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APPLICATION OF PROBABILISTIC NEURAL NETWORK AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR PREDICTING BUS SERVICE QUALITY AND ATTRIBUTE RANKING
Md. Rokibul Islam Graduate Research Assistant, Department of Civil Engineering Bangladesh University of Engineering and Technology (BUET), Dhaka-1000 Tel: 880-2-9665650 Ext. 7225, Fax: 880-2-9665639 ; Email:
[email protected] Md. Mehedi Hasnat Lecturer, Department of Civil Engineering Ahsanullah University of Science and Technology (AUST), Dhaka-1208 Tel: 880-2-8870422 Ext. 114, Fax: 880-2-8870417-18; Email:
[email protected] Dr. Md. Hadiuzzaman, Corresponding Author Assistant Professor, Department of Civil Engineering Bangladesh University of Engineering and Technology (BUET), Dhaka-1000 Tel: 880-2-9665650 Ext. 7225, Fax: 880-2-9665639 ; Email:
[email protected] Irfan Uddin Ahmed Research Assistant, Department of Civil Engineering Bangladesh University of Engineering and Technology (BUET), Dhaka-1000 Tel: 880-2-9665650 Ext. 7225, Fax: 880-2-9665639; Email:
[email protected] Sanjana Hossain Lecturer, Department of Civil Engineering Bangladesh University of Engineering and Technology (BUET), Dhaka-1000 Tel: 880-2-9665650 Ext. 7225, Fax: 880-2-9665639 ; Email:
[email protected]
Word Count: 5186 words + 7*250 (2 tables and 5 figures) = 6936
31 July 2015
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ABSTRACT Probabilistic Neural Network (PNN) and Adaptive Neuro Fuzzy inference System (ANFIS) are two of the most advanced Neural Network (NN) techniques available to model different transportation data sets involving significant human interactions. In this study, PNN and ANFIS are applied to develop bus Service Quality (SQ) prediction model based on user stated preferences. Specifically, using a data set extracted from 655 questionnaire survey samples, this study has developed bus SQ prediction model including 22 attributes for urban context. A comparison on the prediction capability of PNN and ANFIS is presented. From the analysis, ANFIS is found to outperform PNN in prediction capability. Furthermore, selected 22 SQ attributes are ranked according to their significance in the developed model, to identify the key attributes affecting the bus SQ. ‘Punctuality and Reliability’, ‘Seat Availability’, and ‘Service Frequency’ are found to be the top three attributes that mostly affect the decision making process of the bus service users. This study can help the bus service providers and the public transport authority to identify and improve the quality of significant attributes, and thereby, increase bus transit ridership.
Keywords: Public Transport, Service Quality, Probabilistic Neural Network, Adaptive Neuro Fuzzy Inference System.
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INTRODUCTION Public transit systems around the world are concerned to meet the ever-growing demand of mobility in an environmental friendly and energy efficient way. Transportation researchers and practitioners are concerned about solving operating problems, adapting appropriate new technologies and introducing innovations into the transit systems. Modeling of the complex information in this regard from collective data sets have become popular in recent times. Two different approaches: statistics and/or Computational Intelligence (CI) are most commonly used for modeling transportation data (1). Artificial Intelligence (AI) paradigms have become more popular to address some of the complex transportation problems. AI is used to improve the efficiency, safety and environmentalcompatibility of transportation systems (2). AI methods can be divided into two broad categories (2): (i) symbolic AI, which focuses on the development of knowledge-based systems (KBS); and (ii) computational intelligence, which includes such methods as neural networks (NN), fuzzy systems (FS), and evolutionary computing. NN are very generic, accurate and convenient CI based models due to inherent propensity for storing empirical knowledge. NN are able to simulate numerical model components easily and can be used in any of three basic manners (3): i. as models of biological nervous systems. ii. as real-time adaptive signal processors/controllers. iii. as data analytic methods. Probabilistic Neural Network (PNN) is a four layered NN architecture. Based on wellestablished statistical principles, it can map any input pattern to any number of classifications. PNN is gaining popularity in Transportation researches. It has already been used in real-time highway traffic state estimation (4), travel mode choice modeling (5), vehicle identification using pattern recognition (6) etc. Very recently Adaptive Neuro-fuzzy Inference System (ANFIS) has been used in various transportation problems. ANFIS is a hybrid multilayer feed forward network. It is used to plot an output space deriving from an input space using NN learning algorithms and fuzzy reasoning of linguistic expressions. ANFIS has been successfully used in various sectors of transportation studies; i.e. in road accident prediction (7), traffic flow prediction model (8), car following model (9), travel behavior modeling (10) etc. In combination with other ANN approaches, ANFIS was used to analyze the non-linear behavior of mode choice (11). The number of passengers a public transport (PT) system is able to attract and retain is the measure of success of that system (12). Service quality (SQ) is a very important feature of any PT facility. It depends on a series of features relating to the PT service. Berry et al. (13) pointed out that the customers are the sole judges of service