Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data

Claire Y T Chen; Edward W Sun; Ming-Feng Chang; Yi-Bing Lin

doi:10.1007/s10479-023-05223-7

Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data

Ann Oper Res. 2023 Apr 6:1-34. doi: 10.1007/s10479-023-05223-7. Online ahead of print.

Authors

Claire Y T Chen¹, Edward W Sun², Ming-Feng Chang³, Yi-Bing Lin^{4

5}

Affiliations

¹ Montpellier Business School, Montpellier, France.
² KEDGE Business School, Talence, France.
³ Institute of Computational Intelligence, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan.
⁴ College of Artificial Intelligence, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan.
⁵ Miin Wu School of Computing, National Cheng Kung University, Tainan City, Taiwan.

Abstract

With growing environmental concerns and the exploitation of ubiquitous big data, smart transportation is transforming logistics business and operations into a more sustainable approach. To answer questions in intelligent transportation planning, such as which data are feasible, which methods are applicable for intelligent prediction of such data, and what are the available operations for prediction, this paper offers a new deep learning approach called bi-directional isometric-gated recurrent unit (BDIGRU). It is merged to the deep learning framework of neural networks for predictive analysis of travel time and business adoption for route planning. The proposed new method directly learns high-level features from big traffic data and reconstructs them by its own attention mechanism drawn by temporal orders to complete the learning process recursively in an end-to-end manner. After deriving the computational algorithm with stochastic gradient descent, we use the proposed method to perform predictive analysis of stochastic travel time under various traffic conditions (especially for congestions) and then determine the optimal vehicle route with the shortest travel time under future uncertainty. Based on empirical results with big traffic data, we show that the proposed BDIGRU method can (1) significantly improve the predictive accuracy of one-step 30 min ahead travel time compared to several conventional (data-driven, model-driven, hybrid, and heuristics) methods measured with several performance criteria, and (2) efficiently determine the optimal vehicle route in relation to the predictive variability under uncertainty.

Keywords: AI and machine learning; Big data; Predictive analytics; Smart transportation; Travel time.

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