Risk Framework Research Articles

Making Waves: Towards data-centric water engineering

Artificial intelligence (AI) is expected to transform many scientific disciplines, with the potential to significantly accelerate scientific discovery. This perspective calls for the development of data-centric water engineering to tackle water challenges in a changing world. Building on the historical evolution of water engineering from empirical and theoretical paradigms to the current computational paradigm, we argue that a fourth paradigm, i.e., data-centric water engineering, is emerging driven by recent AI advances. Here we define a new framework for data-centric water engineering in which data are transformed into knowledge and insight through a data pipeline powered by AI technologies. It is proposed that data-centric water engineering embraces three principles – data-first, integration and decision making. We envision that the development of data-centric water engineering needs an interdisciplinary research community, a shift in mindset and culture in the academia and water industry, and an ethical and risk framework to guide the development and application of AI. We hope this paper could inspire research and development that will accelerate the paradigm shift towards data-centric water engineering in the water sector and fundamentally transform the planning and management of water infrastructure.

A General Framework for Account Risk Rating on Ethereum: Toward Safer Blockchain Technology

As the largest blockchain platform that supports smart contracts, Ethereum has attracted wide attention from both academia and industry in recent years. Along with the prosperous development of Ethereum, the high-risk illegal practices on it are becoming more and more rampant, seriously jeopardizing the system’s trading security and long-term development. Therefore, the detection and quantification of account risk are of great importance for both cryptocurrency investors and blockchain security researchers. In this article, we propose the first general framework for account risk rating on Ethereum, which includes a devisable suspiciousness metric to adapt to various illicit fraud detection and a network propagation mechanism to formulate the relations between accounts and transactions. By conducting extensive experiments on a real-world dataset from Ethereum, we show the universality of the account risk rating framework. Particularly, statistical analyses on different risk levels of accounts demonstrate that the risk rating framework has access to detect various illicit accounts. And the metric analysis of risk rating results put forward some insights. Moreover, visualization of a suspicious transaction chain reveals the process of illicit activities on Ethereum, enabling investors to obtain an understanding of the risky accounts and avoid significant financial losses.

Integrating ecosystem service supply–demand and ecological risk assessment for urban planning: A case study in Beijing, China

The balance between ecosystem service supply–demand and landscape ecological risk are two crucial aspects influencing the sustainable development of regional ecosystems. Current evaluations ignore these aspects and lead to partial landscape planning in urban areas. As a result, a framework was built for regional planning in urban areas based on multidimensional evaluations of ecosystems for high-efficiency land use planning. First, to determine the ecosystem service balance in regional scale, the supply and demand of five ecosystem services were quantified—biodiversity conservation, carbon sequestration, water conservation, food production, and landscape recreation. Second, landscape ecological risks were identified using the “natural–social–landscape” research framework combined with spatial principal component analysis. Third, spatial clustering analysis of ecosystem service supply–demand ratios and landscape ecological risks was then performed through spatial autocorrelation, which aimed to identify areas for protection and restoration priority. Finally, GeoDetector was used to determine the main factors influencing ecosystem service balance and landscape ecological risks and develop targeted planning measures. The results revealed an imbalance in total ecosystem service supply–demand in Beijing's plain area, coupled with a high level of landscape ecological risk. The combined ecosystem service supply–demand ratios exhibited a significant negative correlation and aggregation with landscape ecological risk, and the significant area accounted for approximately 31.9% of the total area. Priority areas for protection and restoration accounted for 10.39% and 19.94%, respectively, of the total regional area. Land use, distance to settlements, and vegetation cover were the primary environmental variables influencing the distributions of the combined ecosystem service supply–demand ratios and landscape ecological risk. So, this study emphasizes the need to strengthen intensive land use and prevent the uncontrolled expansion of urban land and settlements. It is crucial to enhance urban green infrastructure construction, promote the development of biodiversity-friendly agriculture, and implement measures such as afforestation restoration to increase regional vegetation cover. Overall, by integrating a framework of ecosystem services and landscape ecological risk, a comprehensive scientific reference can be provided for the efficient land use and sustainable development planning in urban areas.

