic congestion in Singapore by introducing DeepPA, a deep-learning framework for real-time parking availability (PA) prediction across 1,687 parking lots. The SINPA dataset, a year-long PA data source enriched with spatial and temporal factors, is used. DeepPA combines Graph Cosine Operator for spatial dependencies and causal Multi-head Self-Attention for temporal sequences, reducing 3-hour forecast errors by 9.2% compared to existing models. The model's efficiency and adaptability make it suitable for urban planning, with a web platform for practical applications. The dataset and code are publicly available for further research and smart city applications. The work highlights the importance of capturing spatial and temporal patterns in large-scale PA prediction and compares favorably with previous studies, demonstrating the effectiveness of DeepPA in reducing traffic congestion and supporting data-driven urban planning.
Mind map
Encouragement for further research and innovation
Release of the SINPA dataset and code
Model efficiency and adaptability analysis
3-hour forecast error reduction (9.2% improvement)
Causal Multi-head Self-Attention for temporal sequences
Graph Cosine Operator (GCO) for spatial dependencies
Public Availability
Real-world impact on traffic congestion reduction
Accuracy and precision of DeepPA predictions
Evaluation
DeepPA Architecture
Spatial and temporal factors included
Source: SINPA dataset - 1-year parking availability data
To contribute to smart city planning and urban management
To improve accuracy and efficiency in PA forecasting
To develop DeepPA: a deep-learning model for PA prediction
Importance of real-time parking availability prediction
Overview of traffic congestion in Singapore
Future directions and potential for scalability
DeepPA's contribution to smart city initiatives
Significance of capturing spatial and temporal patterns in PA prediction
Web Platform
Model Performance
Model Development
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Applications
Method
Introduction
Outline
Introduction
Background
Overview of traffic congestion in Singapore
Importance of real-time parking availability prediction
Objective
To develop DeepPA: a deep-learning model for PA prediction
To improve accuracy and efficiency in PA forecasting
To contribute to smart city planning and urban management
Method
Data Collection
Source: SINPA dataset - 1-year parking availability data
Spatial and temporal factors included
Data Preprocessing
Data cleaning and normalization
Feature extraction for spatial and temporal dependencies
DeepPA Architecture
Graph Cosine Operator (GCO) for spatial dependencies
Causal Multi-head Self-Attention for temporal sequences
Model Development
Model design and implementation
Performance comparison with existing models
Evaluation
3-hour forecast error reduction (9.2% improvement)
Model efficiency and adaptability analysis
Results and Applications
Model Performance
Accuracy and precision of DeepPA predictions
Real-world impact on traffic congestion reduction
Web Platform
Development of a user-friendly web platform
Practical applications for urban planning and smart parking
Public Availability
Release of the SINPA dataset and code
Encouragement for further research and innovation
Conclusion
Significance of capturing spatial and temporal patterns in PA prediction
DeepPA's contribution to smart city initiatives
Future directions and potential for scalability
Key findings
5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of predicting real-time Parking Availability (PA) in Singapore to help alleviate traffic congestion and related social issues in densely populated cities like Singapore . This is not a new problem, but the paper introduces a novel deep-learning framework called DeepPA to collectively and efficiently predict future PA across thousands of parking lots in Singapore, demonstrating a 9.2% reduction in prediction error compared to existing advanced models .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to predicting parking availability by introducing a new dataset called SINPA and developing a data-driven approach named DeepPA to collectively forecast future Parking Availability (PA) across Singapore using complex factors from various domains . The study focuses on addressing the pressing issue of efficiently managing parking space to mitigate traffic congestion and related social problems in densely populated cities like Singapore . The contributions of the paper include introducing the SINPA dataset enriched with spatial and temporal factors, presenting the DeepPA deep-learning framework for predicting PA across thousands of parking lots, and demonstrating a 9.2% reduction in prediction error compared to existing advanced models . The paper also implements DeepPA in a practical web-based platform to provide real-time PA predictions for drivers and urban planning authorities in Singapore .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach" introduces several novel ideas, methods, and models to predict future Parking Availability (PA) across Singapore efficiently . Here are the key contributions outlined in the paper:

  1. New Dataset (SINPA): The paper introduces the SINPA dataset, which contains a year's worth of PA data from 1,687 parking lots in Singapore. This dataset is enriched with various spatial and temporal factors to enhance the prediction accuracy .

