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 …

This paper reviews the activity-travel behaviour literature that employs Machine Learning
(ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which …

We present novel accelerometer-based techniques for accurate and fine-grained detection
of transportation modes on smartphones. The primary contributions of our work are an …

This paper adopts different supervised learning methods from the field of machine learning
to develop multiclass classifiers that identify the transportation mode, including driving a car …

Transportation and locomotion mode recognition from multimodal smartphone sensors is
useful for providing just-in-time context-aware assistance. However, the field is currently …

Just as it has irrevocably reshaped social life, the fast growth of smartphone ownership is
now beginning to revolutionize the driving experience and change how we think about …

Recognition of transportation modes can be used in different applications including human
behavior research, transport management and traffic control. Previous work on …

With the rapid development of mobile Internet, the Internet of Things and other new
technologies, mobile devices are generating massive amounts of spatio-temporal trajectory …

Over the past couple of decades, there has been an exponential increase in the collection of
large-scale GPS data from household/personal travel surveys all over the world. A range of …

We present a prototype mobile phone application that implements a novel transportation
mode detection algorithm. The application is designed to run in the background, and …