Aimed at the multidimensional and complex characteristic of airport competitiveness, a new algorithm is proposed in which BP neural network is optimized by improved double chains quantum genetic algorithm (IDCQGA-BP). The new algorithm is better than existing algorithms in convergence and the diversity of quantum chromosomes. The empirical data of eight airports in Yangtze River Delta in 2011 and 2012 is applied to verify the feasibility of the new algorithm, and then the competitiveness of the eight airports from 2013 to 2015 is gotten through the algorithm. The results show the following. (1) The new algorithm is better than the existing optimization algorithms in the aspects of error accuracy and run time. (2) The gaps of the airports in Yangtze River Delta are narrowing; the competition and cooperation are getting stronger and stronger. (3) The main increase reason of airport competitiveness is the increase of own investment.
In recent years, with the rapid development of China and the improvement of household consumption level, aviation demand has increased enormously in China. Many cities are planning to invest on new airports or on airport extension, which can offer convenience to people’s travel and promote the development of Chinese economy. However, what about the competitiveness of Chinese airports over the years? It has become the center of public concern.
For the past few years, the evaluation of airport competitiveness has been a popular research topic. Lieshout and Matsumoto [
The disadvantages of existing research are as follows.
This paper is structured as follows: firstly, the evaluation index system of airport competitiveness is built; secondly, a new algorithm is proposed in which BP neural network is optimized by improved double chains quantum genetic algorithm (IDCQGA-BP); Thirdly, the real data of 8 Chinese airports in Yangtze River Delta region is applied to do the empirical study. Moreover, the advantages of the new algorithm over other evaluation methods are analyzed from the aspects of error precision and running time. Finally, the eight airports’ dynamic competiveness from 2013 to 2015 is calculated through the new algorithm. The results show that the new algorithm has good applicability.
According to the existing literature [
The evaluation index system of dynamic airport competitiveness.
Index classification | First-class indices | Second-class indices |
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Index system of dynamic airport competitiveness | Regional influence | The proportion of business income divided by city third industry output |
Own strength | Flight zone level |
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The proportion of staff with college degree or above |
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Total assets |
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Investment amount |
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Market condition | City resident transportation expense |
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Service radius |
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Freight throughput |
(2) Service radius is officially defined by Civil Aviation Administration of China as people amount within 100 km from airport or within 1.5 hours driving range. Service radius is defined as people amount of the directly controlled municipalities and prefecture level cities in China [
According to the results of famous scholars and institutes [
The basic structure of BP neural network [
The structure of BP neural network.
If the non-linear smooth activation function is defined as
The output of hidden layer is
If the input is
Gradient descent algorithm (GDA) is the common method to train BP neural network. However, traditional gradient descent algorithm has some disadvantages in converging slowly and falling into the local minimum point easily [
Quantum genetic algorithm (QGA) [
According to literature [
The steps of the algorithm are as follows.
The vector distance of chromosome
The vector distance concentration is
The selective probability is
The expectation reproduction rate is
The formula is
(1)
If there are
Similarly, the change value 1 of freight throughput
(2)
Similarly, the change value 2 of freight throughput
According to the results of competition and cooperation, the passenger throughput of airport
The freight throughput
Then, according to their historical growth rates, this paper predicts the future value of flight zone level, the proportion of staff with college degree or above, investment amount, the number of nonexclusive service desks, city resident transportation expense, service radius, and city third industry output.
The main improvements are embodied in the following.
The basic data in this paper comes from eight airports in Yangtze River Delta in 2011 and 2012. The eight airports are Shanghai Pudong airport, Shanghai Hongqiao airport, Ningbo Lishe airport, Hefei Luogang airport, Hangzhou Xiaoshan airport, Nanjing Lukou airport, Wenzhou Yongqiang airport, and Wuxi Shuofang airport. The data of service radius comes from the definition. Because Shanghai Pudong airport is listed airport, its “proportion of staff with college degree or above,” “total assets,” “investment amount,” “return on asset,” “freight throughput,” “passenger throughput,” “aircraft movements,” and “main business income” come from annual report. The data of other airports comes from research report and network data. Other data comes from the statistical yearbook of the city.
