2016 3rd International Conference on Information and Communication Technology for Education (ICTE 2016) ISBN: 978-1-60595-372-4 Attitudinal Market Segmentation for Transit Riders Using Factor Analysis Sida Luo, Wenyang Gu Transportation Center, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, United States ABSTRACT: Increasing the ridership of urban transit system is crucial for sustainable development of transportation. Previous studies have succeeded in using attitudinal market segmentation to understand the travel demand. However, those related to transit market mostly depend on complex statistical methods including structural equation modeling (SEM) to extract latent attitudinal variables. It adds difficulty to their real-world applications. This study uses simple principle component analysis (PCA) method to help the extraction of attitudinal variables. Results show that PCA is able to provide significant market segmentation. Policy implementations are tailored to the four submarkets, which is valuable to transit system development. 1 INTRODUCTION Developing urban transit system is the way to go for alleviating traffic congestions in cities with large population and dense land use. In order to promote the ridership of public transit, characteristics of travel demand need accurately understanding. An attitudinal market segmentation approach can help capture the travel demand through getting insight into the heterogeneity of transit riders. Accordingly, effective strategies and policies designated to different submarkets of bus users can be proposed to promote the development of transit system (Elmore 1998). Literature has received much attention in using attitudinal market segmentation to understand travel behaviors [Anable 2005, Heinen 2010, Li 2013, Outwater 2003, a, b, Shiftan 2008]. Its advantages in exploring latent contributing factors have been recognized and validated. For example, Shiftan et al. (2008) used SEM to identify potential transit markets, and found that sensitivity to time, need for fixed schedule and willingness to use transit were the best attitudinal factors to define market segments. Similar research was conducted by Li et al. (2013), coming to the result of 6 bicycle commuting submarkets. In addition to policy implementations, they found most socioeconomic attributes do not show substantial characteristics among these submarkets. Meanwhile, the segmentation approach based on other variables such as socioeconomics, level of service and trip attributes has also been used in travel demand modeling (Badoe 1998, Clark 1998, Xing 2009). Those studies mostly used SEM to extract latent attitudinal variables (Shiftan 2008, Outwater 2003, a, b, Li 2013). PCA, a factor analysis approach, is rarely used in transit market analysis. Compared with SEM, PCA is more straightforward and easier to use, so it is more promising for traffic practitioners. As a result, this study applies two factor analysis approaches along with K-means clustering to conduct transit attitudinal market segmentation. Some policy implementations are put forward, which are of significance to the development of sustainable transportation systems. Table 1. Descriptive statistics of the attitudinal questions. Question 1 2 3 4 5 6 7 8 Content I will reserve some time when I go to work or school to arrive on time. I prefer buses with less number of transfers. If the bus is not crowded, I prefer using it. If the bus saves travel time, I can pay more to take the bus. I always fully use my bus travel time or waiting time. If the transfer walking distance is short, I will travel by bus. If the bus is very crowded, I will prefer getting off earlier. If I am more comfortable in travelling, I can pay more for the bus. Mean 4.03 4.15 3.66 3.66 3.50 3.41 3.37 3.57 Std. 0.81 0.71 1.02 0.90 0.77 0.98 0.98 0.92 9 10 11 12 13 14 15 I am usually in a hurry when going to work or school. If there is a bus route without transfer to my destination, I will use that route. At the bus stop, I usually do not board until the coming bus has available seats. Even if the bus fare goes up, I will still use the bus. If I am using the bus, I need to arrive at my destination in possibly shortest time. Even if the transfer stop is crowded, I consider the transfer route as an alternative. I cannot stand waiting a bus for a long time. Figure 1. Pie charts of each socioeconomic attribute. Figure 2. Scree plot for EFA. 3.62 4.18 3.27 3.47 3.86 3.32 4.06 0.83 0.71 1.03 0.88 0.79 0.93 0.89 2 METHODOLOGY 4 RESULTS 2.1 Explanatory factor analysis (EFA) 4.1 Identification of latent attitudinal variables EFA is a statistical method to identify the underlying relationships among a large set of variables, and reduce data dimension such that only a few core factors remain. The remaining variables are known as latent variables. If the slope evidently changes at one point in the scree plot, the corresponding variable should be a latent variable (Cattell, 1966). EFA involves three main steps: extraction, rotation and interpretation. The attitudinal variables are identified by EFA. Using SPSS Statistics 19 to do the analysis, it takes 6 iterations to converge for our dataset. The scree plot is shown in Figure 2. As a result, 4 latent attitudinal variables, A1, A2, A3, A4, are extracted from the 15 original statement variables, V1, V2, …, V15, which explains 64.55% of the total variance. A1 associated with V1, V5, V9, V13, V15 reflects time sensitivity; A2 associated with V3, V7, V11 reflects comfort level. A3 associated with V4, V8, V12 reflects cost sensitivity; A4 associated with V2, V6, V10, V14 reflects transfer sensitivity. The Bartlett’s test of sphericity gives a χ2 of 2327.6 showing the results are significant at 0.05 level. 