Gomes, Rui Jorge Reis
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Gomes
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Rui Jorge Reis
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Rui Gomes
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Rui Gomes holds a BSc and a MSc in Informatics Engineering from the Faculty of Engineering of the University of Porto (FEUP) and a Ph.D. in Transportation Systems from the MIT-Portugal Program at the University of Porto, Portugal. He has more than 20 years of experience in industry and more than 15 years in the academia. Since 2008, he has been Researcher and Invited Professor at several Higher Education Institutions, such as the Universidade Portucalense, University of Porto, the University of Coimbra, the University of Aveiro, the Polytechnic Institute of Coimbra and the Polytechnic Institute of Viana do Castelo. His main research interests are Intelligent Transport Systems, Demand Responsive Transport systems, Artificial Intelligence, Combinatorial Optimisation, and Metaheuristics. Pursuing these research interests resulted in the publication of several scientific papers in peer-reviewed international journals, book chapters, and presentations at conferences, as well as supervision of a number of MSc thesis. Rui Gomes has also been a reviewer for several international scientific conferences and journals in the area of transportation, logistics, and artificial intelligence. Between 2016 and 2018, he was the Principal Investigator in a public funded (FCT) research project at the University of Coimbra in the area of urban mobility. Between 2001 and 2008, Rui Gomes worked for the telecommunication industry, developing Interactive Voice Response services. From 2016 to 2022, Rui Gomes was with ARMIS Group, where he was the Head of ITS Consultancy department, being responsible for the application for funding programs and managing several national and high-profile international projects in Intelligent Transport Systems, most of them bringing together several European Union Member States, totaling more than 100 million Euro. In 2024, Rui Gomes joined Universidade Portucalense as Assistant Professor.
BD1D-F316-C1AA
https://scholar.google.pt/citations?user=SAHc0xsAAAAJ&hl=pt-PT
55938890400
N-7429-2018Research Projects
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REMIT – Research on Economics, Management and Information Technologies
Centro de investigação que que tem como objetivo principal produzir e disseminar conhecimento teórico e aplicado que possibilite uma maior compreensão das dinâmicas e tendências económicas, empresariais, territoriais e tecnológicas do mundo contemporâneo e dos seus efeitos socioeconómicos. O REMIT adota uma perspetiva multidisciplinar que integra vários domínios científicos: Economia e Gestão; Ciências e Tecnologia; Turismo, Património e Cultura.
Founded in 2017, REMIT – Research on Economics, Management and Information Technologies is a research unit of Portucalense University. Based on a multidisciplinary and interdisciplinary perspective it aims at responding to social challenges through a holistic approach involving a wide range of scientific fields such as Economics, Management, Science, Technology, Tourism, Heritage and Culture.
Grounded on the production of advanced scientific knowledge, REMIT has a special focus on its application to the resolution of real issues and challenges, having as strategic orientations:
- the understanding of local, national and international environment;
- the development of activities oriented to professional practice, namely in the business world.
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Publication Open Access Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems2024-05-22 - Gomes, Rui Jorge Reis; Melo, Sandra; Abbasi, Reza; Arantes, AmílcarTransport-on-demand services, such as demand-responsive transport (DRT), involve a flexible transportation service that offers convenient and personalised mobility choices for public transport users. Integrating DRT with mobile data and data analytics enhances understanding of travel patterns and allows the development of improved algorithms to support design-optimised services. This study introduces a replicable framework for DRT that employs an on-demand transport simulator and routing algorithm. This framework is supported by a mobile data set, enabling a more accurate service design grounded on actual demand data. Decision-makers can use this framework to understand traffic patterns better and test a DRT solution before implementing it in the actual world. A case study was conducted in Porto, Portugal, to demonstrate its practicality and proof of concept. Results show that the DRT solution required 135% fewer stops and travelled 81% fewer kilometres than the existing fixed-line service. Findings highlight the potential of this data-driven framework for urban public transportation systems to improve key performance metrics in required buses, energy consumption, travelled distance, and stop frequency, all while maintaining the number of served passengers. Under specific circumstances, embracing this approach can offer a more efficient, user-centric, and environmentally sustainable urban transportation servicePublication Restricted Access Efficient transport simulation with restricted Batch-Mode Active Learning2018-07-24 - Gomes, Rui Jorge Reis; Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco C.Simulation modeling is a well-known and recurrent approach to study the performance of urban systems. Taking into account the recent and continuous transformations within increasingly complex and multidimensional cities, the use of simulation tools is, in many cases, the only feasible and reliable approach to analyze such dynamic systems. However, simulation models can become very time consuming when detailed input-space exploration is needed. To tackle this problem, simulation metamodels are often used to approximate the simulators' results. In this paper, we propose an active learning algorithm based on the Gaussian process (GP) framework that gathers the most informative simulation data points in batches, according to both their predictive variances and to the relative distance between them. This allows us to explore the simulators' input space with fewer data points and in parallel, and thus in a more efficient way, while avoiding computationally expensive simulation