A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information
Abstract
:1. Introduction
- A multi-parking prediction model combining the whole and local aspects is constructed by combining two aspects: the whole block and its interior. Compared with the traditional multi-parking prediction task, the approach splits and transforms the pure point prediction task and is able to capture more factors associated with it. Thus, the accuracy of parking demand prediction improves by using the more factors.
- For the prediction model, a combination of GCN and GRU is chosen to effectively extract temporal and spatial information from the data. The model is based on historical parking demand data, and while using encoder–decoder to extract the characteristics of demand itself. It also fuses the temporal and spatial factors that have an impact on the parking demand of road sections. The combination of such multiple neural network layers obtains the required information in a complex urban road network. Thus, it fuses multiple sources of data effectively and obtains more desirable prediction results.
- A variety of features and influencing factors related to parking demand are analyzed. In addition to considering some inherent external factors, such as weather and holidays, improved spatial class features are also added. The features use more detailed representation methods, such as representing the spatial distance relationship between road sections using their driving times, and representing their semantic functions using the number and distance of various types of points of interest around the road sections, etc. At the same time, these analyzed features are also divided and incorporated into the model from both overall and local perspectives to enrich the influencing factors from the input perspective as meticulously as possible.
- The validation is performed using a real parking dataset from Xiufeng District of Guilin. The good performance of the model in this paper is reflected from the validation results, especially this structure of combining the whole block with the block interior. In addition, the robustness of the model is also reflected by using such a dataset with general accuracy. Finally, the design of the ablation experiment and the comparison of the results demonstrate the importance of considering each feature in the model.
2. Related Work
2.1. Parking Demand Prediction Based on Static Traffic
2.2. Parking Demand Prediction Based on Machine Learning
3. Materials and Methods
3.1. Preliminary
- (1)
- Overall Parking Demand Prediction of Block
- (2)
- Parking Demand Distribution Prediction within Block
3.2. Parking Demand Prediction of Road Sections
3.2.1. Overall Parking Demand Prediction of Block
Feature Analysis
- (1)
- Weather
- (2)
- Holiday Events
- (3)
- Time Factors
Model Design
3.2.2. Parking Demand Distribution Prediction within Block
Feature Analysis
- (1)
- Physical Location
- (2)
- Semantic Function
Model Design
3.2.3. Fusion
4. Experiments
4.1. Datasets
- (1)
- Hour-by-hour weather data from 30 November 2020 to 30 April 2021 for Guilin. Precipitation, wind speed, visibility, and temperature data are screened from it. Data are obtained from the NCEI National Environmental Data website.
- (2)
- The specific scheduling of actual weekdays and holidays is obtained from the holiday scheduling notice issued by the General Office of the State Council of the People’s Republic of China.
- (3)
- Driving path planning and driving distances between different road sections in the region, with data from the Gaode Open Platform.
- (4)
- POI data of different categories within the specific area around each road section, with 23 categories in total, with data from the Gaode Open Platform.
- (5)
- The walking path planning and walking distance between each road section and each POI in the specific area around it, with data from Gaode Open Platform.
4.2. Experimental Settings
4.2.1. Evaluation Metrics
4.2.2. Baselines and Details
- (1)
- Only the P1 component of the overall prediction module in the model is excluded.
- (2)
- Only the P2 component of the overall prediction module in the model is excluded.
- (3)
- The P1 component and the P2 component of the overall prediction module in the model are also excluded.
- (4)
- Only physical adjacency features of the internal distribution prediction module are excluded.
- (5)
- Only the semantic functional similarity relationship features of the internal distribution prediction module are excluded.
4.3. Experimental Results
4.3.1. Results Compared with Baselines
4.3.2. Ablation Experiments with Different Components
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Lequn Road | Sanhuang Road | Sihui Road | Taiping Road | |
---|---|---|---|---|
Lequn Road | 1 | 0.84416343 | 0.89911893 | 0.95502897 |
Sanhuang Road | 0.84416343 | 1 | 0.84044845 | 0.76167333 |
Sihui Road | 0.89911893 | 0.84044845 | 1 | 0.78182478 |
Taiping Road | 0.95502897 | 0.76167333 | 0.78182478 | 1 |
Road | The Number of Parking Spaces |
---|---|
Lequn Road | 42 |
Sanhuang Road | 18 |
Sihui Road | 26 |
Taiping Road | 29 |
Model | RMSE | MAE |
---|---|---|
HA | 4.089 | 2.858 |
ARIMA | 3.885 | 2.653 |
LSTM | 3.354 | 2.568 |
GRU | 3.347 | 2.556 |
GCN+GRU | 3.248 | 2.505 |
HA+B&D | 3.857 | 2.795 |
ARIMA+B&D | 3.581 | 2.450 |
LSTM+B&D | 2.494 | 1.917 |
GRU+B&D | 2.501 | 1.915 |
GCN+GRU+B&D | 2.437 | 1.882 |
ours | 2.254 | 1.791 |
Model | RMSE | MAE |
---|---|---|
without weather | 2.385 | 1.868 |
without external | 2.380 | 1.872 |
without weather and external | 2.429 | 1.874 |
without physical adjacency | 2.418 | 1.898 |
without semantic function adjacency | 2.441 | 1.913 |
ours | 2.254 | 1.791 |
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Wang, T.; Li, S.; Li, W.; Yuan, Q.; Chen, J.; Tang, X. A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information. Sustainability 2023, 15, 7096. https://doi.org/10.3390/su15097096
Wang T, Li S, Li W, Yuan Q, Chen J, Tang X. A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information. Sustainability. 2023; 15(9):7096. https://doi.org/10.3390/su15097096
Chicago/Turabian StyleWang, Tao, Sixuan Li, Wenyong Li, Quan Yuan, Jun Chen, and Xiang Tang. 2023. "A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information" Sustainability 15, no. 9: 7096. https://doi.org/10.3390/su15097096
APA StyleWang, T., Li, S., Li, W., Yuan, Q., Chen, J., & Tang, X. (2023). A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information. Sustainability, 15(9), 7096. https://doi.org/10.3390/su15097096