- Email: [email protected]
The quality of service desired by public transport users
Transport Policy 18 (2011) 217–227
Contents lists available at ScienceDirect
Transport Policy journal homepage: www.elsevier.com/locate/tranpol
The quality of service desired by public transport users Luigi dell’Olio n, Angel Ibeas, Patricia Cecin Department of Transport, University of Cantabria, Avda. de los Castros s/n, 39005, Santander, Cantabria, Spain
a r t i c l e in fo
Keywords: Desired quality Stated preference survey Efficient design Multinomial logit Focus groups
abstract This article describes the methodology used to study the quality of service desired by users of a public transport system. The desired quality is different from the perceived quality because it does not represent the daily experiences of the users, but rather what they desire, hope for or expect from their public transport system. This is why it is important to study the desired quality, knowledge of which gives local authorities the background information for personalised marketing policies based on the user’s requirements rather than their daily perceptions. The methodology goes through several stages, such as the use of focus groups to choose the most important variables for the users, the design and use of unlabelled stated preferences surveys and the calibration of discrete choice models. All of these help determine the weight of the most relevant variables. The analysis is carried out with different categories of users and potential users (those people not currently using public transport). Waiting time, cleanliness and comfort are shown to be the public transport variables that users most valued, but the degree to which they are valued varies according to the category of user. Variables such as driver kindness, bus occupancy and journey time are generally given less weight. The first two vary little by user category, but some variability appears for journey time. For potential users the more important variables when defining expected quality from public transport are waiting time, journey time and above all, level of occupancy. They consider the other variables to be of little importance when defining an efficient public transport service. In order to improve service quality and attract more passengers to public transport in general, the application of this methodology provides the authorities and operating companies with useful information to plan personalised marketing policies specifically directed at different categories of users and potential users of public transport. & 2010 Elsevier Ltd. All rights reserved.
1. Introduction Improving the quality and efficiency of public transport is important if we are to change the daily transport habits of the public. Congestion in urban areas and its immediate and wider consequences on the climate are pushing central and local governments to instigate sustainable transport policies. These policies require an ever more personalised attention to the desires of the customer, to know and quantify the most influential variables on their decision to travel in public transport. This makes it essential to define both the policies and the strata (categories) of users or potential users towards whom the policies should be directed. These factors come within the overall umbrella of improving the quality of the service offered in order to attract more customers. International literature provides much research on user ‘perceived quality’ (dell’Olio et al., 2010) of a public transport
n
Corresponding author. Tel.: +34 942201734; fax: + 34 942201703. E-mail address: [email protected] (L. dell’Olio).
0967-070X/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tranpol.2010.08.005
service or system. Most of these studies relate the quality of each service variable with the importance assigned to it (Glascock, 1997; Foote and Stuart, 1998). The quality of a public transport system is covered by many factors, such as considerations relative to comfort and safety within the vehicle, the time taken to cover the routes and the convenience and existence of any supporting infrastructure (Molinero and Sanchez, 1997). Attempts have been made to relate the quality of service to the conditions stipulated in the initial concessionary contract (known as contracts of interested management). These guarantee a demand for public transport and rationalise the perception of public subsidies to private enterprise (Hensher et al., 2003; Hensher and Stanley, 2003; Hensher and Houghton, 2004). This paper offers a new point of view by introducing a new idea of quality: the quality the user and potential user desires (or hopes for). Traditional research on perceived quality provides the operating companies with knowledge on the impact their decisions have on their customers, while a study on desired quality gives them more in-depth information about their customers and what they want
218
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
2.1. Objectives of the study and its methodology
surveys which are used to model hypothetical, but realistic, situations presented to the users of an urban bus service (Hensher, 1994). Knowledge about the desired service quality provides operating companies with an answer to their investment questions and establishes the basis for designing future policies to encourage greater use of public transport based on the needs and expectations of their existing and potential customers. One of the specific objectives was to ascertain the importance, or weight, that the users of public transport place on certain improvable variables such as waiting time at the bus stop, in-vehicle journey time, vehicle occupancy, cleanliness of the vehicle, standard of customer service of the driver when dealing with passengers (driver kindness) and the comfort of the buses during the journey. The variables were chosen using a process of social participation through focus groups. Three unlabelled stated preferences surveys presented interviewees with a series of alternatives from which they chose the one they found the most satisfactory according to their criteria and personal situation. The design methodology for the stated preferences surveys is detailed in Fig. 1 and summarised as follows:
This research aims to find out what users and potential users of public transport look for in the service they are offered (desired quality) and what they would like to receive in exchange for their ticket and their transfer from one point to another, in other words, what they expect from an efficient and high-quality service. Once we know the desired quality, tools exist that may help improve public transport planning (e.g. for the bus mode) within a setting of sustainable mobility. The concept of ‘desired quality’ must be differentiated from that of ‘perceived quality’ which comes from what the users have experienced and perceived on public transport. The concept of ‘desired quality’ defines what they want to obtain to be fully satisfied with the service offered. Therefore, the ‘desired quality’ reflects the maximum level of utility to which the users and potential users of public transport aspire. It is important to realise that the service desired by the users and potential users must always be feasible. Waiting times and journey times equal to zero belong to a very desirable, but not feasible, scenario. Information is obtained from stated preferences
1. Design a preliminary survey after an extensive state-of-the-art bibliographic review. 2. Test the preliminary version by focus group. 3. Use a SWOT matrix analysis (strengths, weaknesses, opportunities, threats) on the conclusions from the focus group and then design and administer the pilot surveys on a small sample. These pilot surveys check the soundness of the survey design (it was unnecessary to modify the design of the overall form of this survey). 4. Use the data collected in the pilot survey for the efficient design (using a measure known as D-error) of the definitive survey. 5. Administer the definitive surveys to the defined sample. 6. Model bus user and potential user behaviour for their desired quality from public transport after collecting and digitalising the survey data. 7. Quantify the effects of future policies after modelling the sample’s behaviour to find more efficient policies based on the needs and aspirations of existing and potential users.
from the service so they can develop more acceptable policies. Finally, local authorities and transit operators should try to make the perceived quality as similar as possible to the desired quality. This paper could serve as a basis for the future establishment of quality indexes based on the existing differences between desired and perceived quality. Desired quality is shown to vary with user category. This provides valuable information for planning personalised marketing policies directed at different user categories (including potential users) to improve the quality of service and attract more people to public transport and move towards more sustainable mobility. The article is divided into the following sections: objectives and methodology; data collection phase along with some conclusions from the survey performed; the modelling process with a discussion of the results; and finally, the most important conclusions are presented.
