Artificial Neural Networks Applied to Taxi Destination Prediction

By Alexandre de Brébisson, Étienne Simon, Alex Auvolat, Pascal Vincent, Yoshua Bengio. 2015

197 citations recorded by the source corpus.

We describe our first-place solution to the ECML/PKDD discovery challenge on taxi destination prediction. The task consisted in predicting the destination of a taxi based on the beginning of its trajectory, represented as a variable-length sequence of GPS points, and diverse associated meta-information, such as the departure time, the driver id and client information. Contrary to most published competitor approaches, we used an almost fully automated approach based on neural networks and we ranked first out of 381 teams. The architectures we tried use multi-layer perceptrons, bidirectional recurrent neural networks and models inspired from recently introduced memory networks. Our approach could easily be adapted to other applications in which the goal is to predict a fixed-length output from a variable-length sequence.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Improved Recurrent Neural Networks for Session-based Recommendations (2016, methodology): “The first is an application of the sequence preprocessing method proposed in [5].”
An overview of deep learning in medical imaging focusing on MRI (2018, background): “They are, however, not limited to image processing and analysis but are outperforming other approaches in areas like natural language processing [3, 4, 5], speech recognition and synthesis [6, 7]2, and in the analysis of unstructured, tabular-type data using entity embeddings [8, 9].”
Survey on categorical data for neural networks (2020, unknown): “…encoded vectors, and learn lower-dimensional embedded values [27], N/A Use embedding layers for categorical variables to predict taxi destination [28, 29] Entity embedding categorical data from computing system events for intrusion detection [30], N/A Widely used Keras library, provides…”
A Survey on Deep Learning for Human Mobility (2020, methodology): “[127] ECML-PKDD taxi 441 9 months Porto, Portugal [47, 52, 122, 156] Next-Loc.”
TabTransformer: Tabular Data Modeling Using Contextual Embeddings (2020, methodology): “Standard MLPs have been applied to tabular data for many years (De Brébisson et al. 2015).”
Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends (2020, methodology): “Modeling a variable-length trajectory of length 5, using a sequencebased representation and an RNN [23], [26].”
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting (2021, methodology): “Combining these embeddings as features for forecasting yields powerful models like the first place winner of the Kaggle Taxi Trajectory Prediction1 challenge (De Brébisson et al., 2015).”