quality. Therefore, appropriate perceptions of the users about the service on a regular basis are vital to establish efficient transportation strategies. Measuring from the customer's perspective, transit quality depends on the passengers’ perceptions about each attribute characterizing the service (12). Service quality and customer satisfaction can be identified in two different techniques (14): i. using statistical analysis; ii. estimation of the coefficients by modeling. Modeling of service quality attributes is a very challenging task as it is a complex, fuzzy and abstract concept (15), depending on a series of observed and unobserved variables underlying it. Although NN have been used successfully in various complex transportation problems, very few studies have been performed on SQ of public transportation system. Moreover, to the authors’ best knowledge no study can be found on bus SQ using ANFIS. In this study, two of the most advanced modeling techniques namely: PNN and ANFIS are used to predict the user based SQ of Bus transit in Dhaka. Based on a questionnaire data set, this study presents a comparison of prediction capability of PNN and ANFIS. Also PNN and ANFIS models are used to rank the most significant attributes among 22 selected attributes those affect bus SQ the most. Rest of the paper is divided into different sections. The next section briefly discusses previous works on bus SQ using different approaches; which is followed by sections with short description of PNN and ANFIS architecture; study methodology; the key findings of this study, and finally conclusion with future research directions. LITERATURE REVIEW Since the beginning of public transit quality loop in the 1990’s two distinctive measures of quality have been operationalized; namely performance-based quality measures and perception-based quality measures (16). Performance based quality measures evaluates bus performance quality from service providers’ perspectives in terms of productivity, effectiveness, and efficiency. And perception-based quality measures emphasizes on measuring the quality of bus service form users’ perspectives to
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identify different patterns of quality based on the perceptions of different categories of users. This study is concerned with the users’ perception based SQ measures and the SQ attributes those are important to the user group as they decide to use or not to use existing bus transit services. Nature of passengers and pattern of bus operations vary throughout the world with respect to geography, culture, social, economic and other factors. Variations in these aspects led to multiple approaches in measuring the service quality of PT. Methods of measuring SQ and customer satisfaction were identified in two different categories (17): i. techniques of statistical analysis; and ii. estimation of coefficients by modeling. Quadrant and gap analysis, factor analysis, scatter graphs, bivariate correlation, cluster analysis, conjoint analysis and other analytical approaches are of the first category. Regression models, structural equation models (SEM), ANN models and logit models are some modeling techniques to estimate coefficient values relating to the SQ and SQ attributes. Among the statistical methods, some provide an evaluation of the service attributes; others provide the relationship of the service attributes with overall satisfaction (17). SERVQUAL an analytical approach, proposed by Zeithaml et al. (18) is used to measure service quality of different services. Service elements like reliability, assurance, tangibles, empathy and responsiveness (RATER) considered in SERVQUAL model to reflect both expectation and perception of the customer about the provided service. Baidoo and Nyarko (19) applied Discrete Choice Experiment (DCE) and Random Utility Theory (RUT) to measure SQ of public transit. Mahmoud et al. (20) derived user preferences using Analytical Hierarchy Process (AHP) with five attributes constituting of 29 bus quality indicators. Mahmoud et al. (20) emphasized on the necessity to analyze the differential needs of each category of user groups to increase bus transit ridership as different categories of users were found to have different preferences towards bus service. Factor analysis is the most popular statistics method to analyze whether a large number of attributes are linearly related with a smaller number of unobserved variables. This was adopted to predict the satisfaction in airlines (21) and to evaluate the impact of bus transit aspects on global customer satisfaction along with SEM. Modeling of SQ and SQ attributes has become popular for the last few decades. In estimating the co-efficient values, main target is set to relate the global service quality (dependent variables) to some service quality attributes (independent variables). This helps to find out the effect of significant attributes on SQ and customer satisfaction. A number of researches on SQ have been performed using Structural Equation Modeling (SEM). A multi-attribute approach was proposed by Lai and Chen (22) using SEM to measure public transport involvement and service quality. Service attributes such as vehicle safety, facility cleanliness and complaint handing had significant influences on passenger behavioral intentions (22). SEM approach was used by de Oña et al. (12) in order to reveal the latent aspects relating to the service and the relationships between these aspects with the Overall Service Quality. Overall service quality of PT was jointly explained by those two evaluations in that research. Extensive research has been done with SEM to