  2. Data-Driven Approach (DeepPA): The paper presents DeepPA, a novel deep-learning framework designed to collectively and efficiently predict future PA across thousands of parking lots in Singapore. DeepPA aims to mitigate traffic congestion and related social issues by providing real-time PA predictions .

  3. Extensive Experiments and Deployment: DeepPA demonstrates a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models. The framework is implemented in a practical web-based platform to offer real-time PA predictions for drivers and support urban planning in Singapore .

  4. DeepPA Framework: DeepPA consists of multiple DeepPA blocks that interact to capture spatial-temporal dependencies within the data. Each DeepPA block includes sub-blocks like the Spatial Learning Block (SLBlock) to capture intricate spatial relationships between parking lots using a Graph Cosine Operator .

  5. Innovative Techniques: The paper leverages attention mechanisms, neural ordinary differential equations, and causal tools to address complex spatial correlations and computational efficiency challenges in PA forecasting. Techniques like GMAN, STTN, STGODE, and MixRNN are employed to capture dynamic spatial-temporal dependencies effectively .

By introducing the SINPA dataset, DeepPA framework, and utilizing advanced techniques, the paper offers a comprehensive approach to predicting PA in Singapore, contributing significantly to the field of urban traffic management and smart city development . The paper "Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach" introduces the DeepPA framework, which offers several characteristics and advantages compared to previous methods, as detailed in the paper .

Characteristics and Advantages of DeepPA Framework:

  1. Efficiency and Accuracy: DeepPA efficiently predicts future Parking Availability (PA) by capturing complex spatial correlations and ensuring computational efficiency. It outperforms existing models by reducing the prediction error by 9.2% for up to 3-hour forecasts .

  2. Spatial-Temporal Dependencies: DeepPA effectively captures spatial-temporal dependencies within the data through DeepPA blocks that interact to model intricate relationships between parking lots. The Spatial Learning Block (SLBlock) within DeepPA utilizes a Graph Cosine Operator to efficiently capture non-Euclidean spatial relationships .

  3. Real-Time Forecasting: The DeepPA framework is deployed in a practical web-based platform to provide real-time PA predictions for drivers and support urban planning in Singapore. The system offers accurate predictions, as demonstrated by the correlation between predicted and actual PA readings .

  4. Innovative Techniques: DeepPA leverages attention mechanisms, neural ordinary differential equations, and causal tools to address challenges in PA forecasting. It introduces novel methods like the Graph Cosine Operator (GCO) to improve efficiency while maintaining performance .

  5. Model Comparison: DeepPA is compared to existing models like DCRNN, STGCN, GWNET, and MTGNN in terms of parameters, training time, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). DeepPA demonstrates competitive performance in terms of accuracy and efficiency .

By incorporating these characteristics and advantages, the DeepPA framework stands out as an innovative and effective approach for predicting Parking Availability in Singapore, offering improved efficiency, accuracy, and real-time forecasting capabilities compared to previous methods .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of predicting parking availability, with notable researchers contributing to this area. Some of the noteworthy researchers mentioned in the context are Yuxuan Liang, Yutong Xia, Kun Wang, Zhengyang Zhou, and Roger Zimmermann . These researchers have worked on developing innovative approaches and frameworks for predicting future parking availability in urban areas like Singapore.

