A Data-Driven Feature Based Learning Application to Detect Freeway Segment Traffic Status Using Mobile Phone Data

Qiang Liu, Jianguang Xie and Fan Ding
Additional contact information
Qiang Liu: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Jianguang Xie: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Fan Ding: School of Transportation, Southeast University, Nanjing 211189, China

Sustainability, 2021, vol. 13, issue 13, 1-11

Abstract: With the finishing of the construction of the main body of a freeway network, adequately monitoring the traffic status of the network has become an urgent need for both travelers and transportation operators. Various methods are proposed to collect traffic information for this purpose. In this article, a data-driven feature-based learning application is implemented to detect segment traffic status using mobile phone data, building on the practical success of deep learning models in other fields. The traffic status estimation is achieved via the application of a three-level long, short-term memory model. Two phone features are extracted from the raw mobile phone data. A large-scale field experiment was conducted using actual data in Jiangsu, China collected over the “National Holiday Golden Week” of 2014. To evaluate the performance, both precision and recall scores are given along with the overall accuracy. The final results of the large-scale experiment indicate that the proposed application performed well and can be an emerging solution for traffic state monitoring when only limited roadside sensing devices are installed.

Keywords: mobile phone data; deep learning model; feature extraction; traffic status detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/13/7131/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/13/7131/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:13:p:7131-:d:581906

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().