The activation function is
The parameters of IDCQGA-BP.
Input neurons | 12 |
Hidden neurons | 20 |
Output neurons | 1 |
Weights | 260 |
Population size | 1000 |
Mutation rate | 0.1 |
Initial phase | 0.01 |
Target error | 0.01 |
Max iterations | 5000 |
|
|
|
0.0001 |
In order to analyze the advantages and disadvantages of IDCQGA-BP, this paper uses BP neural network [
The results in 2011.
Airports | Shanghai Pudong | Shanghai Hongqiao | Ningbo | Hefei | Hangzhou | Nanjing | Wenzhou | Wuxi |
| ||||||||
Target value | 0.5513 | 0.3404 | 0.0667 | 0.0585 | 0.2329 | 0.1739 | 0.0745 | 0.0391 |
BP | 0.6207 | 0.4898 | 0.1237 | 0.0954 | 0.3615 | 0.2044 | 0.1011 | 0.0594 |
QGA-BP | 0.5090 | 0.4310 | 0.0882 | 0.0472 | 0.2448 | 0.1855 | 0.0909 | 0.0532 |
DCQGA-BP | 0.5409 | 0.3930 | 0.0678 | 0.0496 | 0.2945 | 0.1818 | 0.0891 | 0.0498 |
IDCQGA-BP | 0.5622 | 0.3641 | 0.0732 | 0.0597 | 0.2116 | 0.1691 | 0.0727 | 0.0367 |
The results in 2012.
Airports | Shanghai Pudong | Shanghai Hongqiao | Ningbo | Hefei | Hangzhou | Nanjing | Wenzhou | Wuxi |
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Target value | 0.5745 | 0.3631 | 0.1183 | 0.0735 | 0.2625 | 0.1847 | 0.0858 | 0.0395 |
BP | 0.6386 | 0.4098 | 0.1398 | 0.0996 | 0.3115 | 0.2052 | 0.1062 | 0.0602 |
QGA-BP | 0.5370 | 0.3910 | 0.1088 | 0.0868 | 0.3048 | 0.1955 | 0.1003 | 0.0578 |
DCQGA-BP | 0.6109 | 0.3830 | 0.1278 | 0.0696 | 0.2813 | 0.1618 | 0.0991 | 0.0502 |
IDCQGA-BP | 0.5922 | 0.3741 | 0.1032 | 0.0697 | 0.2416 | 0.1791 | 0.0827 | 0.0387 |
From Tables
The errors and running times are shown in Table
The errors and running times of the four algorithms.
Algorithms | Maximum error | Minimum error | Average error | Average running time (s) |
---|---|---|---|---|
BP | 1.156 | 0.560 | 0.714 | 11.539 |
QGA-BP | 0.784 | 0.425 | 0.592 | 7.891 |
DCQGA-BP | 0.629 | 0.162 | 0.301 | 6.560 |
IDCQGA-BP | 0.627 | 0.146 | 0.287 | 6.195 |
From Table
In this paper, the airport competitiveness in 2011 and 2012 is the initial value to analyze the dynamic airport competitiveness of the eight airports in Yangtze River Delta from 2013 to 2015. This paper supposes that the flight zone levels of the eight airports from 2013 to 2015 are the same as those in 2012. The average increase rate of “the proportion of staff with college degree or above” is 15.2% based on the historical data from 2008 to 2012. The average increase rate of “investment amount” is 13.2%; the number of nonexclusive service desks remains unchanged. The average increase rate of “city resident transportation expense” is 11.08% based on the city statistical yearbook. The average increase rate of “service radius” is 2.8% and the average increase rate of “third industry output” is 17.7%.