The key solution mentioned in the paper is the development of DeepPA, a deep learning framework designed to efficiently predict future Parking Availability (PA) across thousands of parking lots in Singapore. DeepPA utilizes complex spatial and temporal factors, such as weather and geo-location, to enhance the accuracy of PA forecasting. The framework involves transforming historical PA data along with spatial and temporal features into a latent feature space using encoders and Multi-Layer Perceptrons (MLPs), followed by interactions within DeepPA blocks to capture spatial-temporal dependencies . This approach has shown a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing models, demonstrating its effectiveness in improving PA predictions and aiding urban planning efforts .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of the DeepPA model for predicting parking availability in Singapore. The experiments aimed to showcase the accuracy, efficiency, and adaptability of the model . The study involved extensive experiments and deployment of DeepPA, which demonstrated a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models . The model was implemented in a practical web-based platform to provide real-time parking availability predictions to aid drivers and inform urban planning in Singapore . The experiments focused on predicting future parking availability across thousands of parking lots in Singapore by leveraging a new dataset called SINPA, enriched with various spatial and temporal factors . The DeepPA model utilized a deep-learning framework to collectively and efficiently predict future parking availability by incorporating complex factors from various domains .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the SINPA dataset, which contains a year's worth of Parking Availability (PA) data from 1,687 parking lots in Singapore, enriched with various spatial and temporal factors . The dataset is publicly accessible and the source code is open source, available at https://github.com/yoshall/SINPA .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study aimed to predict future Parking Availability (PA) in Singapore using a new dataset and a data-driven approach . The contributions included introducing the SINPA dataset with PA data from 1,687 parking lots enriched with various spatial and temporal factors, presenting the DeepPA deep-learning framework for predicting future PA, and demonstrating a 9.2% reduction in prediction error compared to existing models .

The experiments addressed key research questions such as comparing the performance of DeepPA to existing PA forecasting approaches, analyzing the contribution of each module within DeepPA to overall model performance, assessing the efficiency gains achieved through the implementation of the GCO module, and evaluating the effectiveness of DeepPA for real-time online prediction . These experiments provided valuable insights into the effectiveness and efficiency of the proposed DeepPA model in predicting PA across thousands of parking lots in Singapore.

Furthermore, the study utilized a dataset containing over three years of real-time PA data from 1,921 parking lots in Singapore, which was resampled into 15-minute intervals for experimentation . The dataset was enriched with external attributes such as meteorological data, panning areas, utilization type, and road networks data, providing a comprehensive basis for the analysis and prediction of PA in urban settings like Singapore.

Overall, the experiments conducted in the study, along with the results obtained, effectively validated the scientific hypotheses put forth in the research. The DeepPA model's performance improvements, the analysis of module contributions, and the practical applicability of the model for real-time PA prediction all contribute to the robustness and credibility of the study's findings .


What are the contributions of this paper?

The contributions of the paper "Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach" are as follows:

  • Introduction of a New Dataset: The paper introduces the SINPA dataset, which contains a year's worth of Parking Availability (PA) data from 1,687 parking lots in Singapore, enriched with various spatial and temporal factors .
  • Development of a Data-Driven Approach: The paper presents DeepPA, a novel deep-learning framework designed to collectively and efficiently predict future PA across thousands of parking lots by leveraging complex factors from various domains .
  • Extensive Experiments and Deployment: DeepPA demonstrates a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models. Additionally, the model is implemented in a practical web-based platform to provide real-time PA predictions for drivers and aid urban planning in Singapore .
  • Public Release of Dataset and Source Code: The authors have made the SINPA dataset and the source code available at https://github.com/yoshall/SINPA .
  • Future Research Direction: The paper hints at exploring reinforcement learning to enhance parking recommendation services in the future .

What work can be continued in depth?

To delve deeper into the research on predicting parking availability in Singapore with cross-domain data, several avenues for further exploration can be pursued:

  1. Reinforcement Learning Enhancement: Future research could focus on integrating reinforcement learning techniques to enhance parking recommendation services, thereby improving the overall efficiency and effectiveness of the predictive models .

  2. Exploration of Causal-Based Methods: Investigating causality-based methods like CaST, which address distribution shift problems through causal tools, could provide insights into refining the predictive accuracy of parking availability forecasts .

  3. Incorporation of External Factors: Further studies could delve into the impact and integration of external factors such as meteorological data, panning areas, utilization type, and road networks data from various sources like Data.gov.sg, the Urban Redevelopment Authority (URA), and the Land Transport Authority (LTA) website to enhance the predictive capabilities of the models .

By delving into these areas, researchers can advance the field of predicting parking availability by refining model accuracy, efficiency, and adaptability to real-world scenarios in Singapore.

Tables
2
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