identify the significant attributes affecting SQ. In addition, it has been used to identify the relationships among different attributes and global customer satisfaction (23). Results from these studies helped to prioritize the important SQ attributes while selecting appropriate SQ attributes for this research. A Logit model for calculating a SQ index was proposed based on user stated preferences (17). That model might seem to be less effective in measuring SQ of urban PT, as it was concerned with only one specific user group (student). Costa and Markellos (24) proposed a nonparametric approach based on multi-layer perceptron (MLP) neural networks for measuring performance of public transport services. Considering the advantages and limitations of MLP approach, it was claimed to be superior to traditionally applied techniques since it was both nonparametric and stochastic offering greater flexibility. Uses of hybrid approaches to measure SQ of PT are also found in the literature. For example, a hybrid approach based on SERVQUAL and fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) was used for evaluating service quality of urban transportation systems (25). Tyrinopoulos and Antoniou (26) made a combination of factor analysis and ordered logit modeling to assess the quality implications of the variability of user perceived satisfaction across public transit systems. The inefficiency of the traditional models of prediction, diagnosis and optimization to deal with extremely complex social and human systems was elaborated in NRC (27). To overcome the aforementioned weaknesses, an insight for application of data mining technique was proposed by de Oña et al. (28) to analyze key factors of service quality. A decision tree model which obviates the
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unique assumptions and predefined fundamental relationships was used in that study. Artificial Neural Networks (ANN) is a non-parametric model with advantages in compliment to the tree models. Neural networks have been successfully applied to broad spectrum of data-intensive applications. Also precision in the fields of electronics, computer science, statistics, mathematics, business and medical science were also found by the application of ANN. However, no extensive research has been found on service quality of public transport using PNN or ANFIS. Learning process, capability to model non-linear relationships and scope for model validation put them on higher ground than conventional statistical approaches. As described earlier, these two advanced techniques have been successfully used in different section of transportation studies. This study is the first to adopt PNN and ANFIS to predict user based SQ of urban bus services. The next two immediate sections describe the architecture of PNN and ANFIS, accompanied by some general information about these methods. PROBABILISTIC NEURAL NETWORK PNN is a four layered neural network that can map any input pattern to any number of classifications by minimizing the “expected risk” function (29). Unlike other Artificial Neural Networks (ANNs) such as back-propagation network, it is based on well-established statistical principles derived from Bayes’ decision strategy and non-parametric kernel based estimators of probability density functions (PDFs). Parzen (30) introduced a smooth and continuous class of estimators that asymptotically approach the real density. Later, Specht (29) used it in the PNN design. PNN uses the information during testing which are stored at the time of training the network. That means, for each input data there is a node in the hidden layer. PNN is also an adaptation of radial basis network that is used for classification problems. It has a radial basis portion as activation function and a competitive portion. The four layers of PNN architecture are: input layer, pattern layer, summation layer and output layer. Figure 1 shows a PNN architecture that recognizes classes to determine bus SQ by means of a set of user attributes. The first layer shows the input pattern consists of 22 user attributes.
28 29
FIGURE 1 Architecture of PNN
30 31 32 33 34 35 36 37 38
The number of nodes in the pattern layer is equal to the number of training instances. The number of nodes in the summation layer is equal to the number of classes in the training instances. The input layer is fully connected to the pattern layer. The input layer does not perform any computation and simply distributes the values of user attributes to the neurons in the pattern layer. The pattern layer is semi-connected to the summation layer. Each group of training instances corresponding to each class is just connected to one node in the summation layer. In other words, the summation units simply sum the inputs from the pattern units that correspond to the category from which the training pattern is selected. Finally, output layer shows the estimated class extracted from summation layer. PNNs are found to be the best neural classifiers among all other ANNs due to their
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design architecture (31). Training in PNN is relatively fast as each input is shown to the network only once. Unlike the traditional neural networks, no learning rule is required to train a PNN and no predefined convergence criteria are needed. To build the network, at first, the products of the example vector and the input vector are summed. For each class node, these activations are summed. The pattern node activation (h) shown in the following equation, is simply the product of the two vectors (E is the example vector and F is the input feature vector).
