Francisco C. Pereira

The geography of taste: analyzing cell-phone mobility and social events

This paper deals with the analysis of crowd mobility during special events. We analyze nearly 1 million cell-phone traces and associate their destinations with social events. We show that the origins of people attending an event are strongly correlated to the type of event, with implications in city management, since the knowledge of additive flows can be a critical information on which to take decisions about events management and congestion mitigation.

Future Mobility Survey

The Future Mobility Survey (FMS) is a smartphone-based prompted-recall travel survey that aims to support data collection initiatives for transport-modeling purposes. This paper details the considerations that have gone into the surveys development, including the smartphone apps for iPhone and Android platforms, the online activity diary and user interface, and the background intelligence for processing collected data into activity locations and travel traces. The various trade-offs concerning user comprehension, resource use, and participant burden, including findings from usability tests and a pilot study, are discussed. Close attention should be paid to the simplicity of the user interaction, determinations of activity locations (such as the false positive and false negative trade-off in their automatic classification), and the clarity of interactions in the activity diary. The FMS system design and implementation provide pragmatic, useful insights into the development of similar platforms and approaches for travel and activity surveys.

The importance of retrieval in creative design analogies

Analogy is an important reasoning process in creative design. It enables the generation of new design artifacts using ideas from semantically distant domains. Candidate selection is a crucial process in the generation of creative analogies. Without a good set of candidate sources, the success of subsequent phases can be compromised. Two main types of selection have been identified: semantics-based retrieval and structure-based retrieval. This paper presents an empirical study on the importance of the analogy retrieval strategy in the domain of software design. We argue that both types of selection are important, but they play different roles in the process.

Using CBR for Automation of Software Design Patterns

Software design patterns are used in software engineering as a way to improve and maintain software systems. Patterns are abstract solutions to problem categories, and they describe why, how, and when can a pattern be applied. Their description is based on natural language, which makes the automation of design patterns a difficult task. In this paper we present an approach for automation of design pattern application. We focus on the selection of what pattern to apply, and where to apply it. We follow a Case-Based Reasoning approach, providing a complete framework for pattern application. In our approach cases describe situations for application of patterns.

Using Data From the Web to Predict Public Transport Arrivals Under Special Events Scenarios

The Internet has become the preferred resource to announce, search, and comment about social events such as concerts, sports games, parades, demonstrations, sales, or any other public event that potentially gathers a large group of people. These planned special events often carry a potential disruptive impact to the transportation system, because they correspond to nonhabitual behavior patterns that are hard to predict and plan for. Except for very large and mega events (e.g., Olympic games, football world cup), operators seldom apply special planning measures for two major reasons: The task of manually tracking which events are happening in large cities is labor-intensive; and, even with a list of events, their impact is hard to estimate, especially when more than one event happens simultaneously. In this article, we utilize the Internet as a resource for contextual information about special events and develop a model that predicts public transport arrivals in event areas. In order to demonstrate the feasibility of this solution for practitioners, we apply off-the-shelf techniques both for Internet data collection and for the prediction model development. We demonstrate the results with a case study from the city-state of Singapore using public transport tap-in/tap-out data and local event information obtained from the Internet.

Transportation activity analysis using smartphones

Transportation activity surveys investigate when, where and how people travel in urban areas to provide information necessary for urban transportation planning. In Singapore, the Land Transport Authority (LTA) carries out such a survey amongst households every four years. The survey is conducted through conventional questionnaires and travel diaries. However, the conventional surveys are problematic and error-prone. We are developing a smartphone-based transportation activity survey system to replace the traditional household surveys, which can potentially be used by LTA in future.

Place Enrichment by Mining the Web

In this paper, we address the assignment of semantics to places. The approach followed consists on leveraging from web online resources that are directly or indirectly related to places as well as from the integration with lexical and semantic frameworks such as Wordnet or Semantic Web ontologies. We argue for the wide applicability and validity of this approach to the area of Ubiquitous Computing, particularly for Context Awareness. We present our system, KUSCO, which searches for semantics associations to a given Point Of Interest (POI). Particular focus is provided to the experimentation and validation aspects.

Sequence labeling with multiple annotators

The increasingly popular use of Crowdsourcing as a resource to obtain labeled data has been contributing to the wide awareness of the machine learning community to the problem of supervised learning from multiple annotators. Several approaches have been proposed to deal with this issue, but they disregard sequence labeling problems. However, these are very common, for example, among the Natural Language Processing and Bioinformatics communities. In this paper, we present a probabilistic approach for sequence labeling using Conditional Random Fields (CRF) for situations where label sequences from multiple annotators are available but there is no actual ground truth. The approach uses the Expectation-Maximization algorithm to jointly learn the CRF model parameters, the reliability of the annotators and the estimated ground truth. When it comes to performance, the proposed method (CRF-MA) significantly outperforms typical approaches such as majority voting.

Enrichment of automatically generated texts using metaphor

Computer-generated texts are yet far from human-generated ones. Along with the limited use of vocabulary and syntactic structures they present, their lack of creativeness and abstraction is what points them as artificial. The use of metaphors and analogies is one of the creative tools used by humans that is difficult to reproduce in a computer. A human writer would not have difficulties to find conceptual relations between the domain he is writing about and his knowledge about other domains in the world, using this information in the text avoiding possible confusion. However, this task is not trivial for a computer. This paper presents an approach to the use of metaphors for referring to concepts in an automatically generated text. From a given mapping between the concepts of two domains we intend to generate metaphors for some concepts relating them with the target metaphoric domain and insert these metaphorical references in a text. We also study the ambiguity induced by metaphor and how to reduce it.

Competing risks mixture model for traffic incident duration prediction

Traffic incident duration is known to result from a combination of multiple factors, including covariates such as spatial and temporal characteristics, traffic conditions, and existence of secondary accidents but also the clearance method itself. In this paper, a competing risks mixture model is used to investigate the influence of clearance methods and various covariates on the duration of traffic incidents and predict traffic incident duration. The proposed mixture model considers the uncertainty in any of five clearance methods that occurred. The probability of the clearance method is specified in the mixture by using a multinomial logistic model. Three candidate distributions, namely, generalized gamma, Weibull, and log-logistic are tested to determine the most appropriate probability density function of the parametric survival analysis model. The unobserved heterogeneity is also incorporated into the mixture model in a way that allows parameters to vary across observations based on the three candidate distributions. The methods are illustrated with incident data from Singaporean expressways from January 2010 to December 2011. Regression analysis reveals that the probability of different clearance methods and the duration of traffic incidents are both significantly affected by various factors, such as traffic conditions and incident characteristics. Results show that the proposed mixture model is better than the traditional accelerated failure time model, and it predicts traffic incident duration with reasonable accuracy, as shown by the mean average percent error.