  Background: The development of public transit network can enhance the
efficiency of the system as well as raise interest to use the system. Feeder
bus service fills the area that is far from the railway system; therefore,
designing a feeder network in a gap area causes the expansion of the main
transit network. Purpose: To present a modified potential demand model for
designing a multi-circulation feeder network. Materials and Methods: In this
study, the potential demand is defined based on the traffic demand of each
section related to each link, the average walking distance of passengers to
specified links, the distance from the links to stations, and finally potential
demand of accessing each main station for each link. A labelling method is used
to create continuous circular and separated routes. After generating an initial
solution using a constructive heuristic algorithm, a genetic algorithm was
operated to solve the problem. In the second algorithm, each route is
considered a gene, and each network is considered a chromosome. In fact, in
this step, different routes from diverse solutions are combined as a unique
feeder network. Tehran District 10 was selected as the case study and the model
was tested on this area which is located near the central business districts. A
machine learning approach has been applied to estimate the missing values of
the related database. Results: By running the algorithm, four feeder routes
were obtained with reasonable travel times and distances of 4.8, 5.5, 5.8, and
8.1 km, starting and ending at the same railway station. Moreover, 98% of the
area was covered by resulted feeder network with a maximum access distance of
300m. Conclusion: What stands out from this method is that the combination of
the modified feeder route design model and algorithms is feasible.
