Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …

Short-term trajectory prediction (StTP) for individual metro passengers is of great importance
in intelligent transportation systems and real-time security risk management. Existing …

To provide personalized and precise guidance services to subway passengers, a route
recommendation method for frequent subway riders based on passenger preference …

The development of the automatic fare collection (AFC) systems provides significant support
for predicting passenger flow on urban rail transit. This paper extracts passenger travel …

Mobility pattern recognition is a central aspect of transportation and data mining research.
Despite the development of various machine learning techniques for this problem, most …

Given the rising attention towards the understanding of people's mobility, this paper focuses
on the study of next-place prediction models for mobility demand and path estimation. A …

4.1. What types of representations were learnt? 13 In this section, for each individual data
type, we state the number of articles that 14 considered the problem of learning …

In this study, we employ a probabilistic topic modeling algorithm, known as Latent Dirichlet
Allocation (LDA), to autonomously deduce the purposes of trips based on activity …

In the face of growing incomprehensiveness of traditional data sources, lack of behavioural
information of emerging sources, and changing data requirements of advanced models, the …

We previously defined ItemSB as an extension of the concept of frequent itemsets and a new
interpretation of the association rule expression, which has statistical properties in the …