Autonomous vehicles (AVs) show promise for increasing roadway safety and capacity.
During the AV transition era, which will be characterized by a mixed fleet of AVs and human …

A novel deep learning‐based optimization (DLBO) methodology is proposed for rapidly
optimizing phononic crystal‐based metastructure topologies. DLBO eliminates the need for …

The integration problem of production and transportation (IPPT) is one of the most important
decision issues in real-life manufacturing and flow industries. To illustrate its potential for …

The lack of parking infrastructure continues to pose a problem for urban commuters, as the
parking demand in most cities outstrips supply and significant driver time is expended …

The rise of surveillance systems has led to exponential growth in collected data, enabling
several advances in Deep Learning to exploit them and automate tasks for autonomous …

Growth in urban population, travel, and motorization continue to cause an increased need
for urban projects to expand road capacity. Unfortunately, these projects also cause travel …

This study proposes a methodology for deriving the optimal work zone schedule for the
annual routine maintenance planning in an infrastructure asset management system …

Energy efficiency is at a critical point now with rising energy prices and decarbonisation of
the residential sector to meet the global NetZero agenda. Non-Intrusive Load Monitoring is a …

When a work zone is located at an intersection, it greatly reduces the capacity and change
the traffic flow characteristics. However, the impacts of work zones have not attracted much …

This chapter explores the use of Artificial Intelligence (AI) methods in software project
scheduling, covering key processes like schedule management, activity sequencing, and …