How machine learning informs ride-hailing services: A survey

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed

Self-supervised representation learning for geographical data - a systematic literature review

Self-supervised representation learning (SSRL) concerns the problem of learning a useful data representation without the requirement for labelled or annotated data. This representation can, in turn, be used to support solutions to downstream machine learning problems. SSRL has been demonstrated to be a useful tool in the field of geographical information science (GIS). In this article, we systematically review the existing research literature in this space to answer the following five research questions. What types of representations were learnt? What SSRL models were used? What downstream problems were the representations used to solve? What machine learning models were used to solve these problems? Finally, does using a learnt representation improve the overall performance

Modeling Time-Series and Spatial Data for Recommendations and Other Applications

With the research directions described in this thesis, we seek to address the critical challenges in designing recommender systems that can understand the dynamics of continuous-time event sequences. We follow a ground-up approach, i.e., first, we address the problems that may arise due to the poor quality of CTES data being fed into a recommender system. Later, we handle the task of designing accurate recommender systems. To improve the quality of the CTES data, we address a fundamental problem of overcoming missing events in temporal sequences. Moreover, to provide accurate sequence modeling frameworks, we design solutions for points-of-interest recommendation, i.e., models that can handle spatial mobility data of users to various POI check-ins and recommend candidate locations for the next check-in. Lastly, we highlight that the capabilities of the proposed models can have applications beyond recommender systems, and we extend their abilities to design solutions for large-scale CTES retrieval and human activity prediction. A significant part of this thesis uses the idea of modeling the underlying distribution of CTES via neural marked temporal point processes (MTPP). Traditional MTPP models are stochastic processes that utilize a fixed formulation to capture the generative mechanism of a sequence of discrete events localized in continuous time. In contrast, neural MTPP combine the underlying ideas from the point process literature with modern deep learning architectures. The ability of deep-learning models as accurate function approximators has led to a significant gain in the predictive prowess of neural MTPP models. In this thesis, we utilize and present several neural network-based enhancements for the current MTPP frameworks for the aforementioned real-world applications.Comment: Ph.D. Thesis (2022

Graph Neural Network for spatiotemporal data: methods and applications

In the era of big data, there has been a surge in the availability of data containing rich spatial and temporal information, offering valuable insights into dynamic systems and processes for applications such as weather forecasting, natural disaster management, intelligent transport systems, and precision agriculture. Graph neural networks (GNNs) have emerged as a powerful tool for modeling and understanding data with dependencies to each other such as spatial and temporal dependencies. There is a large amount of existing work that focuses on addressing the complex spatial and temporal dependencies in spatiotemporal data using GNNs. However, the strong interdisciplinary nature of spatiotemporal data has created numerous GNNs variants specifically designed for distinct application domains. Although the techniques are generally applicable across various domains, cross-referencing these methods remains essential yet challenging due to the absence of a comprehensive literature review on GNNs for spatiotemporal data. This article aims to provide a systematic and comprehensive overview of the technologies and applications of GNNs in the spatiotemporal domain. First, the ways of constructing graphs from spatiotemporal data are summarized to help domain experts understand how to generate graphs from various types of spatiotemporal data. Then, a systematic categorization and summary of existing spatiotemporal GNNs are presented to enable domain experts to identify suitable techniques and to support model developers in advancing their research. Moreover, a comprehensive overview of significant applications in the spatiotemporal domain is offered to introduce a broader range of applications to model developers and domain experts, assisting them in exploring potential research topics and enhancing the impact of their work. Finally, open challenges and future directions are discussed

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