Browse by author
Lookup NU author(s): Professor Cheng Chin
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance
Author(s): Yang F, Liu G, Huang L, Chin CS
Publication type: Article
Publication status: Published
Journal: Sensors
Year: 2020
Volume: 20
Issue: 21
Online publication date: 24/10/2020
Acceptance date: 20/10/2020
Date deposited: 11/11/2020
ISSN (print): 1424-8239
ISSN (electronic): 1424-8220
Publisher: MDPI AG
URL: https://doi.org/10.3390/s20216046
DOI: 10.3390/s20216046
Altmetrics provided by Altmetric