8 9
The class output activations (C) are then defined as:
hi = Ei F
Cj = ∑
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
N i=1
(h −1) i λ2
e N
Where, N = total number of example vectors for this class, hi is the hidden-node activation, λ = smoothing factor.
ADAPTIVE NEURO FUZZY INFERENCE SYSTEM ANFIS is a hybrid multilayer feed forward network, which is used to plot an output space deriving from an input space. It uses neural network learning algorithms and fuzzy reasoning of linguistic expressions to get more strategic system (32). This is because, while neural network is trained, it can simultaneously be self-learned and self-improved; whereas, the fuzzy inference system can deal with linguistic expressions. Thus, ANFIS can adjust the membership functions’ parameters and linguistic rules directly from training data with the aim at improving the system performance. To generate fuzzy rules by means of a given input-output dataset, ANFIS implements a Sugeno fuzzy inference system for a logical approach (33). In the modeling process of ANFIS, the first step is the identification of the input and output variables. In a first-order Sugeno fuzzy inference system, two typical IF/THEN fuzzy rules can be expressed when a set of two inputs (x, y) and one output (f) is considered: Rule 1: IF x is A1and y is B1, THEN f1=p1x+q1 y+r1 Rule 2: IF x is A2and y is B2, THEN f2=p2x+q2 y+r2 Where, p1, p2, q1, q2, r1 and r2 are linear parameters; A1, A2, B1 and B2 are nonlinear parameters. The ANFIS architecture consists of five-layers: fuzzification, fuzzy AND, normalization, defuzzification and output layer as shown in figure 2. These layers are connected to each other through direct links and nodes. Nodes are the process units that comprise of some adaptive and fixed parameters. Adaptive parameters can be changed by setting learning rules and thus, the membership functions are reformed. First layer is the fuzzy layer, in which all nodes are adaptive nodes. The membership relationship between the output and input functions of this layer can be expressed as: 𝑂𝑂𝑖𝑖1 = 𝜇𝜇𝐴𝐴𝑖𝑖 (𝑥𝑥); i= 1, 2
𝑂𝑂𝑗𝑗1 = 𝜇𝜇𝐵𝐵𝑗𝑗 (𝑦𝑦); j= 1, 2
Here, x and y are the input of nodes Ai and Bj respectively. Ai and Bj are the linguistic labels used in the fuzzy theory for dividing the membership functions.
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22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
In the second layer, all the nodes are fixed nodes. They perform as a simple multiplier and are labeled with M. The outputs of this layer are firing strengths which can be represented as: 𝑂𝑂𝑖𝑖2 = 𝑤𝑤𝑖𝑖 = 𝜇𝜇𝐴𝐴𝑗𝑗 (𝑥𝑥)𝜇𝜇𝐵𝐵𝑗𝑗 (𝑦𝑦) ; i = 1, 2
In the third layer, the nodes are also fixed nodes. They are labeled with N, indicating that they perform as a normalizer to the firing strengths from the previous layer. The outputs of this layer are called as normalized firing strengths which can be represented as: 𝑂𝑂𝑖𝑖3 = 𝑤𝑤 �𝑖𝑖 =
𝑤𝑤𝑖𝑖
∑ 𝑤𝑤𝑖𝑖
; i = 1, 2
In the fourth layer, the nodes are adaptive nodes. For a first order Sugeno model, the output of each node in this layer is simply the product of the normalized firing strength and a first order polynomial. Hence, the outputs of this layer are given by: 𝑂𝑂𝑖𝑖4 = 𝑤𝑤 �𝑖𝑖 𝑓𝑓𝑖𝑖 = 𝑤𝑤 � 𝑖𝑖 (𝑝𝑝𝑖𝑖 𝑥𝑥 + 𝑞𝑞𝑖𝑖 𝑦𝑦 + 𝑟𝑟𝑖𝑖 ); i = 1, 2
In the fifth layer, the only one single fixed node performs the summation of all incoming signals that is labeled with ∑. Hence, the overall output of the model that comes from fifth layer can be expressed by: 𝑂𝑂𝑖𝑖5 = ∑2𝑖𝑖=1 𝑤𝑤 � 𝑖𝑖 𝑓𝑓𝑖𝑖 =
(∑2𝑖𝑖=1 𝑤𝑤𝑖𝑖 𝑓𝑓𝑖𝑖 ) ∑ 𝑤𝑤𝑖𝑖
; i= 1, 2
FIGURE 2 Architecture of Adaptive Neuro-Fuzzy Inference System (ANFIS) (32) METHODOLOGY A stated preference questionnaire survey is conducted to collect the bus users’ opinion. Collected data are then studied with the help of analyzing tools. The study procedure is chronologically outlined below. Selection of SQ Attributes The procedure suggested by Mahmoud et al. (34) is followed to select the SQ attributes. This process comprises of two main steps: analysis of bus transit users’ demands and the analysis of transit experts’ view towards service quality indicators. Primarily, all the SQ attributes are noted from the focus group discussions for both current and potential users. Another list is made from the recommendations from previous researches and transit experts (26, 35-37). Finally, a concise set of 22 SQ attributes are
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selected to carry out the study. The list of selected 22 attributes is given in table 2 along with the study results.
