The objective of this research was to investigate, develop and evaluate dynamic
load-balancing strategies for parallel execution of microscopic road traffic simulations. Urban road traffic simulation presents irregular, and dynamically varying
distributed computational load for a parallel processor system. The dynamic
nature of road traffic simulation systems lead to uneven load distribution during simulation, even for a system that starts off with even load distributions. Load balancing is a potential way of achieving improved performance by reallocating
work from highly loaded processors to lightly loaded processors leading to
a reduction in the overall computational time. In dynamic load balancing,
workloads are adjusted continually or periodically throughout the computation.
In this thesis load balancing strategies were evaluated and some load balancing
policies developed. A load index and a profitability determination algorithms
were developed. These were used to enhance two load balancing algorithms. One
of the algorithms exhibits local communications and distributed load evaluation
between the neighbour partitions (diffusion algorithm) and the other algorithm
exhibits both local and global communications while the decision making is
centralized (MaS algorithm). The enhanced algorithms were implemented and
synthesized with a research parallel traffic simulation. The performance of the
research parallel traffic simulator, optimized with the two modified dynamic load balancing strategies were studied