288 It affected me so much that I even had to tie my tubes: The Role of Patient-Clinician Communication on Black Women’s Perceptions of Pregnancy Risk

OBJECTIVES/GOALS: This study utilizes a Black Feminist metatheoretical approach in conjunction with the Social Amplification of Risk Framework to: i) assess the role of communication with clinicians on Black women’s perceptions of risk and ii) outline recommendations for empowering communication that attenuates Black women’s perceptions of pregnancy risk. METHODS/STUDY POPULATION: Semi-structured in-depth interviews were conducted with 28 self-identified Black women in 2022 and 2023 to gain insights into their conceptualizations of pregnancy risk, and the role of communication with clinicians in the amplification and/or attenuation of pregnancy risk perceptions.An IRB approved flier was used for both in-person and online recruitment of participants from 12 US states.Each participant signed a consent form and received $20 as compensation for their time. Interviews were recorded and transcribed using a digital transcription tool, and then reviewed and edited to ensure accuracy. This study’s metatheoretical underpinning in Black Feminist Theory informed the use of reflexive thematic analysis in the qualitative analysis of the in-depth interviews. RESULTS/ANTICIPATED RESULTS: This study found thatunclear communication (including insufficiently contextualized history taking), obstetric violence, anddiagnoses with insufficient explanation,led to the amplification of concerns and worries about pregnancy as well as erosion of trust in healthcare. In contrast,clinicians lessenedconcerns about pregnancy through clear communication (including appropriately contextualized history-taking), demonstration of empathy, and by fostering patient agency.The study thus highlights the importance of clinicians' clear communication,demonstration ofempathy and fostering of patient agencyas critical factors to empowering communication that attenuates Black women’s pregnancy-related risk perceptions. DISCUSSION/SIGNIFICANCE: Given the unjust dangers Black women face during pregnancy, this study demonstrates how patient-clinician communication influences Black women’s pregnancy risk perceptions, providing recommendations for clinician communication practices that empower Black women and attenuate their perceptions of pregnancy risk.

ARCHDIOCESE OF JAKARTA: AN ANALYSIS OF ENTREPRENEURIAL PROACTIVENESS AND RISK TAKING

The Archdiocese of Jakarta, Indonesia's oldest diocese with a dynamic megapolitan population, must be proactive and willing to take risks in order to meet the needs of Catholics and fulfill its mission to the general public. With 37 parishes and a diverse parishioner population, Catholic priests encounter a variety of problems in carrying out the church's mission. A proactive approach and the willingness to take risks can boost organizational success. There has been no previous research on the Roman Catholic church with the concept of entrepreneurship, hence this study fills a research gap in terms of entrepreneurship theory and context in religious organizations. Using the framework of entrepreneurial proactiveness and entrepreneurial risk taking, in-depth interviews with 19 parish priests revealed that 19 parish priests applied entrepreneurial concepts to their ministry work. This study adds to the academic side of entrepreneurship in the context of religious non-profit organizations and offers practical guidance to NPO managers who are in a similar scenario.

A cost-benefit ‘source-receptor’ framework for implementation of Blue-Green flood risk management

As floods are a major and growing source of risk in urban areas, there is a necessity to improve flood risk management frameworks and civil protection through planning interventions that modify surface flow pathways and introduce storage. Despite the complexity of densely urbanised areas (topography, buildings, green spaces, roads), modern flood models can represent urban features and flow characteristics in order to help researchers, local authorities, and insurance companies to develop and improve efficient flood risk frameworks to achieve resilience in cities. A cost-benefit driven ‘source-receptor’ flood risk framework is developed in this study to identify (1) locations contributing to surface flooding (sources), (2) buildings and locations at high flood risk (receptors), (3) the cost-benefit nexus between the ‘source’ and the ‘receptor’, and finally (4) ways to mitigate flooding at the ‘receptor’ by adding Blue-Green Infrastructure (BGI) in critical locations. The analysis is based on five steps to identify the ‘source’ and the ‘receptor’ in a study area based on the flood exposure of buildings, damages arising from flooding and available green spaces with the best potential to add sustainable and resilient solutions to reduce flooding. The framework was developed using the detailed hydrodynamic model CityCAT in a case study of the city centre of Newcastle upon Tyne, UK. The novelty of this analysis is that firstly, multiple storm magnitudes (i.e. small and large floods) are used combined with a method to locate the areas and the buildings at flood risk and a prioritized set of best places to add interventions upstream and downstream. Secondly, planning decisions are informed by considering the benefit from reduced damages to properties and the cost to construct resilient BGI options rather than a restricted hydraulic analysis considering only flood depths and storages in isolation from real world economics.

Long-term assessment of social amplification of risk during COVID-19: challenges to public health agencies amid misinformation and vaccine stance

This study employs the Social Amplification of Risk Framework to investigate the stance on COVID-19 vaccines and the spread of misinformation on Twitter in Finland. Analyzing over 1.6 million tweets and manually annotating 4150 samples, the research highlights the challenges faced by the Finnish Institute for Health and Welfare (THL) in steering online vaccination communication. Using BERT models, Botometer, and additional computational methods, the study classifies text, identifies bot-like accounts, and detects malicious bots. Social network analysis further uncovers the underlying social structures and key actors in Twitter discussions during the pandemic. The THL remained a primary source of COVID-19 information throughout the pandemic, maintaining its influence despite challenges posed by malicious bots spreading misinformation and adopting negative vaccine stances. However, THL ceased its Twitter activity at the end of 2022 because its posts were being exploited to gain visibility and traction for misinformation and negative vaccine stance. The study also identifies key influencers in online vaccine discussions, suggesting avenues for improving public health communication. Overall, the research underscores the need to understand social media dynamics to counter misinformation and foster accurate public communication on COVID-19 and vaccination.

Identification of cellular senescence-associated genes as new biomarkers for predicting the prognosis and immunotherapy response of non-small cell lung cancer and construction of a prognostic model

BackgroundGlobally, lung carcinoma remains the leading cause of death, with its associated morbidity and mortality rates remaining elevated. Despite the slow advancement of treatment, the outlook remains bleak. Cellular senescence represents a halt in the cell cycle, encompassing a range of physiological and pathological activities, along with diverse phenotypic alterations, including variations in secretory phenotype, macromolecular harm, and metabolic disturbances. Research has revealed its vital function in the formation and growth of tumors. This study aimed to examine cellular senescence-related mRNAs linked to the outlook of non-small cell lung cancer (NSCLC) and to formulate a predictive risk framework for NSCLC. MethodsWe acquired the NSCLC expression data from The Cancer Genome Atlas (TCGA) to examine mRNAs linked to cellular senescence. Both single-variable and multiple-variable cox proportion risk assessments were utilized to determine the traits of cellular senescence-related mRNAs linked to NSCLC prognosis. Subsequently, the prognostic model for cellular senescence-related mRNAs was integrated with clinical-pathological characteristics to create a prognostic nomogram. Furthermore, the study delved into the risk-oriented predictive model, examining immune infiltration and responses to immunotherapy among both high and low-risk categories. ResultsUtilizing both univariate and multivariate Cox proportion risk assessments, a risk model comprising 12 mRNAs associated with cellular aging was ultimately developed: IGFBP1, TLR3, WT1, ID1, PTTG1, ERRFI1, HEPACAM, MAP2K3, RAD21, NANOG, PRKCD, SOX5. Univariate analysis and multivariate analysis illustrated that the risk score served as a standalone indicator for prognosis, and the hazard ratio (HR) of the risk score were 1.182 (1.139–1.226) (p < 0.001) and 1.162 (1.119 − 1.206) (p < 0.001), respectively. Individual prognoses were forecasted using nomogram, c-index, and principal component analysis (PCA). Furthermore, the risk-oriented model revealed notable statistical variances in immune infiltration and response to immunotherapy among the high and low risk categories. ConclusionsThis study shows that mRNAs related to cell senescence associated with prognosis are reliable predictors of NSCLC immunotherapy reaction and prognosis.