2. Objectives and methodology
Fig. 1. Summary of design process, designing the stated preferences survey and its modelling.
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
The definition of many variables meant that three surveys were administered. Interviewee interest and concentration are known to be limited (Ortu´zar and Willumsen, 1994) when faced with an extensive questionnaire, meaning that the survey should not be too long. Nor should the interviewees be asked to compare too many variables as this complicates the establishment of orders of preference, meaning the results of the survey may turn out to be false. It was, therefore, thought better to break up the stated preference experiments into three surveys, with the result that each individual was presented with eight scenarios, each having three choice alternatives with three defining variables with different degrees of variation. The surveys were conducted both on board the buses and at the bus stops, thereby accessing a wide spectrum of users. Before designing the survey, a focus group was used to deduce the most significant variables for the users of public transport. These variables were later more clearly defined in the pilot surveys. These surveys formed the basis for writing the definitive surveys designed using the D-error (Rose et al., 2008). The D-error is used to create an efficient design and takes the data obtained in the pilot surveys as a basis for defining the situations presented in the definitive surveys. Once the answers obtained in the surveys were digitalised, one unique database could then be used with discrete choice models, particularly multinomial logit models. Studies looked at how different user categories (defined according to age, sex, income level or the frequency they used public transport) interacted with the representative variables of desired quality: waiting time, journey time, vehicle occupancy, level of cleanliness, driver kindness and comfort. The study was carried out in the port town of Santander, capital of Cantabria, located on the north coast of Spain. It has a registered population of about 185,000 inhabitants, but taking peripheral boroughs into account this figure increases to 250,000 people. The municipal urban transport service uses 19 daily bus lines and three nocturnal bus lines throughout the city connecting the periphery with the town centre. The bus network practically covers the whole city. It has been estimated that each person has a bus stop no more than 300 m from their home. In 2008 the number of journeys carried by this network grew to 20 million. Table 1 shows the headway, the average waiting time, average journey time and the reply rate for each line.
219
Table 2 Modal distribution for the cities of Santander, Seville and Graz in 2008. Percentage of trips by mode (%)
Santandera Sevillab Grazc
On foot (%)
Bus (%)
Car (%)
Bicycle (%)
Others (%)
36 31 19
14 12 19
46 50 48
0 0 14
4 7 0
a Data obtained of home survey carried out in Santander (November, 2008) for the project ‘‘PRO-BIKE. Planning and Management Methodologies for Promoting Cycling Strategies’’. CEDEX (Centro de Estudios y Experimentacio´n de Obras Pu´blicas). b Data of the mobility in Seville obtained of the document ‘‘Observatorio de la Movilidad Metropolitana’’ (2009). Ministerio de Fomento y Ministerio de Medio Ambiente y Medio Rural y Marino. c ¨ Data of the mobility in Graz obtained of FGM 2008, Mobilitatsverhalten der ¨ Grazer Wohnbevolkerung.
The data in Table 1 come from a revealed preferences survey administered before the stated preferences survey studied in this research. This revealed preferences survey allowed us to characterise the typical public transport user and journey in Santander. Other cities better known throughout the world than Santander but with a similar modal distribution are Seville in southern Spain and Graz, the second most important town in Austria. In both cases the private car is the most popular mode of transport, favoured by over 45% of the population. Public transport is used by 12–19% of travellers. The most noteworthy difference is in the use of the bicycle. In Graz bicycles are used by 14%, while in Santander and Seville less than 1% uses this mode of transport. The modal distribution is similar when comparing alternative modes (on foot and bicycle) to motorised modes of transport: Santander 36%, Seville 31% and Graz 33% (Table 2). The modal distribution in 2008 for Santander, Seville and Graz is shown in Table 2. The table shows that the most common method for getting around Santander is the private car. This is followed by walking; Santander is a medium-sized town where the average journey distance is less than 5 km. The city’s bus service is the third most popular mode of transport. It is important to know what the public would like from their transport service in order to increase ridership through efficient, targeted policies. 2.2. Focus groups
Table 1 Summary of headway, average waiting time, average journey time and reply rate for each line. Line
Headway (min)
WT (min)
JT (min)
Reply rate (%)
L1 L2 L3 L4 L5C1 L5C2 L6C1 L6C2 L7C1 L7C2 L11 L12 L13 L14 L16 L17 L18 L19 Average
16 16 20 12 12 12 30 30 16 16 30 30 30 20 30 30 30 30 23
7 8 7 8 8 8 15 11 7 7 9 11 7 8 9 13 14 7 9
17 17 15 17 14 13 21 18 14 14 9 18 17 9 14 18 17 18 16
89 82 77 75 80 83 72 76 81 79 78 77 82 85 70 83 65 68 78
Focus groups are a qualitative investigation technique following a workshop-style methodology based on meetings using a predetermined group of individuals chosen for the purpose. The objective is to obtain information about their points of view, life experiences, expectations and knowledge about a specific subject matter (Ibeas et al., 2010). In this case the subject was public transport in the city of Santander. The focus group provides a type of ‘group opinion’ which encapsulates the personal perceptions of each individual, where all the answers are valid and which provides basic information for experimental design. The focus group is a tool used to assist in choosing the relevant variables and is an important starting point to complete and improve the design of the survey (see Fig. 1, points 3 and 4). This particular research work used a focus group of eight participants, four of whom were young people, two adults and two mature adults. The idea was to represent the users of public transport in Santander as closely as possible. The subjects dealt within the focus group allowed us to define the variables used in the pilot stated preferences survey, covering quality in general but particularly desired quality in public transport (Garrido and Ortu´zar, 1994; Powe et al., 2005; Rizzi and Ortu´zar, 2006).
