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Taxi sharing services have been increasing in the past few years in several countries. In this paper we propose a solution to taxi sharing problem based on Vehicle Routing Problem (VRP) with Temporal Restriction. We approach the problem in a static way, for testing purposes. Two metaheuristics were used to determine the best travel plans for taxi sharing service, taking into account the pickup and delivery time of each passenger. Tabu Search (TS) and Simulated Annealing (SA) were used to solve the problem, as literature review suggested, presenting good results. Four test scenario were used with 10, 20, 30 and 40 passengers, knowing their starting location. Analysis of solutions found by TS and SA are compared to the average cost to conclude which is the best travel plan to be used by taxi sharing service.

In the contemporary world, simulation has been gaining a huge importance due to the cost reduction it promotes on many different areas. This paper proposes a validation methodology for the simulation model of an electric bus. This methodology consists in applying the sensitivity analysis method to assess the robustness of the model, compared to real data collected for different scenarios. Data were collected from the operation of the real bus and used in the validation process, which could demonstrate that the model's outcomes are valid within a range of changes in the parameters analyzed, namely number of passengers and accessories energy consumption.

This work proposes a novel framework for the development of new products and services in transportation through an open innovation approach based on automatic content analysis of social media data. The framework is able to extract users comments from Online Social Networks (OSN), to process and analyze text through information extraction and sentiment analysis techniques to obtain relevant information about product reception on the market. A use case was developed using the mobile application Uber, which is today one of the fastest growing technology companies in the world. We measured how a controversial, highly diffused event influences the volume of tweets about Uber and the perception of its users. While there is no change in the image of Uber, a large increase in the number of tweets mentioning the company is observed, which meant a free and important diffusion of its product.

Private individual transportation is becoming cumbersome and expensive, as urban traffic turns more chaotic, fuel prices increase and the effects of pollutant emissions become evident. Public buses are an attractive approach to reducing the cars in use, as they mostly depend on preexistent infrastructure. Making these buses electric would mean even less tailpipe emissions and cheaper consumption costs, when compared to conventional vehicle fleets. However, fully electric bus fleets can prove disadvantageous. We can tackle this with a more conservative approach - using mixed bus fleets, comprised by both electric and conventional buses. This work intends on studying how to obtain a good balance of the different vehicle typologies in the fleet. To fulfill these goals, real data of a bus network in Porto, Portugal, is studied and an evolutionary algorithm devises mixed fleet arrangements, with a brief sensitivity analysis giving us an overview of how to improve our results. As a means of decision support, this work contributes not only with an approach to configure optimized mixed bus fleets, but also with general considerations for managing public transit with electric vehicle fleets.

It is with great pleasure and excitement that I welcome you all to Rio and to IEEE ITSC 2016! In its 19th edition and having travelled many countries already, I'm highly honoured to lead the organization of the prestigious flagship conference of IEEE Intelligent Transportation Systems Society, held for the first time in the Southern Hemisphere, more specifically in Brazil. What a great historical moment to our community!

Over the last decade, the impressive technological advancement around artificial intelligence have encouraged and stimulated much research on driverless cars. Inevitability, such novelty casts huge speculation on its effective deployment in the urban reality. The viability issue of cybercars demands appropriate research so as to infer and validate upcoming impacts at different levels, especially the mobility level, and address new performance measures intrinsic to this novel transportation paradigm. Therefore, this paper presents a short overview perspective about the definitions around the so-called autonomous vehicles. Additionally, as long as multivariate modelling is concerned, this position paper contributes with insights that span some of the future problematics and research questions regarding the automated vehicles topic.

Automatic Vehicle Location (AVL) is becoming an important tool in Intelligent Transportation Systems (ITS) in the past few years, as it is an effective way of collecting and transmitting data regarding the vehicle's trip for real-time or future use. A methodology for analyzing the state of the art regarding the application of these systems is proposed in a form of a systematic literature review, by identifying and systematizing possible transportation network performance metrics that can be calculated or predicted using GPS-based AVL systems and inferring tendencies observed throughout the literature regarding techniques used and sensor data source and usage. As a result of this research, several performance metrics were identified, with Travel Time and Average Speed being the most recurrent ones. The conclusions reveal an increase in the number of publications and research projects regarding this topic over the years, as well as a promising potential of this type of technology, with buses and taxis being the most used probe vehicles.