A novel hybrid deep learning model for taxi demand forecasting based on decomposition of time series and fusion of text data
| Autor: | Wenyu Zhang, Kun Zhu, Shuai Zhang, Zhiqiang Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
050210 logistics & transportation Fusion business.industry Computer science Deep learning 05 social sciences General Engineering 02 engineering and technology Demand forecasting Machine learning computer.software_genre Artificial Intelligence 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Decomposition of time series |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 41:3355-3371 |
| ISSN: | 1875-8967 1064-1246 |
| DOI: | 10.3233/jifs-210657 |
| Popis: | Accurate taxi demand forecasting is significant to estimate the change of demand to further make informed decisions. Although deep learning methods have been widely applied for taxi demand forecasting, they neglect the complexity of taxi demand data and the impact of event occurrences, making it hard to effectively model the taxi demand in highly dynamic areas (e.g., areas with frequent event occurrences). Therefore, to achieve accurate and stable taxi demand forecasting in highly dynamic areas, a novel hybrid deep learning model is proposed in this study. First, to reduce the complexity of taxi demand time series, the seasonal-trend decomposition procedures based on loess is employed to decompose the time series into three simpler components (i.e., seasonal, trend, and remainder components). Then, different forecasting methods are adopted to handle different components to obtain robust forecasting results. Moreover, considering the instability and nonlinearity of the remainder component, this study proposed to fuse the event features (in particular, text data) to capture the unusual fluctuation patterns of remainder component and solve its extreme value problem. Finally, genetic algorithm is applied to determine the optimal weights for integrating the forecasting results of three components to obtain the final taxi demand. The experimental results demonstrate the better accuracy and reliability of the proposed model compared with other baseline forecasting models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|