Traffic on the digital road

By Dr. Karin Kraschl-Hirschmann

Head of System Engineering & Innovation
Siemens Mobility Austria GmbH

December, 2020

 

The digital twin of a road is a virtual replica of its physical assets, infrastructure, vehicles, and the interactions that take place. Digital twins are already used today to develop and test traffic engineering solutions, but we have only just started to realise their potential.

 

In order to design efficient traffic management solutions, it is necessary to model traffic networks of all sizes, from single intersections to regional networks. The digital twin of, for example, a motorway or a city enables the modelling of traffic flows as well as the simulation of the overall transport and infrastructure. A digital twin provides a secure and cost-effective way to experiment with different solutions, their optimisation parameters, and implementation options.

 

 

New traffic control possibilities

Digitisation brings both opportunities and challenges. New traffic control and communication technologies open up new possibilities for traffic management systems; or example, cities are now in a position not only to monitor traffic flows but to dynamically intervene.

 

Optimising traffic has a number of advantages, including reducing traffic-related noise and emissions as well as improving road safety. But how to best optimise all the different parameters that make up traffic management can only really be determined by using innovative software solutions for modelling the overall traffic systems, taking into the account both the infrastructure and travellers’ behaviour.

 

The newest member of the Siemens Mobility family, the software company Aimsun, is a global transport modelling company which builds digital twins of traffic networks and simulates the movement of people and goods. This enables the identification of weak spots in a network’s operation, and allows the testing of solutions to optimise flow. It’s not only localised intersections that can be tested – because Aimsun’s solution models more than just road traffic, multi-modal travel patterns are evaluated and potential solutions identified and trialled.

 

Transport modellers talk of the different levels of granularity in their craft; macro-, meso- and micro-simulation, looking at a whole region, a small collection of roads or a single intersection. These must be more and more interlinked and considered to allow us to make accurate statements about an overall traffic network.

 

Siemens Mobility is already successfully implementing real-time simulations of traffic flow in traffic control centres, in order to anticipate imminent traffic disruption, and to determine the best short-term mitigation strategy to minimise jams. Centres in the USA, United Kingdom, France and Australia are already using the Siemens Mobility and Aimsun solutions to make their cities more intelligent. These centres use the simulation application which constantly monitors and processes real-time data, simulating vehicle movements on any size of network, from an individual artery to a large metropolitan area. The system works so quickly that traffic controllers can virtually test a variety of scenarios and know which is the most effective to implement in the real world. This solution is also being used along with air quality monitoring to use traffic management to reduce congestion in polluted areas, which means fewer emissions.

 

 

Operational improvements for urban transport

In Austria, for example, the area around the capital, Vienna, and the conurbations surrounding the individual state capitals are all now being treated as individual traffic regions. Using simulation within traffic planning at this macroscopic level offers a strategic advantage in urban development. A multimodal transport solution is vital in growing areas to make commuting tolerable and urban living spaces attractive places to be. Multimodal transport solutions need to be designed to give people convenient, safe and quick journeys.

 

Modelling traffic management solutions enable the optimisation of public transport prioritisation, and allow us to understand the effectiveness of traffic signal timing changes or vehicle reduction initiatives such as car pooling. Growing cities and urban regions must also understand the environmental impact of high traffic volumes. Traffic management software has dealt with roadwork sites and safety analysis for years, but the latest versions detail the environmental impact too.

 

Simulations not only deal with vehicles and public transport, but walking and cycling too. This knowledge is vital in understanding the effects of overall traffic control, and with continuously updated data, the time taken to calibrate even the largest models is now shortened.

 

 

Outlook

The latest software means it is straightforward to start a traffic model from scratch. The model takes geographic and socio-economic data into account, meaning a large number of influencing factors can be considered within transport planning.

 

The increasing complexity and dynamics of traffic projects require fast and flexible software solutions. Transport planners and infrastructure operators need simulation tools that are fast. Future traffic networks will not only need to be planned and simulated, but also geared to the needs of connected and autonomous vehicles, and their communication with road users. Aimsun’s transport modelling solution is already taking that into account and gives it a strategic advantage over conventional modelling software.

 

Particularly at the combined meso and micro level, simulations can help plan pedestrian flows and dynamically design traffic allocation. This has a significant advantage in considering the effects of local control and infrastructure measures in the area. Already known source-target relationships are used here to define the demand state. However, continuously updated data can also be integrated for efficient modeling of traffic demands. This method is particularly suitable for creating a larger model and shortening the calibration phase.

 

Link to article – Page 38.

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