Road traffic agencies need to make informed decisions in order to define which road sections have the greatest risk of traffic-related impacts. In this context, the implementation of ATMS may not only improve network efficiency, but also minimize other traffic externalities. However, this implementation should consider both the level of contribution of each externality and its geographical scale; otherwise isolated measures may just migrate the problem elsewhere, there might be conflicts in minimizing different externalities. This can lead to undesirable side-effects and decrease the acceptability of ATMS. Also, despite the importance of the socio-economic impacts of transport, these large single indicators are still not easily perceptible by decision makers/citizens. Thus, in a context of increasing data availability, an important research topic is to unravel the complex relationships of these dynamic externalities, in order to efficiently manage the road network. Data integration is often a difficult problem, since data is usually structured in ways that do not easily match.
This project will be a step forward compared with the state of art in different domains: from the concepts behind the impact assessment models to the optimization algorithms and the attention given to the user’s behaviour. Thus, the main research contributions of this project for the field will be: 1) impute Network Traffic Properties from High Resolution FCD and 2) inclusion of safety/energy/environmental parameters in the assessment of the road network performance, adjusted to local contexts of vulnerability.
A key feature of the @CRUiSE project is the recognition of the heterogeneity of the effects and their vibrant variation over time and space. Thus, it is intended that the decision maker can act in the transport system having a through understanding about the different impacts of the transport system and the main vulnerabilities associated with each link of the network. The main objective will not be to minimize itself a particular parameter, but rather to provide integrated solutions and holistic approaches capable of responding to the questions: What (to minimize)? When? Where? And how? This will be done by accurately assigning dynamic link-based indices of vulnerability and by providing an instrument for evaluating the cost and environmentally effectiveness of different ATMS and suggesting action plans to “where” and “when” they are most necessary.
Given the potential lack of accuracy of macroscopic tools and the difficulty in managing traffic in real-time combined with the use of microscopic models, link-based functional relationships between speed microscale patterns data of individual vehicles and real time macro scale traffic measurements will be developed in order to facilitate integration into optimization algorithms. Links with high intra-variability of speed, road grade and acceleration profiles will be analysed in detail.
The availability of geo-referenced data is increasing quickly, either from nomadic devices as well as from social media, and monitoring sensors networks. One of the challenges of this project is to combine and to increase the potential of each source of information by using innovative data streaming and data mining techniques. One of the main goals of @CRUiSE is to develop a common platform to gather real time information of different sources (see Figure 2) and use this platform to support optimization toolbox at centralized (planning level) and for providing (decentralized) information so that each network user can take advantage of safer roads, better environment and adopt more sustainable choices. Moreover, achieving an accurate prediction of road users’ acceptance to new eco-friendly suggestions is a very challenging task.