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- Data analytics to advance the inference of origin–destination in public transport systems: tracing network vulnerabilities and age-sensitive trip purposesPublication . Cerqueira, Sofia; Arsenio, Elisabete; Barateiro, José; Henriques, RuiKnowing the passengers' final destinations, underlying motifs, and commuting habits is critical to optimise public transportation systems, guide urban planning and contribute to a more sustainable urban mobility. In entry-only Automated Fare Collection systems, the body of literature has focused on the spatial dimension by estimating alighting stops, overlooking the inference of robust alighting times. Moreover, discriminating between transfers and activities is pivotal for determining their ultimate destinations. However, current methods often struggle to adapt to the stochastic nature of passenger behaviour, further disregarding the multiplicity of routes and stops to access specific facilities and individual motivations. Further research is required to address an effective spatio-temporal and contextual inference in both challenges. With the above concerns in mind, this research uses data analytics to propose an enhanced methodology for the inference of OD matrices, with the final goal of providing a comprehensive view of OD mobility patterns across distinct age-sensitive profiles-youth, adults, and older adults. Our methodological framework integrates the following approaches: (i) alighting stop-and-time inference, (ii) ensembled model for transfer classification, (iii) indicators retrieved from statistical analysis of network vulnerabilities (e.g., number of transfers, walkability needs), frequent destinations and their underlying putative motifs against the city amenities and others points-of-interest. The reliability of alighting data (timestamp and location) inference is improved by integrating OpenStreetMap data and the past boarding data from bus and railway systems. Considering Lisbon as the target study case, we apply the methodology over smart card data collected both from metro and bus systems. A comparative analysis with state-of-the-art methods revealed that the enhanced framework for alighting and OD inference led to longer journey times for trips. Furthermore, throughout the day, the older adult group experiences longer transfer times on average compared to both the children and young adult segment and the adult segment.
- The balanced scorecard ontology: a semantic approach to enhance strategy managementPublication . Antunes, António Lorvão; Cardoso, Elsa; Barateiro, JoséThe Balanced Scorecard, developed in 1992 by Kaplan and Norton, has evolved into a communication and strategy execution system widely adopted by organizations across various industries. This article explores the use of an ontology to bridge the gap between strategy management and data within the Balanced Scorecard framework. The Balanced Scorecard Ontology is introduced to store, validate, and analyze knowledge, containing information about the strategy map and quantification frameworks, essential for evaluating the strategy execution. The proposed ontology is designed, developed, and evaluated using competency questions (CQs), and further validated by an online tool. Specifically, the proposed formalization of the Balanced Scorecard framework provides a semantic layer aimed at facilitating an effective Balanced Scorecard implementation, enabling accurate, traceable, and continuous monitoring and improvement of the strategy execution, based on a data-driven approach. The formalization of this knowledge through an ontology encompasses several advantages, such as improved interoperability and validation of the framework’s elements, inference of new knowledge, and enhanced communication between different stakeholders. In addition, managerial implications include ensuring alignment between the Balanced Scorecard and organizational goals, supporting compliance and governance efforts, improving communication and knowledge transfer, enhancing the strategic decision-making process, and facilitating the integration of data into the Balanced Scorecard.
- Strategic analysis in the public sector using semantic web technologiesPublication . Antunes, António Lorvão; Barateiro, José; Cardoso, ElsaThis article addresses the complex challenges that public organizations face in designing, implementing, and evaluating their strategies, where public interest and regulatory compliance often intertwine with strategic objectives. This research investigates the application of ontologies in the field of public sector strategy management to enhance the capacity of organizations to make informed data-driven decisions, efficiently allocate resources, and effectively navigate the intricate landscape of the public sector. The LNEC - National Laboratory for Civil Engineering’s strategy is used as an exploratory case study. Semantic web technologies are used to perform strategy analysis, including validating the strategy formulation and supporting the strategy execution by assessing performance indicators, verifying the design of cause-and-effect relationships between strategic objectives, and monitoring and empirically validating these relationships. The increased interoperability of these technologies enables information sharing across systems and organizations. Following the strategy analysis, recommendations are provided, leading to a more robust and data-driven strategic management approach, enabling accurate, traceable, and continuous monitoring of an organization’s strategy. Theoretical and practical implications are discussed, along with limitations and future work. This research offers a blueprint for public sector organizations seeking to optimize their strategies, foster transparency, and deliver more effective services to the public they serve.
- Ontology-based BIM-AMS integration in European highwaysPublication . Antunes, António Lorvão; Barateiro, José; Marecos, Vânia; Petrović, Jelena; Cardoso, ElsaBIM tools enable decision-making during the lifecycle of engineering structures, such as bridges, tunnels, and roads. National Road Authorities use Asset Management Systems (AMS) to manage and monitor operational information of assets from European Highways, including access to sensor and inspection data. Interoperability between BIM and AMS systems is vital for a timely and effective decision-making process during the operational phase of these assets. The European project Connected Data for Effective Collaboration (CoDEC) designed a framework to support the connections between AMS and BIM platforms, using linked data principles. The CoDEC Data Dictionary was developed to provide standard data formats for AMS used by European NRA. This paper presents the design and development of an Engineering Structures ontology used to encode the shared conceptualization provided by the CoDEC Data Dictionary. The ontology is evaluated, validated, and demonstrated as a base for data exchange between BIM and AMS.
- Moving from classical towards machine learning stances for bus passengers’ alighting estimation: A comparison of state-of-the-art approaches in the city of LisbonPublication . Cerqueira, Sofia; Arsenio, Elisabete; Barateiro, José; Henriques, RuiPassenger alighting estimation is a critical task in public transport (PT) management, especially for entry-only Automatic Fare Collection (AFC) transport systems where passenger alighting are not recorded. Effective estimation methods are necessary for trip analysis and route planning, offering valuable insights into passengers’ mobility patterns and, subsequently, improving the quality of service. However, the stochastic nature of passenger behaviour challenges the degree of successful alighting estimates. A classic approach to infer the alighting stops of passengers is the use of trip-chaining principles. Since these principles are dispersed across the literature in the field, their comprehensive review is pivotal to establish the best practice for alighting estimation. Still, trip chaining approaches are unable to infer the alighting of non-commuting passengers. This paper addresses these two research gaps by: i) providing a critical overview of the existing principles and methods for alighting estimation; ii) proposing an approach to improve alighting estimation that consistently integrates the most effective state-of-the-art principles on trip-chaining; and iii) further introducing a frequent pattern mining and densitybased clustering solutions to support alighting estimation for non-commuting passengers. Considering the public bus transport in Lisbon city as the guiding case study, the achieved estimation rate by the proposed assembled model is 92%. Moreover, the density-based clustering solution is found to improve the estimation of 11pp against classic trip-chaining principles. Furthermore, the proposed model and acquired results yield actionable value to enhance PT operations and services, ultimately leading to improved bus routing and quality of service.