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Transport mode detection based on mobile phone network data: A systematic review


Huang, Haosheng; Cheng, Yi; Weibel, Robert (2019). Transport mode detection based on mobile phone network data: A systematic review. Transportation Research Part C: Emerging Technologies, 101:297-312.

Abstract

The rapid development in telecommunication networks is producing a huge amount of information regarding how people (with their mobile devices) move and behave over space and time. While GPS data, typically collected by smartphone apps, are restricted to rather small samples of the population, mobile phone network data, routinely collected by mobile network operators, potentially allow to analyze travel behaviors and social interaction of the whole population, with full temporal (e.g., longitudinal) coverage at a comparatively low cost. Therefore, recent years have seen an increasing interest in using such data for human mobility studies. However, due to their noisy and temporally infrequent/irregular nature, extracting mobility information such as transport modes from these data is particularly challenging. This paper provides an in-depth, systematic review of transport mode detection based on mobile phone network data. The results of the review show that existing studies tend to identify easy-to-detect modes (e.g., train or metro), or aggregate fine-grained modes into more general groups (e.g., public versus private transport). Rule-based methods making use of geographic data were often developed. More importantly, due to the lack of ground truth data, evaluation of the proposed methods was seldom done and reported. Finally, we identify a list of research gaps currently being faced in this field, particularly regarding robust and real-time data cleaning and mode detection methods, “benchmark” datasets and metrics allowing the comparison of different mode detection methods, as well as privacy and bias issues.

Abstract

The rapid development in telecommunication networks is producing a huge amount of information regarding how people (with their mobile devices) move and behave over space and time. While GPS data, typically collected by smartphone apps, are restricted to rather small samples of the population, mobile phone network data, routinely collected by mobile network operators, potentially allow to analyze travel behaviors and social interaction of the whole population, with full temporal (e.g., longitudinal) coverage at a comparatively low cost. Therefore, recent years have seen an increasing interest in using such data for human mobility studies. However, due to their noisy and temporally infrequent/irregular nature, extracting mobility information such as transport modes from these data is particularly challenging. This paper provides an in-depth, systematic review of transport mode detection based on mobile phone network data. The results of the review show that existing studies tend to identify easy-to-detect modes (e.g., train or metro), or aggregate fine-grained modes into more general groups (e.g., public versus private transport). Rule-based methods making use of geographic data were often developed. More importantly, due to the lack of ground truth data, evaluation of the proposed methods was seldom done and reported. Finally, we identify a list of research gaps currently being faced in this field, particularly regarding robust and real-time data cleaning and mode detection methods, “benchmark” datasets and metrics allowing the comparison of different mode detection methods, as well as privacy and bias issues.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Scopus Subject Areas:Physical Sciences > Civil and Structural Engineering
Physical Sciences > Automotive Engineering
Social Sciences & Humanities > Transportation
Physical Sciences > Computer Science Applications
Uncontrolled Keywords:Automotive Engineering, Transportation, Computer Science Applications
Language:English
Date:1 April 2019
Deposited On:20 Dec 2019 15:06
Last Modified:29 Jul 2020 12:02
Publisher:Elsevier
ISSN:0968-090X
OA Status:Green
Publisher DOI:https://doi.org/10.1016/j.trc.2019.02.008

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