220
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
The users considered the following variables to be the most sensitive when defining desired quality in a public transport service:
Waiting time at the stop Journey time on the bus Vehicle occupancy The cleanliness of the vehicle The driver’s kindness The comfort of the buses
The focus group followed guidelines to help the moderator organise and direct its development, thereby guaranteeing that all the subjects of interest to the research were dealt with. The guidelines followed are shown in Fig. 2. Only one focus group was held because of the ample knowledge already available to the research team on the quality of public transport in the city. This knowledge came from a revealed preferences survey held in the same city some months before the stated preferences survey covered in this article. The revealed preferences survey was used to characterise the users of public
Fig. 2. Guidelines followed in the focus group.
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
transport and the journeys made and covered perceived quality by bus users.
2.3. The definitive survey The pilot surveys were carried out just before preparing the questionnaire for the definitive surveys in order to check how much the interviewees understood the situations with which they were presented and their format. The data collected in the pilot survey also served as the basis for an efficient design, as explained below. The application presented in this work used 36 stated preferences interviews conducted on the buses as part of the pilot surveys in Santander and provided 864 observations. Each interviewee responded to eight scenarios and each scenario provided three observations (this was a hierarchical survey using three cards for each scenario). The team accepted the design of the cards as the public easily understood them and they provided the necessary data on which to base the efficient design of the definitive surveys. The design of the definitive stated preferences surveys followed the methodology proposed by Rose et al. (2008). They suggested a design for estimating parameters with minimum possible standard error by determining the variance–covariance matrix (VCM) based on previous experimentation and already known information, normally the parameters of the utility function. These parameters were obtained from information collected using the 864 observations in the pilot survey. The D-error was used as a measurement to check the efficiency of the design obtained by calculating O (VCM) and applying a scaling factor taking into account the number of parameters K to be estimated, or D-error ¼ detðWÞ1=K
ð1Þ
The experimental design used eight scenarios, considering the following attributes and their levels (varying with the survey performed):
Waiting time at the bus stop: 5, 10, 15 and 20 min Journey time on the bus: 15 and 30 min Vehicle occupancy: 50% and 80% Cleanliness of the vehicle: clean and dirty Driver’s kindness: good and bad Comfort of the buses: good and bad.
The design of these scenarios started from the data obtained in the revealed preferences survey mentioned earlier. This revealed preferences survey allowed us to characterise the journeys made on public transport (waiting timeE 10 min, journey timeE15 min, etc.). This data was then used to define the ‘typical journey’ in the city. The modifications made using this typical journey established the scenarios presented in the survey. The designs with the least D-error were obtained from 5000 iterations using an algorithm based on columns as suggested by Huber and Zwerina (1996). Table 3 shows the size of the D-error obtained and its corresponding iteration. The definitive surveys each had eight scenarios in which the interviewee had to choose from three alternatives in each case. This made it possible to study the six attributes organised as shown in Table 4. The working variables were defined to the interviewee before starting the survey. Furthermore, they were shown photographs which clarified the more complicated concepts or variables they would be asked about (cleanliness (clean or dirty), vehicle
221
Table 3 D-error obtained for each survey. Measure
Survey 1
Survey 2
Survey 3
D-error Iteration
0.202 382
0.05 531
0.07 332
Table 4 Summary of the values of the attributes of the stated preferences surveys on quality. Variables studied
Survey 1
Survey 2
Waiting Time (min) Vehicle Occupancy Cleanliness of the Bus Journey Time (min) Driver kindness Comfort on Journey
5 10 High Clean
5
15 20 Low Dirty
10
15 Good
15
30 Bad
Survey 3 20
5
10
15
15
30
Good
Bad
20
occupancy (half full or full) and comfort (agreeable conditions or not so comfortable)).
3. Collecting the information The three definitive stated preferences surveys were defined by eight situations in which the interviewee must choose from three hypothetical alternatives, each defined by a series of attributes with possible values. Each survey studied a group of three attributes; waiting time was used as a link between the results of the three surveys as it was the only common variable. Survey 1 looked at the relationship between waiting time at the bus stop, vehicle occupancy and its cleanliness, while Survey 2 checked the relationship between waiting time, journey time and driver kindness and Survey 3 studied the relationship between waiting time, journey time and comfort during the journey. Examples of the surveys performed are shown in Figs. 3–5. Three of each of the surveys have the three attributes available in Situation 1, the three cards on the left correspond to Survey 1, the three in the middle to Survey 2 and the three on the right to Survey 3 (Fig. 6). These surveys were asked to both users and potential users of public transport. As these were stated preferences surveys, the sample size is determined by the desired quality of data. The formula used to determine the sample size depends only on the parameters of the models obtained as a function of the data from the pilot survey and their standard deviation: !2 1:96 seðb^ k Þ N4 ð2Þ b^ k
where b^ k are the coefficients obtained from modelling the pilot survey data, seðb^ k Þis the standard deviation of each of the coefficients. The surveys were conducted at rush hour on working days, both on board the buses and at bus stops distributed along the bus routes of Santander. A total of 305 completed surveys were returned covering an ample spectrum of people with their respective opinions (this hierarchical survey provided 7320 observations from eight scenarios with three observations each). The information covered the needs of the ‘frequent’ or ‘very
222
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
Fig. 3. Example of the stated preference cards used in Survey 1.
Fig. 4. Example of the stated preference cards used in Survey 2.
Fig. 5. Example of the stated preference cards used in Survey 3.
Fig. 6. User characterisation survey.
