Pan European Networks discusses the role of intelligent transport within the smart cities of the future, and the developments already underway.
Transport simulation is being implemented in order to make mobility in cities smarter. Using mathematical modelling, computer software helps plan, design and operate transportation systems in a smarter, more efficient way. Furthermore, these mathematical models can be applied to the planning, design and operation stages of transport management, furthering the attainability of sustainable transport.
Performance programmes
Under the European Commission’s Horizon 2020 framework programme, work programme 2016-2017 – titled ‘Smart, green and integrated transport’ – ended in 2017 and detailed how intelligent transport systems (ITS) are integral to achieving seamless transport in both passenger and goods transport. Moreover, the system is also an essential part of guaranteeing that Mobility as a Service (MaaS) is realised, through connecting all the multimodal components of transport systems.
The work programme adds: ‘Seamless transport, provided through mobility as a service, should also allow European citizens to make better use of the existing infrastructure when travelling and could lead to a shift to more environmentally friendly modes of transport.’ However, in order to attain this, it is important that several issues impacting this reality are addressed, including the ‘development on a European-wide basis of a transport information system that provides real-time data, to enable cross-border trips throughout Europe … combining up-to-date information from each relevant transport mode source.’
As a result, the report predicts that through easily accessible, accurate transport data, significant improvements will be delivered to transport networks, in relation to performance, efficiency, visibility, resilience, and collaboration.
Mobility systems of the future
In efforts to continually assert its credentials as a smart city, the Czech Republic capital Prague is to develop an intelligent approach to secure sockets layer (SSL) control of transport under the Smart Prague initiative. From 2018 to 2020, the project will support the development of a system with the ability to manage traffic using aspects of artificial intelligence (AI). The system will make assessments of the traffic landscape across the city and offer a real-time signalling plan.
Providing up-to-date evaluations of traffic status and optimal signalling plans, the system will ensure smoother traffic throughout Prague. Smart Prague details in its plans that the system will: base traffic management on an accurate number of vehicles in the area; extensively use ‘the current telematics structure’; and create the ‘cost savings needed to create new pre-programmed signalling plans’.
However, in order to power such systems, accurate real-time data is needed. In 2016, Transport for London (TfL), UK, held the Data in Motion Hack Week. In this, participants were encouraged to tackle some of TfL’s most high-priority issues, including transport capacity limitations, incessant road congestion, and concerns surrounding air-quality.
Employees from Datatonic, a data analytics consultancy based in Europe, experimented with the role that machine learning (ML) could play in simulating real-time data visualisations for the purpose of transport. As a result, the team found that they were able to predict areas of congestion during commuting hours. Using the Google Cloud Platform (GCP) for storage and data processing, which used data collected over three months from 14,000 sensors across London, the open data tool highlighted its potential in helping to mobilise transport in smart cities.
This article will appear in Pan European Networks: Government 24, which will be published in January, 2018.
