^{1}

^{1}

^{1}

^{1}

^{1}

^{1}

^{1}

The comfort satisfaction of basic facilities of the rail transit transfer station will influence pedestrian choice of vehicle. Aiming at the problem of traffic jams in Changchun in China, we designed a satisfaction questionnaire to investigate the factors which might affect the pedestrian satisfaction in rail transit transfer station in Changchun. By using the statistical methods, including correlation analysis, factor analysis and comparative analysis of satisfaction and importance, we analyzed the survey data, and get the results of analysis. Some suggestions for rail transit transfer station based on the results are given.

With the development of society, the phenomenon of urban traffic is becoming serious day by day. City rail transit transfer stations play an important role in public Transport. It is necessary for us to learn about whether citizens satisfied with city rail transit transfer station. Customer satisfaction models ACSI (American Customer Satisfaction Index) are widely used by people who make research about satisfaction which also is a classical model of research satisfaction made by Fornell et al. (1996) [

There are many people study satisfaction. Joan L. Giese (2000) [

Many researchers also study transportation. Kretz, T. (2007) [

In this paper we use ACSI models to construct the pedestrian satisfaction model in transportation. We designed the satisfaction questionnaire to investigate the factors which might affect the pedestrian satisfaction in rail transit transfer station. By using the statistical methods, including correlation analysis, factor analysis and comparative analysis of satisfaction and importance, we analyze the data of survey. As a result, we propose some suggestions about the rail transit transfer station.

In China, Changchun rapid rail transit network planning was completed in 2002, and was revised in 2009. The planning include 7 subway and light rail lines, the five subway lines (line 1, line 2, line 5, line 6, line 7), two light rail lines (line 3, line 4). Total length of the lines is 253.9 km. Line 3 and line 4 are in used, total length 48.3 km.

Over the past year, aiming at the problem of traffic jams in Changchun, our research group investigate the factors which might influence on pedestrian satisfaction of rail transfer station in Changchun. According to Xu’s (2009) [

The second questionnaire consists of 15 problems, 5 problems are personal information, others are satisfaction, including the comfort satisfaction of various facilities, service attitude satisfaction of the station staff. Such as, “Transfer station environment”, “Lighting and the heating system in transfer station”, “Escalator, chairs and trash can in transfer station”, and so on.

For convenience, we use the letters A_{1}, A_{2}, A_{3}, A_{4}, A_{5}, A_{6}, A_{7}, A_{8}, A_{9} and A_{10} to denote satisfaction of facilities, respectively. That is

A_{1}―Transfer station environment,

A_{2}―Lighting and the heating system in transfer station,

A_{3}―Escalator, chairs and trash can in transfer station,

A_{4}―Crowded degree in transfer station,

A_{5}―Security facilities in transfer station,

A_{6}―Pull in and out station time,

A_{7}―Numbers of ticket windows and check-in path,

A_{8}―Number of vehicles shifts per day,

A_{9}―Information provided by transfer station,

A_{10}―Service attitude of the station staff.

In this paper, we used the statistical methods, including correlation analysis, factor analysis, and comparative analysis of satisfaction and importance to analyze the survey data. The statistical software we used is SPSS.

Correlation analysis is able to obtain the correlation of two variables, and to determine whether the correlation between these variables is statistically significant. In section 4.1, we analyzed the relevance within two transfer station facilities, and found correlation between the various facilities satisfaction.

Factor analysis in multivariate statistics is a kind of dimension reduction. We used factor analysis to simplifying factors, with less common factors to reflect the complex phenomena. In section 4.2, we tried to find the main influencing factors of the two rail transfer station, respectively.

In section 3.3, we compared the relationship between satisfaction and importance in rail transfer station facilities.

Pearson correlation coefficient, Kendall correlation coefficient and Spearman correlation coefficient are widely used. We use Pearson correlation coefficient to analyze the correlation and its formula is as follow:

where

It can be seen that the correlation coefficient of “Security facilities in transfer station” and “Numbers of ticket windows and check-in path” is maximum, 0.996. The second is the correlation coefficient of “Security facilities in transfer station” and “Pull in and out station time”, 0.995. The minimum is the correlation coefficient of “Crowded degree in transfer station” and “Number of vehicles shifts per day”, 0.780. In addition, we analyzed

A_{1} | A_{2} | A_{3} | A_{4} | A_{5} | A_{6} | A_{7} | A_{8} | A_{9} | A_{10} | |
---|---|---|---|---|---|---|---|---|---|---|

