Taxi Demand Method Based on SCSSA-CNN-BiLSTM

Dudu Guo, Miao Sun (), Qingqing Wang and Jinquan Zhang
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Dudu Guo: School of Transportation Engineering, Xinjiang University, Urumqi 830017, China
Miao Sun: School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, China
Qingqing Wang: School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, China
Jinquan Zhang: Xinjiang Hualing Logistics & Distribution Co., Urumqi 830000, China

Sustainability, 2024, vol. 16, issue 18, 1-21

Abstract: 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.

Keywords: taxi; demand prediction; correlation analysis; sparrow optimization algorithm; CNN-BiLSTM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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