# Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists
## 1 Introduction
### Literature Review  
Cyclists can plan their route through the city using a variety of tools and strategies. In particular, they can use the following:  
* Through online journey planning tools: Many cyclists use journey planning tools (i.e., Google Maps) to plan their route [11]. These tools often include bike routes and can provide turn-by-turn directions, estimated travel times, and information about bike lanes or other cyclist-related features. * Local bicycle maps: some cities and towns have specifically designed maps for cyclists; highlighting recommended routes, cycle lanes, and other cycle-friendly features [12]. These maps can often be found online, in local bike shops, or in tourist information centers. * Personal experience and knowledge: Experienced cyclists who are familiar with the city can rely on their personal experience and knowledge to plan their route [13]. They may know the best shortcuts, quiet residential streets, or cycle lanes that are not known to the general public. * Through the cycling community: Cyclists can also receive route suggestions and advice from other cyclists or from local cycling support groups and cycling clubs. These groups can provide information on popular routes, safe routes to ride, and other useful advice. In the international literature, many scientific articles on pathfinding analysis can be found in which the behavior of cyclists is investigated. As an example, the study by [PERSON] et al. [14] focuses on analyzing bicycle route selection through a link-based cost function model. Their objective is to understand how numerous factors, including distance, gradient, safety, road type, and the availability of bicycle facilities, influence cyclist behavior and route choice in urban environments. Developing a nonlinear objective cost minimization function, the study seeks to optimize the trade-off between these attributes to determine the most suitable routes for cyclists. Unlike heuristic or metaheuristic methods, the solution method adopts a systematic approach. The study area encompasses Norrkoping, Sweden, where a part of the city is modeled to predict bicycle route choices. Through a combination of field testing, literature review, and simulations, the developed model is refined. In the same vein, the study by [PERSON] et al. [11] aims to analyze cyclist behavior on routes in Guelph, Ontario, Canada, using Geographic Information Systems (GIS). Employing a heuristic solution method, the study examines the actual commuting routes of cyclists. Decision variables include route characteristics like the number of turns, presence of traffic signals, road type, and terrain. The objective is to identify patterns in route choice based on infrastructure and environmental factors. Guelph's diverse urban cycling routes make it an ideal study area. The study concludes that improving cycling infrastructure should focus on strengthening road networks, considering factors such as road type, signal use, and terrain, to promote cycling as a transportation mode. [PERSON]'s research [15] aims to understand and analyze the bicycle route selection behavior using various methods. The study categorizes literature sources based on their primary research methods for route selection analysis. Decision variables include GPS technology, accelerometer readings, heart rate monitors, and cameras, treated as continuous factors. The study develops nonlinear objective functions to capture cyclists' complex decision-making processes. Primarily heuristic in approach, researchers gather route choice data through methods like GPS tracking, surveys, and observations. Data collection spans multiple cities worldwide, including Utrecht, Guelph, Ottawa, and Phoenix. The research underscores the importance of employing diverse data collection techniques to comprehensively grasp bicycle route choice behavior. [PERSON] et al. [16] evaluate methods for generating cycling route option sets using GPS data from Amsterdam.