Development of a Level of Service Index for Privately Owned Public Transportation Busses in Sri Lanka

Transcription

1 Development of a Level of Service Index for Privately Owned Public Transportation Busses in Sri Lanka Terrance M. Rengarasu 1, W K V Chanaka 2 and G B M Gallage 3 1 Department of Civil and Environmental Engineering Faculty of Engineering, University of Ruhuna, Hapugala, Galle, SRI LANKA 2 KSJ Construction (pvt) Ltd. Kalagedihena, SRI LANKA 3 Edward and Christie (pvt) Ltd. Nugegoda, SRI LANKA rengarasu@cee.ruh.ac.lk Abstract: This study develops a methodology to evaluate the quality of service of privately owned public transportation buses in Sri Lanka by means of Quality of Service Index (QSI). Factors affecting the QSI of buses were analysed using conjoint analysis with special attention to local conditions and user perception. Five factors were selected they are; (1) Bus fare, (2) Bus travel time, (3) General cleanliness, (4) Conductor attitudes and (5) Time of arrival at the bus stop. These factors were designed to have three levels each. Cut-off values for the levels were obtained from a survey conducted among the undergraduates of University of Ruhuna. Partial factorial design was employed to reduce the 3 5 unique services conditions possible to 21 which were arranged in a questionnaire. Total received 156 responses were modelled using linear regression in SPSS and utility values for each level was determined. QSI was developed as the sum of utilities given by the levels of factors in a given service condition. Higher QSI represent a better service level. Keywords: Quality of Service Index, Public Transportation, Partial factorial design, Linear regression, Conjoint analysis. 1. INTRODUCTION Public transportation in Sri Lanka is based heavily on the road network. Other public transportation modes based on air, water and railway only handles a small fraction of the country's transport needs. Busses are the main public transportation mode in Sri Lanka. The first motor bus to Sri Lanka was imported in 1907, and public bus transportation began as a privately owned and operated service. Later in 1958 all privately owned busses were nationalised and no privately owned public transportation busses were allowed to operate. Privately owned public transportation busses were again allowed from 1979 onward. Today, public bus transportation services are provided by the staterun Sri Lanka Transport Board (SLTB) and by privately owned buses. However, number of privately owned buses is higher than that of the government owned busses. Many countries have turned their attention towards developing and improving their public transportation systems, as problems such as traffic congestion in cities, low mobility, high individual costs of transport, and a rural-urban divide in services have arisen (Soh et al. 2014). Extensive studies have been carried out to develop the to give the better solution for developing and improving their public transport system according to their current situations (Belwal and Belwal 2012). In Sri Lanka too, today there are many attempts to increase the attractiveness of the privately owned privately owned public transportation busses and to measure the quality of the services provided by them. To be useful, any such measurement of quality should be linked with the user satisfaction. For example the popular quality index developed by Highway Capacity Manual Level-of-service (LOS) is a concept, which used to assess quality of operations of transportation facilities in the roadside environment was derived based on user satisfaction. Existing literature utilises a concept call Quality of Service Index (QSI) to evaluate busses. For example from year 1999, the Australian Institute of Transport Studies (ITS) have been conducting many studies into public bus transportation service to find out ways to capture customer satisfaction with service levels of busses (Prioni and Hensher 2000, Hensher and Prioni 2002). These studies have developed QSI with many factors. Such an index would provide insights into the effectiveness of 171

