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2018 | OriginalPaper | Buchkapitel

A Deep Prediction Architecture for Traffic Flow with Precipitation Information

verfasst von : Jingyuan Wang, Xiaofei Xu, Feishuang Wang, Chao Chen, Ke Ren

Erschienen in: Advances in Swarm Intelligence

Verlag: Springer International Publishing

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Abstract

Traffic flow prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the governors make reliable traffic control strategies. In this paper, we propose a deep traffic flow prediction architecture P-DBL, which takes advantage of a deep bi-directional long short-term memory (DBL) model and precipitation information. The proposed model is able to capture the deep features of traffic flow and take full advantage of time-aware traffic flow data and additional precipitation data. We evaluate the prediction architecture on the dataset from Caltrans Performance Measurement System (PeMS) and the precipitation dataset from California Data Exchange Center (CDEC). The experiment results demonstrate that the proposed model for traffic flow prediction obtains high accuracy compared with other models.

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Vorheriges Kapitel Mapping the Landscapes, Hotspots and Trends of the Social Network Analysis Research from 1975 to 2017

Nächstes Kapitel Tag Prediction in Social Annotation Systems Based on CNN and BiLSTM

Caltrans Performance Measurement System (PeMS), http://pems.dot.ca.gov.

California Data Exchange Center (CDEC), http://cdec.water.ca.gov.

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Titel: A Deep Prediction Architecture for Traffic Flow with Precipitation Information
verfasst von: Jingyuan Wang
Xiaofei Xu
Feishuang Wang
Chao Chen
Ke Ren
Verlag: Springer International Publishing
Buch: Advances in Swarm Intelligence
Print ISBN: 978-3-319-93817-2

Electronic ISBN: 978-3-319-93818-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93818-9_31

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