Noelia Caceres

Supervised Land Use Inference from Mobility Patterns

This paper addresses the relationship between land use and mobility patterns. Since each particular zone directly feeds the global mobility once acting as origin of trips and others as destination, both roles are simultaneously used for predicting land uses. Specifically this investigation uses mobility data derived from mobile phones, a technology that emerges as a useful, quick data source on people’s daily mobility, collected during two weeks over the urban area of Malaga (Spain). This allows exploring the relevance of integrating weekday-weekend trip information to better determine the category of land use. First, this work classifies patterns on trips originated and terminated in each zone into groups by means of a clustering approach. Based on identifiable relationships between activity and times when travel peaks appear, a preliminary categorization of uses is provided. Then, both grouping results are used as input variables in a K-nearest neighbors (KNN) classification model to determine the exact land use. The KNN method assumes that the category of an object must be similar to the category of the closest neighbors. After training the models, the findings reveal that this approach provides a precise land use categorization, yielding the best accuracy results for the major categories of land uses in the studied area. Moreover, as a result, the weekend data certainly contributes to finding more precise land uses as those obtained by just weekday data. In particular, the percentage of correctly predicted categories using both weekday and weekend is around 80%, while just weekday data reach 67%. The comparison with actual land uses also demonstrates that this approach is able to provide useful information, identifying zones with a specific clear dominant use (residential, industrial, and commercial), as well as multiactivity zones (mixed). This fact is especially useful in the context of urban environments where multiple activities coexist.

Automatic Prediction of Maintenance Intervention Types in Roads using Machine Learning and Historical Records

A methodology to support and automate the prediction of maintenance intervention alerts in transport linear-asset infrastructures can greatly aid maintenance planning and management. This paper proposes a methodology combining the current and predicted conditions of the assets, and unit components of the infrastructure, with operational and historical maintenance data, to derive information about maintenance interventions needed to avoid later severe degradation. By means of data analytics and machine learning techniques, the proposed methodology generates a prioritized listing, ranked on severity levels, corresponding to the pre-alerts and alerts generated for all assets of the transport infrastructure. The methodology is applied and tested in a real case consisting of a road network with different section classes. The analysis of the results shows that the algorithms and tools developed have good predicting capabilities.

Historical maintenance relevant information road-map for a self-learning maintenance prediction procedural approach

A large percentage of transport infrastructures are composed of linear assets, such as roads and rail tracks. The large social and economic relevance of these constructions force the stakeholders to ensure a prolonged health/durability. Even though, inevitable malfunctioning, breaking down, and out-of-service periods arise randomly during the life cycle of the infrastructure. Predictive maintenance techniques tend to diminish the appearance of unpredicted failures and the execution of needed corrective interventions, envisaging the adequate interventions to be conducted before failures show up. This communication presents: i) A procedural approach, to be conducted, in order to collect the relevant information regarding the evolving state condition of the assets involved in all maintenance interventions; this reported and stored information constitutes a rich historical data base to train Machine Learning algorithms in order to generate reliable predictions of the interventions to be carried out in further time scenarios. ii) A schematic flow chart of the automatic learning procedure. iii) Self-learning rules from automatic learning from false positive/negatives. The description, testing, automatic learning approach and the outcomes of a pilot case are presented; finally some conclusions are outlined regarding the methodology proposed for improving the self-learning predictive capability.

Adjustment of Origin-Destination Matrices Based on Traffic Counts and Bootstrapping Confidence Intervals

Mobility studies require, as a preliminary step, that a survey of a sample of users of the transportation system be conducted. The statistical reliability of the data determines the goodness of the results and the conclusions that can be inferred from the analyses and models generated. Because of the high costs of collection, data are partially reused in either a disaggregated or an aggregated manner. In the first case, statistical reliability is not always guaranteed; this condition affects the results that will be derived from projections and estimates of future hypothetical scenarios. A methodology is presented: it is based on bootstrapping techniques and is used for robust statistical estimation of mobility matrices. Confidence intervals of travel between origin–destination pairs defined by each matrix cell derived from a survey are generated. The result is applicable to defining the dimensions of certainty for matrix cells and subsequent adjustment by techniques based on aggregate data (e.g., traffic counts, cordon line matrices, paths). A statistically reliable data mobility study conducted in Spain at a regional level is used. Results derived from disaggregating data at an interprovincial level are presented, along with an application to the posterior mobility matrix adjustment based on traffic count data. The study results demonstrate the potential of the methodology developed and the usefulness of the conclusions.