[HTML][HTML] The promises of big data and small data for travel behavior (aka human mobility) analysis

C Chen, J Ma, Y Susilo, Y Liu, M Wang - Transportation research part C …, 2016 - Elsevier
The last decade has witnessed very active development in two broad, but separate fields,
both involving understanding and modeling of how individuals move in time and space …

Large-scale mobile traffic analysis: a survey

D Naboulsi, M Fiore, S Ribot… - … Surveys & Tutorials, 2015 - ieeexplore.ieee.org
This article surveys the literature on analyses of mobile traffic collected by operators within
their network infrastructure. This is a recently emerged research field, and, apart from a few …

Inferring dynamic origin-destination flows by transport mode using mobile phone data

D Bachir, G Khodabandelou, V Gauthier… - … Research Part C …, 2019 - Elsevier
Fast urbanization generates increasing amounts of travel flows, urging the need for efficient
transport planning policies. In parallel, mobile phone data have emerged as the largest …

What are the factors affecting the adoption and use of electric scooter sharing systems from the end user's perspective?

M Samadzad, H Nosratzadeh, H Karami, A Karami - Transport policy, 2023 - Elsevier
Since their introduction in 2017, Electric Scooter Sharing systems (ESSs) are shown to
provide numerous benefits for both individuals and society, including convenient green …

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 …

DeepSD: Supply-demand prediction for online car-hailing services using deep neural networks

D Wang, W Cao, J Li, J Ye - 2017 IEEE 33rd international …, 2017 - ieeexplore.ieee.org
The online car-hailing service has gained great popularity all over the world. As more
passengers and more drivers use the service, it becomes increasingly more important for the …

Discovering urban activity patterns in cell phone data

P Widhalm, Y Yang, M Ulm, S Athavale, MC González - Transportation, 2015 - Springer
Massive and passive data such as cell phone traces provide samples of the whereabouts
and movements of individuals. These are a potential source of information for models of …

Transport modelling in the age of big data

C Anda, A Erath, PJ Fourie - International Journal of Urban …, 2017 - Taylor & Francis
ABSTRACT New Big Data sources such as mobile phone call data records, smart card data
and geo-coded social media records allow to observe and understand mobility behaviour on …

[HTML][HTML] Emerging data for pedestrian and bicycle monitoring: Sources and applications

K Lee, IN Sener - Transportation research interdisciplinary perspectives, 2020 - Elsevier
Growing attention on the benefits of non-motorized travel has increased the demand for
accurate and timely pedestrian and bicycle travel data. Advancements in technologies and …

Data from mobile phone operators: A tool for smarter cities?

J Steenbruggen, E Tranos, P Nijkamp - Telecommunications Policy, 2015 - Elsevier
The use of mobile phone data provides new spatio-temporal tools for improving urban
planning, and for reducing inefficiencies in present-day urban systems. Data from mobile …