# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 1 Introduction  
Intelligent technologies allow industries and governments to serve citizens better through disruptive applications. Among them, we highlight the ones that are related to Intelligent Transportation Systems (ITS), which are responsible for ensuring efficient and sustainable transportation [1]. Many of these applications explore data from several sources to better understand the city dynamics and adapt to them. This constant search for improvement in urban transportation stimulates new ideas and techniques to help city planners and public administrators better understand urban mobility dynamics. The study of human mobility in urban scenarios has gained more attention recently due to the popularization of location-tracking systems [2], such as Global Positioning Systems (GPS), cellphones, and interactions in location-based social networks. These tracking solutions allow a better understanding of people's movement in a city using buses, subways, taxis, and other means. This understanding is obtained by analyzing their digital footprints. Some smart mobility solutions use data that were collected from human mobility to propose, for instance, urban routing, which focuses on achieving different objectives while suggesting routes to perform trips in the city. Most of the current urban routing applications focus on the shortest or fastest route identification problem; however, different aspects may play essential roles in route selection. In this sense, several studies have investigated different aspects of urban trips. For instance, routing approaches focused on non-motorized means of transport, also called active transportation (e.g., walking, bicycle) [3], enhancing the levels of enjoyment in the trip [4], or even identifying users' preferences using probabilistic frameworks [5]. This paper investigates the use of different transportation modes as a substitute to traditional modes. We devise a model that allows the evaluation of transportation systems in cities with various transportation modes. The same model can be used to recommend routes by also applying user preferences. We describe both cases in this work. Our solution creates hybrid-multimodal routes that combine different transportation modes on a trip. This approach differs from traditional multimodal routing, which suggests multiple options, each with a single transportation mode. We use historical trip data from taxis of New York City to evaluate the hybrid-multimodal routes. The main objective of this study is to evaluate how a different mechanism for creating and recommending routes for urban trips can impact the user. Our model's main contribution is the recommendation of urban trips that is aware of not only cost and duration, but that also is subjective to users' perceptions. Approaches to model perception of users when recommending urban routes have recently gained relevance in the context of smart cities, for instance, with eco-friendly route recommendation [6] being adopted in mainstream route recommendation apps, such as Google Maps. This work extends our previous study [7], where we proposed a method to improve route selection through hybrid private vehicles and traffic information. Previously, the route selection was made by evaluating the duration and cost of urban trips. Here, we advance our previous work by exploring a novel user experience framework that is based on Discrete Choice Analysis [8]. This approach allows us to compare different route options while accounting for variables, such as the acceptability of active transportation modes, cost of transfer between different modes, and user preferences through a user profile operator. Besides that, we also implement other improvements, such as consider more alternative modes (e.g., bicycle) and other datasets to evaluate users' impressions of urban trip segments.