Proactive improvement of transport systems quality and efficiency


Complex urban transportation networks already offer a multitude of modalities and options, including public means such as trains, metros, buses, taxis, shared bicycles, shared cars and electric vehicles. New types of modalities are also expected to emerge within a diverse ecosystem of public, private and non-profit entities. Ideally, an integrated transportation network allows citizens to move easily from point “A” to point “B” regardless of mode or service provider, while sustaining overall user well-being and keeping greenhouse emissions to a minimum. Nonetheless, current systems are fragmented, and attaining a high quality of service while providing a safe, dependable, convenient and comfortable experience for all individuals — while also taking into account ITS optimisations — is not an easy task.

OPTIMUM platform instantiation for Pilot Study 1 will be deployed in three major EU cities, each one with different characteristics in terms of data sources and transportation system particularities. The main aim of the study is to improve the quality and efficiency of multimodal trips, and ITS as a whole, by supporting proactive decisions driven by transportation network and crowdsourcing information data. A multitude of information can be retrieved from the mobility data of people using multimodal and interoperable transport systems. Recent data collection technologies and analysis methods, in combination with modern transport telematics systems, allow for the identification of transport modes and the study of user habits. This will provide the basis for transport information systems and models, which will in turn increase the efficiency of an entire transport system.

The main aim of the three urban pilot studies is to proactively facilitate decision making for efficient integration of transport modes.This will be achieved by implementing a smart multimodal transit concept, which will lead to improved quality, accessibility and utilisation of interconnected transport systems. Thus a complex model of the current traffic conditions, and a short-term prediction of these conditions, will be realised on top of advanced real-time predictive analytics and a multitude of transport information.

From a system-wide viewpoint, it is clear that transportation network capacity is not being used optimally if all travellers within the same pair of origin and destination coordinates travel along the same route. The objective of system-optimal routing is to balance traffic flow volumes within a given network in order to achieve system-wide equilibrium. Since changes in traffic conditions and relevant incidents (such as accidents) can occur unexpectedly at any time, it is necessary to proactively inform travellers and to suggest alternative routes. The integration of various real-time traffic data sources will provide the required information to realise traffic-state-aware routing that can guide travellers along routes to their destinations.