## 1 Introduction
### Brief Literature Review  
In [8], the use of GIS in Utrecht for developing a potential map of future public transportation and supply centers was discussed. The hypothesis of the study was that models in the fields of geography and planning that were integrated into GIS would produce a general picture of accessibility. A new facet of research is outlined in [9] showing the potential user impacts of introducing smartphone-generated and analog-delivered schematic bus maps in Dhaka, Bangladesh. Numerous tools for the accessibility analysis of public transport networks have been developed: a raster-based model of the accessibility of workplaces based on public transportation services [10]; a GIS-based tool developed to allow the rapid analysis of accessibility based on different transport modes using generalized cost to measure transport costs across networks, including monetary and distance components; a tool that was demonstrated in London, UK [11; 12]; a public transport criterion matrix based on existing performance standards, applied to study the public transport networks in three Australian cities, Stonnington, Bayswater, and Cockburn, representing a series of land use and transport policy backgrounds [13]; and the accessibility Interactive Visualisation Tool used in Rome to generate maps of the level of perceived accessibility of different transportation and urban facilities [14]. Consistent multilevel research has been performed for studying the hierarchical process for optimizing bus stop locations in the context of fast-growing multi-modal transit services, with a case study in Wuhan, China [15]; the use of geographic information systems (GIS), particle swarm optimization (PSO), and a genetic algorithm (GA) to model the locations of bus stops in Amman city in Jordan to determine the optimal travel times and serviceability of stops [16]; alternative optimization for the locations of bus stops presented through an optimum proposed model, applied in Sakarya (Turkey) [17]; a bi-level optimization model for locating bus stops to minimize the social cost of the overall transport system [18] and for bus stop time models; and total bus stop time models for bus stops located at mid-blocks and near intersections, based on multivariate regression analysis using an ordinary least squares method that could potentially be used to improve scheduling and transit bus system planning in a dense urban area [19]. Moreover, studies have outlined the analysis of bus dwell times using archived automatic vehicle location (AVL)/automatic passenger counter (APC) data reported at the level of individual bus stops [20], statistical distributions describing and explaining dwell time variability [21], and a method with which to identify bus stops that may need redesigning to reduce the time lost in arriving and departing [22]. The optimization of public transport routes was addressed in [23] for developing a framework for bus network optimization based on geographical information systems (GIS) and genetic algorithms (GAs), and in [24] for describing a methodology for modelling and scaling down a street network to facilitate the optimization of public transport networks, applied to the bus network of Greater Nottingham in the UK. Developing a procedure for generating input datasets for urban transit routing problem (UTRP) algorithms together with developing an interface between the UTRP and the professional transport modeling software PTV Visum for a multi-modal public transport network was performed in [25], the adaptation of a node-based optimization procedure to work with zone-to-zone trips based on a hybrid approach to calculate zone-to-zone journey times through the use of node-based concepts was included in [26], and network analysis tools for public transport optimization with a case study in Lodz, Poland, were described in [27]. Based on the three -dimensional magic cube method [28], transport planning has been studied for determining final land suitability.