2.2 Principle component analysis (PCA) PCA is essentially an orthogonal linear transformation. It is a standard procedure to convert a set of possibly correlated variables into a set of linearly uncorrelated variables. The resultant variables are called principal components. 2.3 K-means clustering (KMC) KMC is one of the widely-used unsupervised learning algorithms. It aims to partition observations into different clusters. The algorithm will perform a clustering to minimize the distance inside each group while maximizing the distance between centers of groups. In this study, the objective of KMC is exactly to group transit riders with similar travel attitudes into submarkets. 3 DATA RESOURCE A survey was conducted in Nanjing, China in April 2014. It includes questions regarding socioeconomic attributes and attitudes towards bus travel. The attitudinal questions (or statements) are carefully designed to reflect travelers’ attitudes that may affect their choice of transit. The effective sample size is 600, which is used in this study. The responses are coded as: 5 – strongly agree; 4 – agree; 3 – somehow agree; 2 – disagree; 1 – strongly disagree. Results of some basic statistical analysis are shown in Table 1 and Figure 1. It shows that people tend to have consensus on the effects of transfer – decreasing transfer times is likely to make transit more appealing. In addition, people do not have extremely strong needs for seats and much variability can be observed among them. Having a seat is not a key factor that affects transit ridership. As for socioeconomic attributes, some bias could be seen from the sample. For instance, there are more men than women, more people with higher education, more unmarried people with no kids, more people without automobiles and more people with bicycles. However, the survey has succeeded covering transit users with different attributes. The bias is not so evident to affect validity of our results. 4.2 Score of latent attitudinal variables The score of those latent attitudinal variables will be utilized for cluster analysis. PCA can help identify 5 principle components, M1, …, M5, from the 15 statement variables. Accordingly, a standardized component matrix can be obtained where the statement variables are ordered and grouped based on the 4 attitudinal variables. The matrix is shown in Table 2. Then, we collapse the statement variables into attitudinal variables and calculate sij, the score of variable i under major component j, according to the information in Table 2. For example, Table 2. Standardized component matrix for PCA. V1 V5 V9 V13 V15 V3 V7 V11 V4 V8 V12 V2 V6 V10 V14 M1 M2 M3 M4 M5 0.248 0.242 0.255 0.315 0.272 0.221 0.231 0.262 0.292 0.296 0.286 0.215 0.197 0.288 0.219 -0.272 -0.196 -0.130 -0.224 -0.233 0.459 0.486 0.432 0.121 0.128 0.053 -0.224 -0.046 -0.198 -0.086 0.130 -0.043 0.029 0.091 0.078 0.270 0.214 0.210 -0.461 -0.471 -0.474 0.262 0.162 0.136 0.176 -0.156 -0.148 -0.152 -0.191 -0.295 -0.116 -0.077 -0.093 0.054 0.026 0.112 0.019 0.635 0.018 0.599 -0.042 0.390 0.353 0.173 0.055 -0.189 0.030 0.218 -0.230 -0.156 0.089 -0.520 0.096 -0.448 0.199 Table 3. Matrix for score calculation. A1 A2 A3 A4 wi M1 s11 s21 s31 s41 0.380 M2 s12 s22 s32 s42 0.204 M3 s13 s23 s33 s43 0.153 M4 s14 s24 s34 s44 0.143 M5 s15 s25 s35 s45 0.119 s11 = 0.248V1+0.242V5+0.255V9+0.315V13+0.272V15 (1) A matrix [sij] can be obtained in this manner. The weights of principle components wi are determined by their eigenvalues that are λ1 = 3.677, λ2 = 1.977, λ3 = 1.486, λ4 = 1.387 and λ5 = 1.156 from the output, respectively. Note that the eigenvalues have already been used to obtain the standardized component matrix in Table 2 where column j is the eigenvector ηj associated with λj. According to the eigenvalues, we obtain the weights wi = λi /(λ1+λ2+λ3+λ4+λ5) (2) The matrix [sij] and results of the weights are shown in Table 3. Then, scores of the 4 latent variables, s1, …, s4, are calculated by si = w1si1+w2si2+w3si3+w4si4+w5si5 (3) The factor analysis approaches have helped extract the latent attitudinal variables, making it ready for the market segmentation. 4.3 Transit market segmentation by KMC 4.3.1 Four transit attitudinal submarkets Time Sensitivity (A1), Comfort Level (A2), Cost Sensitivity (A3) and Transfer Sensitivity (A4) are the 4 variables that is used for KMC. SPSS is also used here. Experiments show that when the number of clusters is 4, results are significant and the clusters are independent of each other. Convergence is achieved in the 9th iteration. Logistic regression is used to test the correlation between different segments. None of the regression parameters are significant at 0.01 level and little correlation is found between the segments and socioeconomic features. This shows the result of clustering is statistically significant. 4 clusters correspond to 4 submarkets and the number of travelers in the submarkets is 76, 121, 140, 263 and 600 in sequence. 