Optimized scheduling of highway work zones

Highway maintenance activities usually require lane closures and disrupt traffic operations. Because of budget constraints, project deadlines, and the resulting traffic impact, the objective of this dissertation is to improve the efficiencies of traffic operation and maintenance work, and minimize the total project cost (i.e., agency cost and road user cost) by optimizing work zone schedules. This dissertation focuses on the maintenance projects on multiple-lane highways. The objective total cost function is formulated while considering a discrete maintenance time-cost function and time-dependent traffic diversions. However, the work zone scheduling problem is a combinatorial optimization problem and difficult to solve analytically. This dissertation transformed the complicated problem into two separate steps: determining the time-dependent traffic diversion by the User Equilibrium Assignment, and minimizing the total project cost by a Genetic Algorithm. An iterative algorithm that integrates the two steps was developed. The optimized work zone schedule and the associated optimal diverted traffic flow can be found simultaneously after multiple iterations. Case studies and extensive sensitivity analyses were conducted to analyze various scheduling scenarios with or without a time-cost function and traffic diversion. The relations among key decision variables were analyzed. Conclusions and recommendations are provided, and directions of future research efforts are discussed

A YBCO RF-squid variable temperature susceptometer and its applications

The Superconducting QUantum Interference Device (SQUID) susceptibility using a high-temperature radio-frequency (rf) SQUID and a normal metal pick-up coil is employed in testing weak magnetization of the sample. The magnetic moment resolution of the device is 1 x 10(exp -6) emu, and that of the susceptibility is 5 x 10(exp -6) emu/cu cm

Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree

Combined Target-Assignment and Path-Finding problem (TAPF) requires simultaneously assigning targets to agents and planning collision-free paths for agents from their start locations to their assigned targets. As a leading approach to address TAPF, Conflict-Based Search with Target Assignment (CBS-TA) leverages both K-best target assignments to create multiple search trees and Conflict-Based Search (CBS) to resolve collisions in each search tree. While being able to find an optimal solution, CBS-TA suffers from scalability due to the duplicated collision resolution in multiple trees and the expensive computation of K-best assignments. We therefore develop Incremental Target Assignment CBS (ITA-CBS) to bypass these two computational bottlenecks. ITA-CBS generates only a single search tree and avoids computing K-best assignments by incrementally computing new 1-best assignments during the search. We show that, in theory, ITA-CBS is guaranteed to find an optimal solution and, in practice, is computationally efficient

What determines online consumers to migrate from PC to Mobile Terminals? -An empirical research on consumers’online channel-migration behaviors

With the improvement of telecommunication and wireless Internet-access technologies, smart mobile terminals have been extensively applied for mobile shopping. In this paper, PPM Model is taken as a theoretical framework and an empirical research method is employed to determine the antecedents influencing consumers’ decisions on migrating from PC-based shopping to mobile shopping. We found that inconvenience, security, perceived usefulness, and perceived ease of use are the significant antecedents influencing consumers’channel migration intention of choosing mobile shopping