Abstract:
Nowadays, the increasing private vehicles have caused severe tra c congestion, environmental
pollution and road accidents in many cities around the world. As such, public transport
has been widely recognized as an e ective way to improve urban life. To develop public bus
service as a competitive alternative to private vehicles, we must design a practical, e cient
and economic transit network. A transit network is composed of several connected routes
for public buses. Transit Network Design Problem (TNDP) determines the transit network
for a city while achieving some objectives and maintaining some constraints. In this modern
age, TNDP involves di erent stakeholders with di erent interests and values. As a result, a
large number of optimization objectives arise naturally. However, most of the researchers have
ignored this issue by using single objective function to express solution quality. In this thesis,
we proposed a new formulation for the many-objective TNDP which allows to generate a diverse
set of alternative solutions. Then we developed problem speci c genetic operators for solving
TNDP using evolutionary algorithms. For the rst time, we adapted several state-of-the-art
many-objective evolutionary algorithms (MaOEAs) to explore the high-dimensional objective
space of TNDP using our genetic operators. Our MaOEA based approach has been rigorously
tested with several benchmark datasets. The experimental results show that, the proposed
methodology is more e ective in addressing modern challenges than the existing approaches.
