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

H Huang, Y Cheng, R Weibel - Transportation Research Part C: Emerging …, 2019 - Elsevier
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 …

Mobile phone data: A survey of techniques, features, and applications

M Okmi, LY Por, TF Ang, CS Ku - Sensors, 2023 - mdpi.com
Due to the rapid growth in the use of smartphones, the digital traces (eg, mobile phone data,
call detail records) left by the use of these devices have been widely employed to assess …

[HTML][HTML] Estimation of truck origin-destination flows using GPS data

MG Demissie, L Kattan - Transportation Research Part E: Logistics and …, 2022 - Elsevier
Large trucking vehicles have a comparatively more significant impact on safety, traffic
congestion, pollution, and pavement wear than passenger vehicles. Appropriate planning …

A data fusion approach with mobile phone data for updating travel survey-based mode split estimates

E Graells-Garrido, D Opitz, F Rowe… - … research part C: emerging …, 2023 - Elsevier
Up-to-date information on different modes of travel to monitor transport traffic and evaluate
rapid urban transport planning interventions is often lacking. Transport systems typically rely …

[HTML][HTML] Passively generated big data for micro-mobility: State-of-the-art and future research directions

HH Schumann, H Haitao, M Quddus - Transportation Research Part D …, 2023 - Elsevier
The sharp rise in popularity of micro-mobility poses significant challenges in terms of
ensuring its safety, addressing its social impacts, mitigating its environmental effects, and …

Inferring fine-grained transport modes from mobile phone cellular signaling data

K Chin, H Huang, C Horn, I Kasanicky… - … , Environment and Urban …, 2019 - Elsevier
Due to the ubiquity of mobile phones, mobile phone network data (eg, Call Detail Records,
CDR; and cellular signaling data, CSD), which are collected by mobile telecommunication …

[HTML][HTML] Artificial intelligence and policy making; can small municipalities enable digital transformation?

I Koliousis, A Al-Surmi, M Bashiri - International Journal of Production …, 2024 - Elsevier
This study investigates digital transformation and the usability of emerging technologies in
policymaking. Prior studies categorised digital transformation into three distinct phases of …

A framework of travel mode identification fusing deep learning and map-matching algorithm

Z Jiang, A Huang, G Qi, W Guan - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The ubiquity of mobile phone signaling data (MPSD) allows us to study travel mode
identification (TMI) of a larger scale of population in cities than GPS data and travel survey …

Exploring strengths and weaknesses of mobility inference from mobile phone data vs. travel surveys

N Caceres, LM Romero, FG Benitez - … A: Transport Science, 2020 - Taylor & Francis
Origin–destination (OD) matrices serve as a basis for travel demand modelling. Traditionally,
they are derived from travel surveys that collect detailed trip information but with several …

Mobile phone data in transportation research: methods for benchmarking against other data sources

A Dypvik Landmark, P Arnesen, CJ Södersten… - Transportation, 2021 - Springer
The ubiquity of personal cellular phones in society has led to a surging interest in using Big
Data generated by mobile phones in transport research. Studies have suggested that the …