A Framework of Travel Mode Identification Fusing Deep Learning and Map-Matching Algorithm-Reference-Cited by-同舟云学术

A Framework of Travel Mode Identification Fusing Deep Learning and Map-Matching Algorithm

Published:2023-06 Issue:6 Volume:24 Page:6401-6415
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Jiang Zhihuan¹^ORCID,Huang Ailing¹^ORCID,Qi Geqi¹^ORCID,Guan Wei¹^ORCID

Affiliation:

1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10139322/10061352.pdf?arnumber=10061352

Reference51 articles.

1. Moving and Calling: Mobile Phone Data Quality Measurements and Spatiotemporal Uncertainty in Human Mobility Studies

2. On data processing required to derive mobility patterns from passively-generated mobile phone data

3. Exploring a new model for mobile positioning based on CDR data of the cellular networks;hadachi;arXiv 1902 09399,2019

4. Estimating crowd flow and crowd density from cellular data for mass rapid transit;li;Proc 6th Int Workshop Urban Comput,2017

5. Discovering urban activity patterns in cell phone data

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