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
frequent’ user when defining the bus service as efficient and the requirements of the ‘sporadic or infrequent user’ with the idea of thinking about a modal change towards using the bus as their normal mode of tranport, i.e. what they hoped to receive from the service in order to guarantee the change of mode. The survey on potential bus users began by asking people on the street (parking their car or walking) if they currently used public transport. If the reply was negative a characterisation survey was carried out along with exactly the same stated preferences survey asked to the users of public transport. Overall city coverage was obtained by conducting the surveys on potential users both in the city centre and in peripheral zones. The total sample was distributed into the socio-economic categories shown in Table 5. The surveys were designed with unlabelled alternatives, i.e. the interviewee was only presented with the situations defined in function of certain journey characteristics. They were asked to give an order of preference in each situation, and the three situations were placed in order from higher to lower preference. 3.1. Data analysis Table 5 shows the socio-economic characterisation of the users and potential users from data obtained in the characterisation survey performed before the stated preferences surveys. The public transport user characterisation form is shown below. The form used to characterise the potential users is very similar and only differs in two questions:
Why did they not use the bus? What mode of transport did they use for their journey? Several important observations appear from studying the sample’s socio-economic characteristics (presented in Table 5) before even starting the modelling phase. These could serve as general guides when planning policies for promoting the use of public transport. Table 5 Distribution of the sample into different socio-economic groups. Category
Sub-category
Total sample Users (%) Potential users (%)
Sex
Man Woman Frequency of use Very frequent Frequent Casual Age Under 30 Between 30 and 60 Over 60 Income level Low household income Medium household income High household income
32.13 67.87 62.62 12.46 24.92 75.74 17.38 6.89 22.3 23.61 54.1
223
One of the first observations was that the population using public transport in Santander is mainly composed of people under 25 years old. The next important group by number was the age range from 25 to 34. The age distribution was practically the same for the potential users of public transport. This classification by age range is important as it helps the authorities know where to direct possible future policy changes to promote transit use. The sex variable shows that most users of public transport are women, approximately seven out of ten, meaning that they should be an important consideration when making decisions on service improvement. There are also more women than men among the potential bus users, but with a rather less important gender difference (56% women against 44% men). Working categories must be defined before proposing the types of users found on public transport. They are defined according to the frequency they use the bus: very frequent users use the bus on a daily basis; frequent users use the bus on a weekly basis, at least twice a week; casual users only occasionally, under certain conditions, use public transport. For the group of potential bus users, frequency of journey referred to the frequency they made the journey they had just finished or were making at the time they were asked the survey. The bus users were also asked to give their overall evaluation of the service, rated by marks from one to five. Table 6 shows how most users give a pass rating to the service provided, the average mark from the three surveys was 3.45 (out of five). The potential users were more critical of the city’s transport system than the users were, with an average score of 2.88 out of five. Quite a high percentage, almost 20% of interviewees, felt they did not have enough knowledge to be able to score the public transport service.
4. Modelling and discussion of the results Once all the possible information from the stated preferences surveys was sorted and placed into a unique database, different models were looked at to explain what the users of public transport expected from the service provided (quality desired or expected). These were reflected in a series of representative journey variables defined beforehand. Multinomial discrete choice models were used in estimating the relative bearing of each variable on the desired service quality. These models are based on random utility theory. It is assumed that this utility can be represented by two components:
44.11 55.89 37.74 27.67 34.59 68.55 25.79 4.4 8.18 31.45 60.37
A systematic or representative component Viq, which is a function of measured attributes (x).
A random component eiq, representing individual idiosyncrasies and tastes, as well as any measurement or observational errors made by the modeller. Therefore, Uiq ¼ Viq þ eiq
ð3Þ
Table 6 Distribution by EVALUATION of the service, differentiating between users and potential users of public transport. % Distribution by evaluation of the service
Users Potential users
1
2
3
4
5
Not applicable
Average score
2.67 4.4
11.00 23.27
37.33 35.22
36.67 13.21
12.33 4.4
– 19.5
3.45 2.88
224
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
The first part of the sum can be expressed as Viq ¼
k X
Table 9 Multinomial logit considering income-level interactions.
yik xikq
ð4Þ
Income level
k¼1
where the parameter q is assumed to be constant for all individuals, though they can vary between different alternatives. These parameters are usually estimated by using the maximum likelihood method. As this is an unlabelled design the intercept has not been considered when designing the models and no socio-economic variables have been introduced (Hensher et al., 2005). It was decided to work with categories of users defined by income level, sex, age, and frequency of use (defined as type of user: very frequent, frequent and casual). Two other categories of users were also studied independently and comparisons made between the results of their corresponding models. These categories were:
Potential users: people who do not currently use the bus. Bus-dependent users: people who depend on the bus to get around. After very exhaustive work, these were the only categories found to give significant results. The results of these model runs are shown in Tables 7–12, which specify the variables used and the interactions with a certain statistical significance within each user characteristic. Other models were run beforehand using all the variables affected by the interactions and the respective socio-economic ranges. Although all of them had the correct signs, some were rejected because of their low statistical significance and these were aggregated.
Log likelihood function Variable Waiting time Waiting time low income Waiting time medium income Vehicle occupancy Cleanliness Journey time Journey time (low income +medium income) Driver kindness Comfort on journey
Coefficient (b)
b/St.Er.
3905.562 P[9Z94 z]
0.092 0.038 0.012 0.102 0.948 0.004 0.004
18.244 3.144 1.630 1.198 13.882 1.598 1.035
0.000 0.002 0.103 0.231 0.000 0.110 0.301
0.257 1.035
3.340 12.652
0.001 0.000
Table 10 Multinomial logit considering user type interactions. Type of User Log likelihood function Variable Waiting time Waiting time very frequent Waiting time frequent Vehicle occupancy Cleanliness Journey time Journey time frequent Driver kindness Comfort on journey Comfort on journey (very frequent + frequent)
Coefficient (b)
b/St.Er.
3901.86 P[9Z94 z]
0.104 0.027 0.025 0.099 0.932 0.008 0.015 0.256 1.259 0.268
13.952 3.188 2.077 1.158 13.829 4.366 2.649 3.318 6.542 1.261
0.000 0.001 0.038 0.247 0.000 0.000 0.008 0.001 0.000 0.207
Table 7 Multinomial logit considering sex interactions. Table 11 Multinomial logit model estimated for the bus-dependent users.
Sex Log likelihood function Variable
Coefficient (b)
b/St.Er.
3903.69 P[9Z94z]
Waiting time Waiting time sex Vehicle occupancy Cleanliness Cleanliness sex Journey time Journey time sex Driver kindness Comfort on journey Comfort on journey sex
0.089 0.009 0.093 0.745 0.288 0.013 0.009 0.257 0.910 0.181
13.656 1.126 1.098 6.975 2.113 4.108 2.501 3.337 6.037 1.010
0.000 0.260 0.272 0.000 0.035 0.000 0.012 0.001 0.000 0.313
Bus dependent users Log likelihood function Variable
Coefficient (b)
b/St.Er.
1017.642 P[9Z94 z]
Waiting time Vehicle occupancy Cleanliness Journey time Driver kindness Comfort on journey
0.094 0.219 1.124 0.006 0.231 1.061
12.80 1.42 9.02 1.66 1.40 6.40
0 0.16 0 0.10 0.16 0
Table 12 Multinomial logit model estimated for potential users of public transport.
