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Characterization of individual mobility for non-routine scenarios from Crowd Sensing and Clustered Data
2019-11-04 - Gomes, Rui Jorge Reis; Cunha, Inês; Simões, João; Alves, Ana; Ribeiro , Anabela
Demand for leisure activities has increased due to some reasons such as increasing wealth, ageing populations and changing lifestyles, however, the efficiency of public transport system relies on solid demand levels and well-established mobility patterns and, so, providing quality public transportation is extremely expensive in low, variable and unpredictable demand scenarios, as it is the case of out-of-routine trips. Better prediction estimations about the trip purpose helps to anticipate the transport demand and consequently improve its planning. This paper addresses the contribution in comparing the traditional approach of considering municipality division to study such trips against a proposed approach based on clustering of dense concentration of services in the urban space.
In our case, POIs (Points of Interest) collected from social networks (e.g. Foursquare) represent these services. These trips were associated with the territory using two different approaches: ‘municipalities’ and ‘clusters’ and then related with the likelihood of choosing a POI category (Points-of-Interest). The results obtained for both geographical approaches are then compared considering a multinomial model to check for differences in destination choice. The variables of distance travelled, travel time and whether the trip was made on a weekday or a weekend had a significant contribution in the choice of destination using municipalities approach. Using clusters approach, the results are similar but the accuracy is improved and due to more significant results to more categories of destinations, more conclusions can be drawn. These results lead us to believe that a cluster-based analysis using georeferenced data from social media can contribute significantly better than a territorial-based analysis to the study of out-of-routine mobility. We also contribute to the knowledge of patterns of this type of travel, a type of trips that is still poorly valued and difficult to study. Nevertheless, it would be worth a more extensive analysis, such as analysing more variables or even during a larger period.
Efficient transport simulation with restricted Batch-Mode Active Learning
2018-07-24 - Gomes, Rui Jorge Reis; Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco C.
Simulation modeling is a well-known and recurrent approach to study the performance of urban systems. Taking into account the recent and continuous transformations within increasingly complex and multidimensional cities, the use of simulation tools is, in many cases, the only feasible and reliable approach to analyze such dynamic systems. However, simulation models can become very time consuming when detailed input-space exploration is needed. To tackle this problem, simulation metamodels are often used to approximate the simulators' results. In this paper, we propose an active learning algorithm based on the Gaussian process (GP) framework that gathers the most informative simulation data points in batches, according to both their predictive variances and to the relative distance between them. This allows us to explore the simulators' input space with fewer data points and in parallel, and thus in a more efficient way, while avoiding computationally expensive simulation runs in the process. We take advantage of the closeness notion encoded into the GP to select batches of points in such a way that they do not belong to the same high-variance neighborhoods. In addition, we also suggest two simple and practical user-defined stopping criteria so that the iterative learning procedure can be fully automated. We illustrate this methodology using three experimental settings. The results show that the proposed methodology is able to improve the exploration efficiency of the simulation input space in comparison with non-restricted batch-mode active learning procedures
From drought to deluge: The complex Journal of Cultural Heritage impacts of climate change on earthen heritage
2025-01-02 - Nakhaei Ashtari, Masoud; Della Ventura, Giancarlo; Correia, Mariana
Purpose
The purpose of this article is to assess climate change impacts on four earthen heritage (EH) sites, highlighting their vulnerability and the need for an integrated approach that considers physical, social and cultural factors.
Design/methodology/approach
The study uses a systematic approach to assess climate change impacts on earthen heritage sites. A literature review informed the development of a conceptual model based on climate change risk guidelines and methodologies. The model evaluates site-specific hazards through quantitative and qualitative assessments, considering exposure, sensitivity and adaptive capacity. Vulnerability is calculated using a formula that categorizes it into five levels. Data collection included meteorological analysis, field observations and interviews with local experts.
Findings
Findings indicate that climate change has significant consequences for earthen heritage sites, including erosion, structural deterioration and loss of cultural value. The regions of Sistan, Yazd, Persepolis and Tchogha Zanbil are particularly vulnerable due to their specific climatic conditions. Key threats include changes in precipitation patterns, extreme weather events, rising temperatures and water scarcity. Socioeconomic factors like limited conservation resources and inadequate stakeholder communication exacerbate these vulnerabilities. The study highlights the need for interdisciplinary collaboration, effective governance and community engagement to develop comprehensive climate change adaptation strategies.
Originality/value
The article’s originality lies in its systematic assessment of climate change impacts on earthen heritage sites, offering a novel framework that integrates interdisciplinary approaches and site-specific factors.