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

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

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

Developing Optimal Peer-to-Peer Ridesharing Strategies

69A43551747123Thanks to recent developments in ride-hailing transit services, the Peer-to-Peer (P2P) ridematching problem has been actively considered in academia in recent years. P2P ridematching not only reduces travel costs for riders but also benefits drivers by saving them money in exchange for their additional travel time and costs. However, assigning riders to drivers in an efficient way is a complex problem that requires a focus on maximizing the benefits for both riders and drivers. This study first aims to formulate a multi-driver multirider (MDMR) P2P ride-matching problem based on rational preferences and cost allocation for both driver and rider. This model also enables riders to transfer between multiple drivers to complete their journeys if needed. To solve the ride-matching problem, a Tabu Search (TS) for system optimum ride-matchings and Greedy Matching (GM) algorithm for the stable ridematchings were created to produce stable ride-matchings

Mathematical Optimization for Routing and Logistic Problems

In this thesis, we focus on mathematical optimization models and algorithms for solving routing and logistic problems. The first contribution regards a path and mission planning problem, called Carrier-Vehicle Traveling Salesman Problem (CVTSP), for a system of heterogeneous vehicles. A Mixed-Integer Second Order Conic Programming (MISOCP) model and a Benders-like enumeration algorithm are presented for solving CVTSP. The second work concerns a class of routing problems, referred to as Interceptor Vehicle Routing Problems (IVRPs). They generalize VRPs in the sense that target points are allowed to move from their initial location according to a known motion. We present a novel MISOCP formulation and a Branch-and-Price algorithm based on a Lagrangian Relaxation of the vehicle-assignment constraints. Other two contributions focus on waste flow management problems: the former considers a deterministic setting in which a Mixed-Integer Linear Programming (MILP) formulation is used as a Decision Support System for a real-world waste operator, whereas the latter deals with the uncertainty of the waste generation amounts by means of Two-Stage Multiperiod Stochastic Mixed-Integer Programming formulations. Finally, we give an overview on the optimization challenges arising in electric car-sharing systems, both at strategic and tactical planning level

VANET-enabled eco-friendly road characteristics-aware routing for vehicular traffic

There is growing awareness of the dangers of climate change caused by greenhouse gases. In the coming decades this could result in numerous disasters such as heat-waves, flooding and crop failures. A major contributor to the total amount of greenhouse gas emissions is the transport sector, particularly private vehicles. Traffic congestion involving private vehicles also causes a lot of wasted time and stress to commuters. At the same time new wireless technologies such as Vehicular Ad-Hoc Networks (VANETs) are being developed which could allow vehicles to communicate with each other. These could enable a number of innovative schemes to reduce traffic congestion and greenhouse gas emissions. 1) EcoTrec is a VANET-based system which allows vehicles to exchange messages regarding traffic congestion and road conditions, such as roughness and gradient. Each vehicle uses the messages it has received to build a model of nearby roads and the traffic on them. The EcoTrec Algorithm then recommends the most fuel efficient route for the vehicles to follow. 2) Time-Ants is a swarm based algorithm that considers not only the amount of cars in the spatial domain but also the amoumt in the time domain. This allows the system to build a model of the traffic congestion throughout the day. As traffic patterns are broadly similar for weekdays this gives us a good idea of what traffic will be like allowing us to route the vehicles more efficiently using the Time-Ants Algorithm. 3) Electric Vehicle enhanced Dedicated Bus Lanes (E-DBL) proposes allowing electric vehicles onto the bus lanes. Such an approach could allow a reduction in traffic congestion on the regular lanes without greatly impeding the buses. It would also encourage uptake of electric vehicles. 4) A comprehensive survey of issues associated with communication centred traffic management systems was carried out