runs in the process. We take advantage of the closeness notion encoded into the GP to select batches of points in such a way that they do not belong to the same high-variance neighborhoods. In addition, we also suggest two simple and practical user-defined stopping criteria so that the iterative learning procedure can be fully automated. We illustrate this methodology using three experimental settings. The results show that the proposed methodology is able to improve the exploration efficiency of the simulation input space in comparison with non-restricted batch-mode active learning proceduresPublication Restricted Access Characterization of individual mobility for non-routine scenarios from Crowd Sensing and Clustered Data2019-11-04 - Gomes, Rui Jorge Reis; Cunha, Inês; Simões, João; Alves, Ana; Ribeiro , AnabelaDemand for leisure activities has increased due to some reasons such as increasing wealth, ageing populations and changing lifestyles, however, the efficiency of public transport system relies on solid demand levels and well-established mobility patterns and, so, providing quality public transportation is extremely expensive in low, variable and unpredictable demand scenarios, as it is the case of out-of-routine trips. Better prediction estimations about the trip purpose helps to anticipate the transport demand and consequently improve its planning. This paper addresses the contribution in comparing the traditional approach of considering municipality division to study such trips against a proposed approach based on clustering of dense concentration of services in the urban space. In our case, POIs (Points of Interest) collected from social networks (e.g. Foursquare) represent these services. These trips were associated with the territory using two different approaches: ‘municipalities’ and ‘clusters’ and then related with the likelihood of choosing a POI category (Points-of-Interest). The results obtained for both geographical approaches are then compared considering a multinomial model to check for differences in destination choice. The variables of distance travelled, travel time and whether the trip was made on a weekday or a weekend had a significant contribution in the choice of destination using municipalities approach. Using clusters approach, the results are similar but the accuracy is improved and due to more significant results to more categories of destinations, more conclusions can be drawn. These results lead us to believe that a cluster-based analysis using georeferenced data from social media can contribute significantly better than a territorial-based analysis to the study of out-of-routine mobility. We also contribute to the knowledge of patterns of this type of travel, a type of trips that is still poorly valued and difficult to study. Nevertheless, it would be worth a more extensive analysis, such as analysing more variables or even during a larger period.Publication Restricted Access Automatic POI Matching using an Outlier Detection Based Approach2018-10-05 - Gomes, Rui Jorge Reis; Almeida, Alexandre; Alves, AnaPoints of Interest (POI) are widely used in many applications nowadays mainly due to the increasing amount of related data available online, notably from volunteered geographic information (VGI) sources. Being able to connect these data from different sources is useful for many things like validating, cor- recting and also removing duplicated data in a database. However, there is no standard way to identify the same POIs across different sources and doing it manually could be very expensive. Therefore, automatic POI matching has been an attractive research topic. In our work, we propose a novel data-driven machine learning approach based on an outlier detection algorithm to match POIs automatically. Surprisingly, works that have been presented so far do not use data-driven machine learning approaches. The reason for this might be that such approaches need a training dataset to be constructed by manually matching some POIs. To mitigate this, we have taken advantage of the Crosswalk API, available at the time we started our project, which allowed us to retrieve already matched POI data from different sources in US territory. We trained and tested our model with a dataset containing Factual, Facebook and Foursquare POIs from New York City and were able to successfully apply it to another dataset of Facebook and Foursquare POIs from Porto, Portugal, finding matches with an accuracy around 95%. These are encouraging results that confirm our approach as an effective way to address the problem of automatically matching POIs. They also show that such a model can be trained with data available from multiple sources and be applied to other datasets with different locations from those used in training. Furthermore, as a data-driven machine learning approach, the model can be continuously improved by adding new validated data to its training datasetPublication Open Access A GRASP-based approach for demand responsive transportation2014-02-01 - Gomes, Rui Jorge Reis; Sousa, Jorge Pinho de; Dias, Teresa GalvãoDemand Responsive Transportation (DRT) systems try to provide quality public transportation in low, variable and unpredictable demand scenarios, with routes and frequencies that may vary according to observed demand, possibly in real-time. The design and operation of DRTs involve multiple criteria and have a combinatorial nature that prevents the use of traditional optimization methods. To obtain an approximation of the Pareto solution set, we have designed a heuristic approach involving the construction of a feasible route through a greedy randomized procedure, followed by a local search phase, latter embedded in a Decision Support System that also uses simulation. The goal is not only to minimize operating costs but also to maximize the quality of the service. Experiments with simple cases, inspired in real problems, have shown the potential of this approach for efficiently designing and managing DRT servicesPublication Open Access Demand modelling for responsive transport systems using digital footprints2015-08-15 - Gomes, Rui Jorge Reis; Silva, Paulo; Antunes, Francisco; Bento , CarlosTraditionally, travel demand modelling focused on long-term multiple socio-economic scenarios and land-use configurations to estimate the required transport supply. However, the limited number of transportation requests in demand-responsive