A multi-pattern deep fusion model for short-term bus passenger flow – NLPIR自然语言处理与信息检索共享平台

自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

A multi-pattern deep fusion model for short-term bus passenger flow

NLPIR SEMINAR Y2019#12

INTRO

In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.

Arrangement

This week’s seminar is organized as follows:

  1. The seminar time is 1.pm, Mon, at Zhongguancun Technology Park ,Building 5, 1306.
  2. The lecturer is Nihad, the paper’s title is A multi-pattern deep fusion model for short-term bus passenger flow forecasting.
  3. The seminar will be hosted by Ziyu Liu.
  4. Attachment is the paper of this seminar, please download in advance.

Everyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper.

A multi-pattern deep fusion model for short-term bus passenger flow

Yun Bai, Zhenzhong Sun, Bo Zeng, Jun Deng, Chuan Li

Abstract

Short-term passenger flow forecasting is one of the crucial components in transportation systems with data support for transportation planning and management. For forecasting bus passenger flow, this paper proposes a multi-pattern deep fusion (MPDF) approach that is constructed by fusing deep belief networks(DBNs) corresponding to multiple patterns. The dataset of the short-term bus passenger flow is first segmented into different clusters by an affinity propagation algorithm. The passenger flow distribution of these clusters is subsequently analyzed for identifying different patterns. In each pattern, a DBN is developed as a deep representation for the passenger flow. The outputs of the DBNs are finally fused by chronological order rearrangement. Taking a bus line in Guangzhou city of China as an example, the present MPDF approach is modeled. Five approaches, non-parametric and parametric models, are applied to the same case for comparison. The results show that, the proposed model overwhelms all the peer methods in terms of mean absolute percentage error, root-mean-square error, and determination coefficient criteria. In addition, there exists significant difference between the addressed model and the comparison models. It is recommended from the present study that the deep learning technique incorporating the pattern analysis is promising in forecasting the short-term passenger flow.

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