A predictive continuum dynamic user-optimal (PDUO-C) model is formulated in this study to investigate the dynamic characteristics of traffic flow and the corresponding route-choice behavior of travelers within a region with a dense urban road network. The modeled region is arbitrary in shape...

This paper presents an application of the continuum traffic equilibrium model for the cordon-based congestion pricing problem. The traffic equilibrium model treats densely spaced roads as a continuum over which commuters are continuously dispersed. Suppose in the morning peak-hour, all commuters...

Consider a city with several facilities competing for multi-class users that are distributed continuously over space. Within the city region, the road network is relatively dense and is considered as a continuum. A user equilibrium condition results when users freely make their choice of...

Purpose The purpose of this paper is to accurately obtain the deformation of a hybrid robot and rapidly enable real-time compensation in friction stir welding (FSW). In this paper, a prediction algorithm based on the back-propagation neural network (BPNN) optimized by the adaptive genetic...

Purpose As complex analysis of contact models is required in the traditional assembly strategy, it is still a challenge for a robot to complete the multiple peg-in-hole assembly tasks autonomously. This paper aims to enable the robot to complete the assembly tasks autonomously and more...

In this paper, we propose a user equilibrium transit assignment model that takes into account transit schedules and individual vehicle capacities explicitly. The model assumes that passengers use travel strategies that can be adaptive over time and graphically represented as subgraphs. When...

In this paper, we extend the toll pricing framework previously developed for vehicular traffic networks to ones with the potential to include many modes of transportation such as walking, driving, and using public conveyance (e.g., buses, subways, and trains). To determine tolls, we construct a...