A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip

TL;DR

This paper introduces ST-NN, a neural network model that jointly predicts taxi trip time and distance using raw GPS data, outperforming existing methods and demonstrating robustness to outliers.

Contribution

The paper presents a novel deep neural network model that estimates both travel time and distance from raw GPS data without feature engineering, improving accuracy and robustness.

Findings

Reduces mean absolute error by about 17% for travel time prediction

Outperforms state-of-the-art methods in accuracy

More robust to dataset outliers

Abstract

In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [23], this paper investigates the use of deep neural networks to jointly predict taxi trip time and distance. We propose a model, called ST-NN (Spatio-Temporal Neural Network), which first predicts the travel distance between an origin and a destination GPS coordinate, then combines this prediction with the time of day to predict the travel time. The beauty of ST-NN is that it uses only the raw trips data without requiring further feature engineering and provides a joint estimate of travel time and distance. We compare the performance of ST-NN to that of state-of-the-art travel time estimation methods, and we observe that the proposed approach generalizes better than state-of-the-art methods. We show that ST-NN approach significantly reduces the mean absolute error for both predicted travel time and distance, about 17% for travel time prediction. We also observe that the proposed approach is more robust to outliers present in the dataset by testing the performance of ST-NN on the datasets with and without outliers.