Table 8 Multinomial logit considering age interactions.
Potential users of public transport Age Log likelihood function Variable
Coefficient (b)
b/St.Er.
3910.4 P[9Z94z]
Waiting time Vehicle occupancy Cleanliness Journey time Driver kindness Comfort on journey Comfort on journey ageo 30 Comfort on journey age 30–60
0.083 0.094 0.915 0.006 0.254 1.351 0.331 0.428
22.856 1.109 13.689 3.701 3.305 5.194 1.192 1.336
0.000 0.268 0.000 0.000 0.001 0.000 0.233 0.182
Log likelihood function Variable
Coefficient (b)
b/St.Er.
815.5148 P[9Z94z]
Waiting time Vehicle occupancy Journey time
0.077 1.583 0.038
5.4 6.64 7.71
0 0 0
In the particular case of the SEX category (Table 7), two variables had very little influence whether or not the user was a man or a woman: vehicle occupancy (boccup: 0.093) and driver
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
kindness (bkind: 0.257). In both cases the low statistical relevance meant the interaction had to be removed in order to study this variable globally. Cleanliness (bman: 0.745; bwoman: 0.745+ 0.288) and comfort during the journey (bman: 0.910; bwoman: 0.910 +0.181) have much more weight for women than for men, who appear to be much less demanding about what they hope for from these two variables within the overall quality of service. However, the opposite occurs with the variables covering the overall journey times: journey time (bman: 0.013; bwoman: 0.013 +0.009) and waiting time, though the values for the two sexes are very similar for the latter variable (bman: 0.089; bwoman: 0.089 + 0.009). Three variables in the SEX category with coefficients not significant to 95% have been allowed to form part of the model: vehicle occupancy (test t: 1.098) and the interactions of waiting time with gender (test t: 1.126) and comfort with gender (test t: 1.010). In all three cases the sign of the parameters is as expected. Vehicle occupancy has been left because of its importance within this research. The cases of the two interactions of gender with waiting time and comfort have also been left because they could provide relevant information for policy proposals. If the model is looked at as a function of the AGE of the interviewee (Table 8) (separating young users, adults and older adults (over 60s)), only one variable justifies studying the interactions separately (based on the statistical significance of the variable associated with these interactions); comfort during the journey. In this case, the associated weight is lower for younger users than older adults and even less for adults (b 4 60: 1.351; b o 30:1.351–0.331; b30–60:1.351–0.428). This was to be expected because older adults are physically less adaptable to travel and usually have greater difficulties in staying in uncomfortable positions for long periods; therefore, they place greater value on comfort than the younger age groups do. Three variables in the AGE category have coefficients not significant to 95%: vehicle occupancy (test t: 1.019), the interactions of people under 30 (test t: 1.192) and comfort for those in the age range between 30 and 60 (test t: 1.336). Vehicle occupancy, as in the sex and user-type categories, has been left in the model because it has the correct sign and is relevant to this study. The interactions of the age ranges with comfort, apart from having the expected signs, show how the concept of comfort varies with age. Income level (Table 9), separated into low income, medium income and high income, showed that it was only beneficial to separate the effect of the different categories of income in the case of waiting time. It appeared that the weight given by users to waiting time at the bus stop as part of the quality hoped for from an efficient service increased with income (bh.incom: 0.092; bm.incom: 0.092 +0.012; bl.incom: 0.092 +0.038). It was evident that people with more resources valued their time more and saw time spent waiting at the bus stop as lost, useless time. There are four variables in the Income level category with coefficients that are not significant at a 95% confidence level: vehicle occupancy (test t: 1.198), journey time (test t: 1.598), the interactions of journey time with the low-medium income level (test t: 1.035) and waiting time with medium income level (test t: 1.630). Vehicle occupancy and journey time have been left because they are fundamental variables within this research and also have the correct sign. Income level is an important socioeconomic variable when evaluating the different times used for journeys and also had the expected signs. People did not value the times equally, independently of their income level. These are important variables when establishing future policies for efficient action. An analysis according to type of user (Table 10) shows that vehicle occupancy (boccup: 0.099), cleanliness (bclean: 0.932) and
225
driver kindness (bkind: 0.256) are not worth separating as a function of user journey frequency because the statistical significance of the interactions studied is hardly relevant. It is worth pointing out that very frequent and frequent users are less impressed by comfort during their journey than the other users are (bcasual: 1.259; bfrec, v.frec: 1.259–0.268). This could be due to the fact that the company running the bus fleet in Santander has recently been named as Exemplary Company of the Year in Spain in 2005), boasting vehicles which are comfortable, safe and adapted to the needs of all the routes. The weight given to waiting time at the bus stop within the quality of service desired by users diminishes as use of public transport grows (bcasual: 0.104; bfrec: 0.104+ 0.025; bv.frec: 0.104+ 0.027). Passengers use public transport more efficiently when they have more knowledge of the timetables and spend less waiting time at the bus stop. This variable is highly valued when defining a service as efficient. There are two variables in the type of user category with coefficients not significant at 95% which have been allowed to form part of the definitive model: vehicle occupancy (test t: 1.158) and the interaction of comfort with the group of users covering very frequent and frequent (test t: 1.261). As in the case of the sex category, vehicle occupancy has been left because of its relevance within this study and its correct sign. The interaction of comfort with very frequent and frequent users, apart from having the expected sign, is important for evaluating comfort. People who least use the bus demands the most comfort which is important information when deciding policies to attract potential bus users. The model obtained for the bus-dependent users shows that the variables with most weight are similar to the rest of the categories studied up to now: waiting time (bwt: 0.094), cleanliness (bclean: 1.124) and comfort (bcomfort: 1.124). The variable with least weight for this group is journey time (bjt: 0.006). Perhaps their dependence on this mode of transport to get around means they accept the time is necessary for the bus journey and have accounted for it in their plans. However, the cost associated with waiting at the bus stop is one of the variables they value most when defining efficiency in a public transport system. The potential users of public transport do not value the variables being studied in the same way as the different types of actual users. They do not consider the variables defining cleanliness, driver kindness and comfort as important in defining expected quality from public transport. The multinomial logit model run for the potential users showed that these three variables had a t-test lower than 1, meaning they were barely statistically significant and had incorrect signs (prohibiting their inclusion in the model). The incorrect sign of these variables means the potential users do not consider them when making their choice, they only consider journey time, waiting time and level of occupancy. These results appear to show that potential users only value variables directly related to the journey, the times (both for waiting (bwt: 0.077) and journey (bjt: 0.038)) and the level of occupancy (boccup: 1.583), i.e. the space available to them when travelling together with other people. At this point it is of interest to see the information all together and graphically represent the contribution each of the variables makes to the utility function according to the socio-economic characteristics used in this study. Having knowledge of what users and potential users desire from an efficient and goodquality service makes it easier to establish lines of action or personalised marketing policies orientated at improving the quality of public transport. Fig. 7 shows that the contribution each variable makes to the utility functions estimated for the different categories follows a very similar line in all cases except for the potential users.
