Factors affecting passengers’ decisions to use the metropolitan rapid transit chalong ratchadham line (purple line)

The results of studying the relationship showed that predictor factors influencing the possibility of using Purple Line MRT included available routes, fast, safe, and convenient transpor

Factors affecting Passengers’ Decisions to Use the Metropolitan Rapid

Transit Chalong Ratchadham Line (Purple Line)

Krongthong Heebkhoksung

Bansomdejchaopraya Rajabhat University

Abstract

The objectives of this research were to study factors affecting passengers’ decisions to use the Metropolitan Rapid Transit Chalong Ratchadham Line (Purple Line) and to study whether passengers decided to use MRT Chalong Ratchadham Line The setting of this study was area around MRT Chalong Ratchadham Line (Purple Line) Data were collected in March 2017 The aim of this research was to study predictor factors and passengers’ behaviors influencing the possibility of using Purple Line MRT Mathematical models could be developed to predict the decision to use Purple Line MRT The predictive results were used to plan transport management Logistic Regression Analysis was used to determine the relationship The results of studying the relationship showed that predictor factors influencing the possibility of using Purple Line MRT included available routes, fast, safe, and convenient transport as well as other factors By comparing Purple Line MRT usage, the passengers’ decision to use this line was more than their decision not to use the service

Keywords: Metropolitan Rapid Transit Chalong Ratchadham Line, Purple Line

1 Introduction

The policy of decentralizing the prosperity to local administration areas has been formulated by the government meanwhile number of population in the metropolitan area is still increasing The price of typical residences in Bangkok is expensive Most people are unable to find a residence in the city and are forced to live in the metropolitan area However, most employment sources are still located in Bangkok and are increasing The greater number of people living in outer areas needs for staying in Bangkok, resulting in traffic jam Metropolitan Rapid Transit system is important to facilitate the public transport without the usage of private car Presently, MRT structure is constructed in Bangkok and Metropolitan Area The Metropolitan Rapid Transit Chalong Ratchadham Line (Purple Line) contains total distance of 23 km and 16 stations The behaviors of using Purple Line MRT service were studied in this research to analyze basic data and decision criteria to decide to use the Purple Line MRT towards the development of a Binary Choice Model to predict the probability of passengers to use the Purple Line MRT

2 Objectives

1.To study the demographic characteristics affecting the decision to use Purple Line MRT service

2.To study behaviors of Purple Line passengers affecting possibility of using Purple Line MRT service

3 To develop a mathematical model for predicting the selection of using Purple Line MRT service when factors and behaviors affecting passengers’ decision to use Purple Line MRT service

3 Methodology

Data analysis in this research could be divided into 4 parts as follows:

Analysis of demographic data

Analysis of factors affecting the usage of Purple Line MRT

Analysis of behaviors of Purple Line MRT passengers

Analysis of data for hypothesis testing and summary

- Hypothesis 1: The samples with different demographic characteristics emphasize on factors affecting the decision to use Purple Line MRT

- Hypothesis 2: Demographic characteristics of Purple Line MRT passengers affect the decision to use or not use Purple Line MRT

- Hypothesis 3: Factors affecting the decision to use or not use Purple Line MRT

- Hypothesis 4: Behaviors of the Purple Line MRT passengers affect the decision to use or not use Purple Line MRT

4 Logistic Regression Analysis

Logistic regression analysis or Logit Analysis is the analysis of the predictive equation to study the effect

of a predictor variable on a dichotomous variable or polytomous variable Logistic Function represents the relationship between predictor variable and probability of the occurrence of events according to the criterion variable (Sirichai Kanchanawasee 2005: 39) In conclusion, there are three concepts of regression analysis in case that dependent variables are dichotomous variables (Sriridej Sucheewa: 1996: p 17)

 Conditional mean of regression equation shall be converted to be from 0 to 1, which is suitable for logistic regression analysis

 The distribution of errors must be binomial, which will be the basic statistical distribution for further analysis

 Other principles of linear regression analysis can be applied to logistic regression

Goodness of fit must be validated by the researcher

 If Chi-square is significant, it means that an independent variable or a set of independent variables

is related to the dependent variable

 If -2 Log Likelihood is close to 0, it means that the model has higher goodness-of-fit than other models (if its value is 0, it is the perfect model.)