A_{1} | 1 | 0.971 | 0.983 | 0.922 | 0.970 | 0.975 | 0.970 | 0.938 | 0.948 | 0.898 |

A_{2} | 0.971 | 1 | 0.957 | 0.830 | 0.988 | 0.993 | 0.978 | 0.973 | 0.965 | 0.920 |

A_{3} | 0.983 | 0.957 | 1 | 0.915 | 0.961 | 0.950 | 0.949 | 0.883 | 0.915 | 0.852 |

A_{4} | 0.922 | 0.830 | 0.915 | 1 | 0.882 | 0.867 | 0.900 | 0.780 | 0.880 | 0.854 |

A_{5} | 0.970 | 0.988 | 0.961 | 0.882 | 1 | 0.995 | 0.996 | 0.953 | 0.987 | 0.954 |

A_{6} | 0.975 | 0.993 | 0.950 | 0.867 | 0.995 | 1 | 0.994 | 0.978 | 0.988 | 0.957 |

A_{7} | 0.970 | 0.978 | 0.949 | 0.900 | 0.996 | 0.994 | 1 | 0.957 | 0.995 | 0.972 |

A_{8} | 0.938 | 0.973 | 0.883 | 0.780 | 0.953 | 0.978 | 0.957 | 1 | 0.961 | 0.936 |

A_{9} | 0.948 | 0.965 | 0.915 | 0.880 | 0.987 | 0.988 | 0.995 | 0.961 | 1 | 0.989 |

A_{10} | 0.898 | 0.920 | 0.852 | 0.854 | 0.954 | 0.957 | 0.972 | 0.936 | 0.989 | 1 |

Changchun rail transfer center and Linhe street transfer station, respectively. The correlation coefficients of Changchun rail transfer center are roughly the same with the total data. Linhe Street is slightly different. The maximum correlation coefficient is “Number of vehicles shifts per day” and “Pull in and out station time”, 0.997. The second is “Number of vehicles shifts per day” and “Numbers of ticket windows and check-in path”, 0.982. The minimum is “Crowded degree in transfer station” and “Information provided by transfer station”, 0.718. The fact is that line 3 and line 4 transfer channel is very long in Linhe street transfer station. The results of the survey reflected in that channel length of the transfer station affects the degree of correlation between the various facilities.

Factor analysis usually needs to do non-dimensionalization, that is data normalization. It can keep the relative stability of original values which can also bring a lot of convenience. The formula is as follow:

where

Factor analysis extracts common factors from the variables and finds the correlation between each variable. Our purpose is to avoid bias caused by the correlation between the variable. Using factor analysis needs to a strong correlation of these variables. In general, it is not suitable for factor analysis when the majority of coefficient of correlation matrix is less than 0.3.

The eigenvalues of the first principal component is 9.483 which is shown in

The coefficients of the first principal component is as follow:

Component matrix^{a} | |
---|---|

Component | |

1 | |

Transfer station environment | 0.983 |

Lighting and the heating system in transfer station | 0.984 |

Escalator, chairs and trash can in transfer station | 0.962 |

Crowded degree in transfer station | 0.905 |

Security facilities in transfer station | 0.995 |

Pull in and out station time | 0.996 |

Number of vehicles shifts per day | 0.962 |

Numbers of ticket windows and check-in path | 0.998 |

Information provided by transfer station | 0.989 |

Service attitude of the station staff | 0.959 |

Extraction method: principal component; ^{a}The components 1 have been extracted.

The total variance explained | |||
---|---|---|---|

Ingredients | Square of extraction and load | ||

Total | % of the variance | Cumulative% | |

1 | 9.483 | 94.832 | 94.832 |

Extraction method: principal component analysis.

After dividing

Component 1 in

We get the component score is as follow

Obviously, the highest scores are “Security facilities in transfer station”, “Pull in and out station time” and “Number of vehicles shifts per day”, 0.105, the lowesr score is “Crowded degree in transfer station”, 0.095.

We also obtain that in Linhe Street transfer station, “Service attitude of the station staff” in component 1 with the largest eigenvalue has the maximum contribution rate.

We analyzed comfort satisfaction of facilities in the rail transfer stations in Changchun, and used analytic hierarchy method to get higher score of satisfaction according to the first questionnaire in July 2014, see Zhou (2015). In order to do comparative analysis, in the second questionnaire we added to the questions of the facility comfort importance. So we obtained the data of comfort importance and got their scores by using the method as follow.