226
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
Waiting Time Vehicle Occupancy Cleanliness Journey Time Driver Kindness Comfort
Contribution of each variable to the Utility Function
1.6 1.4 1.2
Contribution to the Utility Function
1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -1.2 -1.4 -1.6 -1.8 Woman
Man
Very Frequent Casual Age<30 Age frequent 30-60
Age>60
Low Medium High Dependt Potentioal Income Income Income user user
Fig. 7. Contribution of each variable to the utility function.
Certain variables contribute greater weight to the utility function for all the defined categories of current bus users, independently of socio-economic characteristics. These variables are waiting time (with a negative sign), comfort on board and cleanliness in the bus (both with positive signs). However, the contribution of comfort and cleanliness to the utility functions can be seen to have no weight for potential bus users. These variables have not been considered when defining what they expect from a high-quality public transport service. Other variables make scarcely any contribution to the utility function by category of actual user. These are mainly vehicle occupancy and journey time, both being variables whose value is taken away from the overall utility. Nevertheless, these two variables carry more weight in the estimated utility functions for the potential users, being the only ones they value along with waiting time at the bus stop. Driver kindness stays practically constant for each type of actual bus users. The potential bus users do not place much importance on this variable. Fig. 7 provides a simple idea of the variables with greatest weight within the utility function defining the quality desired from public transport (bus mode, in the particular case of Santander).
The end goal of this research is to provide information for future policy making leading to the creation of an efficient transport service which lives up to the quality standards that the users expect or desire and which motivates the potential users (those who do not actually use it) to make that modal change to public transport. Once the authorities know the variables that actual users value most they can define and direct more efficient lines of investment at those specific points. Knowledge of the variables more highly valued by the potential users of public transport means policies can be designed to create the conditions that would encourage them to change over to using public transport. Knowing which variables are most important to both actual and potential users is useful information for public transport operating companies as they provide guidelines to follow when asked to improve the service. The three variables that stand out independently of the grouping criteria and define the quality desired from an efficient and safe public transport service are:
5. Conclusions
Cleanliness turns out to have practically the same weight in all the cases studied so all the interactions needed to be aggregated. The only case where it was worth studying separately was in the case of sex, as women value this variable more than men. Comfort during the journey turns out to have very similar values in all the models. The users who gave most weight to this were people over 65 years old and those passengers that only used the service sporadically or circumstantially. Both results
The objective of this research was to study the quality desired by users and potential users of public transport when defining an efficient and reliable system. The municipal bus service in the city of Santander was used as a practical example. A pilot stated preferences survey was performed and efficient design was used to prepare the definitive version.
1. Waiting time 2. Cleanliness 3. Comfort
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
have a certain logic, as older people do not like to suffer long periods of discomfort in uncomfortable or unstable postures; and those customers that use the service casually expect that once they have to depend on the timetables and routes of public transport their journey should be as least annoying as possible, as compensation for their loss of independence. Waiting time is always one of the variables that usually gets the most weight in the utility functions of a transport mode, because it represents time that the user sees as lost and the loss of time is irritating (Lirman, 2008). In this study it stands out as one of the most important factors when defining what people want from an efficient or high-quality service. The users that give most weight to waiting time are those who use the service casually or circumstantially. This could be explained because their infrequent use of the service gives them little knowledge of timetables and therefore they cannot optimise their waiting times, which consequently turn out to be longer, more irritating and of greater importance to them when defining an efficient system. The least important variable was found to be vehicle occupancy. The resulting weight turned out to be almost equal in all the cases studied, to such an extent that it became necessary to aggregate them all, as the different interactions studied (with the socio-economic characteristics) are hardly statistically significant. This could be due to the company distributing the fleet in such a way that it provides a service to all parts of the city and allocates more buses where there is greater demand, therefore the users do not see it as a problem that needs solving to improve the quality of service. The variables which are most valued by the potential users are different from those valued by the various types of actual users. It appears that not being a bus user is very significant when evaluating the different variables influencing quality in public transport. The potential users hardly take into account cleanliness, employee attitude and comfort during the journey. In fact, these variables showed incorrect signs in the modelling process, implying that these variables were ignored in most cases when they were choosing between the different scenarios. The only variables valued by the potential users are journey time, much more important than for the actual bus users, waiting time and the level of bus occupancy. This latter variable had the most weight for the potential bus users. The results and conclusions show that any policies aimed at increasing the number of users of public transport in Santander should be orientated at: 1. Reducing waiting times: perhaps with more information panels, more publicity campaigns aimed at getting closer to the people (so they know the routes, timetables and fares); by improving bus frequency; by improving the traffic management system which includes public transport; or by improving the location of bus stops and bus-only lanes. 2. Improving comfort during the journey: keeping the vehicles in perfect condition; cleaning them regularly and maintaining their current good condition; providing courses for drivers on calm driving, consequently increasing the perception of safety during the journey; introducing systems that provide constant good ventilation and an agreeable temperature inside the bus throughout its journey. 3. Introducing information campaigns aimed at potential bus users which outline, for example, the real journey times by bus
227
between significant city landmarks, thereby countering the erroneous belief of the non-users that it is a slow mode of transport. 4. Strengthening the busiest lines at rush hour by providing more services which would allow the public to make their journey in greater comfort. This action would also counter the non-bus users’ perception of overcrowding as they observe the running of the public transport system. Finally, it is worth pointing out that the analysis done for this work has clearly defined the population strata that use the public transport service in Santander as well as those that do not use it. Based on the information and characteristics studied in this work, the corresponding public authorities and bus operating companies can set out policies aimed at capturing specific types of users. The model designed here provides the variables most valued by the users for a desired quality of service and the results can help in the design of a greater range of measures to improve the service.
