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 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 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 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 Methodology 181 Data analysis in this research could be divided into 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 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 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 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 182 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 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 5rating 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 Table 1: Mean and standard deviation of the samples classified with factors affecting the decision to use Purple Line MRT service Items x S.D 3.93 0.737 meet the passengers’ need 4.07 0.755 Purple Line MRT provides more convenient transport 3.97 0.798 3.79 0.886 3.72 0.808 3.75 0.782 easily understood 3.83 0.916 Station location is near major landmarks such as office, department store and educational institution 3.585 0.918 Routes are convenient 3.76 0.887 Number of stations is enough 3.78 0.856 3.40 0.884 Purple Line MRT provides fast service without waiting long time Purple Line MRT provides sufficient number of trains to Purple Line MRT provides convenient transport of BTS or MRT linkage Passenger fare is reasonable and based on distance Passenger fares are classified by passenger’s age Children and students are charged cheaper than general passengers A list of fares is clearly displayed on ticketing machine and Public relations are available through various media 183 Symbol Items x S.D Discount is offered for top-up card 3.59 0.753 Documents are distributed to educate routes 3.44 0.978 3.73 0.965 3.69 0.911 3.63 0.968 3.97 0.813 passengers is consistent 3.93 0.723 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 MRT station is clean and tidy 3.74 0.870 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 Ticket is issued easily 3.67 0.966 MRT officers are polite and friendly Symbol Number of stationed officers in each station is enough to meet the demand MRT officers pay their attention to clients and make them feel impressive Steps of using MRT service are not complicated and passengers can self-service MRT service is punctual Period of waiting for the To study the behaviors of using the Purple Line MRT service, 15-item scale was used as shown in table Table : Number and percentage of the samples classified by behaviors of using Purple Line MRT service Items Frequency Percentage – times/week 113 28.25 – times/week 116 29 – times/week 64 16 Over times/week 107 26.75 05.30 – 09.30 hrs 150 37.5 Items Frequency Percentage 184 Symbol Symbol 09.31 – 12.30 hrs 53 12.31 – 15.30 hrs 56 14 15.31 – 18.30 hrs 85 21.25 18.31 – 22.40 hrs 56 Routes available meet the passenger’s need 183 45.75 MRT transport is convenient 140 35 MRT transport is safe and convenient 71 17.75 Station is not located in the area where a passenger lives 190 47.50 Number of co-users is large 34 8.50 143 35.75 Expense is high 13.25 14 Table 3: Statistics of the model of Purple Line MRT passengers Omnibus Test Chi-Square Sig 0.000* Cox&Snell R2 0.289 Nakelkerke R2 0.507 Hosmer and Lemeshow Test Chi-Square 8.233 Sig 0.411 136.457 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 was considered and Sig value was less than 0.05, only variables as in Table influenced predictive equation of Purple Line MRT usage 185 Table 4: Variables affecting predictive equation of Purple Line MRT service Variables Number of trains is Variables enough to meet β S.E Wald df Sig Exp (β) 0.857 0.320 7.152 0.007* 2.356 β S.E Wald df Sig Exp (β) the 1.892 passengers’ needs 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 -2 times/week Usage frequency of -4 time/week Period of using the service during 12.31-15.30 hrs Constant 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 0.016* 0.000* 0.027* 0.016* 0.024* 1.000 0.153 16734186.077 Logistic Regression Equation was obtained as follows: Table 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 186 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 in next five years because of available routes, fast, safe and convenient transport 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 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Marketing Management Faculty of Business Administration Dhurakij Pundit University 188 ... 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... 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. .. (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,