3 Literature Review (Preliminary list of attributes)
4 5 6
Panel of Specialists’ Opinion
Bus Users’ Desired Quality
7
Academicians
Present Users
8
Operators
Potential Users
9
Policy Makers
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
Local Authorities Bus Users Hierarchy of Importance
Desired Quality Attributes
Defined Concise sets of SQ Attributes FIGURE 3 Framework for SQ attributes selection Data Collection The bus users of Dhaka metropolitan area are targeted for this research. 30 enumerators are involved to carry out face-to-face interviews at the main bus stops around Dhaka city. The survey is conducted throughout the month of December, 2014. The survey questionnaire is planned into four sections. The first section aims to acquire information regarding socioeconomic characteristics of passengers (gender, age, occupation) and purpose of travelling. The second section focuses on 22 SQ attributes provided in a close ended layout with pertinent alternatives. The respondents are asked to mark the checkboxes from their point of view and assess the present situation of the service. The third section focuses on collecting benchmark points at a quantitative scale of 1 to 5 to rate the SQ for a particular service on a route. In that scale, 1 corresponds excellent quality and 5 corresponds to very poor quality. The fourth section aims at the respondents’ responsiveness on the significant attributes considered for rating the service. The users are asked to select at least 12 attributes upon which they have decided the SQ rating of a bus service in the previous section. The strategy followed in this survey is to assess the opinion of different age groups, gender and occupation type. Acquired sample size is 655. 70% of the respondents are male and 30% are female. In case of income (in Bangladeshi taka or BDT) distribution, 68% of the respondents’ monthly income is less than 10000 BDT, 19% respondents have income 10000 to 30000 BDT, 9% respondents’ income is in between 30000 to 50000 BDT, and 4% is have more than 50000 BDT monthly income. Age distribution falls between 18 to more than 45 years. It is found that 23%, 40%, 28% and 9% of the respondents fall in the category of 18 to 25 years, 26 to 35 years, 36 to 45 years, and more than 45 years, respectively. Occupation type revealed that 58% of the respondents are job holder (different service holder, government employee, teacher etc.), 30% are student, and 12% are businessman. Among various options, respondents are asked to tick the most pursuing reason behind choosing bus transit as their mode of travel. 75% of the respondents described necessity as their motivation, 16% respondents selected bus as the service is economical than other modes, and remaining 9% of the respondents choose bus for occasional travel (non-work based; mainly recreational) trips. According to 3% of the respondents the SQ is A (very good); 21% respondent
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indicated SQ as B (good); 42% respondent indicated SQ as C (average); 22% respondent indicated SQ as D (poor); 13% respondent indicated SQ of bus transit as E (very poor). Development of Input Data The qualitative survey is transformed to quantitative form. The selected option among the alternatives for each attributes is labeled as corresponding number. Thus, an excel sheet is prepared in such a manner that has to fed the models. The entire set of sample is randomly divided into two sub-sets containing 524 (80% of whole sample set) and 131 (20% of whole sample set) observations, respectively. Models Development (Training) The out-of-sample forecasting technique is applied to examine the predictive power of the models. Accordingly, the sample is randomly divided into two sub-samples: a training sample (80% of whole sample set) and a forecasting sample (20% of whole sample set). Two models are developed by this training sample using PNN and ANFIS tools of MATLAB 14. To get the best performance, the parameters of the PNN and ANFIS models are randomly altered by trial-and-error technique. The parameters of PNN and ANFIS models are shown in table 1. TABLE 1 Parameters related to PNN and ANFIS for Bus SQ prediction model PNN Number of input variables Number of layers Initial function Performance function Performance parameter Scaling method Training algorithm