Acknowledgement The authors would like to thank the Spanish Ministry of Science and Innovation for financing the project TRA2006-14663/ MODAL, which has allowed this research to take place. References dell’Olio, L., et al., 2010. Modelling user perception of bus transit quality. Transport Policy, doi:10.1016/j.tranpol.2010.04.006. Foote, P.J., Stuart, D.G., 1998. Customer satisfaction contrasts express versus local bus service in Chicago’s North Corridor. Transportation Research Record (1618), 143–152. Garrido, R.A., Ortu´zar, J.D., 1994. Deriving public transport level of service weights from a multiple comparison of latent and observable variables. The Journal of the Operational Research Society 45. Glascock, J., 1997. Research on customer requirements for transit service design and delivery. Transportation Research Record (1604), 121–127. Hensher, D.A., 1994. Stated preference analysis of travel choices: the state of the practice. Transportation 21, 107–133. Hensher, D.A., Houghton, E., 2004. Performance-based quality contracts for the bus sector: delivering social and commercial value for money. Transportation Research Part B 38, 123–146. Hensher, D.A., Rose, J.M., Greene, W.H., 2005. Applied Choice Analysis: A Primer. Cambridge University Press. Hensher, D.A., Stanley, J., 2003. Performance-based quality contracts in bus service provision. Transportation Research Part A (37), 519–538. 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 37, 499–517. Huber, J., Zwerina, K., 1996. The importance of utility balance and efficient choice designs. Journal of Marketing Research (33), 307–317. Ibeas, A., dell’Olio, L., Barreda, R., 2010. Citizen involvement in promoting sustainable mobility. Journal of Transport Geography. doi:10.1016/j.jtrangeo. 2010.01.005. Lirman, T., 2008. Valuing transit service quality improvements. Journal of Public Transportation 11 (2), 43–63. ˜ o, operacio´n y Molinero, A., Sanchez, L., 1997. Transporte pu´blico: Planeacio´n, disen administracio´n. Publicaciones UAEM. Ortu´zar, J.D., Willumsen, L., 1994. Modeling Transport. John Wiley and Sons. Powe, N.A., Garrod, G.D., McMahon, P.L., 2005. Mixing methods within stated preference environmental valuation: choice experiments and post-questionnaire qualitative analysis. Ecological Economics. Rizzi, L.I., Ortu´zar, J.D., 2006. Road safety valuation under a state choice framework. Journal of Transport Economics and Policy. Rose, J.M., Bliemer, M., Hensher, D., Collins, A., 2008. Designing efficient stated choice experiments in the presence of reference alternatives. Transportation Research Part B 42, 395–406.
Contents lists available at ScienceDirect
Transport Policy journal homepage: www.elsevier.com/locate/tranpol
The quality of service desired by public transport users Luigi dell’Olio n, Angel Ibeas, Patricia Cecin Department of Transport, University of Cantabria, Avda. de los Castros s/n, 39005, Santander, Cantabria, Spain
a r t i c l e in fo
Keywords: Desired quality Stated preference survey Efficient design Multinomial logit Focus groups
abstract This article describes the methodology used to study the quality of service desired by users of a public transport system. The desired quality is different from the perceived quality because it does not represent the daily experiences of the users, but rather what they desire, hope for or expect from their public transport system. This is why it is important to study the desired quality, knowledge of which gives local authorities the background information for personalised marketing policies based on the user’s requirements rather than their daily perceptions. The methodology goes through several stages, such as the use of focus groups to choose the most important variables for the users, the design and use of unlabelled stated preferences surveys and the calibration of discrete choice models. All of these help determine the weight of the most relevant variables. The analysis is carried out with different categories of users and potential users (those people not currently using public transport). Waiting time, cleanliness and comfort are shown to be the public transport variables that users most valued, but the degree to which they are valued varies according to the category of user. Variables such as driver kindness, bus occupancy and journey time are generally given less weight. The first two vary little by user category, but some variability appears for journey time. For potential users the more important variables when defining expected quality from public transport are waiting time, journey time and above all, level of occupancy. They consider the other variables to be of little importance when defining an efficient public transport service. In order to improve service quality and attract more passengers to public transport in general, the application of this methodology provides the authorities and operating companies with useful information to plan personalised marketing policies specifically directed at different categories of users and potential users of public transport. & 2010 Elsevier Ltd. All rights reserved.
1. Introduction Improving the quality and efficiency of public transport is important if we are to change the daily transport habits of the public. Congestion in urban areas and its immediate and wider consequences on the climate are pushing central and local governments to instigate sustainable transport policies. These policies require an ever more personalised attention to the desires of the customer, to know and quantify the most influential variables on their decision to travel in public transport. This makes it essential to define both the policies and the strata (categories) of users or potential users towards whom the policies should be directed. These factors come within the overall umbrella of improving the quality of the service offered in order to attract more customers. International literature provides much research on user ‘perceived quality’ (dell’Olio et al., 2010) of a public transport
n
Corresponding author. Tel.: +34 942201734; fax: + 34 942201703. E-mail address: [email protected] (L. dell’Olio).