We use N_{1}, N_{2}, N_{3}, N_{4}, N_{5} to denote the five important degree of each variable, that is most unimportant, unimportant, general, important and very important. Let

Counting N_{1}, N_{2}, N_{3}, N_{4}, N_{5} and calculate the proportion of each number compare with total number

The formula is as follows:

We can get the importance degree of each variable from 475 questionnaire survey which is shown as

Through the comparative analysis of satisfaction and importance, we choose three typical questions to analyze specifically. They are “Security facilities in transfer station” (Abbreviated “Security”), “Crowded degree in transfer station” (“Crowded degree”) and “Information provided by transfer station” (“Information”). The average scores of importance of these three questions in of rail transit transfer stations in Changchun are 4.2274, 4.1200 and 4.1832, see

“Security” has the highest score of importance in the all questions, 4.2274, and its score of satisfaction comparing to others is also higher, 3.6929. With the increase of urban population and traffic congestion, rail transportation has been becoming a first choice by the transport. Rail transit transfer stations are the places where

A_{1} | A_{2} | A_{3} | A_{4} | A_{5} | A_{6} | A_{7} | A_{8} | A_{9} | A_{10} |
---|---|---|---|---|---|---|---|---|---|

4.0968 | 4.1347 | 4.1432 | 4.1200 | 4.2274 | 4.0189 | 4.5254 | 4.0842 | 4.1832 | 4.1453 |

Factor | Weight | Quite unsatisfactory | Unsatisfactory | General | Satisfactory | Very satisfactory | Mean |
---|---|---|---|---|---|---|---|

Efficiency | 0.1143 | 0.0614 | 0.0557 | 0.2176 | 0.3445 | 0.3208 | 3.8076 |

Pull in and out station time | 0.2941 | 0.0673 | 0.0884 | 0.2295 | 0.3074 | 0.3074 | 3.6992 |

Number of vehicles shifts per day | 0.7059 | 0.0590 | 0.0421 | 0.2126 | 0.3600 | 0.3263 | 3.8525 |

Convenience | 0.2196 | 0.0800 | 0.0800 | 0.2337 | 0.2968 | 0.3095 | 3.6758 |

Numbers of ticket windows and check-in path | 1 | 0.0800 | 0.0800 | 0.2337 | 0.2968 | 0.3095 | 3.6758 |

Comfort | 0.2723 | 0.0962 | 0.0993 | 0.2575 | 0.2782 | 0.2688 | 3.5241 |

Transfer station environment | 0.1667 | 0.0653 | 0.0737 | 0.2758 | 0.3221 | 0.2631 | 3.644 |

Lighting and the heating system in transfer station | 0.1667 | 0.0821 | 0.1137 | 0.2232 | 0.3158 | 0.2652 | 3.5683 |

Escalator, chairs and trash can in transfer station | 0.1667 | 0.0758 | 0.0989. | 0.2758 | 0.2863 | 0.2632 | 3.5622 |

Crowded degree in transfer station | 0.4999 | 0.1179 | 0.1032 | 0.2568 | 0.2484 | 0.2737 | 3.4568 |

Safety | 0.3136 | 0.0568 | 0.0926 | 0.2505 | 0.3011 | 0.2990 | 3.6929 |

Security facilities in transfer station | 1 | 0.0568 | 0.0926 | 0.2505 | 0.3011 | 0.2990 | 3.6929 |

Service | 0.0802 | 0.0668 | 0.0670 | 0.2310 | 0.3011 | 0.3341 | 3.7687 |

Information provided by transfer station | 0.4286 | 0.0632 | 0.0779 | 0.2526 | 0.2926 | 0.3137 | 3.7157 |

Service attitude of the station staff | 0.5714 | 0.0695 | 0.0589 | 0.2147 | 0.3074 | 0.3495 | 3.8085 |

Σ | 1 | 0.0740 | 0.0853 | 0.2434 | 0.2989 | 0.2984 | 3.6624 |

large pedestrian flow. So its security gets the government and passengers more and more attention. On the other hand, reasonable allocation of safety facilities in rail transit transfer station has also contributed to the people’s choices of rail transit.

“Crowded degree” has the higher importance score, and the lowest satisfaction score. This result explains the gap of reality and passengers expectations. With the social progress and economic development, convenient and fast become the main choice, so rail transportation became passengers main travel way. Crowded degree in rail transit transfer stations also became a problem people pay attention to. In addition, rail transit transfer station is the site passengers travel on transportation. The pedestrian within the transfer station have higher density. Crowded conditions can effect the traveler satisfaction. So “crowded degree” is the lowest.

The satisfaction and importance scores of “Information” are both higher. On one hand, rail transit transfer station information can help people better and faster choosing transportation routes, saving time and easy to understand. On the other hand, detailed and accurate information is further standardized of rail transfer station facilities. The more information gets higher satisfaction score.

In this section, we used analytic hierarchy method to analyze the satisfaction of facilities in rail transit transfer stations, including Changchun rail transfer center and Linhe Street transfer station. Facilities satisfaction scores are shown in

In

From the results in

This work was support by NSF grant 51278221 and grant 51378076 from the National Natural Science Funds in China and Collage innovation projects in China: 40103-129645.

ChuanshengZhou,LijieXie,ZhenLian,MengDu,XiaoyangLi,PinchaoMeng,SanzhiShi, (2015) Investigation and Analysis of Satisfaction of Rail Transit Transfer Station Facilities in Changchun. Applied Mathematics,06,2311-2318. doi: 10.4236/am.2015.614203