21 22 23 24 25 26 27 28 29 30 31 32 33
34 35 36 37
22 4 initlay mse regularization normalization radial basis
ANFIS Number of input variables Number of layers Number of Membership Functions MF type Transfer function of hidden layer Sealing method Transfer function of output layer Training algorithm Training cycles, epochs Training goal
22 5 430 Gaussian tansigmoid normalization linear back-propagation 10 0.01
RESULTS AND DISCUSSIONS Models Evaluation There are several ways to evaluate the model performance. However, the performance evaluation techniques used in this study are: confusion matrix, root-mean-square error (RMSE), correlation coefficient (R). These are explained below. Confusion matrix is used to check the one-to-one matching between output classes (1 to 5) and target classes (1 to 5). The diagonal green boxes show the amounts and percentages that are identical in both output and corresponding target classes. The red boxes show the amounts of misclassifications. The right-bottom blue box shows the total correct classifications (green) and misclassifications (red) in percent (%). Correlation co-efficient(R) is defined as 𝑅𝑅 = Where, Oavg = mean of target classes; Oi = ith target class; and
∑𝑁𝑁 𝑖𝑖=1�𝑂𝑂𝑖𝑖 − 𝑂𝑂𝑎𝑎𝑎𝑎𝑎𝑎 ��𝑃𝑃𝑖𝑖 − 𝑃𝑃𝑎𝑎𝑎𝑎𝑎𝑎 � 2
�∑𝑁𝑁 �∑𝑁𝑁 𝑖𝑖=1�𝑂𝑂𝑖𝑖 − 𝑂𝑂𝑎𝑎𝑎𝑎𝑎𝑎 � 𝑖𝑖=1�𝑃𝑃𝑖𝑖 − 𝑃𝑃𝑎𝑎𝑎𝑎𝑎𝑎 �
2
Pavg = mean of predicted classes; Pi = ith predicted class.
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Root-mean-square error is defined as 2 ∑𝑁𝑁 𝑖𝑖=1(𝑂𝑂𝑖𝑖 − 𝑃𝑃𝑖𝑖 ) � 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = 𝑁𝑁
2 3 4 5 6 7 8
9 10
Where, N = total number of observations.
The predicted classifications of developed PNN and ANFIS models are shown in figure 4 by means of confusion matrix. It can be seen that, PNN and ANFIS have 75.6% and 84.0% accuracy in prediction, respectively. That is, a total of 99 predictions out of 131 match with the actual SQ value in PNN. Whereas, in ANFIS, a total of 110 prediction out of 131 match with the actual SQ value.
(a)
(b)
11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Attributes Ranking This study used 22 attributes in PNN and ANFIS models to estimate the bus SQ. As the relationship between input variables (SQ attributes) and the output variable is indistinct, ranking of these SQ attributes can be performed by analytical techniques (38). Cross-correlation, principal component analysis (PCA), and stepwise approach are some of the analytical techniques. However, this study implements stepwise approach. In addition, public opinion is also considered to rank the attributes.
26 27 28 29 30 31
Stepwise Approach In stepwise approach, cases are formed by dropping one of the attributes consecutively from input variable matrix. Separate networks are trained with the ‘training sample’ for each new case. The predictions of these networks for the ‘forecasting sample’ are estimated. After that, model performances are assessed for each case. Although model performance can be assessed by calculating the dissimilarity between actual and predicted results through average percentage error, co-efficient of
FIGURE 4 Confusion matrix for model evaluation: (i) PNN; and (ii) ANFIS Also, analyzing R and RMSE values between the predicted and actual SQ of the forecasting sample (131 data), comments can be made on the model performances. The R values of PNN and ANFIS prediction are 0.70788 and 0.79932, respectively. Whereas, the RMSE values for those models are 0.63607 and 0.50190, respectively. It can be seen that R value of ANFIS model is greater than PNN model. Whereas, RMSE value of ANFIS model is smaller than PNN model. It indicates that based on user stated preferences, ANFIS performs better than PNN in bus SQ prediction.
