Paper Info
Title
Dynamic origin‐destination flow estimation using automatic vehicle identification data: A 3D convolutional neural network approach
Abstract
AbstractAbstractDynamic origin‐destination (OD) flow estimation is one of the most fundamental problems in traffic engineering. Despite numerous existing studies, the OD flow estimation problem remains challenging, as there is large dimensional difference between the unknown values to be estimated and the known traffic observations. To meet the needs of active traffic management and control, accurate time‐dependent OD flows are required to understand time‐of‐day traffic flow patterns. In this work, we propose a three‐dimensional (3D) convolution‐based deep neural network, “Res3D,” to learn the high‐dimensional correlations between local traffic patterns presented by automatic vehicle identification observations and OD flows. In this paper, a practical framework combining simulation‐based model training and few‐shot transfer learning is introduced to enhance the applicability of the proposed model, as continuously observing OD flows could be expensive. The proposed model is extensively tested based on a realistic road network, and the results show that for significant OD flows, the relative errors are stable around 5%, outperforming several other models, including prevalent neural networks as well as existing estimation models. Meanwhile, corrupted and out‐of‐distribution samples are generated as real‐world samples to validate Res3D's transferability, and the results indicated a 60% improvement with few‐shot transfer learning. Therefore, this proposed framework could help to bridge the gaps between traffic simulations and empirical cases.
Year
DOI
Venue
2021
10.1111/mice.12559
Periodicals
DocType
Volume
Issue
Journal
36
1
ISSN
Citations 
PageRank 
1093-9687
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Keshuang Tang193.67
Yumin Cao200.34
Can Chen300.34
Jiarong Yao421.07
Chaopeng Tan521.08
Jian Sun66014.76