How to provide actionable information on weather and climate impacts?–A summary of strategic, methodological, and technical perspectives

Climate change will result in more intense and more frequent weather and climate events that will continue to cause fatalities, economic damages and other adverse societal impacts worldwide. To mitigate these consequences and to support better informed decisions and improved actions and responses, many National Meteorological and Hydrological Services (NMHSs) are discussing how to provide services on weather and climate impacts as part of their operational routines. The authors outline how a risk framework can support the development of these services by NMHSs. In addition to the hazard information, a risk perspective considers the propensity for a given hazard to inflict adverse consequences on society and environment, and attempts to quantify the uncertainties involved. The relevant strategic, methodological and technical steps are summarized and recommendations for the development of impact-related services are provided. Specifically, we propose that NMHSs adopt an integrated risk framework that incorporates a hazard-exposure-vulnerability model into operational services. Such a framework integrates all existing forecast and impact services, including the underlying impact models, and allows for flexible future extensions driven by the evolving collaboration with partners, stakeholders and users. Thereby, this paper attempts to unify existing work streams on impact-related services from different spatial and temporal scales (weather, climate) and disciplines (hydrology, meteorology, economics, social sciences) and to propose a harmonized approach that can create synergies within and across NMHSs to further develop and enhance risk-based services.

Don’t Call It a Failure: Systemic Risk Governance for Complex Financial Systems

The probability that an event will avalanche into an impairment of essential services constitutes a “systemic risk.” Owing to the inherent complexities of modern societies, the outbreak of a novel disease or the failure of a financial institution can rapidly escalate into an impact significantly larger than the initial event. Through the lens of complex system theory, this article draws a parallel between financial crises and disasters to contend that the regulatory framework for financial systemic risk is unequipped to address its fundamental dynamics. Epitomized by the market failure rationale, financial regulation is premised on a reductionist view that purports both systemic risk and law as external to the actions of market participants. Conversely, this article advances a twofold conceptual framework. First, it shows that systemic risk emerges from the same complex dynamics that generate the financial system. Second, it understands law as an agent of complexity, thus contributing to the emergence of finance and its inherent instability. Normatively, this conceptual framework reveals the limits of current regulatory approaches and constructs a holistic risk governance framework that is akin to the one adopted to govern disaster risks.

Sustainability matters : Best practices and challenges on sustainability data and how to integrate ESG and climate risk into your operating model and risk framework

Securities services companies need to respond to changing risks and the impacts they could have on operational resilience. Much of the focus has been on identifying risks relating to asset protection, or factors that can have an impact on revenue, such as falling interest rates. With the potential threat of climate change on asset prices, and the risk more broadly of climate change to financial markets, custodians need to rethink risk, both at an operational level and how they support asset owners and asset managers to help mitigate these risks.

Annual Research Review: Neuroimmune network model of depression: adevelopmental perspective.

Depression is a serious public health problem, and adolescence is an 'age of risk' for the onset of Major Depressive Disorder. Recently, we and others have proposed neuroimmune network models that highlight bidirectional communication between the brain and the immune system in both mental and physical health, including depression. These models draw on research indicating that the cellular actors (particularly monocytes) and signaling molecules (particularly cytokines) that orchestrate inflammation in the periphery can directly modulate the structure and function of the brain. In the brain, inflammatory activity heightens sensitivity to threats in the cortico-amygdala circuit, lowers sensitivity to rewards in the cortico-striatal circuit, and alters executive control and emotion regulation in the prefrontal cortex. When dysregulated, and particularly under conditions of chronic stress, inflammation can generate feelings of dysphoria, distress, and anhedonia. This is proposed to initiate unhealthy, self-medicating behaviors (e.g. substance use, poor diet) to manage the dysphoria, which further heighten inflammation. Over time, dysregulation in these brain circuits and the inflammatory response may compound each other to form a positive feedback loop, whereby dysregulation in one organ system exacerbates the other. We and others suggest that this neuroimmune dysregulation is a dynamic joint vulnerability for depression, particularly during adolescence. We have three goals for the present paper. First, we extend neuroimmune network models of mental and physical health to generate a developmental framework of risk for the onset of depression during adolescence. Second, we examine how a neuroimmune network perspective can help explain the high rates of comorbidity between depression and other psychiatric disorders across development, and multimorbidity between depression and stress-related medical illnesses. Finally, we consider how identifying neuroimmune pathways to depression can facilitate a 'next generation' of behavioral and biological interventions that target neuroimmune signaling to treat, and ideally prevent, depression in youth and adolescents.

