• Transport Research

The effectiveness of traffic management strategies is essential in reducing traffic congestion. Developing an effective traffic management strategy using traffic simulation involves calibration of the simulator parameters and evaluating the traffic management strategy. This thesis enhances both parts of the development process. With better calibration efficiency, practitioners can analyse traffic scenarios with a lower computational budget. In terms of traffic management, a unified framework which combines vehicle rerouting and traffic light control strategies is proposed in this thesis to reduce traffic congestion.

This paper describes a novel procedure for identifying peak events, starting from the data of presence from mobile phones. The aim is to support the identification of aggregation of peoples with respect to time-series of presences in a given territory. Specifically, the data used in the work consist of a database containing the values of presence of mobile cell phone users with a time interval of 15 minutes. The approach used for the peak identification is based on statistical and machine learning (ML) methods. The study describes the procedure used to recognize a combination of percentiles suitable for the recognition of peak events linked to scenarios related to large events of collective interest. Furthermore, to validate this procedure, supervised learning methods are adopted, namely Logistic Regression (LR) and Support Vector Machines (SVM). Test results confirmed that both methods can recognize peak events with remarkable precision when obtained with the optimal percentile combination method.

Traffic congestion continues to be the bane of many metropolitan areas and has exercised the minds of experts for at least the last 60 years. With the advent of smart (intelligent) mobility, aligned with digital disruption and future connected and collaborative transport including extensions to autonomous vehicles, the question of whether we have a new window of opportunity to tame congestion is now high on the list of possibilities. It is however very unclear what the future will look like in respect of congestion on the roads, especially if we rely on ‘smart’ technology and continue to reject reform of road user charging and new opportunities to fund the sharing model. This paper looks at a number of themes as a way of highlighting possibilities and challenges.

There are varios challenges to be won for the sustainable transport in cities, such as ever-growing congestion, no more space for new routes, and hence the need to optimizing the use of current infrastructure. In addition, people are wasting their time in congestions. Moreover, alternative (public, non-motorized) transport should be easier to use & and more attractive. The environmental quality, CO2 & energy efficiency targets are tightening year by year. Traffic safety, especially for vulnerable road users, is still not perfect.

International audience; New technologies for Intelligent Vehicles will be presented, with an emphasis on multisensors Embedded Perception, Situation Awareness & Collision Risk Assessment, and Decision making for safe navigation and maneuvering. It will be shown that Bayesian approaches are mandatory for developing these technologies and for obtaining the required robustness in presence of uncertainty and complex traffic situations. Results obtained with Toyota and Renault will be presented.

