Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment

This study analyses factors associated with cyclist injury severity, focusing on vehicle type, route environment, and interactions between them. Data analysed was collected by Spanish police during 2016 and includes records relating to 12,318 drivers and cyclist involving in collisions with at least one injured cyclist, of whom 7230 were injured cyclists. Bayesian methods were used to model relationships between cyclist injury severity and circumstances related to the crash, with the outcome variable being whether a cyclist was killed or seriously injured (KSI) rather than slightly injured. Factors in the model included those relating to the injured cyclist, the route environment, and involved motorists. Injury severity among cyclists was likely to be higher where an Heavy Goods Vehicle (HGV) was involved, and certain route conditions (bicycle infrastructure, 30 kph zones, and urban zones) were associated with lower injury severity. Interactions exist between the two: collisions involving large vehicles in lower-risk environments are less likely to lead to KSIs than collisions involving large vehicles in higher-risk environments. Finally, motorists involved in a collision were more likely than the injured cyclists to have committed an error or infraction. The study supports the creation of infrastructure that separates cyclists from motor tra c. Also, action needs to be taken to address motorist behaviour, given the imbalance between responsibility and risk.European Regional Development Fund (Ref. FEDER BU300P18)

The Impact of Smart City Initiatives on Cities’ Local Economic Development

The problem explored in this mixed-method action research is that challenges to sustainable economic development and basic community services increase, as populations in cities and towns increase. A city is a human settlement with well-defined demarcation points. A city’s infrastructure consists of complex systems, such as sewage treatment plants, water treatment plants, police stations, fire departments, utility services, schools, libraries, business, houses, etc. A smart city, on the other hand, is an urban vision that fosters citizens’ engagement and technological integration of the city’s infrastructure. The purpose of this mixed-method action research was to identify the characteristics of a smart city and determine to what extent smart city initiatives impact economic development. Using a combative analysis methodology, the study examined five major smart cities. The research results revealed that cities apply smart solutions by focusing on 5 major areas: Economic Development, Public Safety, Energy & Environment, Infrastructure, and Transportation. The study concluded that Smart city initiatives contribute directly and indirectly to the economic growth of cities in the United States. The study indicated that smart cities are socially engaged, financially stable, business-oriented, data-driven, environmentally friendly, and energy-efficient cities. The study also concluded that smart city initiatives can alleviate cities’ challenges, thus, enhancing economic development

Methodological evolution and frontiers of identifying, modeling and preventing secondary crashes on highways

© 2018 Elsevier Ltd Secondary crashes (SCs) or crashes that occur within the boundaries of the impact area of prior, primary crashes are one of the incident types that frequently affect highway traffic operations and safety. Existing studies have made great efforts to explore the underlying mechanisms of SCs and relevant methodologies have been e volving over the last two decades concerning the identification, modeling, and prevention of these crashes. So far there is a lack of a detailed examination on the progress, lessons, and potential opportunities regarding existing achievements in SC-related studies. This paper provides a comprehensive investigation of the state-of-the-art approaches; examines their strengths and weaknesses; and provides guidance in exploiting new directions in SC-related research. It aims to support researchers and practitioners in understanding well-established approaches so as to further explore the frontiers. Published studies focused on SCs since 1997 have been identified, reviewed, and summarized. Key issues concentrated on the following aspects are discussed: (i) static/dynamic approaches to identify SCs; (ii) parametric/non-parametric models to analyze SC risk, and (iii) deployable countermeasures to prevent SCs. Based on the examined issues, needs, and challenges, this paper further provides insights into potential opportunities such as: (a) fusing data from multiple sources for SC identification, (b) using advanced learning algorithms for real-time SC analysis, and (c) deploying connected vehicles for SC prevention in future research. This paper contributes to the research community by providing a one-stop reference for research on secondary crashes

Implementacija umjetne inteligencije i njezin budući potencijal

Firstly, in the paper, I explored the history of artificial intelligence (AI) thought spanning from the early conceptual beginnings, then through early examples of primitive AI applications and all the way to recent feats in this field. Next, I analyzed types of AI, both present and future, encompassing two wide schools of thought; after which I detailed the pathways to achieving practical implementation of AI through machine learning (ML) and deep learning (DL) as well as a brief history of TensorFlow. The following chapters focused on analyzing case studies of AI application in the fields of banking and finance from the financial sector, and transportation in general, with the ensuing critical analyses. The final chapter is concerned with future implementation of AI