Climate‐related credit risk: Rethinking the credit risk framework

AbstractClimate change and the challenges associated with the transition to a zero‐carbon economy pose significant financial risks. Climate‐related risks (CRR) indirectly impact banks through their loan portfolios. To examine the integration of CRR into banks' credit risk assessment and monitoring, this article reviews academic and institutional literature using quantitative bibliometric techniques and content analysis of 145 academic documents from policymakers and financial supervisors. A framework emerges that incorporates CRR into credit risk management. We find four thematic areas in the literature: CRR drivers, CRR tools, CRR data and CRR pricing. Overall, uncertainty, non‐linearity, geographic and industrial dependency and non‐reversibility of CRR difficult climate‐related credit risk assessment. Moreover, CRR data present comparability, availability and reliability issues, which Artificial Intelligence can improve. Finally, evidence reveals that current financial prices do not fully reflect CRR. Our findings provide important implications to policymakers for assessing ex‐ante the financial impacts of climate transition regulations, the potential for prudential regulatory action, and the need for supra‐national policies that facilitate access to reliable and comparable climate data.

An integrative decision-making framework to guide policies on regulating ChatGPT usage.

Generative artificial intelligence has created a moment in history where human beings have begin to closely interact with artificial intelligence (AI) tools, putting policymakers in a position to restrict or legislate such tools. One particular example of such a tool is ChatGPT which is the first and world's most popular multipurpose generative AI tool. This study aims to put forward a policy-making framework of generative artificial intelligence based on the risk, reward, and resilience framework. A systematic search was conducted, by using carefully chosen keywords, excluding non-English content, conference articles, book chapters, and editorials. Published research were filtered based on their relevance to ChatGPT ethics, yielding a total of 41 articles. Key elements surrounding ChatGPT concerns and motivations were systematically deduced and classified under the risk, reward, and resilience categories to serve as ingredients for the proposed decision-making framework. The decision-making process and rules were developed as a primer to help policymakers navigate decision-making conundrums. Then, the framework was practically tailored towards some of the concerns surrounding ChatGPT in the context of higher education. In the case of the interconnection between risk and reward, the findings show that providing students with access to ChatGPT presents an opportunity for increased efficiency in tasks such as text summarization and workload reduction. However, this exposes them to risks such as plagiarism and cheating. Similarly, pursuing certain opportunities such as accessing vast amounts of information, can lead to rewards, but it also introduces risks like misinformation and copyright issues. Likewise, focusing on specific capabilities of ChatGPT, such as developing tools to detect plagiarism and misinformation, may enhance resilience in some areas (e.g., academic integrity). However, it may also create vulnerabilities in other domains, such as the digital divide, educational equity, and job losses. Furthermore, the finding indicates second-order effects of legislation regarding ChatGPT which have implications both positively and negatively. One potential effect is a decrease in rewards due to the limitations imposed by the legislation, which may hinder individuals from fully capitalizing on the opportunities provided by ChatGPT. Hence, the risk, reward, and resilience framework provides a comprehensive and flexible decision-making model that allows policymakers and in this use case, higher education institutions to navigate the complexities and trade-offs associated with ChatGPT, which have theoretical and practical implications for the future.

Real-time estimation of the Potential Loss of Life in case of ship-to-ship collisions

The flooding risk assessment for passenger ships is a topic mainly addressed during the design phase of the vessel. However, risk pertains to the whole life cycle of a ship. In this sense, the operational phase requires methodologies for risk assessment while the vessel is sailing, thus a real-time estimation of the flooding risk. The framework developed during the EU project FLARE for the design phase allows for determining flooding risk by estimating the Potential Loss of Life. Such a metric can be extended to real-time applications thanks to the flexibility of the risk framework. The execution of direct calculations for ship-to-ship collisions and flooding simulations for vessel survivability permit the generation of fast surrogate models to determine potential damage dimensions and survivability for a specific event, thus the flooding risk. Such a process, including uncertainties due to the onboard instrumentations, is applied to two reference passenger ships: a Cruise ship and a RoPax. Simulations of four scenarios considering different weather conditions show the real-time variations of the flooding risk (through the Potential Loss of Life) following collision with a target vessel, thus demonstrating the applicability of the process in real time.

A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics.

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20-25min. The RTCF framework provides new pathways for proactive safety management at signalised intersections.

