Multi-modal interactions at the network-level remain unexplored due to the lack of high-
resolution data for all transportation modes involved. The current work investigates the effect …

Multi-layer networks could reveal the carbon emission structure of urban traffic formed after
residents choose the means and purpose of trips. In this paper, a multi-layer network model …

Mobile device location data (MDLD) have been widely recognized for their rich human
mobility information and thus considered as a supplementary data source for the current …

The ubiquity of mobile phone signaling data (MPSD) allows us to study travel mode
identification (TMI) of a larger scale of population in cities than GPS data and travel survey …

Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control,
transportation project prioritization, road maintenance planning, and more. Traditional …

During the coronavirus disease 2019 pandemic, the activity engagement and travel
behavior of city residents have been impacted by government restrictions, such as …

This paper presents a novel microsimulation framework (BayABM) that integrates population
synthesis, Bayesian Networks (BNs), and activity-based models (activity-BM) to model …

To reduce earthquake-induced casualties, it is essential to analyze the capability and
demand of post-earthquake medical rescue in a city. This study proposes a joint method for …

Travel data is vital for understanding individual travel behaviors and estimating travel
demand. Compared with traditional travel surveys in which respondents were asked to recall …

Stimulating more citizens to walk plays an essential role in building a healthy city. This paper
explores the mismatch between walkability and walking behavior, using mobile phone data …