Taxi Destination Prediction Based on Convolution, Attention and Multilayer Perceptron

Taxi destination prediction based on location services is an important aspect for reasonable planning of urban transportation. However, there exist problems of data sparsity and single feature of trajectory data, which affect the accuracy of destination prediction. To address the problem of data sparsity, the original taxi trajectory data (i. e. taxi speed, direction angle, and time) is combined with trajectory truncation method to determine the model input features, thus overcoming data sparsity. To address the problems of excessive parameters and overfitting caused by a large number of parameters of the multilayer perceptron (MLP), a parameter sharing mechanism based on convolution is proposed to solve parameter redundancy. Furthermore, an attention mechanism is used to allocate more computing resources to more important tasks, focusing on important information to improve the prediction performance of the model. Based on this, a taxi destination prediction method (CCMLP) that combines convolution, attention modules, and MLP is proposed, which achieves more accurate destination prediction while overcoming the structural deficiencies of MLP and convolution. The reliability of the CCMLP model is verified through different data and experiments. The experimental results show that selecting the trajectory features of the first m points and the last n points, as well as their corresponding direction angles, speeds, license plate numbers, and time features as the input features of the model can effectively improve the accuracy of destination prediction. The proposed CCMLP method has good feature learning ability. Compared with the MLP prediction model, the distance error is reduced by 10%, and compared with the ensemble learning algorithm, the distance error is reduced by 19.6%. The CCMLP model’s generalization ability for different data distributions is verified by dividing the data set based on weekdays and weekends, with distance losses of 2.25 km and 2.23 km respectively. The effect of different trajectory completeness on destination prediction is verified based on the first ten points of the trajectory, with distance losses of 2.23 km, 1.80 km, 0.97 km, and 0.68 km for completeness rates of 40%, 60%, 80%, and complete trajectories respectively. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.