flexible transport systems require a higher resolution zoning. This work analyses users short-term destination choice patterns, with a careful analysis of the available data coming from various different sources, such as GPS traces and social networks. We use a Multinomial Logit Model, with a social component for utility and characteristics, both derived from Social Network Analyses. The results from the model show meaningful relationships between distance and attractiveness for all the different alternatives, with the variable distance being the most significant.Publication Open Access Mapping accessible transport for persons with reduced mobility [report]2020-12-10 - Gomes, Rui Jorge Reis; Carvalho, Daniela; Rodrigues, Alexandra; Rodrigues, Maria; Teoh, Tharsis; Tanis, Jasper; Matthews, Bryan; Costa, CarlosThe aim of this study is to facilitate and increase the distribution of personalised travel information for persons with disabilities and reduced mobility (PRM). To this end, the study team carried out two steps: an inventory of existing information tools and the information they provide on barrier-free mobility within the European Union, and a pilot experiment integrating accessibility information in an interactive web map application entirely developed and tested. The study highlighted the need for better data concerning the nature of accessibility features in order to ensure reliable journey planning when accessibility constraints need to be used as criteria in journey planning search engines. Although it is expected that the Regulation on multimodal travel information services (MMTIS) will boost the provision of multimodal travel information in general and more specifically for persons with disabilities and reduced mobility, data heterogeneity and gaps are blocking the process, with major efforts being still required to achieve the desired levels. Evidence collected through our analysis and pilots, showed that the data fusion process1 to amalgamate and process information from a range of sources is highly complex and inefficient, and that it is virtually impossible to carry out accurate route accessibility planning with most of the data available. The European Commission could take further actions, notably throughout communication and support actions for data collection and harmonization, in order to provide guidance to those willing to take steps in this field and take the maximum benefit from the ongoing deployments of journey planners.Publication Restricted Access Sustainable demand responsive transportation systems in a context of austerity: The case of a Portuguese city2015-07-26 - Gomes, Rui Jorge Reis; Sousa, Jorge Pinho de; Dias, Teresa GalvãoIn a time of economic austerity, more pressure is being put on the existing transport systems to be more sustainable and, at the same time, more equitable and socially inclusive. Regular public road transportation traditionally uses xed routes and schedules, which can be extremely expensive in rural areas and certain periods of the day in urban areas due to low and unpredictable demand. Demand Responsive Transportation systems are a kind of hybrid transportation approach between a taxi and a bus that try to address these problems with routes and frequencies that may vary according to the actual observed demand. Demand Responsive Transportation seems to have potential to answer the sustainability and social inclusion challenges in a context of austerity. However, DRT projects may fail: it is not only important to solve the underlying model in an ef cient way, but also to understand how different ways of operating the service affect customers and operators. To help design DRT services, we developed an innovative approach integrating simulation and optimization. Using this simulator, we compared a real night-time bus service in the city of Porto, Portugal, with a hypothetical exible DRT service for the same scenario.Publication Restricted Access Urban mobility: Mobile crowdsensing applications2018-11-05 - Simões, João; Gomes, Rui Jorge Reis; Alves, Ana; Bernardino , JorgeMobility has become one of the most difficult challenges that cities must face. More than half of world’s population resides in urban areas and with the continuously growing population it is imperative that cities use their resources more efficiently. Obtaining and gathering data from different sources can be extremely important to support new solutions that will help building a better mobility for the citizens. Crowdsensing has become a popular way to share data collected by sensing devices with the goal to achieve a common interest. Data collected by crowdsensing applications can be a promising way to obtain valuable mobility information from each citizen. In this paper, we study the current work on the integrated mobility services exploring the crowdsensing applications that were used to extract and provide valuable mobility data. Also, we analyze the main current techniques used to characterize urban mobility.Publication Restricted Access Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal2016-02-23 - Gomes, Rui Jorge ReisA rise in population, along with urbanization, has been causing an increase in demand for urban transportation services in the sub-Saharan Africa countries. In these countries, mobility of people is mainly ensured by bus services and a large-scale informal public transport service, which is known as paratransit (e.g., car rapides in Senegal, Tro Tros in Ghana, taxis in Uganda and Ethiopia, and Matatus in Kenya). Transport demand estimation is a challenging task, particularly in developing countries, mainly due to its expensive and time-consuming data collection requirements. Without accurate demand estimation, it is difficult for transport operators to provide their services and make other important decisions. In this paper, we present a methodology to estimate passenger demand for public transport services using cell phone data. Significant origins and destinations of inhabitants are extracted and used to build origin-destination matrices that resemble travel demand. Based on the inferred travel demand, we are able to reasonably suggest strategic locations for public transport services such as paratransit and taxi stands, as well as new transit routes. The outcome of this study can be useful for the development of policies that can potentially help fulfill the mobility needs of city inhabitants.