An intelligent model for road traffic management in the motorway network around barcelona

This paper presents an advanced knowledge-based environment to develop real time traffic management applications called TRYS. The building process supported by the architecture is guided by the progressive definition of knowledge features from the knowledge level to the symbolic level. Firstly, the problem is presented showing the shortcomings perceived in the state of the art of traffic management systems. Secondly, a description of the KSM tool, aimed at supporting the organization of structured models at the knowledge level is commented. Thirdly, the generic model, intended to deal with traffic management, is described using the KSM format. Finally, the domain model of the application developed for Barcelona is described. Document type: Part of book or chapter of boo

Adaptive performance optimization for large-scale traffic control systems

In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort and time to devote to the successful deployment and operation of traffic control systems due to the lack of an automated well-established systematic approach. We investigate the adaptive fine-tuning algorithm for determining the set of design parameters of two distinct mutually interacting modules of the traffic-responsive urban control (TUC) strategy, i.e., split and cycle, for the large-scale urban road network of the city of Chania, Greece. Simulation results are presented, demonstrating that the network performance in terms of the daily mean speed, which is attained by the proposed adaptive optimization methodology, is significantly better than the original TUC system in the case in which the aforementioned design parameters are manually fine-tuned to virtual perfection by the system operators

Assessment of incident management strategies using aimsun

PRIME (Prediction of Congestion and Incidents in Real Time, for Intelligent Incident Management and Emergency Traffic Management) is a project of the 5ht Framework Program of the European Union which objectives are to develop: methods for estimating incident probability in real-time, which can activate traffic management strategies to reduce the likelihood of incidents, improved systems and algorithms for detecting incidents, an improved integration of incident verification to increase the reliability of incident management, and the integration of aspects of motorway and urban-network incident management strategies to increase the effectiveness of incident and traffic management strategies in urban / interurban areas. This paper deals with the use of microscopic simulation to assess the potential impacts of the incident management strategies. A methodological scheme on how to use simulation to achieve these objectives is presented and the experimental plan for the test site in Barcelona is described and the preliminary testing results are presented.Peer ReviewedPostprint (author’s final draft

Performance evaluation of a hybrid sensor and vehicular network to improve road safety

In the last years, wireless networks have become a widely spread type of communication technology and also a challenging scientific area for new fields of research. Many contributions in ad hoc networks, such as WSNs (Wireless Sensor Networks) and VANETs (Vehicular Ad Hoc Networks), have been proposed. Nowadays, the huge amount of cars in transit has raised a big interest in vehicular communication technologies. A new type of network has been developed, named HSVN (Hybrid Sensor and Vehicular Network) in which WSNs and VANETs cooperate with the aim of improving road safety. Recent projects, such as CVIS [1] and COMeSafety [2], are focused on improving the road driving. This type of approaches will warn the driver and the co-pilot of any event occurred in the road ahead, such as traffic jam, accidents, bad weather, etc. This way, the number of traffic accidents may decrease and many lives might be saved. Besides, a better selection of non-congested roads will help to reduce pollution. In addition, other attractive services, such as downloading of multimedia services or Internet browsing, would be easily available through infrastructure along the roadside. Transportation in motorways will be easier, safer and more comfortable for passengers. In this paper a HSVN platform is presented, also a communications protocol between VANETs and WSNs is described and evaluated using the NCTUns [3] simulator.Postprint (published version

Motorway tidal flow lane control

The expansion of road infrastructure, in spite of increasing congestion levels, faces severe restrictions from all sorts: economical, environmental, social, or technical. An efficient and, usually, less expensive alternative to improve mobility and the use of available infrastructure is the adoption of traffic management. A particular case of interest occurs when inbound and outbound traffic on a given facility is unbalanced throughout the day. This scenario may benefit of a lane management strategy called tidal flow (or reversible) lane control, in which case the direction of one or more contraflow buffer lanes is reversed according to the needs of each direction. This paper proposes a simple and practical real-time strategy for efficient motorway tidal flow lane control. A state-feedback switching policy based on the triangular fundamental diagram, that requires only aggregated measurements of density, is adopted. A theoretical analysis based on the kinematic wave theory shows that the strategy provides a Pareto-optimal solution. Microsimulations using empirical data from the A38(M) Aston Expressway in Birmingham, UK, are used to demonstrate the operation of the proposed strategy. The robustness of the switching policy to parameter variations is demonstrated by parametric sensitivity analysis. Simulation results confirm an increase of motorway throughput and a smooth operation for the simulated scenarios

Computational Intelligence in Highway Management: A Review

Highway management systems are used to improve safety and driving comfort on highways by using control strategies and providing information and warnings to drivers. They use several strategies starting from speed and lane management, through incident detection and warning systems, ramp metering, weather information up to, for example, informing drivers about alternative roads. This paper provides a review of the existing approaches to highway management systems, particularly speed harmonization and ramp metering. It is focused only on modern and advanced approaches, such as soft computing, multi-agent methods and their interconnection. Its objective is to provide guidance in the wide field of highway management and to point out the most relevant recent activities which demonstrate that development in the field of highway management is still important and that the existing research exhibits potential for further enhancement

Improving adaptation and interpretability of a short-term traffic forecasting system

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyse the situation in a commercial real-time prediction system with its current problems and limitations. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. We test this predictive system in different real-work scenarios, and evaluate its performance integrating a multi-task learning paradigm for the sake of the traffic prediction task.Peer ReviewedPostprint (published version

Fuzzy-Based Variable Speed Limits System Under Connected Vehicle Environment: A Simulation-Based Case Study in the City of Naples

This paper handles the problem of controlling speed limits on freeways in a connected traffic environment to reduce traffic congestion and improve both the operational and environmental performance of the road network. In order to achieve this objective, we present a Variable Speed Limit (VSL) system that utilizes fuzzy logic, which adjusts the speed limits that connected vehicles must comply with by leveraging traffic data such as vehicle flow, occupancy, and speed obtained from loop detectors installed along the road. To evaluate the effectiveness of the proposed Fuzzy-based VSL system and its potential benefits compared to the conventional rule-based VSL system in terms of traffic congestion and environmental impact, we conducted a simulation analysis using the microscopic traffic simulator, VISSIM. Specifically, three simulation scenarios are taken into account: i) no VSL, where the VSL system is not enabled; ii) Rule-based VSL system, where a typical a decision tree-based system is considered; iii) Fuzzy-based VSL system, where the herein proposed approach is appraised. The results demonstrate that the proposed approach enhances road efficiency by decreasing speed variation, increasing average speed and vehicle volume, and reducing fuel consumption

2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor