M2-CNN: A macro-micro model for taxi demand prediction

• Main Authors: CHENG, Shih-Fen, RATHNAYAKA MUDIYANSELAGE, Prabod Manuranga
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8543; https://ink.library.smu.edu.sg/context/sis_research/article/9546/viewcontent/taxi_demand_bigdata23__1_.pdf
• Institution: Singapore Management University

In this paper, we introduce a macro-micro model for predicting taxi demands. Our model is a composite deep learning model that integrates multiple views. Our network design specifically incorporates the spatial and temporal dependency of taxi or ride-hailing demand, unlike previous papers that also utilize deep learning models. In addition, we propose a hybrid of Long Short-Term Memory Networks and Temporal Convolutional Networks that incorporates real world time series with long sequences. Finally, we introduce a microscopic component that attempts to extract insights revealed by roaming vacant taxis. In our study, we demonstrate that our approach is competitive against a large array of approaches from the literature on the basis of detailed moving logs of more than 20,000 taxis and 12 million trips per month over a three-month period. Our analysis of the effectiveness of individual components reveals that microscopic information is essential for generating high-quality predictions.