Taxi Demand Method Based on SCSSA-CNN-BiLSTM

The randomness of passengers’ travel and the blindness of empty drivers seeking passengers can lead to a serious imbalance in the spatio-temporal distribution of taxi supply and demand. In order to realize the accurate prediction of taxi demand, promote a balance between taxi supply and demand, and...

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Vydáno v:Sustainability Ročník 16; číslo 18; s. 7879
Hlavní autoři: Guo, Dudu, Sun, Miao, Wang, Qingqing, Zhang, Jinquan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2024
Témata:
Accuracy
Algorithms
Deep learning
Efficiency
Energy consumption
Forecasts and trends
Fuzzy sets
Neural networks
Passengers
Supply and demand
Sustainable development
Taxicabs
China
ISSN:2071-1050, 2071-1050
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Shrnutí:The randomness of passengers’ travel and the blindness of empty drivers seeking passengers can lead to a serious imbalance in the spatio-temporal distribution of taxi supply and demand. In order to realize the accurate prediction of taxi demand, promote a balance between taxi supply and demand, and respond to the requirements of the sustainable development of urban transportation, a travel demand prediction model based on Sparrow Search Algorithm incorporating sine-cosine and Cauchy variants (SCSSA), Convolutional Neural Network (CNN), and Bi-directional Long Short-Term Memory (BiLSTM) is proposed. The key factors affecting travel demand are identified by constructing a set of influencing factors for feature correlation analysis. In order to overcome the overfitting or underfitting phenomenon caused by the improper parameter configuration of the CNN-BiLSTM model, the SCSSA algorithm is utilized to optimize the model. By fine tuning the model parameters, the algorithm enhanced the model’s adaptability to dataset characteristics and improved the accuracy of the prediction results. Compared with CNN, LSTM, CNN- LSTM, CNN-BiLSTM, and SSA-CNN-BiLSTM models, the Root Mean Square Error is decreased by 10.77 on average.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:2071-1050
2071-1050
DOI:10.3390/su16187879