Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA

Next Generation intelligent transport systems: a multidimensional framework for eCall implementation

The present use of Intelligent Transport Systems (ITS) can be defined as a hybrid between information and communication technologies to improve different aspects of mobility and transport. The potential value of the next generation ITS can be assessed as an integrated array of services satisfying customer preferences, optimising policy objectives and generating business revenues. Based on industry interviews, the analysis of a traffic information service and an 'emergency call' service permitted the multidimensional appreciation of deployment scenarios of these next generation Intelligent Transport Systems. The implementation of an on-board emergency call (eCall) is an ITS service which has already been deployed in different countries. Several private and public initiatives have already resulted into preliminary and purely private eCall services, mainly proprietary to the car industry, each with different underlying revenue and cost models. On the European level, a Memorandum of Understanding (MoU) instigated on the national enactment to implement a standardised eCall system. The research question involved in this paper is whether the specified ecosystem for the Belgian case confirms that all stakeholders have a particular interest in the effectuation of eCall. The findings are the result of a case study performed within the Flemish IBBT research project NextGenITS. --

Towards an Intelligent Workflow Designer based on the Reuse of Workflow Patterns

In order to perform process-aware information systems we need sophisticated methods and concepts for designing and modeling processes. Recently, research on workflow patterns has emerged in order to increase the reuse of recurring workflow structures. However, current workflow modeling tools do not provide functionalities that enable users to define, query, and reuse workflow patterns properly. In this paper we gather a suite for both process modeling and normalization based on workflow patterns reuse. This suite must be used in the extension of some workflow design tool. The suite comprises components for the design of processes from both legacy systems and process modeling