4.3.2 Characteristics of submarkets The four transit submarkets have distinct attitudinal characteristics, which can be visualized in Figure 3. Submarket 1 is a group of transit users with high time sensitivity, low demand of comfort level, medium cost sensitivity and high transfer sensitivity; submarket 2 is a group of transit users with low time sensitivity, medium demand of comfort level, low cost sensitivity and medium transfer sensitivity; submarket 3 is a group of transit users with high time sensitivity, high demand of comfort level, medium cost sensitivity and low transfer sensitivity; submarket 4 is a group of transit users with high time sensitivity, high demand of comfort level, high cost sensitivity and high transfer sensitivity. 4.3.3 Policy implications for submarkets This approach groups travelers with similar attitudes towards transit. Characteristics of various segments can help traffic planners develop targeted policies that best serve the needs of each submarket and therefore promote transit ridership. Travelers in submarket 1 and 4 are very sensitive in time and bus transfers. To encourage them to use transit, rapid transit service that connects their major work and residential districts can be designed. This makes travel time shorter and more predictable, and the number of transfers is expected to decrease. Another direction is to improve the transfer facilities to decrease transfer distance and time and improve transfer safety and environment. Also, bus information can be available at the facilities such that the travelers will feel more certain about their waiting time if a transfer is needed. Travelers in submarket 2 have both low time and cost sensitivity. They tend to be regular transit riders who are rather insensitive to the level of service. Captive riders probably belong to submarket 2, whose travel demand by bus is basically inelastic. Travelers in submarket 3 have strong desires in comfort level. Providing buses with better environment such as more seating space and less noise can make them more competitive to private cars. A good strategy is to update transit vehicles with moderate cost, which is highly likely to attract these travelers. Travelers in submarket 4 have high cost sensitivity. It will be a bad strategy to increase the ridership of buses if transit companies raise the fares when a large proportion of these transit riders is observed. Providing conventional transit service with normal price, which is relatively low, will be good for this submarket. Figure 3. Radar chart of various clusters. 5 CONCLUSIONS This paper uses attitudinal market segmentation approach to study the transit market. With the help of factor analysis approaches, latent attitudinal variables are extracted, based on which the K-means clustering is applied to achieve the segmentation. 4 attitudinal segments are found, and policy implementations are specifically provided according to the characteristics of each submarket. For future research, the SP survey on attitudes is expensive to some extent, and it is unable to know the attitude of transit riders in future years. These pose some limitations for the approach to be applied to transport planning. Therefore, it is of great significance to directly predict the attitudinal segmentation based on socioeconomic attributes that can be obtained from some data used for urban planning. Machine learning can play the role. Finding good machine learning methods is the direction for follow-up studies. 6 ACKNOWLEDGMENT The authors would like to thank the colleagues in the Transportation Center of Northwestern University for their valuable time and advice. REFERENCES [1] Anable, J. 2005. ‘Complacent car addicts’ or ‘aspiring environmentalists’? Identifying travel behavior segments using attitude theory. Transport Policy 12 (2005): 65-78. [2] Badoe, D.A. & Miller, E.J. 1998. An automatic segmentation procedure for studying variations in mode choice behavior. Journal of Advanced Transportation 32 (2): 190-215. [3] Clark, D.E. 1998. Estimation future bicycle and pedestrian trips from a travel demand forecasting model. Transportation Research Board, Washington, DC. [4] Elmore, Y.R. 1998. A Handbook: Using Market Segmentation to Increase Transit Ridership. TCRP Report 36. Transportation Research Board, Washington, DC. [5] Heinen, E., Maat, K. & Wee, B.V. 2011. The role of attitudes toward characteristics of bicycle commuting on the choice to cycle to work over various distances. Transportation Research Part D 16 (2011): 102-109. [6] Li, Z.B., Wang, W., Yang, C. & Ragland, D.R. 2013. Bicycle commuting market analysis using attitudinal market segmentation approach. Transportation Research Part A 47 (2013): 56-68. [7] Outwater, M.L., Castleberry, S., Shiftan, Y., Ben-Akiva, M.E., Zhou, Y.S. & Kuppam, A. 2003. Use of structural equation modeling for an attitudinal market segmentation approach to mode choice and ridership forecasting. 10th International Conference on Travel Behavior Research 1015 August 2003. Lucerne, Switzerland. [8] Outwater, M.L., Castleberry, S., Shiftan, Y., Ben-Akiva, M.E., Zhou, Y.S. & Kuppam, A. 2003. Attitudinal market segmentation approach to mode choice and ridership forecasting: Structural equation modeling. Transportation Research Record 1854: 32-42. [9] Shiftan, Y., Outwater, M.L. & Zhou Y.S. 2008. Transit market research using structural equation modeling and attitudinal market segmentation. Transport Policy 15 (2008): 186-195. [10] Xing, Y., Handy, S.L. & Mokhtarian P.L. 2010. Factors associated with proportions and miles of bicycling for transportation and recreation in six small US cities. Transportation Research Part D 15 (2010): 73-81.
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