Mobility trajectory data is of great significance for mobility pattern study, urban computing,
and city science. Self-driving, traffic prediction, environment estimation, and many other …

This article comprehensively surveys the development of trajectory data classification.
Considering the critical role of trajectory data classification in modern intelligent systems for …

Owing to the increased public interest in passive brain–computer interface (pBCI)
applications, many wearable devices for capturing electroencephalogram (EEG) signals in …

Recently, short-term traffic state prediction for urban transportation networks has become a
popular topic. However, due to the uncontrollable and unpredictable elements of special …

In this paper we present a new conceptual model of trajectories, which accounts for
semantic and indoor space information and supports the design and implementation of …

This study identifies the limitations and underlying characteristics of urban mobility networks
that influence the performance of the gravity model. The gravity model is a widely-used …

With the development of information technique and wireless communication, a vast number
of taxis' and ride-sharing cars' trajectory data that provide a rich and detailed source to study …

Cities around the world suffer from congestion and pollution caused by road traffic.
Intermodal transport, which integrates multiple modes in a single journey, is a promising …

In recent years, technologies of indoor crowd positioning and movement data analysis have
received widespread attention in the fields of reliability management, indoor navigation, and …

Due to the ubiquitous utilization of GPS devices, traffic cameras, and sensing devices, data
are collected more readily in a smart city. If all the cabs of this city are used as data carriers …