2 service levels from a passenger viewpoint and identify which service aspects are working best and which need more improvement. The concept of transit evaluation through the measurement of level of service is discussed in terms of usefulness, past work theory, and the presentation of a set of characteristic attributes. It is concluded that transit service can be quantified and evaluated but that considerable effort is necessary to achieve a comprehensive and equitable system (Jr et al. 1976). The concept of the Level of Service of Safety (LOSS) was developed at the Colorado Department of Transportation (CDOT) in 2000.LOS reflects how a roadway segment or an intersection is performing in reference to the expected frequency and severity of crashes predicted by its Safety Performance Function (SPF) (Kononov et al. 2015). Existing literature suggest many factors affecting QSI of buses. Paulley et al. (2006) suggests factors such as fares, quality of service and income and car ownership. Bus travel time, Bus fare, Buses per hour at this bus Time of arrival at bus stop, Time walking to bus stop, Seat availability on bus, Information at bus stop, Access to bus Wide entry, Bus stop facilities, Temperature on bus, Driver attitude, General Cleanliness on board are suggested by Hensher et al. (2003). Alter, C. H. (1976) used basic accessibility, travel time, reliability, directness of service, frequency of service and passenger density as parameters in the evaluation of the public transit service. Guo et al. (2010) used average waiting time at the bus stop as a factor. Passengers occupation, travel time/delay, information regarding timetable, having unfavourable experience when using buses are identified as a factors affecting the quality of service of public transportation busses in Western Province of Sri Lanka (Ranawana and Hewage 2015). With the increased demand for public transportation in Sri Lankan and growing number of complaints against privately owned public transportation busses it is very important to have a system which can measure the quality of service provided. In this backdrop, aim of this study is to develop a method to evaluate the quality of service provided by the privately owned public transportation buses. Specific objectives of this study are: to identify effect of related factors on the quality of service of privately owned public transportation buses; and to develop a SQI for the privately owned public transportation buses. 2. METHODOLOGY Conjoint analysis was selected as the tool to design the experiment and analyze the data. Methodology of this study can be divided in to 7 stages. First the factors affecting the quality of service of privately owned public transportation buses were identified. Second the levels of the factors were determined. In the third stage questionnaire was designed. In the fourth stage data were collected by the questionnaire. In the fifth stage linear regression model was developed. In the sixth stage results of linear regression was converted into SQI. In the seventh stage developed linear regression model was verified. Following paragraphs give detailed account of the methodology. Identification of correct factors for which affect the quality of service is very important. A one to one survey was conducted among 200 participants in Galle and in Colombo-Pettha bus terminals. In the interview the interviewees were asked list important factors that they are important in determining the quality of service of a privately owned public transportation bus. In this study, Bus travel time, Bus fare, Conductor attitudes, General Cleanliness and Time of arrival at the bus stop were identified as the most frequently appearing factors affect for quality of service of privately owned public transportation buses. Selected five factors (attributes) were decided to be divided into three levels each. Selection of threshold values has to be so that the levels are meaningful and easily understandable by the interviewees. Thresholds values for the factors conductor attitudes, cleanliness and time of arrival at the bus stop were determined using the previous study (Hensher et al. 2003). But thresholds of bus fare and bus travel time had to be determined according to the Sri Lankan situation and there were no any references Therefore a questionnaire survey was conducted inside the Faculty of Engineering, University of Ruhuna premises in Hapugala, Galle among the Engineering undergraduates to identify the most desirable values for the thresholds of bus fare and bus travel time for the each three levels. Final levels and the threshold levels are shown Table

3 Level Bus Fare (%)* Table 1 Attributes and their corresponding levels Bus Travel Time (%)* Attributes Conductor Attitudes General Cleanness Time of Arrival at Bus Stop Very friendly Very clean on time Friendly enough Clean enough 5 min late Generally unfriendly Not clean enough 10 min late * Compared to that of a three wheel hire of the same origin and destination A unique service condition is represented by combining different levels of each factor. Each unique service condition was represented to the interviewee as a profile card. Different combinations levels of all five attributes are included within a profile card. In total there were 3 5 unique combinations. It would be very hard work for the participating interviewee if not impossible to rate all 3 5 profile cards. Number of profile cards was limited to 21 by partial factorial design with the aid of orthogonal matrix shown on Table 2. Out of 21 generated cards, 5 were kept as holdout cards and 16 were used as design cards. Design cards are used to develop the model and while simulation or holdout cards were used to validate the model. 21 questions were formed using the sixteen number of design profile cards. At the end of bottom each profile card a rating scale with description were also provide for the ratings (See Figure 1 for an example card). Questions were hard printed and distributed to bus passengers in Colombo-Pettah bus terminal. Out of 200 questionnaires distributed 160 retuned, however only 156 were complete and used in this study consequently. Table 2 Orthogonal matrix of attributes and their corresponding levels Bus Fare Bus Travel Conductor General Time of Arrival Time Attitudes Cleanness at Bus Stop Status level 2 level 2 level 1 level 1 level 1 Design level 3 level 2 level 2 level 1 level 1 Design level 3 level 2 level 1 level 1 level 1 Design level 1 level 1 level 1 level 2 level 3 Design level 2 level 1 level 3 level 1 level 3 Design level 1 level 1 level 1 level 1 level 1 Design level 1 level 2 level 3 level 3 level 1 Design level 2 level 2 level 1 level 2 level 2 Design level 1 level 3 level 2 level 1 level 2 Design level 1 level 1 level 1 level 3 level 2 Design level 1 level 1 level 1 level 1 level 1 Design level 1 level 3 level 3 level 2 level 1 Design level 3 level 1 level 3 level 1 level 2 Design level 1 level 2 level 2 level 1 level 3 Design level 3 level 3 level 1 level 3 level 3 Design level 2 level 2 level 2 level 1 level 3 Design level 2 level 2 level 2 level 1 level 3 Simulation level 3 level 1 level 1 level 1 level 3 Simulation level 3 level 3 level 2 level 3 level 1 Simulation level 3 level 1 level 3 level 1 level 1 Simulation level 3 level 3 level 3 level 2 level 3 Simulation 173

