Traffic Prediction Based on Improved Neural Network

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Journal of Convergence Inf
ormation Technology

Volume 5, Number 9. November 2010

Traffic Prediction Based on Improved Neural Network

Cui Jian-ming

School of Information Engineering, Chang’an University, Xi’an 710064, China.
Shaanxi Road Traffic Detection and Equipment Engineering Research Center, Xi’an
doi:10.4156/jcit.vol5. issue9.8

Artificial neural networks and genetic algorithms derived from the corresponding simulation of
biology, anatomy. The paper analyzes the advantages and the disadvantages of the artificial neural
networks and genetic algorithms. The artificial neural networks and genetic algorithms to be combine
in the prediction model. This method is used to predict traffic volume in a road, the accuracy of
forecasting results improved significantly. Therefore, this simulation method in traffic prediction have
a good prospect.

Keywords: Improved Neural Networks, Traffic, Prediction Models

1. Introduction

Traffic prediction is an important item in the transport planning[1]. It is also an indispensable
component technology in traffic guidance system. The accuracy of its forecasting results will directly
affect the reasonableness of the transport planning, and the correctness of the traffic guidance. Choice
of prediction method directly related to the objectives of the forecast results and the accuracy of
forecast. Common forecasting methods are often heavy workload, but accuracy is not high.
In recent years, artificial neural networks and genetic algorithms are applied gradually. Neural
network model is the network level structure in some rules by a multi-neuron connections. It has many
advantages: a strong adaptive capacity and ability to learn, nonlinear mapping capabilities, robustness
and fault tolerance. But its essence is gradient search method, this easily result in a conspicuous
shortcomings of local minimum.
Genetic algorithm is adaptive random Global Optimization Algorithm. This algorithm will be every
possible problems result from "chromosome" to describe. Through the chromosome replication,
exchange and variations to find the optimal calculation. Its operation is the target of a feasible solution
for a number of groups, with the nature of the characteristics of parallel computing, so its search speed,
but it also shortcomings: First, some issues do not know in advance the value scope of variables;
Second, rely entirely on random probability for optimal operation, it is difficult to obtain optimal
solutions, a greater impact on human factors. In view of the above their existing problems, In this paper,
genetic algorithms and neural network algorithm organically integrate, and make full use of their
respective advantages of genetic neural network model constructed using genetic algorithm to train the
neural network weights, Overcome the neural network of local Poles problems. Finally, this model is
applied to traffic volume forecast for the test results. The results showed that this method is feasible
traffic forecasting methods.

2. Basic principles of Genetic Algorithm

For the average parameter optimization, can be expressed as:
Objective function:


Genetic Algorithm[1] basic idea is: First, the optimization problem for a group of basic feasible
for a group of binary encoding of the string, (Each string containing multiple
Traffic Prediction Based on Improved Neural Network
Cui Jian-ming

sub-string, each sub-string in a one or a combination of several known as a gene, also known as
chromosome), and then again some of these chromosomes parameter optimization and operations .
Then, in order to achieve parameter optimization to operating these chromosomes.
Basic processes in genetic algorithm are including replication, exchange and variation, that
implementation item process of optimization by imitation use the three methods.

3. Basic principles of neural network

The neurons is base unit in Neural networks[2,3],an n-inputs

is connection weight ,u
is outputs.
were called transfer function(activation function). effect is as follows: output
change from possible infinite field to finite field, and power processing of nonlinear by imitation the
neurons.In this method system, it imitate one processor with multiple inputs and single output. among


W and X are structure column vector by
Transpose by W Matrix.

4. Based on genetic algorithm neural network weights learning

Here, through the repeated use of genetic algorithm optimization weights of neural network, until
there is no longer any increase in average.
Decoding the parameters of this combination has been fully close to the best combination of
parameters. On this basis, re-use neural network algorithm for them to fine-tune(trainings),this method
has strong applicability.
According to need of the traffic forecasts, using multiple-input and multiple-output feed-forward
neural network, the type of neural network algorithm used for BP. Its core ideal is the first given t
group initial weights of network, using BP network algorithms training t group weight, and from the
minimum and maximum levels corresponding by this t group weights determine the interval value of
each weight. Following this, the use of specific coding to generating gene group and using genetic
algorithm optimization. Genetic Algorithm chromosome is weights of the neural network. See

Figure 1. Neural Network in the learning process under the Genetic Algorithm

4.1. Coding

Journal of Convergence Inf
ormation Technology

Volume 5, Number 9. November 2010

Using real coding, various weight of the neural network to connect a long string according to a
certain order, each location corresponding to a network weight in the string. This code means avoiding
a coding error in the process of calculation, while reducing the time of the encoding and decoding, and
improve the computational efficiency.

