The Gross Potential for Cycling: planning for human scale urban mobility

Silva, Cecília; Marques, Joana S.; Lopes, Miguel; Dias, Ana Mélice

doi:10.4337/9781800370517.00024

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…Likewise, a relationship has been established between school performance and this type of learning, with which students express feeling mostly satisfied and are less likely to abandon their studies (Liborius et al 2019), because this type of learning is implemented appropriately. generates greater emotional, social and cognitive maturity in students, so by introducing it into the learning processes with strategies such as problem solving and critical thinking, it will be opportune to make changes and introduce innovative forms of teaching and learning that lead to better educational outcomes (Milheiro Silva et al 2021).…”

Section: Autonomous Learningmentioning

confidence: 99%

Exploring the Relationship Between Artificial Intelligence, Autonomous Learning, and Skills Required for Success in The 21st Century

Lengua-Cantero,

Caro-Piñeres,

Pérez

2024

RGSA

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Objective: The topic of this study focuses on how autonomous learning enhances 21st century competencies through the use of artificial intelligence and educational technologies. The objective is to explore the relationship between autonomous learning, artificial intelligence and 21st century competencies. Theoretical Framework: The main concepts and theories underpinning the research were: artificial intelligence in education, autonomous learning and 21st century skills. Method: The methodology adopted for this research was a systematic mapping of 991 scientific articles published in the last ten years in three databases Results and Discussion: The results obtained revealed that artificial intelligence is still in its infancy in the use of data and machine learning systems, until its impact on society as a whole is better understood. Likewise, critical thinking, collaborative work and problem solving are the competencies that show a strong relationship with both autonomous learning and artificial intelligence. Research implications: the main fields of study were: self-regulated learning, 21st century competencies and their relationship with artificial intelligence. The impact could be seen in the educational, economic, environmental and social sectors.

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Section: Autonomous Learningmentioning

confidence: 99%

Exploring the Relationship Between Artificial Intelligence, Autonomous Learning, and Skills Required for Success in The 21st Century

Lengua-Cantero,

Caro-Piñeres,

Pérez

2024

RGSA

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“…Many mobility models have been proposed to represent particular dimensions of the cycling reality of a city or region. These mobility models include compound indexes to assess bikeability [9][10][11] or bike-friendliness [12][13][14], custom tools to estimate the potential for cycling [15,16] or the economic value of cycling [17] and a few commercial services based mostly on data from mobile applications [18]. There is also a very broad range of smaller and more focused models, serving specific perspectives of analysis, such as hilliness [19], pollution [20], route choice [21], comfort [22] or accessibility [23].…”

Section: Introductionmentioning

confidence: 99%

Cycling Analytics for Urban Environments: From Vertical Models to Horizontal Innovation

Carvalho

Pessoa²,

José

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The full potential of ITS can only be achieved in a global scale and combining the efforts and the knowledge of multiple entities. This is also true for the current efforts towards the application of data, communications and services, to improve cycling and its integration into general mobility systems. The currently prevailing paradigm is based on disperse and self-contained custom processes, which fail to promote distributed and open innovation. These models are hard to reproduce, generalize, recombine or improve outside the context in which they were originally implemented. A digital platform strategy might offer a viable and scalable way to support convergence between multiple models and promote their usage as shared references for cycling ecosystems. In this work, we aim to validate our assumptions about the limitations of current development paradigms and analyse the extent to which a platform strategy could offer a fundamentally different approach to address those limitations. To validate the problem and uncover generalisation opportunities, we study 3 cycling mobility models and make an initial analysis of how the general principles of digital platforms could be applied as a general framework for a new type of solution for cycling analytics. The results confirm a high potential for horizontal features and outline a set of key design principles for the development of a digital platform strategy for cycling analytics. This should constitute a major contribution to inform the development of a new generation of cycling platforms for urban environments.

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“…. The CPT is composed of the base environmental module -based on eight weighted factors (population density, hilliness, physical barriers, access to services, existing cycling mode share, distance to work and school, and road speed) -and the quality of service module, which evaluates each area of interest based on eight factors (surface condition, adjacent cyclists, comfort factor, conflict, distance between junctions, slope, access to services, and origin/destination).A more recent areal-based approach is the designed the Gross Potential for Cycling (GPC) tool to prioritize areas for cycling infrastructure and other cycling measures33 . The GPC uses two sets of indicators: populationbased indicators (age, potential demand density, employment density and motorization rate) and area-based indicators (accessibility, education, public transport, connectivity, land use mix and relative performance).…”

mentioning

confidence: 99%

Cycle Route Uptake and Scenario Estimation (CRUSE): an approach for developing strategic cycle network planning tools

Lovelace,

Talbot,

Vidal-Tortosa

et al. 2024

Preprint

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This paper describes an approach for developing strategic cycle network planning tools. Based on our experience developing and deploying the Cycle Route Uptake and Scenario Estimation (CRUSE) Tool for Ireland, we outline the underlying methods, including disaggregation of origin-destination data, incorporation of additional trip purposes, routing, scenario generation, and development of an intuitive user interface that has been tested and used by practitioners. Commissioned and used by Transport Infrastructure Ireland, CRUSE provides estimates of current and potential future cycling levels under ‘snapshot’ scenarios to inform investment decisions. The publicly available results at https://cruse.bike/ enable planners, engineers, and other stakeholders to make more evidence-based decisions. CRUSE goes beyond previous work by: modeling networks at high spatial resolution; simulating multiple trip purposes (social, shopping, personal utility, recreational, and cycle touring) in addition to using official origin-destination data; and providing estimates of ‘quietness’ (a proxy for cyclist comfort and route preference) at the route segment level. Three network types — ‘Fastest’, ‘Balanced’, and ‘Quietest’ — help plan both arterial and residential cycle networks. Workshops and unstructured interviews with stakeholders show that the tool is already being used in practice to inform urban, inter-urban, and rural network planning. The approach is flexible and open-source, allowing the underlying ideas and code to be adapted, supporting effective cycling policies and interventions internationally.

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The Gross Potential for Cycling: planning for human scale urban mobility

Cited by 4 publications

References 30 publications

Exploring the Relationship Between Artificial Intelligence, Autonomous Learning, and Skills Required for Success in The 21st Century

Exploring the Relationship Between Artificial Intelligence, Autonomous Learning, and Skills Required for Success in The 21st Century

Cycling Analytics for Urban Environments: From Vertical Models to Horizontal Innovation

Cycle Route Uptake and Scenario Estimation (CRUSE): an approach for developing strategic cycle network planning tools

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