4 To model the data obtained from the questionnaire additive composition model was adopted. This model is the simplest and the most frequently used, and assumes that the overall evaluations are formed by the sum of the separate part-worths (partial standardized utility) of the attributes. It is represented mathematically in Equation 1. m Total UUtility = UU 0 + UU ij i=1 (1) Where UU 0 is the mean utility score with respect to all attributes,uu ij represents the partial standardized utility of a factor i at levelj and m is the number attributes. To estimate the partial standardized utility user rating were taken as the dependent variable of the linear regression model with Very uncomfortable was coded as 3, Uncomfortable was coded as 2, Little uncomfortable was coded as 1, Neither uncomfortable nor comfortable was coded as 0, Little comfortable was coded as 1, Comfortable was coded as 2, and Very comfortable was coded as 3. Factors and their levels were taken as independent variables. Dummy variable coding was used to code the factors and their corresponding levels. SPSS was used to run the regression. Model will only estimate partial standardized utilities (n 1) level only for the other level it can be calculated because the utility values of all of its levels n will be zero. As shown in Equation 2. n UU ij = 0 j=1 (2) Importance value is calculated as in equation 3. I i = Max UU ij Min UU ij for each i (3) More conveniently I i is normalized and presented as percentage as shown in Equation 4. W i = I i m i=1 I i 100% (4) Service quality index developed in this study is proposed as the sum of utilities of the factors which are present in a given service condition as shown in Equation 5. That means in an ideal case SQI will have rage of -3 to SQI = UU 0 + UU ij i=1 (5) 3. RESULTS 3.1. Average Importance and Utility Values Results of the regression analysis of the averaged importance of each Attribute and the part-worth utility of each attribute level. Normalised importance values (W i ) of each attribute is shown in Figure 1. According to the normalised importance values the most significant attribute is the Bus travel time while Bus fare has second highest normalised importance value. Conductor attitudes and Time of arrival at bus stop has similar normalised importance values. General cleanliness has the least normalised importance value among the attributes considered in this study. Partial standardized utility values for all factors and levels are shown in Figure 2. It can be noted that level 1 of each attribute take positive values while level 3 take negative values. Level 2 also take negative values expect the 'conductor attitude'. The range between utility levels of 'bus travel time' is wider than other attributes. According to the results of utility values, conditions defined under level 1 are the most preferable for the passengers. Conditions under level 2 and level 3 are not preferable for passengers. 174

5 Figure 1 Normalised importance values of factors Conditions under level 3 are the worst case for each attribute Overall Model Fit Additional statistical assessments are given through a Pearson's R and Kendall's tau values. Pearson's R correlation coefficient is an indication of how well the model fits the data. Model achieved Pearson correlation coefficient of and Kendall's tau value of These statistics are highly significant indicating that choice of additive composition model is correct further it also indicates a high level of correlation between the observed and estimated preference Development of SQI Total utility value can be calculated by substituting individual utility values according to the data given in Table 3 for the model given below In Equation 6. Total UUtility = UU BBBBj + UU BBBBj + UU CCCCj + UU CCCCj + UU BBCCj (6) Where UU BBBBj is the partial standardized utility for bus travel time at level j UU BBBBj is the partial standardized utility for bus fare at level j UU CCCCj is the partial standardized utility for general cleanliness at level j UU CCCCj is the partial standardized utility for conductor attitudes at level j UU BBCCj is the partial standardized utility for time of arrival at level j Here it is obvious that the maximum total utility value was obtained under the service condition where all attributes were at level 1 and the minimum total utility value was obtained when all attributes were at level 3. SQI is defines as to have range between the maximum total utility value and the minimum total utility value. SQI range is divided in to three and named A-B, C-D and E-F in the descending order. Relationship between the Total Utility Ranges and SQI are shown in Table Model Validation When developing the model the holdout cards or the simulation cards were not used. Data obtained from the holdout cards now can be used validate the model results. For the five holdout cards total 175

6 Figure 2 Partial standardized utility values for all factors and levels utility values were calculated by using the developed model (Table 3 and Equation 6). These values were compared with the user stated utility values. Figure 3 shows the graphical representation of the calculated and the observed total utility values and the ideal results line (y=x line). The utility values of the hold out cards lie close to the ideal results line. They have Mean Root Square error of ±0.20. Therefore it can be stated that the results of this model are has error of ± Segmentation Analysis Segmentation analysis is very important for any study based on questionnaire. The obtained data were divided into two categories as male and female. Then normalised importance values of each attributes were calculated separately. The comparison of the normalised importance values between Table 3 Relationship between the total utility ranges and SQI Attribute Symbol Specification Level Utility value Bus fare Bus travel time Conductor attitudes General cleanliness Time of arrival at bus stop UU BBBB1 10% Level UU BBBB2 50% Level UU BBBB3 70% Level UU BBBB1 30% Level UU BBBB2 60% Level UU BBBB3 80% Level UU CCCC1 Very friendly Level UU CCCC2 Friendly enough Level UU CCCC3 Generally unfriendly Level UU CCCC1 Very clean Level UU CCCC2 Clean enough Level UU CCCC3 Not clean enough Level UU TTTT1 On time Level UU TTTT2 5 min late Level UU TTTT3 10 min late Level

