Short-term forecasting of travel time utilizing deep learning approach

YingFang Tong; Jie Fang; ZhiJia Liu; PingHui Xiao

doi:10.1117/12.2637079

15 June 2022 Short-term forecasting of travel time utilizing deep learning approach

YingFang Tong, Jie Fang, ZhiJia Liu, PingHui Xiao

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122850G (2022) https://doi.org/10.1117/12.2637079
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

Travel time prediction is a fundamental part of traffic analysis. Meanwhile it affected by spatial correlations, temporal dependencies, external conditions (e.g. weather, meta data, traffic conditions). In this paper, we propose a deep learning framework that integrates CNN and Bi-LSTM to learn spatial-temporal feature representations of travel time prediction. The short-term (5 minutes interval) historical traffic data which fully utilize to capture the patterns and trend of the travel time. Our paper sorted the feature into two categories: time-varying attributes, non-time-varying attributes. The proposed models called MV-FCL were evaluated on a network in the City of Zhangzhou, China. The results demonstrate that the proposed MV-FCL model outperform state-of-art baselines.

Citation Download Citation

YingFang Tong, Jie Fang, ZhiJia Liu, and PingHui Xiao "Short-term forecasting of travel time utilizing deep learning approach", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122850G (15 June 2022); https://doi.org/10.1117/12.2637079

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available