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Cartesian Diagram Or IPA Diagram (Supranto, 1997

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Cartesian Diagram Or IPA Diagram (Supranto, 1997 3rdConference of Transportation Research Group of India (3rd CTRG) Performance Analysis of Sub Urban Rail System in Delhi - A Case Study Rahul Raoniara, Amudapuram Mohan Raob*, S. Velmuruganc a Post Graduate Student, Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Road Research Institute, New Delhi-110025, India. b Senior Scientist, Traffic Engineering and Safety Division, CSIR-Central Road Research Institute, New Delhi-110025, India. [email protected] c Senior Principal Scientist and Head of Division Traffic and Safety Division, CSIR-Central Road Research Institute, New Delhi- 110025, India. * Corresponding Author Abstract Today customer’s satisfaction is one of the key parameter which is considered by the transport organizations for assessment of service quality. Due to growing importance of quality in our life, customer’s desire for good quality of transport and superior quality of services has been increased. India is one of the leading nations at world level contributing better living standards, economy, and employment and also having rapid growth of population which contribute to an increase in demand of better safe transportation facilities. Delhi’s Suburban Rail transportation system is one of the cheapest modes for commuting. This paper aims to identify the major cause of lesser use of suburban rail transport system and identify the attributes, on whose improving may further leads to increase in service quality. Three techniques namely Important Performance Analysis (IPA), Customer Satisfaction Index (CSI) and Structure Equation Modeling (SEM) are used to identify the current service quality in terms of Current Service Satisfaction and Expectation. Results indicates the current passengers of Delhi’s Suburban Rail system are not satisfied with the overall service quality provided. CSI value obtained is 0.45 which is quite low compared to the highest level of service quality measure i.e. ranges in between 0.81 to 1 and SEM analysis indicates, Delhi Suburban rail transit system isn’t having a positive impact on passenger satisfaction in Delhi City. Keywords: Performance Evaluation Techniques, Survey and Scale Design, IPA, CSI, SEM. 1. Introduction Transportation facilities evolve day by day in all cities irrespective of their status. Delhi transportation system connectivity lies at par with major cities across the world. Public transit systems such as Metro; Bus and Rail are the most extensively used by the commuters on daily basis. The study aims to identify the service quality and performance gap in terms of user perception and to identify the major attribute that defines transit service performance. 2. Literature Review The performance of transit system can be enumerated based on two distinct dimensions i.e., Service and Service quality. Service is described as “the business transaction that takes place between a donor (Service provider) and Receiver (Customer) in order to produce an outcome that satisfies the customer” (Ramaswamy, 1996) [1]. Whereas, Service quality gives the measure of how well the service level is delivered to the commuters as per its expectation. Parasuraman (1988) defines service quality as a comparison between customer expectation and perception of service [2]. 3rdConference of Transportation Research Group of India (3rd CTRG) The model proposed by Parasuraman (1985) known as SERVICE QUALITY (SERVQUAL) which comprises ten dimensions namely; Reliability, Responsiveness, Competence, Access, Courtesy, Communication, Credibility, Security, Understandability and Tangibles which later in 1988 reduced to five dimensions: Reliability, Assurance, Tangibles, Empathy and Responsiveness, known as RATER. This method calculates the service quality by measuring the gap in between service delivered and service perceived by the commuters [3]. Federal Administration of the U.S (1999) developed a simple and effective measurement method of customer satisfaction for transit services termed as Impact Score Technique [4]. 3. Study Methodology 3.1 Study area Delhi suburban rail system is operated by northern railway for the National Capital Region (NCR) which was started in 1975 to serve the goods and passenger around Delhi city covering 25 km around the city. Delhi suburban railway services cover Delhi along with adjoining district of Faridabad, Ghaziabad and other places of Haryana and Uttar Pradesh. Most of the railway path covered by the EMU and MEMU passenger trains covering the Delhi use the same track used by long distance trains. The study area covers the suburban rail system covering south Delhi is considered, i.e. the line going from New Delhi railway station to Badarpur and ring rail which covers many area of Delhi is covered. User perception data is collected at three rail stations namely Tughlakabad, Okhla and Lajpat Nagar. 