The COVID-19 pandemic strongly affected the mobility of people. Several studies have
quantified these changes, for example, measuring the effectiveness of quarantine measures …

Understanding choice behavior regarding travel mode is essential in forecasting travel
demand. Machine learning (ML) approaches have been proposed to model mode choice …

Machine learning (ML) solutions have been proposed to make public transportation more
attractive. Works that employ ML in bus transportation focus on various problems, such as …

Abstract Travel Diary Survey (TDS) collects comprehensive attributes such as
sociodemographic attributes, trip purpose, and trip chain attributes of the trips taken by a …

Transit-oriented development (TOD) is a planning strategy to encourage the use of public
transportation by clustering urban development centering on transit stations with dense and …

The poor predictability and the misspecification arising from hand-crafted utility functions are
common issues in theory-driven discrete choice models (DCMs). Data-driven DCMs improve …

In the airline, business a passenger's purpose of travel (business or leisure) has a strong
relationship with the price elasticity of that passenger. Full-service network carriers (FSNCs) …

Understanding the underlying reasons for potential human risky driving behaviors is crucial
for improving road safety. Recent technologies allow the analysis of driving behaviors at a …

Smart card data is a rich and available dataset for monitoring and planning public
transportation systems. The trip purpose is a valuable attribute of the trip that is missing from …

Alighting bus stops inferring is of great significance for origin–destination estimation.
Cellular signaling data (CSD), a kind of individual trajectory generated by mobile phones …