Predictability of public transport usage: a study of bus rides in Lisbon, Portugal

This paper presents a study of the predictability of bus usage based on massive bus ride data collected from Lisbon, Portugal. An understanding of public bus usage behavior is important for future development of personalized transport information systems that are equipped with proactive capabilities such as predictive travel recommender systems. In this study, we show that there exists a regularity in the bus usage and that daily bus rides can be predicted with a high degree of accuracy. In addition, we show that there are spatial and temporal factors that influence bus usage predictability. These influential factors include bus usage frequency, number of different bus lines and stops used, and time of rides

Catch me if you can: predicting mobility patterns of public transport users

Direct and easy access to public transport information is an important factor for improving the satisfaction and experience of transport users. In the future, public transport information systems could be turned into personalized recommender systems which can help riders save time, make more effective decisions and avoid frustrating situations. In this paper, we present a predictive study of the mobility patterns of public transport users to lay the foundation for transport information systems with proactive capabilities. By making use of travel card data from a large population of bus riders, we describe algorithms that can anticipate bus stops accessed by individual riders to generate knowledge about future transport access patterns. To this end, we investigate and compare different prediction algorithms that can incorporate various influential factors on mobility in public transport networks, e.g., travel distance or travel hot spots. In our evaluation, we demonstrate that by combining personal and population-wide mobility patterns we can improve prediction accuracy, even with little knowledge of past behaviour of transport users

An Ontology for Service Semantic Interoperability in the Smartphone-Based Tourist Trip Planning System

This paper presents an ontology-based approach for semantic interoperability tourist trip planning services. The proposed ontology describes a tourist, an attraction route and context information about tourist and his/her environment. This ontology is developed within the Tourist Trip Planning System, which consists of a set of interacting services. All services work accordingly to the proposed ontology which leads to service semantic interoperability and allows to increase interaction speed between them

Distributing Tourists Among POIs with an Adaptive Trip Recommendation System

Traveling is part of many people leisure activities and an increasing fraction of the economy comes from the tourism. Given a destination, the information about the different attractions, or points of interest (POIs), can be found on many sources. Among these attractions, finding the ones that could be of interest for a specific user represents a challenging task. Travel recommendation systems deal with this type of problems. Most of the solution in the literature does not take into account the impact of the suggestions on the level of crowding of POIs. This paper considers the trip planning problem focusing on user balancing among the different POIs. To this aim, we consider the effects of the previous recommendations, as well as estimates based on historical data, while devising a new recommendation. The problem is formulated as a multi-objective optimization problem, and a recommendation engine has been designed and implemented for exploring the solution space in near real-time, through a distributed version of the Simulated Annealing approach. We test our solution using a real dataset of users visiting the POIs of a touristic city, and we show that we are able to provide high quality recommendations, yet maintaining the attractions not overcrowded

Adaptive Trip Recommendation System

Travel recommendation systems provide suggestions to the users based on di erent information, such as user preferences, needs, or constraints. The recommendation may also take into account some characteristics of the points of interest (POIs) to be visited, such as the opening hours, or the peak hours. Although a number of studies have been proposed on the topic, most of them tailor the recommendation considering the user viewpoint, without evaluating the impact of the suggestions on the system as a whole. This may lead to oscillatory dynamics, where the choices made by the recommendation system generate new peak hours. This paper considers the trip planning problem that takes into account the balancing of users among the di erent POIs. To this aim, we consider the estimate of the level of crowding at POIs, including both the historical data and the e ects of the recommendation. We formulate the problem as a multi- objective optimization problem, and we design a recommendation engine that explores the solution space in near real-time, through a distributed version of the Simulated Annealing approach. Through an experimental evaluation on a real dataset of users visiting the POIs of a touristic city, we show that our solution is able to provide high quality recommendations, yet maintaining the attractions not overcrowded

Personalized Travel Itineraries with Multi-access Edge Computing Touristic Services

International audienceThe 5G networks enable new touristic services with challenging communication requirements, such as augmented reality (AR) applications, and allow the visitors to enjoy a touristic experience that involves both the physical and virtual space. Here, we propose a novel multiuser travel itinerary planning framework based on an optimal problem formulation that considers both individual trip itinerary (e.g., tourist's preferences, time or cost) and touristic service constraints (e.g., nearby edge cloud resources and application requirements). The main idea is to maximize the itinerary score of individual visitors, while also optimizing the resource allocation at the edge. We consider two services, video streaming and AR, and evaluate our framework using data from Flickr. Results demonstrate gains up to 100% in the resource allocation and user experience in comparison with a state-of-the-art solution adapted to this scenario

PlanFitting: Tailoring Personalized Exercise Plans with Large Language Models

A personally tailored exercise regimen is crucial to ensuring sufficient physical activities, yet challenging to create as people have complex schedules and considerations and the creation of plans often requires iterations with experts. We present PlanFitting, a conversational AI that assists in personalized exercise planning. Leveraging generative capabilities of large language models, PlanFitting enables users to describe various constraints and queries in natural language, thereby facilitating the creation and refinement of their weekly exercise plan to suit their specific circumstances while staying grounded in foundational principles. Through a user study where participants (N=18) generated a personalized exercise plan using PlanFitting and expert planners (N=3) evaluated these plans, we identified the potential of PlanFitting in generating personalized, actionable, and evidence-based exercise plans. We discuss future design opportunities for AI assistants in creating plans that better comply with exercise principles and accommodate personal constraints.Comment: 22 pages, 5 figures, 1 tabl

We are the Change that we Seek: Information Interactions During a Change of Viewpoint

There has been considerable hype about filter bubbles and echo chambers influencing the views of information consumers. The fear is that these technologies are undermining democracy by swaying opinion and creating an uninformed, polarised populace. The literature in this space is mostly techno-centric, addressing the impact of technology. In contrast, our work is the first research in the information interaction field to examine changing viewpoints from a human-centric perspective. It provides a new understanding of view change and how we might support informed, autonomous view change behaviour. We interviewed 18 participants about a self-identified change of view, and the information touchpoints they engaged with along the way. In this paper we present the information types and sources that informed changes of viewpoint, and the ways in which our participants interacted with that information. We describe our findings in the context of the techno-centric literature and suggest principles for designing digital information environments that support user autonomy and reflection in viewpoint formation