Development of a Decision Making Algorithm for Traffic Jams Reduction Applied to Intelligent Transportation Systems

This paper is aimed at developing a decision making algorithm for traffic jams reduction that can be applied to Intelligent Transportation Systems. To do so, these algorithms must address two main challenges that arise in this context. On one hand, there are uncertainties in the data received from sensor networks produced by incomplete information or because the information loses some of the precision during information processing and display. On the other hand, there is the variability of the context in which these types of systems are operating. More specifically, Analytic Hierarchy Process (AHP) algorithm has been adapted to ITS, taking into account the mentioned challenges. After explaining the proposed decision making method, it is validated in a specific scenario: a smart traffic management system

Development of a Decision Making Algorithm for Traffic Jams Reduction Applied to Intelligent Transportation Systems

This paper is aimed at developing a decision making algorithm for traffic jams reduction that can be applied to Intelligent Transportation Systems. To do so, these algorithms must address two main challenges that arise in this context. On one hand, there are uncertainties in the data received from sensor networks produced by incomplete information or because the information loses some of the precision during information processing and display. On the other hand, there is the variability of the context in which these types of systems are operating. More specifically, Analytic Hierarchy Process (AHP) algorithm has been adapted to ITS, taking into account the mentioned challenges. After explaining the proposed decision making method, it is validated in a specific scenario: a smart traffic management system

Feasibility of expanding traffic monitoring systems with floating car data technology

Trajectory information reported by certain vehicles (Floating Car Data or FCD) can be applied to monitor the road network. Policy makers face difficulties when deciding to invest in the expansion of their infrastructure based on inductive loops and cameras, or to invest in a FCD system. This paper targets this decision. The provided FCD functionality is investigated, minimum requirements are determined and reliability issues are researched. The communication cost is derived and combined with other elements to assess the total costs for different scenarios. The outcome is to target a penetration rate of 1%, a sample interval of 10 seconds and a transmission interval of 30 seconds. Such a deployment can accurately determine the locations of incidents and traffic jams. It can also estimate travel times accurately for highways, for urban roads this is limited to a binary categorization into normal or congested traffic. No reliability issues are expected. The most cost efficient scenario when deploying a new FCD system is to launch a smartphone application. For Belgium, this costs 13 million EUR for 10 years. However, it is estimated that purchasing data from companies already acquiring FCD data through their own product could reduce costs with a factor 10

A Review of Traffic Signal Control.

The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

Patterns of mobility in a smart city

Transportation data in smart cities is becoming increasingly available. This data allows building meaningful, intelligent solutions for city residents and city management authorities, the so-called Intelligent Transportation Systems. Our research focused on Lisbon mobility data, provided by Lisbon municipality. The main research objective was to address mobility problems, interdependence, and cascading effects solutions for the city of Lisbon. We developed a data-driven approach based on historical data with a strong focus on visualization methods and dashboard creation. Also, we applied a method based on time series to do prediction based on the traffic congestion data provided. A CRISP-DM approach was applied, integrating different data sources, using Python. Hence, understand traffic patterns, and help the city authorities in the decision-making process, namely more preparedness, adaptability, responsiveness to events.Os dados de transporte, no âmbito das cidades inteligentes, estão cada vez mais disponíveis. Estes dados permitem a construção de soluções inteligentes com impacto significativo na vida dos residentes e nos mecanismos das autoridades de gestão da cidade, os chamados Sistemas de Transporte Inteligentes. A nossa investigação incidiu sobre os dados de mobilidade urbana da cidade de Lisboa, disponibilizados pelo município. O principal objetivo da pesquisa foi abordar os problemas de mobilidade, interdependência e soluções de efeitos em cascata para a cidade de Lisboa. Para alcançar este objetivo foi desenvolvida uma metodologia baseada nos dados históricos do transito no centro urbano da cidade e principais acessos, com uma forte componente de visualização. Foi também aplicado um método baseado em series temporais para fazer a previsão das ocorrências de transito na cidade de Lisboa. Foi aplicada uma abordagem CRISP-DM, integrando diferentes fontes de dados, utilizando Python. Esta tese tem como objetivo identificar padrões de mobilidade urbana com análise e visualização de dados, de forma a auxiliar as autoridades municipais no processo de tomada de decisão, nomeadamente estar mais preparada, adaptada e responsiva