Inferring Activities from Social Media Data

Social media produce an unprecedented amount of information that can be extracted and used in transportation research, with one of the most promising areas being the inference of individuals’ activities. Whereas most studies in the literature focus on the direct use of social media data, this study presents an efficient framework that follows a user-centric approach for the inference of users’ activities from social media data. The framework was applied to data from Twitter, combined with inferred data from Foursquare that contains information about the type of location visited. The users’ data were then classified with a density-based spatial classification algorithm that allows for the definition of commonly visited locations, and the individual-based data were augmented with the known activity definition from Foursquare. On the basis of the known activities and the Twitter text, a set of classification algorithms was applied for the inference of activities. The results are discussed according to the types of activities recognized and the classification performance. The classification results allow for a wide application of the framework in the exploration of the activity space of individuals

Exploiting the Knowledge of Dynamics, Correlations and Causalities in the Performance of Different Road Paths for Enhancing Urban Transport Management

The great abundance of multi-sensor traffic data (traditional traffic data sources - loops, cameras and radars accompanied or even replaced by the most recent - Bluetooth detectors, GPS enabled floating car data) although offering the chance to exploit Big Data advantages in traffic planning, management and monitoring, has also opened the debate on data cleaning, fusion and interpretation techniques. The current paper concentrates on floating taxi data in the case of a Greek city, Thessaloniki city, and proposes the use of advanced spatiotemporal dynamics identification techniques among urban road paths for gaining a deep understanding of complex relations among them. The visualizations deriving from the advanced time series analysis proposed (hereinafter referred also as knowledge graphs) facilitate the understanding of the relations and the potential future reactions/outcomes of urban traffic management and calming interventions, enhances communication potentials (useful and consumable by any target group) and therefore add on the acceptability and effectiveness of decision making. The paper concludes in the proposal of an abstract Decision Support System to forecast, predict or potentially preempt any negative outcomes that could come from not looking directly to long datasets. © 2019, Springer Nature Switzerland AG

An intelligent decision support system for managing natural and man-made disasters

Climate change-induced disasters are gaining in both severity and intensity. In view of their associated increased impacts on human assets and systems, public and private stakeholders have lately turned their attention to efficiently and effectively managing their effects, both prior as well as during an event’s occurrence. In an effort to address this issue, the DECIDE intelligent decision support systems (iDSS) has been developed, validated and tested under realistic conditions, aiming to assist public authorities in safe-proofing and protecting transport networks and operations in case of extreme weather events and related natural hazards. The iDSS is a web-based, GIS-enabled platform, able to suggest protection and management measures that optimally address occurring events, the affected modes of transport and transport networks and infrastructures. Optimal protection actions are derived from a database containing measures, strategies and policies that have been deployed at EU level in the last decades

Human factor methodological framework. Deliverable D2.5 of the SAFER-LC Project

This deliverable presents the revised version of the Human Factors (HF) methodological framework which has been developed in the SAFER-LC project as part of Work Package 2 (WP2). The objective of Task 2.2 of WP2 is to develop a Human Factors methodological framework to evaluate the effectiveness of selected safety measures in terms of making level crossings (LCs) more self-explaining and forgiving, and hence increasing their safety. The methodological framework includes a practical Human Factors Assessment Tool (HFAT) accompanied by an implementation guide which presents how the HFAT can be used in a real case study. The purpose of this deliverable is to summarise the theoretical background of the Human Factors methodological framework and the development process of the first version of the Human Factors Assessment Tool. In addition, this deliverable aims to explain how the HFAT was adjusted and updated in the second part of the project based on feedback obtained during the HFAT testing phase in four of the project’s pilot tests, covering 14 measures. The overall objectives and structure of this deliverable is described in Chapter 1. Chapter 2 reviews and summarises the most important theoretical aspects of the Human Factors methodological framework in the LC context. The framework was developed in line with the principles self-explaining and forgiving infrastructure and by considering LCs as socio-technical systems, where individual road users and the technical infrastructure interact. Models on human information processing and human behaviour in terms of errors and violations at LCs have also been considered. These theoretical aspects represent the theoretical backbone of the HFAT, and were presented in detail in deliverable D2.2 (Havârneanu et al., 2018). Further, Chapter 3 shows how the HFAT was applied in the SAFER-LC pilot tests and presents the feedback received from the pilot test leaders. The two-step evaluation of the HFAT by the pilot test leaders was a useful and productive exercise. It allowed collecting valuable inputs, suggestions and ideas on how to improve specific parts of the tool. While most of the evaluation feedback was taken into account during the HFAT revision process, not all received suggestions could be implemented within the SAFER-LC timeframe and resources. Other suggestions were subject to group discussion during the project meetings and were implemented only partially, following the collective decision. Chapter 4 explains the differences between the first version of the tool and the revised version. Based on the received feedback, changes concerned only the classification criteria (orange form) and the criteria to assess the behavioural safety effects (green forms). Major changes involved the revision of effect mechanism list in the classification criteria table and the regrouping of areas of psychological function in assessment of behavioural safety effects. Chapter 5 provides an overall discussion of the HFAT, its strengths and limitations, its current utility as a stand-alone methodology, and possible directions in its further development. For example, the HFAT could be used in the future as a checklist to support the consideration of human factors perspective in the evaluation of LC safety measures. The HFAT will also be included in the SAFERLC toolbox, accessible through a user-friendly interface

Service network enabler of multi-modal transport information & booking services: a Greek case study

Μη διαθέσιμη περίληψηNot available summarizationΠαρουσιάστηκε στο: 5th European Congress and Exhibition on Intelligent Transport Systems and Service