4.2. Evaluation function

All right value in chromosomes to be assigned to the network structure, training samples for input
and output of network,
is evaluation function of chromosomes. In this,

4.3. Initialization process

In initial chromosome set, weights of network are determine base on the probability of random,
initial weight is random numbers in uniform distribution on from -1.0 to 1.0.

4.4. Genetic Operators

The form of genetic operator is multifarious for all application of different, here, using operator of
weight of crossover and mutation.
Operator of weight crossover, When offspring chromosome select the weights of each , crossover
operator selected randomly a number of cross-cutting position form two-parent chromosome and cross
computing in this generation of chromosome position. So, the offspring chromosome will contain two
parental genes.
Operator of weight mutation, input every weights for offspring chromosome, operator select random
a value in the distribution of the initial probability, then with the combined weight of the input position.

4.5. Selection Method

Here, using method of proportional options. Namely, choice random base on the value of formula

are the samples to learning,
is real output of

4.6. Improved Method for Parameters

Value size of Cross-rate
and mutation
have a great influential for performance of genetic
algorithm, under ideal circumstances, with the values of the change of adaptation to change to get
values of
.We used fitness to measure algorithm status of convergence, get lower values
for the solution to high value, the solution was to eliminate. Increasing value
when mature before the time of convergence, and speed up the formation of the new
entity. with adapt to the changes in value of the fitness, formula of(3)and(4) are formula of

Traffic Prediction Based on Improved Neural Network
Cui Jian-ming



are constant less than 1;
is a large fitness of the two cross individuals .
is a variation of the individual to individual fitness

is the greatest fitness and the average fitness in groups.

5. Application of an improved arithmetic on Neural Networks in traffic

5.1. Traffic prediction

Using genetic algorithms and neural network forecast the annual average traffic volume of a road
section. Passenger volume in 1989 to 2000 input and training as learning samples, look Fig.4 Passenger
volume in 2001 to 2004 is the sample set. Neural networks use the three-tier network when the actual
forecast, nodes number of input and out is 3,nodes of hide layer is 6,error is 0.001,t is 8,crossover
probability p
=0.8,Mutation probability p
=0.01,Max number of iterations is 100, Base on the above
parameters and data are as follows Table.1.

Table 1. passenger volume annual from 1989 to 2004 [4]

passenger volume total


passenger volume total

































5.2. Traffic prediction test results

According to comparison the results of genetic algorithms, see Table.2, neural network algorithm
with improved neural network algorithm. The results can be considered that improved neural network
algorithm has a good performance forecast.

Journal of Convergence Inf
ormation Technology

Volume 5, Number 9. November 2010

Table 2. Results: genetic algorithms, neural networks algorithm, improved neural network algorithm

6. Results and Discussions

Improved neural network algorithm optimization calculation model was established, In this paper,
the advantages of this model using parallel computing and can be rapid and global search, such a model
to resolve that the neural network model has inherent shortcomings, theses are the search slow and the
local maturity easy. That model has also used the advantages of the neural network that has a strong
ability described problems and has good adaptability for incomplete information, and compensate for
the shortcomings of the genetic algorithm coding difficult. And this method has been applied to predict
the volume of traffic, the results contrast with forecast of the genetic algorithms and the neural network
model, and found that its significantly reduced error in the limited time, have a great degree of increase
for forecast accuracy and efficiency. This shows, the predicted method is feasible, neural networks and
genetic algorithms organic integration that is an effective tool can be used as predicted the volume of

7. Acknowledgements

The Project was Supported by the Special Fund for Basic Scientific Research of Central
Colleges(CHD2010JC035), Chang′an University, by the Special Fund for Basic Research Program of
Chang′an University and by the open Fund for Shaanxi Road Traffic Detection and Equipment
Engineering Research Center.

8. References

[1] CHEN Hong, "An Approach to Traffic Volume Forecasting Based on Ant Colony Neural
Network", JDCTA: International Journal of Digital Content Technology and its Applications, Vol.
4, No. 4, pp. 58 ~ 63, 2010X.L.
[2] Du, L. Han and L.P. Jiang. “An efficient global optimization Algorithm: Empirical genetic
algorithm”, Journal of Beijing Polytechnic University, vol.32,no. 11,pp.992-995,2006
[3] Shifei Ding, Weikuan Jia, Chunyang Su, Xiaoliang Liu, Jinrong Chen, "An Improved BP Neural
Network Algorithm Based on Factor Analysis", JCIT: Journal of Convergence Information
Technology, Vol. 5, No. 4, pp. 103 ~ 108, 2010
[4] National bureau of Statistics of China.《 China Statistical Yearbook 2005》.Beijing, China Statistics





volume in

passenger volume
prediction by
improved neural
network in 2008

alues of prediction






verage deviation