3.2 Parameters Identification and Scale Design The selection of performance evaluation parameters and scale design solely depends upon the transit system. The parameters were formulated using all possible demographic and performance variables to capture passenger’s responses. The questions include 10 demographic questions and 19 performance measuring variables. The details of the demographic parameters are presented in Table 1 and performance variable are presented in Table 2. In this study a dual level questionnaire prepared by incorporating satisfaction level with the base line of comparison Expectation level. Hence a constant scale of 5 Likert scale [5] units has been selected which indicates minimum satisfaction and expectation level as 1 and maximum satisfaction and expectation level as 5. The questionnaire was designed to capture the existing satisfaction level for the system and also to capture the expected level of each parameter. 3.3 Data Collection The user opinion survey was conducted at entry/exit and on platforms of railway stations. The survey was conducted at three stations for one day from 6:00 am to 10:00 pm. The survey was conducted by trained enumerators. The samples are collected by random sampling technique by considering various socio economic parameters. The partly filled forms are rejected and not considered for the analysis. Around 1195 samples were collected at three selected stations, these data is used for further analysis. The user characteristics obtained from collected sample are presented in Table 1. Table 1 User Characteristics Characteristics Statistics 1. Gender Male (85.4%), Female (14.6%) 2. Age <20 (16.13%), 21-30 (43.19%), 31-40 (21.22%), 41-50 (12.14%), 51-60 (6.12%), 61-70 (1.11%), 71-80 (0.09%). Raoniar, Mohan Rao and Velmurugan 3rdConference of Transportation Research Group of India (3rd CTRG) 3. Income <5000 (39.5%), 5001-15000 (32.9%), 15001-30000 (18.6%), 30001-45000 (5.0%), 45001-60000 (1.4%), 60001-75000 (2.1%), >75000 (0.5%). 4. Education <Matric (20.2%), XII (30.9%), Graduate (40.1%), Post Graduate (8.8%). 5. Profession Employee (49.1%), Manager (6.1%), Entrepreneur (5.8%), Self Employed Worker (4.2%), Unemployed (2.9%), Student (21.4%), House Wife (2.1%), Pensioner (0.5%), Other (7.9%). 6.Vehicle Ownership Cycle (11.7%), Two Wheeler (36.8%), Car (10.6%), Haven’t any Vehicle (40.9%). 7.Trip Purpose Work (61.0%), Education (20.3%), Recreation (3.5%), Social (7.1%), Other (8.1%). 8.Mode Used to Walk (43.8%), Car as a Driver (1%), Car as a Passenger Reach Rail Station (2.0%), Bicycle (3.3%), Motorcycle (5.4%), Auto (24.5%), Taxi (3.2%), Train (3.8%), Other Mode (11.2%), More than One Mode (1.8%). 9. Mode Used to Walk (46.4%), Car as a Driver (1.2%), Car as a Passenger Reach Destination (2.3%), Bicycle (2.9%), Motorcycle (3.1%), Auto (27.5%), Taxi (3.6%), Train (3.3%), Other Mode (8.1%), More than One Mode (1.7%). 10. Ticket Type One Way Ticket (35.0%), Return Ticket (11.5%), Weekly Travel Card (1.7%), Monthly Travel Card (51.7%). The demographic of present study represented in Table 1 shows majority of respondents are male (85.4%) with aged between 21 and 30, profession of the majority (49.1%) are employees. More than half (59.1%) of the user have a personal mode (Two wheeler/car) for commuting. More than half (51.7 %) passengers are regular commuters with monthly travel card. 3.4 Analysis of Data The processed data was analyzed to find what are the parameters are important and based on which parameters user are rating the system. Important performance analysis, overall satisfaction of the system is calculated. An attempt has been made to model the user satisfaction using SEM model technique. 3.4.1 Important Performance Analysis (IPA) The performance appraisal of public transport can be carried out using Important Performance Analysis (IPA) is also known as quadrant analysis in order to measure the relationship between user perception and priority, (Martilla 1977) [6]. In IPA, user satisfaction is translated into Cartesian diagram where two lines perpendicularly divide it into four quadrants shown in Figure 1. Where (Q) represents the average of average scores of level of implementation of all factors and (P) represents the average of average scores of the importance of all factors. Raoniar, Mohan Rao and Velmurugan 3rdConference of Transportation Research Group of India (3rd CTRG) Fig. 1 Cartesian Diagram or IPA Diagram (Supranto, 1997) The different terms of the Figure 1 are as follows: Point (P) as the middle point of the score level expectations, obtained by dividing the total score of the average level of expectation per respondent each dimension with the existing number of dimensions. Point (Q) as the middle point of the performance level score, obtained by dividing the total score of the average level of performance per respondent each dimension with the existing number of dimensions. Quadrant 1: The factors that are considered important by the customer but in reality these factors have not been in line with expectations. Attributes that are included in this quadrant should get more attention or repaired so that the performance is increased.
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