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Personalized Air Travel Prediction: A Multi-factor Perspective

Published: 19 December 2017 Publication History

Abstract

Human mobility analysis is one of the most important research problems in the field of urban computing. Existing research mainly focuses on the intra-city ground travel behavior modeling, while the inter-city air travel behavior modeling has been largely ignored. Actually, the inter-city travel analysis can be of equivalent importance and complementary to the intra-city travel analysis. Understanding massive passenger-air-travel behavior delivers intelligence for airlines’ precision marketing and related socioeconomic activities, such as airport planning, emergency management, local transportation planning, and tourism-related businesses. Moreover, it provides opportunities to study the characteristics of cities and the mutual relationships between them. However, modeling and predicting air traveler behavior is challenging due to the complex factors of the market situation and individual characteristics of customers (e.g., airlines’ market share, customer membership, and travelers’ intrinsic interests on destinations). To this end, in this article, we present a systematic study on the personalized air travel prediction problem, namely where a customer will fly to and which airline carrier to fly with, by leveraging real-world anonymized Passenger Name Record (PNR) data. Specifically, we first propose a relational travel topic model, which combines the merits of latent factor model with a neighborhood-based method, to uncover the personal travel preferences of aviation customers and the latent travel topics of air routes and airline carriers simultaneously. Then we present a multi-factor travel prediction framework, which fuses complex factors of the market situation and individual characteristics of customers, to predict airline customers’ personalized travel demands. Experimental results on two real-world PNR datasets demonstrate the effectiveness of our approach on both travel topic discovery and customer travel prediction.

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Published In

cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 9, Issue 3
Regular Papers and Special Issue: Urban Intelligence
May 2018
370 pages
ISSN:2157-6904
EISSN:2157-6912
DOI:10.1145/3167125
  • Editor:
  • Yu Zheng
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 December 2017
Accepted: 01 April 2017
Revised: 01 March 2017
Received: 01 December 2016
Published in TIST Volume 9, Issue 3

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Author Tags

  1. Air travel demand
  2. latent dirichlet allocation
  3. travel topic model
  4. urban computing

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  • Refereed

Funding Sources

  • National Science Foundation of Tianjin
  • National Science Foundation of China

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  • (2023)Systematic review of passenger demand forecasting in aviation industryMultimedia Tools and Applications10.1007/s11042-023-15552-182:30(46483-46519)Online publication date: 1-Dec-2023
  • (2022)Towards the Inference of Travel Purpose with Heterogeneous Urban DataIEEE Transactions on Big Data10.1109/TBDATA.2019.29218238:1(166-177)Online publication date: 1-Feb-2022
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