Modeling and Forecasting of Urban Logistics Demand Based on Improved Simulated Annealing Neural Network

Owing to the urban logistics system was an uncertain, nonlinear, dynamic and complicated system, and it was difficult to describe it by traditional methods, a modeling and forecasting method of urban logistics demand based on improved simulated annealing neural network (ISANN) is presented in this paper. First the simulated annealing algorithm with the best reserve mechanism is introduced and it is organic combined with Powell algorithm to form improved simulated annealing mixed optimize algorithm, instead of gradient falling algorithm of BP network to train network weight. It can get higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. The main parameters of affecting urban logistics demand are studied. With the ability of strong self-learning and faster convergence of ISANN, the modeling and forecasting method can truly forecast the logistics demand. The actual forecasting results show that this method is feasible and effective.