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Publicações

Publicações por CESE

2020

Design of a Route-Planner for Urban Public Transport, Promoting Social Inclusion

Autores
Dias, R; Fontes, T; Galvao, T;

Publicação
INTELLIGENT TRANSPORT SYSTEMS

Abstract
People that do not have access to the transport system and therefore, a facilitated access to goods and services essential to daily life, can be regarded as transport-related social excluded. This is a big issue, namely for groups of people that have physical, sensorial and/or cognitive limitations. This paper provides guidelines to design route planners for socially excluded groups, by promoting social inclusion in public transportation. For this purpose, a set of mock-up user-interfaces of an inclusive inter-modal route planning application were developed. These interfaces will deliver ready availability of information about infrastructures and other journey related data.

2020

Process discovery on geolocation data

Autores
Ribeiro, J; Fontes, T; Soares, C; Borges, JL;

Publicação
Transportation Research Procedia

Abstract
Fleet tracking technology collects real-time information about geolocation of vehicles as well as driving-related data. This information is typically used for location monitoring as well as for analysis of routes, vehicles and drivers. From an operational point of view, the geolocation simply identifies the state of a vehicle in terms of positioning and navigation. From a management point of view, the geolocation may be used to infer the state of a vehicle in terms of process (e.g., driving, fueling, maintenance, or lunch break). Meaningful information may be extracted from these inferred states using process mining. An innovative methodology for inferring process states from geolocation data is proposed in this paper. Also, it is presented the potential of applying process mining techniques on geolocation data for process discovery. © 2020 The Authors. Published by Elsevier B.V.

2020

A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions

Autores
Fontes, T; Correia, R; Ribeiro, J; Borges, JL;

Publicação
Transport and Telecommunication

Abstract
This work apply a deep learning artificial neural network model-the Multilayer Perceptron- A s a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: Individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays). © 2020 Tânia Fontes et al., published by Sciendo.

2020

Accessibility as an indicator to estimate social exclusion in public transport

Autores
Ribeiro, J; Fontes, T; Soares, C; Borges, JL;

Publicação
Transportation Research Procedia

Abstract
Accessibility is one of the key measures of urban transportation planning, which quantify how easy is the access to a facility. Public transport accessibility concerns of the access level of geographical locations to public transport. In this paper, accessibility is used as an indicator to estimate social exclusion based on the maximum distance that someone has to walk to reach the public transport. The concept of the 6-minute walking distance (6MWD) is applied to measure accurately the walking ability for different groups of the population. A real life case study is conducted to get insight into the transportation network of the Porto Metropolitan Area, Portugal. For this purpose, geographic, demographic and infrastructure data were collected and integrated. Also, webservices are used to measure walking distances between locations. The results of this study allowed to characterize regions by different levels of accessibility, providing insight into the social exclusion in public transport. This assessment is used not only to identify inequities but also to get an overview of the service quality of public transport. © 2020 The Authors. Published by ELSEVIER B.V.

2020

A multi objective approach for DRT service using tabu search

Autores
Torgal, M; Dias, TG; Fontes, T;

Publicação
Transportation Research Procedia

Abstract
Urban population is increasing fast. This is creating new challenges to public transport systems since some groups of citizens as elderly people may have sensory, cognitive or motor impairments that need to be addressed. This work explores the potential of a Demand Responsive Transport (DRT) system for people with reduced mobility in an urban environment. For this purpose, the Dial-A-Ride Problem (DARP) was implemented using a multivariable minimisation approach. In this approach, an Assigning Request to Vehicles (ARV) algorithm is used to obtain an initial solution. Then a Multi-Objective Tabu Search Algorithm (MOTSA) is applied to the initial solution to search for the non-dominated solution (optimisation phase). In this optimisation phase, the total travelled distance, the deadheading distance and the number of vehicles were minimised. The performance of the model was computed combining different parameters' values of the number of requests, boarding time for each user, the number of seats in each vehicle, vehicle's speed, the total number of iterations, and candidate threshold number (the algorithm's parameter). The computational results found a strong positive correlation between the number of requests and the: total travelled distance (rs = 0.977, p-value<0.001) and the number of vehicles (rs =0.883, p-value<0.001); and a low positive correlation between the number of requests and the optimised total travelled distance (rs =0.331, p-value<0.001) and the optimised number of vehicles (rs =0.340, p-value<0.001). © 2020 The Authors. Published by ELSEVIER B.V.

2020

Desafios, barreiras e aprendizagens com a remanufatura

Autores
Medeiros, FSB; Simonetto, EdO; Castro, HCGAd;

Publicação
Revista de Gestão dos Países de Língua Portuguesa

Abstract
Este artigo tem como objetivo identificar os desafios, as barreiras e as aprendizagens com a atividade de remanufatura. Por meio de uma busca realizada na internet foram encontradas empresas de diferentes regiões do país que operam no setor. Desse modo, como procedimento de coleta, foi adotado o estudo de casos múltiplos e, como técnica de coleta, foi utilizada a entrevista semiestruturada, uma vez que a intenção era obter dos entrevistados o relato sobre o seu dia a dia e o seu ambiente de negócio na remanufatura. Os resultados mostraram que a atividade é carente de incentivos por parte do poder público. Outro ponto que prejudica é o custo da logística reversa. Há, ainda, a falta de locais apropriados na fase de descarte dos materiais, cujas condições de reaproveitamento no processo não são mais viáveis. Destarte, o estudo permitiu conhecer um pouco mais da remanufatura por meio do que as empresas contatadas vivenciam no mercado.

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