Traffic Flow Prediction Model Based on BDBO-TCN
Zhang Xijun and Chen Xuan
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
Keywords: Traffic Flow Prediction, TCN, DBO.
Abstract: In order to improve the accuracy of short-term traffic flow prediction and overcome the shortcomings of
single prediction model and the limitations of traditional depth learning based on experience to set
hyperparameters, a time convolution network (TCN) model based on improved dung beetle algorithm
(DBO) is proposed. In order to solve the problem of slow convergence of traditional TCN model, the dung
beetle algorithm is introduced, and the Bernoulli chaotic mapping algorithm is used to improve the initial
value, considering the randomness and diversity of the initialization of dung beetle algorithm, the traffic
flow prediction model based on BDBO-TCN is constructed. To verify the predictive effect of the
experiment, experiments were conducted on two different real data sets, the multi-step prediction is
compared with the TCN model optimized by DBO based on various chaotic mapping algorithms to further
verify the prediction performance of the model. This model is superior to other models.
1 INTRODUCTION
Traffic flow prediction is the basis of traffic control
and traffic guidance. At present, the common short-
term traffic flow prediction models are LSTM(Ma et
al., 2015), GRU (Wu et al., 2018), TCN(Lea et al.)
etc., in the field of traffic flow prediction, the
common optimization algorithms such as particle
swarm optimization (Kennedy and Eberhart) ,
genetic algorithm (Goldberg, 1989)etc., in this
paper, dung beetle Optimizer algorithm(Xue and
Shen, 2023) is used to solve the hyperparameters of
TCN model, and chaos mapping algorithm(Yu et al.,
2018)is introduced into intelligent optimization
algorithm to increase population diversity. Chaotic
mapping algorithms include Tent mapping(Zhao,
2012), Logistic mapping(Zhang and Liang, 2012)
Bernoulli mapping(Saito and Yamaguchi, 2016)and
so on. The hyperparameters of TCN are optimized
by DBO algorithm of dung beetle, and the traffic
flow prediction of TCN is made by the optimal
hyperparameters. The main contributions are as
follows:
(1) Aiming at the problem that the
hyperparameters of TCN are difficult to determine in
the traffic flow prediction, in this paper, TCN traffic
flow prediction model based on improved dung
beetle algorithm is designed by combining TCN
with improved dung beetle algorithm. The
simulation results show that the proposed model is
superior to other optimized TCN prediction models.
(2) Using the method of randomly generating the
initial population in traditional dung beetle
algorithm, the distribution of the initial population is
not uniform, which leads to the decrease of the
population diversity and the low quality of the
population, the problem of unbalanced global
exploration and local development capability affects
the convergence speed of the algorithm. In this
paper, chaotic maps are introduced to improve the
quality of initial population distribution in the search
space, thus strengthening the global search
capability.
2 MODEL
2.1 Dung Beetle Optimizer
Dung Beetle Optimizer (DBO) is a new heuristic
swarm intelligence optimization algorithm inspired
by the behavior of Dung beetles in nature. The dung
beetle algorithm selects the optimal solution by
modelling dung beetle, survival behavior , ball
rolling and dancing behavior, foraging behavior,
breeding behavior and stealing behavior.
The rolling behavior of dung beetles can be
divided into barrier mode and barrier-free mode. The
Xijun, Z. and Chen, X.