Submitted by Nadir Basilio (nadirsb@uninove.br) on 2019-06-17T21:07:34Z No. of bitstreams: 1 Cassiano Galhardo.pdf: 1451346 bytes, checksum: 0cd59c3f51ec5fc5baaac6e38067c5fb (MD5) Made available in DSpace on 2019-06-17T21:07:34Z (GMT). No. of bitstreams: 1 Cassiano Galhardo.pdf: 1451346 bytes, checksum: 0cd59c3f51ec5fc5baaac6e38067c5fb (MD5) Previous issue date: 2019-03-28 The process of urbanization shows a consistent trend that follows the 20th and 21st centuries. As population densities increase, cities become progressively more administratively complex, requiring the application of new tools, techniques and procedures for city management. This is the role of innovation laboratories, which aim to create an environment for idealizing, experimenting, prototyping and analyzing new solutions, bringing together companies, citizens, public authorities and other institutions. The present research, which focuses on the MobiLab - Innovation Laboratory in Mobility of São Paulo, aims to analyze the learning of the various actors involved in structuring this laboratory and describe them in the form of lessons learned. The lessons were elaborated based on the bibliographical, documentary and knowledge review of stakeholders related to MobiLab, through 3 stages of interviews, in order to review and triangulate information at the end of each one. As a result, 6 lessons were learned, covering topics such as political support, autonomy, open data, team, contracting model and publicity and dissemination, as well as the design of motivating factors for the MobiLab structuring and main useful knowledge for the development of similar initiatives. These lessons and learning expose relevant elements for the survival, effectiveness, efficiency and reach of a unit such as MobiLab and its contribution to materialize innovation in the public sector, as well as explain an important feature of MobiLab, the approximation of public power and startups, This relationship enables an alternative, agile and inexpensive way of consuming technology by the state entity, as well as boosting the development of solutions for the city. It is concluded that, despite all the difficulties and weaknesses that innovation labs such as MobiLab may have, these structures offer significant contributions to public management and bring tangible benefits to citizens O processo de urbanização apresenta uma tendência consistente que se segue pelos séculos XX e XXI. À medida que adensamentos populacionais crescem, tornam-se progressivamente mais complexos de serem administrados, havendo a necessidade de aplicação de novas ferramentas, técnicas e procedimentos para a gestão de cidades. Este é o espaço em que se inserem os laboratórios de inovação, os quais têm por finalidade criar um ambiente próprio para idealizar, experimentar, prototipar e analisar novas soluções, aproximando empresas, cidadãos, poder público e outras instituições. A presente pesquisa, que tem como foco o MobiLab – Laboratório de Inovação em Mobilidade de São Paulo, tem por objetivo analisar os aprendizados dos vários atores envolvidos na estruturação deste laboratório e descrevê-los na forma de lições aprendidas. As lições foram elaboradas a partir da revisão bibliográfica, documental e dos conhecimentos de stakeholders relacionados ao MobiLab, por meio de 3 etapas de entrevistas, no intuito de, ao final de cada uma, revisar e triangular informações. Como resultado, obteve-se 6 lições aprendidas, abrangendo temas como apoio político, autonomia, abertura de dados, equipe, modelo de contratação e publicidade e divulgação, além do delineamento dos fatores motivadores da estruturação do MobiLab e principais conhecimentos úteis para o desenvolvimento de iniciativas similares. Estas lições e aprendizados expõem elementos relevantes para a sobrevivência, eficácia, eficiência e alcance de uma unidade como o MobiLab e sua contribuição para materializar a inovação no setor público, bem como explanam uma importante característica do MobiLab, a aproximação do poder público e startups, relação esta que possibilita uma forma alternativa, ágil e barata de consumo de tecnologia pelo ente estatal, além de impulsionar o desenvolvimento de soluções para a cidade. Conclui-se que, a despeito de todas as dificuldades e fragilidades que laboratórios de inovação como o MobiLab possam ter, estas estruturas oferecem contribuições significativas para a gestão pública e trazem benefícios palpáveis para os cidadãos.

Table ronde "Vehicule Autonome et Ville de Demain - Technologie, Business et Société: Quels enjeux ?" Issy-les-Moulineaux; Cette intervention se place dans le cadre d'une table ronde intitulée "Véhicule Autonome et Ville de Demain - Technologie, Business et Société: Quels enjeux ?". Le volet abordé dans cette intervention est celui de l'arrivée future des véhicules autonomes dans les villes, et de l’impact de ces nouvelles technologies sur l'évolution des aspects sociétaux et technologiques liés à la mobilité des personnes.

Whilst bus lanes are an important tool to ensure bus time reliability their presence can be detrimental to urban traffic. In this paper a Non-dominated Sorting Genetic Algorithm (NSGA-II) has been adopted to study the effect of bus lanes on urban traffic in terms of location and time of operation. Due to the complex nature of this problem traditional search would not be feasible. An artificial arterial route has been modelled from real data to evaluate candidate solutions. The results confirm this methodology for the purpose of studying and identifying bus lane locations and times of operation. Additionally it is shown that bus lanes can exist on an arterial link without exclusively occupying a continuous lane for large periods of time. Furthermore results indicate a use for this methodology over a larger scale and potential near real-time operation.

There are varios challenges to be won for the sustainable transport in cities, such as ever-growing congestion, no more space for new routes, and hence the need to optimizing the use of current infrastructure. In addition, people are wasting their time in congestions. Moreover, alternative (public, non-motorized) transport should be easier to use & and more attractive. The environmental quality, CO2 & energy efficiency targets are tightening year by year. Traffic safety, especially for vulnerable road users, is still not perfect.