Exploring new traffic prediction models to build an intelligent transport system for Smart Cities

Mehta, Vatsal, Mapp, Glenford E. ORCID: https://orcid.org/0000-0002-0539-5852 and Gandhi, Vaibhav ORCID: https://orcid.org/0000-0003-1121-7419 (2022) Exploring new traffic prediction models to build an intelligent transport system for Smart Cities. In: IEEE/IFIP Network Operations and Management Symposium, 25-29 Apr 2022, Hungary. ISBN 978166540601. [Conference or Workshop Item]

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Abstract

The demand for passenger transportation, especially by road, has been increasing due to globalisation, resulting in further delays and traffic congestion. This paper addresses issues to minimise delays and traffic congestion using source and destination information in an urban environment. A journey is defined as the traversal of several road links and junctions. The delays on the links are analysed using M/M/K Markov technique.

The delays at a junction are examined using the Zero-Server Markov Chain technique. In order to study multiple junctions, this technique is combined with the Jackson Network model. This combined approach is then used to evaluate the delays at multiple junctions using the Middlesex University VANET Testbed (a real-time vehicular network in London, UK). Initial results indicate that there is more congestion at traffic junctions and hence the Markov Chain analysis will allow better traffic algorithms to reduce congestion at traffic

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