7 Table 4 Relationship Between the Total Utility Ranges and SQI Range Name Indicating Letter Max Total Utility Min Total Utility High A-B Medium C-D Low E-F two results is shown in Figure 4. Accordingly most striking difference between genders on bus service is on the 'general cleanliness. Female passengers considered 'general cleanliness' as the most important attribute while males considered it as the least importance attribute. For both the genders bus fare is a considerable factor when selecting a bus for short distance travel. However, female passengers give more importance bus fare than male passengers. 4. DISCUSSION AND CONCLUSIONS Normalised importance values can be used to infer the importance of each factor relative to other in determining the quality of service. Results indicate that the 'bus travel time' is the most important factor determining the quality of service of the privately owned public transportation busses. Importance of the bus fare is the second most important factor and closely followed by Time of arrival. Conductor attitudes take the fourth important palace and General cleanliness shows the least importance in determining the quality of service of the privately owned public transportation busses. From the results it can be understood that passengers on public transportation want to travel to their destination with less or no time wastage. The reason might be that the considerable majority of passengers on public transportation use it to get to their work, schools and etc on time. Further it can see that bus passenger have little concern for the General cleanliness or Conductor attitude. Although the general model indicates that the general cleanliness is to be the least important factor, segmentation analysis revealed that it is the most important factor for model developed for the females. This fact has to further investigate especially for example if implementing a ladies only bus service etc. Any future studies might also have to look at the segmentation analysis for the income of the passenger and education level. Based on additive composition model a liner regression model was developed to represent the quality of service quantitatively namely QSI. Perceived quality of service by of privately owned public transportation busses increases with the increase of QSI. The QSI has range of to and has been divided into three laves of service. Ideally the range should run from 3 to -3, it is believed that with more data collected this shortcoming can be rectified. Figure 3 Calculated and the observed total utility values and the ideal results line 177

8 With limited data this study has obtained firm results however it recommended that this study has to be continued at different places on the country representing different social groups and thinking patterns so that the end result will give further more realistic results. Number of responds should be raised and Figure 4 Normalised Importance Values of Factors with Respect to Gender more data should be collected. It will further enhance the validity of the obtained results. REFERENCES Alter, C. H. (1976), Evaluation of public transit services: the level-of-service concept (abridgment), Transportation Research Board. Belwal, R. & Belwal, S. (2012), 'Public Transportation Services in Oman: A Study of Public Perceptions', Journal of Public Transportation Vol. 13(No. 4), Guo, S.; Yu, L.; Chen, X. & Zhang, Y. (2010), The Modeling of Waiting Time for Passengers to Transfer from Rail to Buses Based-on Passenger Classification, in 'Transportation Research Board 89th Annual Meeting'. Hensher, D. A. & Prioni, P. (2002), 'A service quality index for area-wide contract performance assessment', Journal of Transport Economics and Policy (JTEP) Vol. 36 (No.1), Hensher, D. A.; Stopher, P. & Bullock, P. (2003), 'Service quality-developing a service quality index in the provision of commercial bus contracts', Transportation Research Part A: Policy and Practice Vol.37 (No.6), Jr, A.; William, G. & DiCesare, F. (1976), 'Transit service evaluation: preliminary identification of variables characterizing level of service', Transportation Research Record: Journal of the Transportation Research Board Vol 606, Kononov, J., Durso, C., Lyon, C. and Allery, B., Level of Service of Safety Revisited. Transportation Research Record: Journal of the Transportation Research Board, (2514), pp Paulley, N.; Balcombe, R.; Mackett, R.; Titheridge, H.; Preston, J.; Wardman, M.; Shires, J. & White, P. (2006), 'The demand for public transport: The effects of fares, quality of service, income and car ownership', Transport Policy Vol.13 (No.4), Prioni, P. & Hensher, D. A. (2000), 'Measuring service quality in scheduled bus services', Journal of Public Transportation Vol.3 (No.2),

9 Ranawana, H. & Hewage, D. (2015), 'Factors Affecting Service Quality in Public Bus Transportation in Sri Lanka', Proceedings of 8th International Research Conference, KDU, Sri Lanka Soh, K. L.; Le Chong, C.; Wong, W. P. & Hiew, Y. H. (2014), 'Proclivity of University Students to Use Public Bus Transport Service' 179