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determination (R2), root-mean-square error (RMSE) and correlation coefficient (R), this study used the last two measures to evaluate the effects of attributes. These criteria are compared for both PNN and ANFIS models in table 2. Here, the lesser the value of ‘R’ and greater the value of ‘RMSE’, the corresponding excluded attribute is more significant and vice versa. As an example, in case of the model developed by excluding ‘Punctuality and Reliability’, the prediction is the most inaccurate. Because, among all the other models, this model has the least R value and the largest RMSE. It means, this variable has significant control on the bus SQ determination. Conversely, ‘Commuting Period (weekends)’ is less important due to the corresponding R and RMSE values.
9 10 11 12 13 14 15 16 17 18
To determine the relative importance of the attributes, all the respondents are asked to select at least 12 out of 22 attributes that affect mostly the bus SQ. Note that these public opinion data is independent of the data used for PNN and ANFIS models development. Figure 5 shows the percentages of public opinion corresponding to all the attributes. According to 75% respondents, ‘Punctuality and reliability’ is one of the most important attribute. Around 7074% opined that ‘Service Frequency’, ‘Commuting Experience’, ‘Seat availability’, ‘Ticketing System’, ‘Comfort Level of Seats’, ‘Average travel time (weekdays)’ are also important and have significant impact on the SQ.
10
73.44 50.23
50.53
67.18 35.42
38.63
28.09
Commuting Frequency (daily)
29.62
30.38
Proximity from Workplace
19.85
30.84
30 20
39.69
40
63.97
50
53.59
72.21
Seat Comfort
58.63
73.13
Seat Availability
68.4
74.66
Punctuality and Reliability
72.82
60
70.08
73.74
70
Route Information
Commuting Experience
Noise Level
Interior Cleanliness
Structural condition
Bus Staffs Courtesy
On-time Performance
Female Harassment
On-board Security
Air Ventilation system
Accessibility to/from bus
Fare Expenditure (daily)
Ticketing System
Commuting period (weekends)
Service Frequency
0
Commuting period (weekdays)
19 20 21 22 23 24 25 26 27 28 29 30 31 32
80
Proximity from Home
PERCENTAGE OF IMPORTANCE (%)
Public Opinion
BUS SQ ATTRIBUTES
FIGURE 5 Percentages of Public Opinion for each of the Attributes
The percentages of public inclination to each attribute shown in figure 5 have been normalized and compared with each other termed as ‘Relative Importance’ in the table 2. Table 2 elucidates that ‘Punctuality and Reliability’ of bus service is the most important attribute according to all of the three methods. The sequences of relative significance within first ten of the SQ attributes revealed by PNN and ANFIS models are almost analogous to public opinion. Slight variation in the relative importance of the later attributes is observed here. This may occur due to the less sensitivity of bus users to these SQ attributes. Analyzing all the three methods, it is obvious that ‘Punctuality and Reliability’, ‘Service Frequency’, ‘Seat Availability’, ‘Commuting Experience’, ‘Ticketing System’, ‘Commuting period (weekdays)’ and ‘Structural condition’ are some of the significant SQ attributes.
12 Islam, Hasnat, Hadiuzzaman, Ahmed, Hossain 1
TABLE 2 Attributes ranking comparison among PNN, ANFIS and Public Opinion PNN Sl. No. 1 2 3
2 3 4 5 6 7 8 9 10 11 12
R
RMSE
Rank
R
RMSE
Rank
Relative Importance
Rank
Proximity from Home
0.45685
0.85605
13
0.40703
0.90377
12
0.20056
18
0.44569
0.87806
12
0.39091
0.92050
11
0.1922
19
0.54212
0.80552
15
0.57723
0.77163
15
0.15042
21
0.05790
1.19477
2
0.15452
1.23251
3
0.98329
2
0.23759
1.10516
7
0.16227
1.20114
5
0.91643
7
0.69393
0.65963
21
0.77681
0.52422
21
0.00000
22
0.17312
1.09475
5
0.18427
1.19157
6
0.96657
5
0.55845
0.78146
16
0.59264
0.75159
16
0.17827
20
0.05398
1.15910
1
0.13416
1.27812
1
1
1
Proximity from Workplace Commuting Frequency (daily) Service Frequency
5
Commuting period (weekdays) Commuting period (weekends)
7
Ticketing System
8
Fare Expenditure (daily) Punctuality and Reliability
9
Public Opinion
Excluded attribute
4
6
ANFIS
10
Seat Availability
0.08405
1.17220
3
0.14655
1.26612
2
0.97214
4
11
Seat Comfort
0.19997
1.13245
6
0.27965
1.16567
7
0.95543
6
12
Accessibility to/from bus
0.66690
0.68238
20
0.79031
0.50190
22
0.34262
16
13
Air Ventilation system
0.70537
0.64204
22
0.72882
0.57955
19
0.88579
8
14
On-board Security
0.40590
0.90377
11
0.44291
0.86932
13
0.70752
11
15
Female Harassment
0.62217
0.71516
19
0.63332
0.69896
17
0.6156
12
16
On-time performance
