# GIS-Based Multi-Objective Routing Approach for Street-Based Sporting-Event Routing
## 1 Introduction
### Literature Review
#### 1.2.2 The Vehicle-Routing Problem with GIS  
In the literature on the vehicle-routing problem (VRP), GIS is seen as a powerful database. The GIS is a platform used to store, manage, and visualize large amounts of city-related data and to support analyses by exploring relationships between various pieces of information [8; 9; 10]. Some countries offer large amounts of open-source data on specific geographic details, road infrastructure, and transport [11].  
Against this background, GIS data have been utilized in the literature related to pathfinding and path evaluation for the past several decades [12]. The VRP field for route optimization is usually based on GIS-based network analysis [13]. In addition, routing research using GIS has been conducted in the railway, waste, distribution, and transportation sectors. [PERSON] et al. [14] examined optimal railway tracks, and [PERSON] et al. [15] proposed the optimization of a solid-waste-collection route.  
The simplest route-selection model is based on the shortest path [16]. However, minimizing the general cost, time, or distance might not be the only influence governing route selection [17].  
An important factor in the route-selection model is the purpose of the system. Therefore, it is necessary to define selection criteria to determine a suitable route for a given purpose [18].  
The aim of a street-based sporting event in a city is the dynamic presentation of the event based on the safety of citizens. The routing of the event involves several factors. Algorithms that support the general shortest-longest distance or minimum-maximum action are not available. In sum, an algorithm that satisfies or converges toward a multi-objective factor should be used.  
Evolutionary (e.g., genetic) algorithms should be used to determine the optimal path that solves the multi-objective problem (MOP) [19]. The literature mentions the fusion methodology of the Dijkstra algorithm and the evolutionary algorithm. In particular, the non-dominated sorting algorithm-II (NSGA-II) [20], used for path multi-objective optimization based on non-domination levels, presented by [PERSON] et al. [21], is an effective technique for solving the MOP. The simulation results for various factors using NSGA-II show that it is superior to other optimization approaches [22].  
Recently, the NSGA-III methodology for large-scale data optimization was proposed. This methodology is used to overcome high data-calculation costs, which are created by the current NSGA-II approach [23]. For example, [PERSON] et al. [24] proposed the NSGA-III methodology for optimizing biomedical ontology alignments, in which concepts of similarity measures cannot be independently distinguished under any circumstances, such as biomedical information systems.  
However, the NSGA-III approach is generally used for many-objective optimization (MaOP) with more than four objectives [25], and computational experiments in MOP with fewer than ten showed that NSGA-III outperformed NSGA-II [26]. In accordance with these results, the NSGA-II approach was adopted in this study.  
Moreover, since the variables considered in GIS data vary according to the purpose, previous studies cannot be directly used for distance-based event-path searches.  
In other words, in this study, the NSGA-II methodology presented in previous studies was applied to select the optimal route that can satisfy the elements required by street events and minimize the impact on cities. Furthermore, we propose a comprehensive decision-making process, including the quantitative evaluation of the derived routes by linking them with BIM.