The significance of each independent variable in the model was validated with Wald statistic ( t-statistics were used in Linear Regression) The model’s ability to forecast is considered by % of Classification When the goodness-of-fit was validated, the next steps are the analysis and determination of statistics from the analysis

 The relationship between a set of independent variables and dependent variable was tested by -2 log Likelihood and Chi-square statistics

 The model’s Goodness of fit was tested with Chi-square statistics

 The significance of each independent variable in the model was validated with Wald statistic

 The model’s predictability was tested by considering % of Classification

Studying the passengers’ decision to use or not use Purple Line MRT is useful to prepare or manage to plan the implementation efficiently and effectively The variables affecting the decision to use the Purple Line MRT are classified into 3 groups as follows:

 Variables related to passengers’ personal characteristics are gender, age and occupation

 Variables related to the factors influencing the use of the Purple Line MRT including Purple Line MRT service usage, fares, station location, marketing promotion, employee service, service process, station facilities

 Variables related to the behavior of using the Purple Line MRT including weekly usage frequency, most frequently period of time to use the service, main objective of using the service, reason to use the service, and reason not to use the service

Some variables described above had positive impact(this implied that data collected from the questionnaire were then analyzed through table and Logistic Regression Analysis

5 Results

The results of data analysis showed that the samples were males(46.76%) and females (53.24%) Most of the samples were those aged between 21-30 years (33.5%) Most of the samples were employees (33.50%) To study demographic factors of the samples who have used the Purple Line MRT service, 11-item questionnaire was used To study factors affecting the decision to use Purple Line MRT, 24-item questionnaire was used with 5-rating scale If an answer is “5” means “highest” level and “1” means “lowest” level From evaluating factors affecting the decision to use Purple Line MRT, data collected were then analyzed to determine the mean and standard deviation as shown in Table 1

Table 1: Mean and standard deviation of the samples classified with factors affecting the decision

to use Purple Line MRT service

Purple Line MRT provides fast service without waiting

Purple Line MRT provides sufficient number of trains to

Purple Line MRT provides more convenient transport 3.97 0.798

Purple Line MRT provides convenient transport of BTS or

Passenger fare is reasonable and based on distance

3.72 0.808 Passenger fares are classified by passenger’s age Children

and students are charged cheaper than general passengers 3.75 0.782

A list of fares is clearly displayed on ticketing machine and

Station location is near major landmarks such as office,

department store and educational institution

3.585 0.918

Public relations are available through various media

3.40 0.884

Items x S.D Symbol

Documents are distributed to educate routes 3.44 0.978

MRT officers are polite and friendly

3.73 0.965 Number of stationed officers in each station is enough to

MRT officers pay their attention to clients and make them

Steps of using MRT service are not complicated and

MRT service is punctual Period of waiting for the

Queuing is formed to access to the service 3.82 0.921

MRT provide facilities for disable people such as entranceexit,

toilet for priority, disable people, and Braille signs

3.74 0.970

Elevator, toilet, telephone box, ATM, and shops are available 3.23 0.995

Both Thai and English signage are available for the passengers 3.69 0.979

To study the behaviors of using the Purple Line MRT service, 15-item scale was used as shown in table 2

Table 2 : Number and percentage of the samples classified by behaviors of using Purple Line MRT service

09.31 – 12.30 hrs 53

Station is not located in the area where a passenger lives 190 47.50

Expense is high

Table 3: Statistics of the model of Purple Line MRT passengers

Omnibus Test Cox&Snell

R2 0.289

Nakelkerke

R2 0.507

Hosmer and Lemeshow Test Chi-Square Sig

0.000*

Chi-Square 8.233

Sig

Initial -2 Log Likelihood : 338.167

-2LL of Full Model : 201.711

* Reject the hypothesis at a significance level of 0.05

From Table 3, the Chi-square statistics was 136.457 (sig = 0.000) This meant that at least one factor influenced the decision to use Purple Line MRT with -2 log likelihood value approaching zero This implied that constructed equation or model had good quality or consistency with data Cox & Snell R Square value was 0.289 which was not close to zero This indicated the model’s consistency in terms of comparing the quality

of the model created with the worst model, a null model with no independent variable The Nagelkerke R Square value was 0.507 This meant that independent variables could explain 50.70% of the variation in the service usage When Wald Statistic of over 1 was considered and Sig value was less than 0.05, only variables

as in Table 4 influenced predictive equation of Purple Line MRT usage

Table 4: Variables affecting predictive equation of Purple Line MRT service

enough to meet the

passengers’ needs

0.638 -1.099 1.084 1.036 -1.876 16.633

0.264 0.309 0.489 0.431 0.834 46160.889

5.817 12.645 4.910 5.781 5.064 0.000

1

1

1

1

1

1

0.016*

0.000*

0.027*

0.016*

0.024*

1.000

1.892 Convenient transport of both

BTS or MRT linkage

Uncomplicated steps of

using the service and

self-service

0.333

2.955 2.818 Usage frequency of 1 -2

times/week

Usage frequency of 3 -4

time/week

Period of using the service

during

12.31-15.30 hrs

0.153

Logistic Regression Equation was obtained as follows:

Table 4 shows that factors influencing the decision to use the Purple Line MRT were as follows Sufficient number of MRT trains influenced the increase in using Purple Line MRT service by 2.356 times Convenient transport of BTS or MRT linkage influenced the increase in using Purple Line MRT service by 1.892 times Uncomplicated service usage steps and selfservice influenced the increase in using Purple Line MRT service

by 0.333 times Queuing of passengers influenced the increase in using Purple Line MRT service by 2.660 times The service usage of 1-2 times /week influenced the increase in using Purple Line MRT service by 2.995 times The service usage of 3-4 times /week influenced the increase in using Purple Line MRT service by 2.818 times The service usage during 12.31-15.30 hrs influenced the increase in using Purple Line MRT service by 0.153 times

