Outdoor air pollution causes approximately 1.3 million deaths every year worldwide and approximately 310,000 premature deaths in Europe as revealed by the Department of Business, Innovation and Skills. Given road traffic and more specifically congestion is a major source of pollution, there is an urgent need to apply network management aimed at delivering air quality as well as carbon targets. Intelligent Transport Systems can be used for traffic management application and as a by-product of their control produce huge volumes of data that are useful to support traffic operators in their decision-making. Due to the increasing amount of available ITS, traffic operators are faced with an increasing amount of information overload. More sophistication is needed to achieve multiple policy objectives and across modes of transport. Autonomic computing is a software environment with the ability of self-management and dynamic adaption in relation to business policies and objectives alternatively defined as automation of system adaptation. Autonomic computing is a technology that comes into play where there is need to minimise cost and maximise efficiency through management of resources and applications. Following an overview of the policy context, this paper presents an autonomic system and demonstrates self- optimization of lane choice on a UK motorway and a Dutch trunk road through a case study. By creating an autonomic capability which can reason within the data analysis layer of a data platform, traffic control networks can begin to manage more effectively routine control decisions from day to day and as a next step against multiple objectives and thus free up engineers time to devote to the more complex tasks.