The overlap conditions of the eight airports’ service radius are shown in Table
The overlap conditions of the eight airports’ service radius.
Overlap or not | Pudong | Hongqiao | Ningbo | Hefei | Hangzhou | Nanjing | Wenzhou | Wuxi |
---|---|---|---|---|---|---|---|---|
Pudong | — | 1 | 1 | 0 | 1 | 0 | 0 | 1 |
Hongqiao | 1 | — | 1 | 0 | 1 | 0 | 0 | 1 |
Ningbo | 1 | 1 | — | 0 | 1 | 0 | 0 | 0 |
Hefei | 0 | 0 | 0 | — | 0 | 1 | 0 | 0 |
Hangzhou | 1 | 1 | 1 | 0 | — | 0 | 0 | 1 |
Nanjing | 0 | 0 | 0 | 1 | 0 | — | 0 | 1 |
Wenzhou | 0 | 0 | 0 | 0 | 0 | 0 | — | 0 |
Wuxi | 1 | 1 | 0 | 0 | 1 | 1 | 0 | — |
The international lines of the eight airports in 2012 are shown in Table
The international lines of the eight airports in 2012.
Airports | Shanghai Pudong | Shanghai Hongqiao | Ningbo | Hefei | Hangzhou | Nanjing | Wenzhou | Wuxi |
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Number | 93 | 10 | 13 | 8 | 27 | 18 | 7 | 7 |
The proportion of the passenger throughput in international lines divided by the total passenger throughput is
The dynamic airport competitiveness of the eight airports from 2013 to 2015.
Airports | Shanghai Pudong | Shanghai Hongqiao | Ningbo | Hefei | Hangzhou | Nanjing | Wenzhou | Wuxi |
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2013 | 0.6254 | 0.3905 | 0.1204 | 0.0965 | 0.2535 | 0.1847 | 0.0993 | 0.0547 |
2014 | 0.6544 | 0.4197 | 0.1449 | 0.1172 | 0.2703 | 0.1951 | 0.1025 | 0.0666 |
2015 | 0.6675 | 0.4252 | 0.1637 | 0.1362 | 0.2913 | 0.2201 | 0.1138 | 0.0883 |
Average increase rate (%) | 3.3194 | 4.3940 | 16.6617 | 18.8312 | 7.1982 | 9.2223 | 7.1235 | 27.1688 |
As shown in Table
In this paper, the airport competitiveness when airports are facing competition and cooperation is studied. The empirical study is based on the data of eight airports in Yangtze River Delta in 2011 and 2012, and the eight airports’ dynamic competitiveness under competition and cooperation from 2013 to 2015 is calculated. The average increase rate of feeder airports is bigger than international hub airport and regional hub airports. The average increase rate of regional hub airports is bigger than international hub airport. The gaps of the airports in Yangtze River Delta are narrowing; the competition and cooperation are getting stronger and stronger.
On the whole, the contribution of this paper to the literature is embodied in two aspects. Firstly, a new algorithm—IDCQGA-BP algorithm—is proposed. From the results, it can be concluded that IDCQGA-BP is better than the existing optimization algorithms in the aspects of error accuracy and run time. IDCQGA-BP has satisfactory imitative effect and high accuracy; its applicability has been verified. Secondly, The paper simulates the influence of competition and cooperation on airports’ market share and calculates the dynamic airport competitiveness when airports are facing competition and cooperation. It fills the gap of the existing literature in which only static airport competitiveness has been evaluated and lays good foundation for evaluating dynamic airport competitiveness reasonably.
However, it should be noted that the parameter selection process has certain randomness. This paper runs the algorithm ten times to minimize the effect caused by this randomness, but this will increase the workload. Future research could focus on avoiding the randomness to reduce the workload.
This research is funded by National Nature Science Foundation of China (nos. 71073016, 71273037, and 71320107006) and the Fundamental Research Funds for the Central Universities (no. 3132013336) and supported by Program for Liaoning Innovative Research Team in University.