0.35688
0.99618
10
0.35688
0.99618
10
0.80501
10
17
Bus Staffs Courtesy
0.48098
0.84257
14
0.50169
0.83803
14
0.36212
15
18
Structural condition
0.24548
1.07717
8
0.29986
1.05208
9
0.86351
9
19
Interior Cleanliness
0.57173
0.77163
17
0.68781
0.63607
18
0.28412
17
20
Noise Level
0.58848
0.76667
18
0.76364
0.53859
20
0.55989
13
21
Commuting Experience
0.08580
1.15910
4
0.15757
1.22006
4
0.97772
3
22
Route Information
0.28725
1.06291
9
0.28626
1.06649
8
0.55432
14
CONCLUSIONS
Predicting service quality based on users’ perception is a non-linear process. Artificial neural network is a dependable tool in case of non-linear relationship. Two of the most advanced and popular techniques of artificial neural network: PNN and ANFIS have been implemented in this study to predict bus service quality based on selected SQ attributes. This study is conducted with two main objectives: i) comparison of prediction capability of PNN and ANFIS, and ii) evaluation of bus SQ attributes according to their importance. To reach the goals, two models are constructed using PNN and ANFIS structure involving all the 22 attributes. From the results, it is found that ANFIS outperforms PNN in SQ prediction capability with 84% accurate prediction. ANFIS stands superior to PNN in
13 Islam, Hasnat, Hadiuzzaman, Ahmed, Hossain 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
prediction because, ANFIS executes the combined algorithm of both neural network and fuzzy inference system; whereas PNN uses only neural network algorithm. Using R and RMSE values, most influential attributes are ranked from 1 to 22 using both the architectures. To have a better understanding, the SQ attributes are also ranked based on relative importance from the questionnaire survey. Findings of this study support the user stated preferences collected from the survey. According to both PNN and ANFIS, ‘Punctuality and Reliability’ is found to be the most significant attribute that affect the bus SQ. ‘Seat Availability’, ‘Service Frequency’ and ‘Commuting Experience’ are found to be the next three most significant attributes. ‘Ticketing System’, ‘Commuting period (weekdays)’ and ‘Structural condition’ are the also important factors. This model reflects users’ perceptions about the provided quality of bus service. From the result, the first attribute to prioritize is the punctuality of the bus service, that is the arrival time and departure time in every bus stop must be maintained properly, and reliability, that is the certainty with which the service will help the users to reach the destination in time. Seat availability is the second most important attribute which can only be improved by introducing more public buses in desired routes. This will also increase the service frequency. Similarly, an in depth examination of the results presented in table 2 will help to initiate a multitude of possible initiatives to increase the satisfaction levels of the users and thereby increase the number of PT users. Base data for the developed models are collected from Dhaka city, Bangladesh. Bus is the most dominant among all travel modes and represents 31% of all trips within the city. Due to the meteoric increase in population, the existing service system is crippling to meet the rising travel demand. The result of this study will carry some valuable information to the service providers, operators, policy makers and transportation authorities about how to improve the bus SQ. However, improving all the significant attributes’ quality at once is not possible. Being a developing country the authorities must acknowledge the limitations imposed by inadequate resources. This research provides a platform for staged development with the most significant attribute to start with. Since the application of neural network is greatly prolific and operative, author encourages ample study to be performed in context of transportation problems of developing countries. The data set used here consists of a sample size equal to 655, which is comparatively large compared to other similar studies performed on SQ of PT. However, a metropolitan area with more than 15 million inhabitants might call for a larger sample size. Further research can be done by targeting a specific groups of users, i. e. students and low income people as they are the significant portion of the bus users in Dhaka. Additionally, to attract the private vehicle owner to use PT, inputs from the higher income group are also important. ACKNOWLEDGEMENT
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