The purpose of this article is to present a structured review of publications utilizing machine
learning methods to aid in engineering risk assessment. A keyword search is performed to …

The analysis of travel mode choice is important in transportation planning and policy-making
in order to understand and forecast travel demands. Research in the field of machine …

Energy consumption prediction of buildings has drawn attention in the related literature
since it is very complex and affected by various factors. Hence, a challenging work is …

Prediction of mode choice for travelers has been the subject of keen interest among
transportation planners. Traditionally, mode choice analysis is conducted by statistical …

Although many studies have explored the relationship between the built environment and
travel behavior, the literature offers limited evidence about the collective influence of built …

Previous studies on the built environment and urban vitality often assume that they follow a
pre-defined (mostly linear in parameters) relationship, and few studies substantiate whether …

This study investigated the importance of applying explainable artificial intelligence (XAI) on
different machine learning (ML) models developed to predict the strength characteristics of …

To inform the station-area planning, previous studies use direct ridership models to examine
the relationship between the built environment around stations and transit ridership. Based …

With the global climate change and the rapid urbanization process, there is an increase in
the risk of urban floods. Therefore, undertaking risk studies of urban floods, especially the …

Very few studies have examined the impact of built environment on urban rail transit
ridership at the station-to-station (origin-destination) level. Moreover, most direct ridership …