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2016 | OriginalPaper | Buchkapitel

Effective Traffic Flow Forecasting Using Taxi and Weather Data

verfasst von : Xiujuan Xu, Benzhe Su, Xiaowei Zhao, Zhenzhen Xu, Quan Z. Sheng

Erschienen in: Advanced Data Mining and Applications

Verlag: Springer International Publishing

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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).

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Vorheriges Kapitel DAPPFC: Density-Based Affinity Propagation for Parameter Free Clustering

Nächstes Kapitel Understanding Behavioral Differences Between Short and Long-Term Drinking Abstainers from Social Media

The trip data was not created by the TLC, and TLC makes no representations as to the accuracy of these data.

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Titel: Effective Traffic Flow Forecasting Using Taxi and Weather Data
verfasst von: Xiujuan Xu
Benzhe Su
Xiaowei Zhao
Zhenzhen Xu
Quan Z. Sheng
Verlag: Springer International Publishing
Buch: Advanced Data Mining and Applications
Print ISBN: 978-3-319-49585-9

Electronic ISBN: 978-3-319-49586-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-49586-6_35

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