Boosting Ride Sharing With Alternative Destinations

People living in highly populated cities increasingly experience decreased quality of life due to pollution and traffic congestion. With the objective of reducing the number of circulating vehicles, we investigate a novel approach to boost ride-sharing opportunities based on the knowledge of the human activities behind individual mobility demands. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed in many different places. Therefore, when there is the possibility of sharing a ride, people having a pro-environment behavior or interested in saving money can accept to fulfill their needs at an alternative destination. We thus propose activity-based ride matching (ABRM), an algorithm aimed at matching ride requests with ride offers, possibly reaching alternative destinations where the intended activity can be performed. By analyzing two large mobility datasets extracted from a popular social network, we show that our approach could largely impact urban mobility by resulting in an increase up to 54.69% of ride-sharing opportunities with respect to a traditional destination-oriented approach. Due to the high number of ride possibilities found by ABRM, we introduce and assess a subsequent ranking step to provide the user with the top-k most relevant rides only. We discuss how ABRM parameters affect the fraction of car rides that can be saved and how the ranking function can be tuned to enforce pro-environment behaviors. This is the a pre-print version. Full version is available at the IEEE Transactions in Intelligent Transportations Systems https://ieeexplore.ieee.org/document/837006

Mining Human Mobility Data and Social Media for Smart Ride Sharing

CAPES People living in highly-populated cities increasingly suffer an impoverishment of their quality of life due to pollution and traffic congestion problems caused by the huge number of circulating vehicles. Indeed, the reduction the number of circulating vehicles is one of the most difficult challenges in large metropolitan areas. This PhD thesis proposes a research contribution with the final objective of reducing travelling vehicles. This is done towards two different directions: on the one hand, we aim to improve the efficacy of ride sharing systems, creating a larger number of ride possibilities based on the passengers destination activities; on the other hand, we propose a social media analysis method, based on machine learning, to identify transportation demand to an event. Concerning the first research direction, we investigate a novel approach to boost ride sharing opportunities based, not only on fixed destinations, but also on alternative destinations while preserving the intended activity of the user. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed at many different locations (e.g. all the shopping malls in a given area). Our assumption is that, when there is the possibility of sharing a ride, people may accept visiting an alternative destination to fulfill their needs. Based on this idea, We thus propose Activity-Based Ride Matching (ABRM), an algorithm aimed at matching ride requests with ride offers to alternative destinations where the intended activity can still be performed. By analyzing two large mobility datasets, we found that with our approach there is an increase up to 54.69% in ride-sharing opportunities compared to a traditional fixed-destination-oriented approach. For the second research contribution, we focus on the analysis of social media for inferring the transportation demands for large events such as music festivals and sports games. In this context, we investigate the novel problem of exploiting the content of nongeotagged posts to infer users’ attendance of large events. We identified three temporal periods: before, during and after an event. We detail the features used to train the event attendance classifiers on the three temporal periods and report on experiments conducted on two large music festivals in the UK. Our classifiers attained a very high accuracy, with the highest result observed for Creamfields festival (∼91% accuracy to classify users that will participate in the event). Furthermore, we proposed an example of application of our methodology in event-related transportation. This proposed application aims to evaluate the geographic areas with a higher potential demand for transportation services to an event. Pessoas que vivem em cidades altamente populosas sofrem cada vez mais com o declínio da qualidade de vida devido à poluição e aos problemas de congestionamento causados pelo enorme número de veículos em circulação. A redução da quantidade de veículos em circulação é de fato um dos mais difíceis desafios em grandes áreas metropolitanas. A presente tese de doutorado propõe uma pesquisa com o objetivo final de reduzir o número de veículos em circulação. Tal objetivo é feito em duas diferentes direções: por um lado, pretendemos melhorar a eficácia dos sistemas de ride-sharing aumentando o número de possibilidades de caronas com base na atividade destino dos passageiros; por outro lado, propomos também um método baseado em aprendizagem de máquina e análise de mídia social para identificar demanda de transporte de um evento. Em relação à primeira