# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 2 Related Work
### Routing and Modeling  
Different factors affect decision making when choosing urban trip routes. Most commercial systems and IT studies map the problem to the shortest/fastest pathfinding [5; 17]. The literature of Discrete Choice Modeling [8] and Transport Economics [18] for route choice modeling is well-studied. Yet, some proposed models are prohibitive due to data restrictions that are present in real-world applications and the difficulty in assessing every aspect that drives route selection. Our model builds upon discrete choice modeling frameworks and defines premises to ease the implementation in the real world. Additionally, we identify data sources that can be used to better model the user's behavior.  
This study assumes that cost, duration, and quality of experience (QoE) play a central role in the mode/route choice process. Other studies model these choices while considering more variables. The FAVOUR algorithm [5], for instance, uses a probabilistic model over historical data. It represents the route's cost/utility as a weighted sum of the cost of route segments. Every segment is evaluated with different features, leading to different weights for the same segment. Furthermore, the algorithm considers that some route options have costs that are associated with the entire route, rather than every segment to contemplate different phenomena, such as weather. We use similar variables to those that are used in FAVOUR, such as distance, duration, price, and the number of transport mode changes. However, the FAVOUR algorithm does not consider the acceptability of using active transportation modes, which we do. In their study, walking and cycling long distances are evaluated in the same way as short ones. Another difference is that we combine the features into a familiar metric to users, such as a monetary price or a duration. The FAVOUR model is based on a Bayesian learning technique to define the weights for the sum and recommend routes for users. Differently, we use Discrete Choice Analysis [8] to model the costs of a trip and apply nominal values for the quality of service (QoS). Finally, we use a profile operator to suggest routes separately that provide ways to compare routes with or without the bias of individuals. FAVOUR always counts on the existence of individual user data to adapt its recommendations. Finally, our model considers traffic conditions to select transition mode points, while FAVOUR does not consider real-time traffic information.  
[PERSON] et al. [19] proposed a method for selecting bicycle routes using different criteria. They defined a generic framework based on a graph with geo-coordinates, altitude, the horizontal length of cycling paths segments, and other features of route segments. For the experiment, they defined the features to be the ones that are provided by OpenStreetMap ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)), accessed on 14 May 2021). The authors modeled the route selection problem as multi-criteria search optimization and proposed heuristics to make this search feasible. This study focuses on maximizing different utility functions that are applied to routes. They do not provide methods to obtain scores that aid in ranking or personalizing the results, such as our proposal. They only consider one transportation mode (i.e., bicycles), which causes important variables to be omitted, such as the acceptability to chose a given route using active transportation. When taking active transportation, users may feel discouraged to perform long trips, which is considered in our model.