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An Intelligent Particle Swarm Optimization for Short-Term Traffic Flow Forecasting Using on-Road Sensor Systems.
- Source :
- IEEE Transactions on Industrial Electronics; Oct2013, Vol. 60 Issue 10, p4714-4725, 12p
- Publication Year :
- 2013
-
Abstract
- On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, 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: 1) 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 2) 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 (IPSO) algorithm is proposed to develop short-term traffic flow predictors by integrating the mechanisms of PSO, neural network and fuzzy inference system, 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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 02780046
- Volume :
- 60
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Industrial Electronics
- Publication Type :
- Academic Journal
- Accession number :
- 87693916
- Full Text :
- https://doi.org/10.1109/TIE.2012.2213556