Effective traffic flow forecasting using taxi and weather data

Xiujuan Xu, Benzhe Su, Xiaowei Zhao, Zhenzhen Xu*, Quan Z. Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

5 Citations (Scopus)

Abstract

Short-term traffic flow forecasting is an important component of intelligent transportation systems. The forecasting results can be used to support intelligent transportation systems to plan operation and manage revenue. In this paper, we aim to predict the daily floating population by presenting a novel model using taxi trajectory data and weather information. We study the problem of floating traffic flow prediction with weather-affected New York City, and a new methodology called WTFPredict is proposed to solve this problem. In particular, we target the busiest part of the city (i.e., the airports), and identify its boundary to compute the traffic flow around the area. The experimental results based on large scale, real-life taxi and weather data (12 million records) indicate that the proposed method performs well in forecasting the short-term traffic flows. Our study will provide some valuable insights to transport management, urban planning, and location-based services (LBS).

Original languageEnglish
Title of host publicationAdvanced data mining and applications
Subtitle of host publication12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12–15, 2016, proceedings
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages507-519
Number of pages13
ISBN (Print)9783319495859
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event12th International Conference on Advanced Data Mining and Applications, ADMA 2016 - Gold Coast, Australia
Duration: 12 Dec 201615 Dec 2016

Publication series

NameLecture Notes in Artificial Intelligence
Volume10086 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th International Conference on Advanced Data Mining and Applications, ADMA 2016
CountryAustralia
CityGold Coast
Period12/12/1615/12/16

Keywords

  • Big data
  • Intelligent transportation systems
  • Prediction model
  • Weather data

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