Comprehensive Risk Assessment Framework for Flash Floods in China

Accurately assessing the risk of flash floods is a fundamental prerequisite for defending against flash flood disasters. The existing methods for assessing flash flood risk are constrained by unclear key factors and challenges in elucidating disaster mechanisms, resulting in less-than-ideal early warning effectiveness. This article is based on official statistics of flash flood disaster data from 2017 to 2021. It selects eight categories of driving factors influencing flash floods, such as rainfall, underlying surface conditions, and human activities. Subsequently, a geographical detector is utilized to analyze the explanatory power of each driving factor in flash flood disasters, quantifying the contribution of each factor to the initiation of flash flood; the flash flood potential index (FFPI) was introduced to assess the risk of flash flood disasters in China, leading to the construction of a comprehensive assessment framework for flash flood risk. The results indicate that (1) Flash floods are generally triggered by multiple factors, with rainfall being the most influential factor, directly causing flash floods. Soil type is the second most influential factor, and the combined effects of multiple factors intensify the risk of flash floods. (2) The southeastern, southern, and southwestern regions of China are considered high-risk areas for flash floods, with a high danger level, whereas the northwestern, northern, and northeastern plain regions exhibit a lower danger level. The above research results provide reference and guidance for the prevention and control of flash flood disasters.

Global monitoring for biodiversity: Uncertainty, risk, and power analyses to support trend change detection.

Global targets aim to reverse biodiversity declines by 2050 but require knowledge of current trends and future projections under policy intervention. First, given uncertainty in measurement of current trends, we propose a risk framework, considering probability and magnitude of decline. While only 11 of 198 systems analyzed (taxonomic groups by country from the Living Planet Database) showed declining abundance with high certainty, 20% of systems had a 70% chance of strong declines. Society needs to decide acceptable risks of biodiversity loss. Second, we calculated statistical power to detect trend change using ~12,000 populations from 62 systems currently showing strong declines. Current trend uncertainty hinders our ability to assess improvements. Trend change is detectable with high certainty in only 14 systems, even if thousands of populations are sampled, and conservation action reduces net declines to zero immediately, on average. We provide potential solutions to improve monitoring of progress toward biodiversity targets.

Towards a dynamic earthquake risk framework for Switzerland

Abstract. Scientists from different disciplines at ETH Zurich are developing a dynamic, harmonised, and user-centred earthquake risk framework for Switzerland, relying on a continuously evolving earthquake catalogue generated by the Swiss Seismological Service (SED) using the national seismic networks. This framework uses all available information to assess seismic risk at various stages and facilitates widespread dissemination and communication of the resulting information. Earthquake risk products and services include operational earthquake (loss) forecasting (OE(L)F), earthquake early warning (EEW), ShakeMaps, rapid impact assessment (RIA), structural health monitoring (SHM), and recovery and rebuilding efforts (RRE). Standardisation of products and workflows across various applications is essential for achieving broad adoption, universal recognition, and maximum synergies. In the Swiss dynamic earthquake risk framework, the harmonisation of products into seamless solutions that access the same databases, workflows, and software is a crucial component. A user-centred approach utilising quantitative and qualitative social science tools like online surveys and focus groups is a significant innovation featured in all products and services. Here we report on the key considerations and developments of the framework and its components. This paper may serve as a reference guide for other countries wishing to establish similar services for seismic risk reduction.

Refining a crash risk framework for urban bus safety assessment: Evidence from Sardinia (Italy)

Assessing the crash risk on bus routes would be pivotal to increasing the transit system safety. All past studies largely focused on the proposal of some crash risk indexes, and only one (recent) research has computed the bus crash risk in transit services by developing a bivariate (frequency and severity) risk model. Conversely, as far as the authors’ knowledge, no research attempted to quantify the probability of bus crash occurrence as a risk component and present results by geographic information systems. This study covers these gaps by refining an existing framework to better adhere to the original and well-accepted definition of risk. Specifically, it models the bus crash risk on (parts of) routes as a function of the probability of having a crash, the related severity, and exposure terms. Next, (parts of) routes are ranked according to the risk value and classified by a multilevel scale, to show those with the highest risk. Experiments highlight the viability of this framework using 3000+ raw bus crash data records. The results are synthesised by straightforward charts and maps. This framework helps support public transport companies in enhancing safety performance because it could be implemented in a transit safety managerial system for monitoring and certification purposes according to safety norms and EU directives.