Implementacija umjetne inteligencije i njezin budući potencijal

Firstly, in the paper, I explored the history of artificial intelligence (AI) thought spanning from the early conceptual beginnings, then through early examples of primitive AI applications and all the way to recent feats in this field. Next, I analyzed types of AI, both present and future, encompassing two wide schools of thought; after which I detailed the pathways to achieving practical implementation of AI through machine learning (ML) and deep learning (DL) as well as a brief history of TensorFlow. The following chapters focused on analyzing case studies of AI application in the fields of banking and finance from the financial sector, and transportation in general, with the ensuing critical analyses. The final chapter is concerned with future implementation of AI

Prevenção e alerta da sinistralidade rodoviária com o contributo da inteligência artificial

Na atualidade, a aplicação de sistemas baseados em inteligência artificial (IA) no combate à sinistralidade rodoviária já é uma realidade, apresentando resultados significativos. Em Portugal já foram dados os primeiros passos nesta área, principalmente através de projetos de investigação desenvolvidos por algumas Academias, dos quais se destaca o projeto da Universidade de Évora MOPREVIS. Partindo do argumento que a aplicação de metodologias de IA na construção de modelos preditivos potencia a prevenção e o alerta da sinistralidade rodoviária, afigurou-se pertinente analisar de que forma podem ser potenciados os contributos da IA no combate ao flagelo da sinistralidade rodoviária, usando como caso de estudo o modelo MOPREVIS. Para cumprir este desiderato, recorreu-se a um processo metodológico assente no raciocínio indutivo, materializado por uma estratégia de investigação mista, na qual se recorreu a técnicas de análise documental, inquéritos por entrevista e inquéritos por questionário, num desenho de pesquisa de estudo de caso. Foi possível concluir que a forma de potenciar os contributos da IA para a prevenção e alerta da sinistralidade rodoviária deve ser sustentada nas seguintes linhas de orientação estratégica: LOE1 - Assegurar qualidade e rigor; LOE2 - Automatizar; LOE3 - Desenvolver e diversificar; e LOE 4 - Cooperar e colaborar.Currently, the application of artificial intelligence (AI) systems in combating road accidents is already a reality, with significant results. In Portugal, the first steps have already been taken in this area, mainly through research projects developed by some universities, including the MOPREVIS project at the University of Évora. Given that the application of AI methodologies in the construction of predictive models can enhance the prevention and alert of road accidents, it was pertinent to analyse how the contributions of AI can be enhanced in combating the scourge of road accidents, using the MOPREVIS model as a case study. To achieve this goal, a methodological process based on inductive reasoning was used, materialized by a mixed research strategy, which included techniques of documentary analysis, interview surveys, and questionnaire surveys in a case study research design. It was possible to conclude that the way to enhance the contributions of AI to the prevention and alert of road accidents should be based on the following strategic guidelines: LOE1 - Ensuring quality and rigor; LOE2 - Automating; LOE3 - Developing and diversifying; and LOE 4 - Cooperating and collaborating.N/

Green Cities Artificial Intelligence

119 pagesIn an era defined by rapid urbanization, the effective planning and management of cities have become paramount to ensure sustainable development, efficient resource allocation, and enhanced quality of life for residents. Traditional methods of urban planning and management are grappling with the complexities and challenges presented by modern cities. Enter Artificial Intelligence (AI), a disruptive technology that holds immense potential to revolutionize the way cities are planned, designed, and operated. The primary aim of this report is to provide an in-depth exploration of the multifaceted role that Artificial Intelligence plays in modern city planning and management. Through a comprehensive analysis of key AI applications, case studies, challenges, and ethical considerations, the report aims to provide resources for urban planners, City staff, and elected officials responsible for community planning and development. These include a model City policy, draft informational public meeting format, AI software and applications, implementation actions, AI timeline, glossary, and research references. This report represents the cumulative efforts of many participants and is sponsored by the City of Salem and Sustainable City Year Program. The Green Cities AI project website is at: https://blogs.uoregon.edu/artificialintelligence/. As cities continue to evolve into complex ecosystems, the integration of Artificial Intelligence stands as a pivotal force in shaping their trajectories. Through this report, we aim to provide a comprehensive understanding of how AI is transforming the way cities are planned, operated, and experienced. By analyzing the tools, applications, and ethical considerations, we hope to equip policymakers, urban planners, and stakeholders with the insights needed to navigate the AI-driven urban landscape effectively and create cities that are not only smart but also sustainable, resilient, and regenerative.This year's SCYP partnership is possible in part due to support from U.S. Senators Ron Wyden and Jeff Merkley, as well as former Congressman Peter DeFazio, who secured federal funding for SCYP through Congressionally Directed Spending. With additional funding from the city of Salem, the partnerships will allow UO students and faculty to study and make recommendations on city-identified projects and issues