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dc.contributor.authorChan, Kit Yan
dc.contributor.authorDillon, Tharam
dc.contributor.authorChang, Elizabeth
dc.identifier.citationChan, Kit Yan and Dillon, Tharam S. and Chang, Elizabeth. 2013. A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems. IEEE Transactions on Industrial Electronics. 60 (10): pp. 4714-4725.

On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, in order to reduce traffic congestion and improve vehicular mobility. This paper intends to tackle the impractical time-invariant assumptions which underlie the methods currently used to develop short-term traffic flow predictors: i) the characteristics of current data captured by on-road sensors are assumed to be time-invariant with respect to those of the historical data, which is used to developed short-term traffic flow predictors; and ii) the configuration of the on-road sensor systems is assumed to be time-invariant. In fact, both assumptions are impractical in the real world, as the current traffic flow characteristics can be very different from the historical ones, and also the on-road sensor systems are time-varying in nature due to damaged sensors or component wear. Therefore, misleading forecasting results are likely to be produced when short-term traffic flow predictors are designed using these two time-invariant assumptions. To tackle these time-invariant assumptions, an intelligent particle swarm optimization algorithm, namely IPSO, is proposed to develop short-term traffic flow predictors by integrating the mechanisms of particle swarm optimization, neural network and fuzzy inference system, in order to adapt to the time-varying traffic flow characteristics and the time-varying configurations of the on-road sensor systems. The proposed IPSO was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information can be captured on-line by the on-road sensor system. These results clearly demonstrate the effectiveness of using the proposed IPSO for real-time traffic flow forecasting based on traffic flow data captured by on-road sensor systems.

dc.publisherInstitute of Electrical and Electronic Engineers
dc.subjectparticle swarm optimization
dc.subjectsensor systems
dc.subjecttraffic contingency
dc.subjectfuzzy inference system
dc.subjecttraffic flow forecasting
dc.subjectneural networks
dc.subjecttime-varying systems
dc.subjectsensor data
dc.titleA intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Industrial Electronics

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curtin.accessStatusOpen access

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