contribuição da pesquisa, nós investigamos uma nova abordagem para aumentar o compartilhamento de caronas baseando-se não apenas em destinos fixos, mas também em destinos alternativos enquanto que preservando a atividade pretendida do usuário. Observamos que em muitos casos a atividade que motiva o uso de um carro particular (por exemplo ir a um shopping center) pode ser realizada em muitos locais diferentes (por exemplo todos os shoppings em uma determinada área). Nossa suposição é que, quando há a possibilidade de compartilhar uma carona, as pessoas podem aceitar visitas a destinos alternativos para satisfazer suas necessidades. Nós propomos o Activity-Based Ride Matching (ABRM), um algoritmo que visa atender às solicitações de caronas usando destinos alternativos onde a atividade pretendida pelo passageiro ainda pode ser executada. Através da análise de dois grande conjuntos de dados de mobilidade, mostramos que nossa abordagem alcança um aumento de até 54,69% nas oportunidades de caronas em comparação com abordagens tradicionais orientadas a destinos fixos. Para a segunda contribuição nos concentramos na análise de mídias sociais para inferir as demandas de transporte para grandes eventos tais como concertos musicais e eventos esportivos. Investigamos um problema que consiste em explorar o conteúdo de postagens não geolocalizadas para inferir a participação dos usuários em grandes eventos. Nós identificamos três períodos temporais: antes, durante e depois de um evento. Detalhamos as features usadas para treinar classificadores capazes de inferir a participação de usuários em um dado evento nos três períodos temporais. Os experimentos foram conduzidos usando postagens em mídias sociais referentes a dois grandes festivais de música no Reino Unido. Nossos classificadores obtiveram alta accuracy, com o maior resultado observado para o festival Creamfields (∼91% de accuracy para classificar os usuários que participarão do evento). Propusemos também uma aplicação de nosso método que visa avaliar as áreas geográficas com maior potencial de demanda por serviços de transporte para um evento. Le persone che vivono in città densamente popolate subiscono sempre più un impoverimento delle loro qualità della vita a causa dell’inquinamento e dei problemi di congestione del traffico causati dall’enorme numero di veicoli circolanti. La riduzione dei veicoli circolanti è una delle sfide più difficili nelle grandi aree metropolitane. Questa tesi di dottorato propone un contributo di ricerca con l’obiettivo finale di ridurre i numeri di veicoli in viaggio. Questo eśtato sviluppato verso due direzioni: da un lato, vogliamo migliorare l’efficacia dei sistemi di ride sharing, aumentando la possibilità di ricevere e dare passaggi in base alla attività di destinazione dei passeggeri. D’altra parte, vogliamo proporre un metodo basato sul machine learning e analisi dei social media, per identificare demanda de transporte a un evento. Per quanto riguarda il primo contributo di ricerca, abbiamo studiato un nuovo approccio per aumentare la condivisione dei passagi non solo su destinazioni fisse, ma anche su destinazioni alternative preservando l’attività prevista dall’utente. Osserviamo infatti che in molti casi l’attività che motiva l’uso di un’auto privata (ad es. andare in un centro commerciale) può essere eseguito in molti luoghi diversi (ad esempio tutti i centri commerciali in una determinata area). La nostra ipotesi è che, quando c’è la possibilità di condividere un passaggio, le persone possono accettare di visitare una destinazione alternativa per soddisfare i loro bisogni. Basato su questa idea, proponiamo Activity-Based Ride Matching (ABRM), un algoritmo che mira a soddisfare le richieste di carpool utilizzando destinazioni alternative, dove l’attività desiderata dal passeggero può ancora essere eseguita. Attraverso l’analisi di due grandi insiemi di dati di mobilità, mostriamo che il nostro approccio raggiunge un aumento fino al 54,69% nelle opportunità di condivisione di car pooling rispetto agli approcci tradizionali rivolti a destinazioni fisse. Per il secondo contributo della ricerca ci concentriamo sull’analisi dei social media per inferire le richieste di trasporto verso grandi eventi come concerti musicali e giochi sportivi. In questo contesto, indaghiamo sul nuovo problema dello sfruttamento del contenuto di non geotagged post per inferire la presenza di utenti a grandi eventi. Abbiamo identificato tre periodi temporali: prima, durante e dopo un evento. Descriviamo in dettaglio le caratteristiche utilizzate per addestrare i classificatori per inferire la partecipazione all’evento sui tre periodi temporali. Riportiamo gli esperimenti condotti su due grandi festival musicali nel Regno Unito. I nostri classificatori raggiungono uma alta accuracy, con il risultato più alto osservato per il festival Creamfields (∼91% di accuracy per classificare gli utenti che parteciperanno all’evento). Inoltre, abbiamo proposto un’applicazione della nostra metodologia che ha come scopo valutare le aree geografiche con il maggior potenziale di domanda di servizi di trasporto per un evento. Document type: Conference objec

A Recommendation System for Shared-Use Mobility Service through Data Extracted from Online Social Networks

