A Short-Term Traffic Forecasting Model Based on Echo State Network Optimized by Improved Fruit Fly Optimization Algorithm

Abstract Short-term traffic forecasting is an important part of contemporary intelligent transportation systems. In this paper, based on echo state network optimized by improved fruit fly optimization algorithm (ESN-IFOA), a model is proposed to provide a five-minute forecast of traffic volume. In IFOA, parallel searching strategy and uniform crossover operator are applied to enhance searching range and communication between swarms. Simultaneously, the source allocation strategy is proposed to calculate the number of fruit flies generated by each swarm in the next iteration. The five main parameters of ESN are optimized by the proposed IFOA. Massive of prediction results and algorithm comparisons demonstrates that ESN-IFOA has very good forecasting ability for the five-minutes forecast of traffic volume.