# Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists
## 1 Introduction
### Literature Review
Employing a data-driven approach, the study aims to identify option sets based on cyclists' observed behavior and preferences. Decision variables involve discrete route choices made by cyclists, while the objective function is nonlinear. The routes were optimized based on criteria related to distance, travel time, and network-related objectives. The study area is Amsterdam, where GPS bicycle route data were collected during \"Bicycle Count Week.\" The solution method compares different option set generation algorithms, including deterministic and stochastic methods. Heuristic methods are utilized to address the complexity of generating choice sets efficiently. The study concludes that the data-driven approach provides valuable insights into cycling route selection behavior. In the work of [PERSON] et al. [17], cyclists' route choices in the Regional Municipality of Waterloo, Ontario, are analyzed to understand how road network design affects travel distances. Using a trip prediction model, the study aims to minimize redundant routes, highlighting the importance of efficient route selection. Decision variables involve the continuous calculations of linear distances and shortest routes based on road network maps. The approach is heuristic, utilizing GIS and [PERSON]'s algorithm to find the most efficient routes based on predefined criteria. [PERSON] et al. [18] developed a route selection model for cyclists using GPS data from Zurich. Their model, based on a multinomial logit framework, seeks to address gaps in understanding factors influencing cyclists' route choices. Decision variables include route length, the presence of bike lanes, slope, traffic signals, and traffic volume, which interact to capture route choice complexity. The nonlinear objective function reflects variable interactions. Data are driven from various sources to form a detailed road network dataset. Model estimation using the BIOGEME software (3.2.14) provides insights into cyclists' preferences and trade-offs. The results emphasize factors like route length, gradients, and cycle lane availability, crucial for designing cycling networks and improving route choice modeling. [PERSON]'s study [19] aims to analyze cyclists' route choice behavior using stated preference approaches. The model estimates the partial service value of trip attributes like travel time, surface quality, traffic volume, and cycling facility type. The linear objective function determines the utility of each alternative based on attribute combinations. Maximum likelihood techniques are used for model parameter estimation, focusing on maximizing the probability of observed choices. The study area focuses on urban cycling route choices, emphasizing the role of cycling infrastructure. This approach provides insights into cyclists' route choice trade-offs, emphasizing factors like travel time and surface quality in decision making. Similarly, [PERSON] et al. [20] develop a mixed multinomial logit (MNL) model for analyzing bicycle route selection, aiming to address individual heterogeneity based on observed and unobserved characteristics. Decision variables include route attributes like parked vehicle turnover rate, parking lot length, and parking occupancy rate. The nonlinear objective function estimates the likelihood function for individual choices based on route characteristics. Data collection in Texas involves an online bicyclist preference survey. The solution method utilizes a \"quasi-Monte Carlo\" (heuristic) simulation to approximate integrals in the likelihood function, enabling more generalized model estimation. The study underscores the importance of considering both route-related characteristics and cyclist demographics in route choice decisions, providing insights into cyclists' preferences for factors like parking length and occupancy rates. [PERSON] et al. [21] aim to model route selection by bicycle commuters using bi-objective routing methods, considering travel time and suitability as the primary objectives.