Vision of Smart Network Operations

As towns and cities expand, pressure to rationalise competing priorities for road-space will grow. It will be vital to harness ITS for a variety of transport management measures. In metropolitan areas integrated public transport operations that interface with traffic management systems will become increasingly important. They will provide reliable public transport services as well as reducing the traffic load and environmental burden. The needs of cyclists, pedestrians and other vulnerable road users have also to be considered and integrated. See Safety of Vulnerable Road Users and Vulnerable Road Users

In Road Network Operations ITS helps to improve decision making in real time by transport network controllers and other users – thereby improving the operation of the entire transport system. In future, self-recognition systems will have a part to play in traffic management, travel substitution and “smart” access controls, taking account of the individual characteristics of the vehicle, the load and the journey purpose.

On our highways, better logic, connectivity and knowledge of the spatial requirements is needed for the dynamic allocation of traffic priorities in time and space. This is the case also for journey planning, goods distribution and freight logistics and for demand-responsive collective transport modes. The automated highway or a “smart” intersection will also require a kinaesthetic capability.

Traffic management tools aim to optimise the operation of transport networks in time and space. Although there are clear benefits from “smart” traffic management, it also introduces the risk of gridlock and a “superjam” in the event of system failure, making things much worse. Future systems need to be robust and intelligent enough to deal with worst case scenarios. This is particularly so if the vehicles themselves are automated.

There are other reasons for incorporating more intelligence into integrated road transport and mobility management systems.

First, today’s traffic management and control systems show limitations when facing critical traffic conditions and widespread congestion. This is an almost permanent problem in many metropolitan and urban areas and is often caused by a locally conceived analysis of traffic behavior – when more strategic, high-level control methods, such as demand management, are required. See Demand Management

Secondly, the role of human operators in traffic management centres is still crucial in day-by-day operations. No matter how sophisticated and advanced the traffic control technology is, the “person in the loop” paradigm still prevails today in most centralised traffic control systems. See Human Performance and Traffic Control Centres

Thirdly, the introduction and progressive integration of extended monitoring and management facilities in the new generation of ITS architectures (for example improved road condition monitoring, traffic monitoring, incident detection, collective and individual route guidance systems) has prompted demand for increased, on-line operator support tools. These are to help cope with the complexity of the data to be managed and the resulting, integrated traffic management schemes. See Network Monitoring

Intelligent traffic management systems need to be capable of analysing traffic behaviour and its evolution in a similar way to an expert traffic controller. These systems – for example, self-learning autonomic management systems may replace human operators in the future – and will certainly act as intelligent assistants that cooperate in defining and applying traffic control decisions. Several techniques are being applied in this context including evolutionary algorithms, knowledge-based systems, neural networks and multi-agent systems. See Traffic Management Strategies and System Monitoring

Future dynamic traffic management systems will be required to support network-wide, pro-active traffic management and to replace locally-oriented, reactive traffic management that is common today. Improved intelligence is also needed to deal with the huge amount of real time traffic data generated from detectors and other sources (for example probe vehicles that are equipped to report their position and traffic conditions in real time). The data needs to be interpreted and analysed by the operators to support the decision making process.