6 Discussions

Firstly, the service usage of 1-2 times /week influenced the increase in using Purple Line MRT service by 2.995 times This implied that number of passengers of Purple Line MRT was 92 and will be increased by 271

in the next five years because of available routes, fast, safe and convenient transport

Secondly, the service usage of 3-4 times /week influenced the increase in using Purple Line MRT service

by 2.818 times This implied that number of passengers of Purple Line MRT was 94 and will be increased by

264 in the next five years because of available routes, fast, safe and convenient transport

Thirdly, queuing of passengers influenced the increase in using Purple Line MRT service by 2.660 times This implied that number of passengers of Purple Line MRT was 340 and will be increased by 904 in the next five years because of available routes, fast, safe and convenient transport

Fourthly, sufficient number of MRT trains influenced the increase in using Purple Line MRT service by 2.356 times This implied that number of passengers of Purple Line MRT was 340 and will be increased by 801

in the next five years because of available routes, fast, safe and convenient transport

Fifthly, convenient transport of BTS or MRT linkage influenced the increase in using Purple Line MRT service by 1.892 times This implied that number of passengers of Purple Line MRT was 340 and will be increased by 643 in the next five years because of available routes, fast, safe and convenient transport Sixthly, uncomplicated service usage steps and self-service influenced the increase in using Purple Line MRT service by 0.333 times This implied that number of passengers of Purple Line MRT was 340 and will be increased by in the next five years because of available routes, fast, safe and convenient transport

Seventhly, the service usage during 12.31-15.30 hrs influenced the increase in using Purple Line MRT service by 0.153 times This implied that number of passengers of Purple Line MRT was 53 and will be increased by 8 in next five years 5 because of available routes, fast, safe and convenient transport

7 Suggestions

More questionnaire examples should be prepared as greater information leads to lower errors This also provides benefits to make Purple Line MRT service improvement plan

The study period was from 08.00-18.00 hrs from Monday to Friday Therefore, other periods of time should

be explored to increase data Additional survey should be conducted during Saturdays and Sundays for data coverage

Convenient access to sky train stations should be studied

-Greater studies on public needs for station locations should be conducted

-More studies should be conducted to investigate whether the public accepts fare or not

References

Kamol Tharuarak ( 2005) Simulation Model of Route Selection Behaviors with Binary Logit Model (Revealed Preference) Report of Bureau of Road Research and Development

Kietiphong Jearanuntanakij ( 2006) Simulation Model of Route Selection Behaviors with Multinomial Logit Model (The Case Study of Bangkok-Pattaya Route) Bureau of Road Research and Development

Chalita Padungmit ( 2009) Factors Affecting the Selection of Bangkok- Chiang Mai Routes International Business Administration, Logistics & Transport, Thammasat University

Napat Lekawatthana ( 2013) Developing the Model of Selection of Transport for Students Between School Bus and Other Vehicles Faculty of Transport engineering, Suranaree University of Technology

Tawinan Simajaruk (2013) Cost Reduction in Transport: The Case Study of Chemical Plant, Faculty of Logistics, Burapha University Narucha Chaodee (2005) Opinions towards the Service of State Railway of Thailand: The Case Study of the East Line (Bangkok - Aranyaprathet) Burapha University

Paweena Khampukka (2016) The Willingness of Students and Personnel to Pay for Public Van for Ubon Ratchathani University-Ubon Ratchathani Bus Terminal Academic Journal:

Far Eastern University

Piti Jantruthai (2016) Factors Influencing the Decision to Select Public System in the City Area Academic Journal: Princess of Naradhiwas University

Pachara Jitchang (2010) The Study of Decision to Use MRT of People In Bangkok General Business Management Silpakorn University Bhawat Chaichannawatik (2013) Long Distance Travel Behaviors of Bangkok Residents: The Study of Selecting Transport Model Academic Journal Kasem Bundit University)

Yuth Kaiyawan ( 2012) Principles and Applications of Logistic Regression Analysis for Research Research Journal: Rajamangala University of Technology Srivijaya

Weerayuth Wattanatham (2012) Factors Influencing the Decision of Passengers to Select the Railway Transport Service of Suvarnabhumi Airport and the Airport Terminal in the City Department of Civil Engineering Faculty of Engineering Kasetsart University Sirichai Karnchanawasee ( 2005) Multi-level Analysis Bangkok: 3 rd Edition Chulalongkorn University Press

Siridej Sujiva ( 1996, January - June) Logistics Logistic Analysis: Concept, Analysis, and Interpretation Research Methodology 8 (1):

10-34

Atcharaphorn Jirachartphong (2013) Using Railway Transport Service of Suvarnabhumi Airport and the Airport Terminal in the City in Passengers’ Perspective Department of

Marketing Management Faculty of Business Administration Dhurakij Pundit University