In recent years, the shared mobility service hasincreased in many countries across the world because its low cost and several shared-use mobility applications on mobile devices. Commonly, if a ride is shared between people with similar preferences, users likely feel both more comfortable and safer.In this context, the main goal of this article is to classify userswith similar preferences, in automatic manner, to improve user’s quality of experience in ridesharing service. To obtain initial data, subjective tests are carried out using questionnaires and their results are used to determine ridesharing profiles. Then, some basic user profile information is extracted from Online Social Networks (OSN) to determine an user profile based on preferences in ridesharing service. The user profile classification is performed through different machine learning algorithms, which use as input the data extracted from OSN. Two case studies of shared-mobility are treated, (i) sharing a ride with a passenger with a similar hobby [2], and (ii) sharing a ride with people thatsupport an opposite football teams. In this work, a novel contribution is the use of Hybrid Discriminative Restricted Boltzmann Machines (HDRBM) technique for classification, which results overcomes other algorithms, such as Random Forest, SVM and DRBM. The experimental results presented a correctly classified instance of 96:9% and 97:3% for the cases of sharing a ride with people with similar hobby and support different football team, respectively. Finally, a Recommendation System (RS) is proposed, which efficiency is compared with a basic RS, obtaining a Pearson correlation coefficient of 0:97 and 0:79, respectively

A Recommendation System for Shared-Use Mobility Service through Data Extracted from Online Social Networks

In recent years, the shared mobility service hasincreased in many countries across the world because its low cost and several shared-use mobility applications on mobile devices. Commonly, if a ride is shared between people with similar preferences, users likely feel both more comfortable and safer.In this context, the main goal of this article is to classify userswith similar preferences, in automatic manner, to improve user’s quality of experience in ridesharing service. To obtain initial data, subjective tests are carried out using questionnaires and their results are used to determine ridesharing profiles. Then, some basic user profile information is extracted from Online Social Networks (OSN) to determine an user profile based on preferences in ridesharing service. The user profile classification is performed through different machine learning algorithms, which use as input the data extracted from OSN. Two case studies of shared-mobility are treated, (i) sharing a ride with a passenger with a similar hobby [2], and (ii) sharing a ride with people thatsupport an opposite football teams. In this work, a novel contribution is the use of Hybrid Discriminative Restricted Boltzmann Machines (HDRBM) technique for classification, which results overcomes other algorithms, such as Random Forest, SVM and DRBM. The experimental results presented a correctly classified instance of 96:9% and 97:3% for the cases of sharing a ride with people with similar hobby and support different football team, respectively. Finally, a Recommendation System (RS) is proposed, which efficiency is compared with a basic RS, obtaining a Pearson correlation coefficient of 0:97 and 0:79, respectively

MOBILITY ANALYSIS AND PROFILING FOR SMART MOBILITY SERVICES: A BIG DATA DRIVEN APPROACH. An Integration of Data Science and Travel Behaviour Analytics

Smart mobility proved to be an important but challenging component of the smart cities paradigm. The increased urbanization and the advent of sharing economy require a complete digitalisation of the way travellers interact with the mobility services. New sharing mobility services and smart transportation models are emerging as partial solutions for solving some tra c problems, improve the resource e ciency and reduce the environmental impact. The high connectivity between travellers and the sharing services generates enormous quantity of data which can reveal valuable knowledge and help understanding complex travel behaviour. Advances in data science, embedded computing, sensing systems, and arti cial intelligence technologies make the development of a new generation of intelligent recommendation systems possible. These systems have the potential to act as intelligent transportation advisors that can o er recommendations for an e cient usage of the sharing services and in uence the travel behaviour towards a more sustainable mobility. However, their methodological and technological requirements will far exceed the capabilities of today's smart mobility systems. This dissertation presents a new data-driven approach for mobility analysis and travel behaviour pro ling for smart mobility services. The main objective of this thesis is to investigate how the latest technologies from data science can contribute to the development of the next generation of mobility recommendation systems. Therefore, the main contribution of this thesis is the development of new methodologies and tools for mobility analysis that aim at combining the domain of transportation engineering with the domain of data science. The addressed challenges are derived from speci c open issues and problems in the current state of the art from the smart mobility domain. First, an intelligent recommendation system for sharing services needs a general metric which can assess if a group of users are compatible for speci c sharing solutions. For this problem, this thesis presents a data driven indicator for collaborative mobility that can give an indication whether it is economically bene cial for a group of users to share the ride, a vehicle or a parking space. Secondly, the complex sharing mobility scenarios involve a high number of users and big data that must be handled by capable modelling frameworks and data analytic platforms. To tackle this problem, a suitable meta model for the transportation domain is created, using the state of the art multi-dimensional graph data models, technologies and analytic frameworks. Thirdly, the sharing mobility paradigm needs an user-centric approach for dynamic extraction of travel habits and mobility patterns. To address this challenge, this dissertation proposes a method capable of dynamically pro ling users and the visited locations in order to extract knowledge (mobility patterns and habits) from raw data that can be used for the implementation of shared mobility solutions. Fourthly, the entire process of data collection and extraction of the knowledge should be done with near no interaction from user side. To tackle this issue, this thesis presents practical applications such as classi cation of visited locations and learning of users' travel habits and mobility patterns using historical and external contextual data