Wednesday, 27 November 2013
An Intelligent Particle Swarm Optimization for Short-Term Traffic Flow Forecasting Using on-Road Sensor Systems
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.