Journal Article

Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning

Yan, Xuejin, Li, Shuqi, Yang, Xiaoxian, Kuang, Li, Tan, Xianhan

2019

Overview

Taxi demand can be divided into pick-up demand and drop-off demand, which are firmly related to human’s travel habits. Accurately predicting taxi demand is of great significance to passengers, drivers, ride-hailing platforms and urban managers. Most of the existing studies only forecast the taxi demand for pick-up and separate the interaction between spatial correlation and temporal correlation. In this paper, we first analyze the historical data and select three highly relevant parts for each time interval, namely closeness, period and trend. We then construct a multi-task learning component and extract the common spatiotemporal feature by treating the taxi pick-up prediction task and drop-off prediction task as two related tasks. With the aim of fusing spatiotemporal features of historical data, we conduct feature embedding by attention-based long short-term memory (LSTM) and capture the correlation between taxi pick-up and drop-off with 3D ResNet. Finally, we combine external factors to simultaneously predict the taxi demand for pick-up and drop-off in the next time interval. Experiments conducted on real datasets in Chengdu present the effectiveness of the proposed method and show better performance in comparison with state-of-the-art models.

• Publisher: MDPI AG
• Subject: Artificial intelligence / Artificial neural networks / Car sharing / convolutional neural network / Data mining / Deep learning / Demand / Economic forecasting / Embedding / Feature extraction / International conferences / Learning / Long short-term memory / Machine learning / Methods / multi-task learning / Natural language / Neural networks / Predictions / Remote sensing / Short term / spatiotemporal data / taxi demand prediction / Taxicabs / Traffic control / Voice recognition