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Aside from using Agent-based simulations in road traffic itself, it can further be used in designing a passenger-centered design of buses to enhance concepts designs of buses to improve service to different passengers. An approach discussed by Schelenz et al. was to use a passenger model where passengers' preferences and behavior are modeled with modern bus layouts considering seat typologies and orientations, types and number of doors, ticketing machines and the fact that passengers are not only classified by gender, age and ethnicity but also by their physical needs and psychological features \cite{Schelenz_2012}.  \subsubsubsection \emph{Hybrid Routing Model}\\  Before the Hybrid Routing Model became the result of He et al. studies, random networks, scale-free networks, small-world networks and a square lattice was used to study efficient generation of routing methods to mitigate congestion. Square lattices are typically used as the base model to study road networks. All four generated networks have the number of nodes \emph {N} = 400 with an average degree {\emph{k}} \approx 4. Transport demand was assumed to be homogeneously distributed. The total transport volume \emph{V} was increased to 10,000 to 35,000 to simulate different levels of congestion in the networks. The transport cost of a link was defined by the Bureue of Public Roads function, which is widely used in civil engineering where Transport cost \emph{Cij} between two neighboring nodes \emph{i} and \emph{j} is defined as : \\  cij(f) = lij + α(f /Capacity) β  lij\\  where  lij is the distance between the two nodes, f is the link flow, Capacity is the link capacity, and α and β determine the  correlation between congestion cost and f . Noting that the volume over capacity VOC = f /Capacity, can be written as:\\  cij(f) = lij + α(VOC)  β  lij. \\  Combining shortest path (SP) routing and minimum cost (MC) routing, a hybrid routing model was created by He et al. This model requires only a small fraction of the total number of agents to use MC routes, and mitigates congestion under heterogeneous or homogeneous transport demand. \cite{He_2015} Usually, if it concerns network flow, the agent's first choice is the shortest path. MC routing mitigate networks in congestion, but it is difficult to induce all agents to choose MC routes. Using the hybrid routing model, targeted agents are forced to use MC routes. This is generated by calculating transport cost for each agent in SP routing scenario then the fraction of agents with the largest transport cost were assigned as a target to use MC routes. The remaining agents use SP routes, while the link flow is calculated. With the updated link cost, targeted agents are kept to use MC routes until the condition of convergence is satisfied.    \section{Review of Related Software}