# Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists
## 1 Introduction
### Literature Review
The model identifies efficient routes based on these criteria, with decision variables representing discrete routes. Nonlinear objective functions simultaneously minimize travel time and route suitability. A two-objective heuristic routing algorithm is employed to derive a solution. The case study focuses on morning peak travel between Point Chevalier and Auckland's central business district. The research lays a foundation for cyclists' route assignment and suggests the potential application of the methodology as an interactive route-finding guide to help cyclists select efficient routes based on preferences and needs. The objective of the route choice analysis study by [PERSON] and [PERSON] [22] is to explore factors influencing cyclists' route choices in Northern England utilizing OpenStreetMap (OSM) as a transport network. The model incorporates content variables and observes routes to analyze network constraints' impact on cyclist behavior. Decision variables include distance, time, roadway categories, bike lane availability, bridges, crossings, parking, and green space, reflecting the diverse characteristics influencing route choices. The nonlinear objective function aims to capture complex interactions between the identified variables and their influence on route choice. Focused on home-to-work travel, the study employs a combination of statistical techniques and parametric and non-parametric tests to assess network constraints' importance on commuter cyclists' travel. [PERSON] et al. [23] aim to evaluate the factors influencing cyclists' route choices in a medium-sized city in Brazil, focusing on the characteristics influencing route preferences. Using a questionnaire with 18 factors assessed by 65 cyclists commuting by bicycle, decision variables include roadway characteristics like pavement condition, continuity, hierarchy, and environmental factors such as safety concerns and adjacent land uses. These variables can be both continuous (e.g., pavement condition) and integer (e.g., number of intersections). The nonlinear objective function captures complex interactions between the factors influencing route choice. Sao Carlos, Brazil, with limited cycling infrastructure, serves as the research area where most bicycle trips occur on shared roads. The solution method involves a combination of statistical analysis and modeling techniques to analyze multiple factors influencing route choice. [PERSON] et al. [24] develop a model for bicycle route selection using GPS data from Portland cyclists, aiming to understand the factors influencing route preferences like infrastructure and traffic conditions. Their route choice model incorporates variables to predict cyclists' decisions, including factors like route distance, bicycle facilities, and traffic volume. These continuous decision variables reflect route characteristics quantitatively. The nonlinear objective function captures complex interactions among variables to predict route choices accurately. Using deterministic methods, mathematical optimization techniques estimate model parameters and predict route choices based on the collected data. Portland, Oregon, serves as the research area, where GPS data from cyclists were collected to develop and validate the route selection model. Finally, [PERSON] et al. [25] examine the application of the on-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing multi-objective routes within transportation networks. NSGA-II provides a framework for dealing with the complexity associated with optimizing conflicting objectives, such as minimizing travel time and maximizing route safety. Through the use of a genetic algorithm, NSGA-II efficiently navigates the search space, generating a diverse set of representationally optimal solutions that capture the tradeoffs between competing objectives. The paper discusses the key principles of the algorithm, including non-dominated classification and crowding distance, which promote the preservation of solution diversity across generations.