Abstract

Intelligent Transportation Systems emerged to meet the increasing demand for more efficient, reliable and safer transportation systems. These systems combine electronic, communication and information technologies with traffic engineering to respond to the former challenges. The benefits of Intelligent Transportation Systems have been extensively proved in many different facets of transport and Soft Computing has played a major role in achieving these successful results. This book chapter aims at gathering and discussing some of the most relevant and recent advances of the application of Soft Computing in four important areas of Intelligent Transportation Systems as autonomous driving, traffic state prediction, vehicle route planning and vehicular ad hoc networks. Document type: Part of book or chapter of book

Abstract

The Internet of Things (IoT) is one of the key ingredients for the realization of Smart Cities. IoT devices are essential components of the Smart Cities infrastructure, as they can provide information collected from the environment through sensors or allow other systems to reach out and act on the world through actuators. IoT data collection, however, is not limited to sensors and machines, but to data from social networks, and the web. Social networks have a huge impact on the amount of data being produced daily, becoming an increasingly central and important data source. The exploitation of these data sources, combined with the growing popularity of mobile devices, can lead to the development of better solutions to improve people’s quality of life. This paper discusses how to take advantage of the benefits of mobile devices and the vast range of information sources and services, such as traffic conditions, and narrow, closed or conditioned roads data. The proposed system uses a real-time collection, organization, and transmission of traffic and road conditions data to provide efficient and accurate information to drivers. With the purpose of supporting and improving traffic data collection and distribution, an Android application was developed to collect information about extraordinary events that take place in a city, providing warnings and alternative routes to drivers and helping them to improve their time management. The developed solution also exploits the existing gaps in other applications, implementing a more specific solution for the Madeira Island traffic condition problems. info:eu-repo/semantics/acceptedVersion Document type: Part of book or chapter of book

Abstract

We propose a novel technique for answering routing queries in public transportation networks that allows unrestricted walking. We consider several types of queries: earliest arrival time, Pareto-optimal journeys regarding arrival time, number of transfers and walking time, and profile, i.e. finding all Pareto-optimal journeys regarding travel time and arrival time in a given time interval. Our techniques uses hub labeling to represent unlimited foot transfers and can be adapted to both classical algorithms RAPTOR and CSA. We obtain significant speedup compared to the state-of-the-art approach based on contraction hierarchies. A research report version is deposited on HAL with number hal-02161283.

Abstract

International audience; We study the problem of robust routing in urban public transportation networks. In order to propose solutions that are robust for typical delays, we assume that we have past observations of real traffic situations available. In particular, we assume that we have "daily records" containing the observed travel times in the whole network for a few past days. We introduce a new concept to express a solution that is feasible in any record of a given public transportation network. We adapt the method of Buhmann et al. [Buhmann et al., ITCS 2013] for optimization under uncertainty, and develop algorithms that allow its application for finding a robust journey from a given source to a given destination. The performance of the algorithms and the quality of the predicted journey are evaluated in a preliminary experimental study. We furthermore introduce a measure of reliability of a given journey, and develop algorithms for its computation. The robust routing concepts presented in this work are suited specially for public transportation networks of large cities that lack clear hierarchical structure and contain services that run with high frequencies.

Abstract

International audience; Transfer pattern routing is a state-of-the-art speed-up technique for finding optimal paths which minimize multiple cost criteria in public transportation networks. It precomputes sequences of transfer stations along optimal paths. At query time, the optimal paths are searched among the stored transfer patterns, which allows for very fast response times even on very large networks. On the other hand, even a minor change to the timetables may affect many optimal paths, so that, in principle, a new computation of all optimal transfer patterns becomes necessary. In this paper, we examine the robustness of transfer pattern routing towards delay, which is the most common source of such updates. The intuition is that the deviating paths caused by typical updates are already covered by original transfer patterns. We perform experiments which show that the transfer patterns are remarkably robust even to large and many delays, which underlines the applicability and reliability of transfer pattern routing in realistic routing applications.

Abstract

TIMON project (www.timon-project.eu) aims at providing Real-Time (RT) information and services through a cooperative open web-based platform and a mobile application to different transport users – drivers, Vulnerable Road Users (VRU), and businesses."br" Problems such as the traffic congestion and accidents cause cities to be the principal source of pollutant emissions. TIMON provides a solution by creating a cooperative ecosystem which connects people, vehicles, infrastructure and business, with the aim of improving safety, sustainability, flexibility and efficiency of the road transport."br" The system collects and stores RT data from infrastructure, cars, VRUs and open data; it processes the information using several innovative technologies, such as open data management, hybrid vehicle communications, artificial intelligence and cooperative positioning; and delivers RT services to drivers, VRUs and business particularly information on nearby accidents, traffic jams or air pollution, a multi-modal commuter and dynamic route planner.

Abstract

TIMON is an European research project under the Horizon 2020 programme. The main objective of"br" this project is to provide real-time services through a web based platform and a mobile APP for drivers,"br" Vulnerable Road Users (VRUs) and businesses. These services will contribute to increasing drivers"br" and VRUs assistance and safety. To provide these services, one of the core technologies developed"br" inside TIMON will be the design and development of Artificial Intelligence (AI) techniques for traffic"br" prediction and route planning. The DeustoTech-Mobility research group is in charge of this part of the"br" project. The objective of this technical paper is to describe the approach followed in TIMON to"br" develop traffic congestion prediction and route planning services based on AI techniques and the"br" progress done so far. Additionally, the deployment and the result obtained in the first test done is also"br" detailed in this study.