0967-070X/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tranpol.2010.08.005
service or system. Most of these studies relate the quality of each service variable with the importance assigned to it (Glascock, 1997; Foote and Stuart, 1998). The quality of a public transport system is covered by many factors, such as considerations relative to comfort and safety within the vehicle, the time taken to cover the routes and the convenience and existence of any supporting infrastructure (Molinero and Sanchez, 1997). Attempts have been made to relate the quality of service to the conditions stipulated in the initial concessionary contract (known as contracts of interested management). These guarantee a demand for public transport and rationalise the perception of public subsidies to private enterprise (Hensher et al., 2003; Hensher and Stanley, 2003; Hensher and Houghton, 2004). This paper offers a new point of view by introducing a new idea of quality: the quality the user and potential user desires (or hopes for). Traditional research on perceived quality provides the operating companies with knowledge on the impact their decisions have on their customers, while a study on desired quality gives them more in-depth information about their customers and what they want
218
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
2.1. Objectives of the study and its methodology
surveys which are used to model hypothetical, but realistic, situations presented to the users of an urban bus service (Hensher, 1994). Knowledge about the desired service quality provides operating companies with an answer to their investment questions and establishes the basis for designing future policies to encourage greater use of public transport based on the needs and expectations of their existing and potential customers. One of the specific objectives was to ascertain the importance, or weight, that the users of public transport place on certain improvable variables such as waiting time at the bus stop, in-vehicle journey time, vehicle occupancy, cleanliness of the vehicle, standard of customer service of the driver when dealing with passengers (driver kindness) and the comfort of the buses during the journey. The variables were chosen using a process of social participation through focus groups. Three unlabelled stated preferences surveys presented interviewees with a series of alternatives from which they chose the one they found the most satisfactory according to their criteria and personal situation. The design methodology for the stated preferences surveys is detailed in Fig. 1 and summarised as follows:
This research aims to find out what users and potential users of public transport look for in the service they are offered (desired quality) and what they would like to receive in exchange for their ticket and their transfer from one point to another, in other words, what they expect from an efficient and high-quality service. Once we know the desired quality, tools exist that may help improve public transport planning (e.g. for the bus mode) within a setting of sustainable mobility. The concept of ‘desired quality’ must be differentiated from that of ‘perceived quality’ which comes from what the users have experienced and perceived on public transport. The concept of ‘desired quality’ defines what they want to obtain to be fully satisfied with the service offered. Therefore, the ‘desired quality’ reflects the maximum level of utility to which the users and potential users of public transport aspire. It is important to realise that the service desired by the users and potential users must always be feasible. Waiting times and journey times equal to zero belong to a very desirable, but not feasible, scenario. Information is obtained from stated preferences
1. Design a preliminary survey after an extensive state-of-the-art bibliographic review. 2. Test the preliminary version by focus group. 3. Use a SWOT matrix analysis (strengths, weaknesses, opportunities, threats) on the conclusions from the focus group and then design and administer the pilot surveys on a small sample. These pilot surveys check the soundness of the survey design (it was unnecessary to modify the design of the overall form of this survey). 4. Use the data collected in the pilot survey for the efficient design (using a measure known as D-error) of the definitive survey. 5. Administer the definitive surveys to the defined sample. 6. Model bus user and potential user behaviour for their desired quality from public transport after collecting and digitalising the survey data. 7. Quantify the effects of future policies after modelling the sample’s behaviour to find more efficient policies based on the needs and aspirations of existing and potential users.
from the service so they can develop more acceptable policies. Finally, local authorities and transit operators should try to make the perceived quality as similar as possible to the desired quality. This paper could serve as a basis for the future establishment of quality indexes based on the existing differences between desired and perceived quality. Desired quality is shown to vary with user category. This provides valuable information for planning personalised marketing policies directed at different user categories (including potential users) to improve the quality of service and attract more people to public transport and move towards more sustainable mobility. The article is divided into the following sections: objectives and methodology; data collection phase along with some conclusions from the survey performed; the modelling process with a discussion of the results; and finally, the most important conclusions are presented.
2. Objectives and methodology
Fig. 1. Summary of design process, designing the stated preferences survey and its modelling.
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
The definition of many variables meant that three surveys were administered. Interviewee interest and concentration are known to be limited (Ortu´zar and Willumsen, 1994) when faced with an extensive questionnaire, meaning that the survey should not be too long. Nor should the interviewees be asked to compare too many variables as this complicates the establishment of orders of preference, meaning the results of the survey may turn out to be false. It was, therefore, thought better to break up the stated preference experiments into three surveys, with the result that each individual was presented with eight scenarios, each having three choice alternatives with three defining variables with different degrees of variation. The surveys were conducted both on board the buses and at the bus stops, thereby accessing a wide spectrum of users. Before designing the survey, a focus group was used to deduce the most significant variables for the users of public transport. These variables were later more clearly defined in the pilot surveys. These surveys formed the basis for writing the definitive surveys designed using the D-error (Rose et al., 2008). The D-error is used to create an efficient design and takes the data obtained in the pilot surveys as a basis for defining the situations presented in the definitive surveys. Once the answers obtained in the surveys were digitalised, one unique database could then be used with discrete choice models, particularly multinomial logit models. Studies looked at how different user categories (defined according to age, sex, income level or the frequency they used public transport) interacted with the representative variables of desired quality: waiting time, journey time, vehicle occupancy, level of cleanliness, driver kindness and comfort. The study was carried out in the port town of Santander, capital of Cantabria, located on the north coast of Spain. It has a registered population of about 185,000 inhabitants, but taking peripheral boroughs into account this figure increases to 250,000 people. The municipal urban transport service uses 19 daily bus lines and three nocturnal bus lines throughout the city connecting the periphery with the town centre. The bus network practically covers the whole city. It has been estimated that each person has a bus stop no more than 300 m from their home. In 2008 the number of journeys carried by this network grew to 20 million. Table 1 shows the headway, the average waiting time, average journey time and the reply rate for each line.
219
Table 2 Modal distribution for the cities of Santander, Seville and Graz in 2008. Percentage of trips by mode (%)
Santandera Sevillab Grazc
On foot (%)
Bus (%)
Car (%)
Bicycle (%)
Others (%)
36 31 19
14 12 19
46 50 48
0 0 14
4 7 0
a Data obtained of home survey carried out in Santander (November, 2008) for the project ‘‘PRO-BIKE. Planning and Management Methodologies for Promoting Cycling Strategies’’. CEDEX (Centro de Estudios y Experimentacio´n de Obras Pu´blicas). b Data of the mobility in Seville obtained of the document ‘‘Observatorio de la Movilidad Metropolitana’’ (2009). Ministerio de Fomento y Ministerio de Medio Ambiente y Medio Rural y Marino. c ¨ Data of the mobility in Graz obtained of FGM 2008, Mobilitatsverhalten der ¨ Grazer Wohnbevolkerung.
The data in Table 1 come from a revealed preferences survey administered before the stated preferences survey studied in this research. This revealed preferences survey allowed us to characterise the typical public transport user and journey in Santander. Other cities better known throughout the world than Santander but with a similar modal distribution are Seville in southern Spain and Graz, the second most important town in Austria. In both cases the private car is the most popular mode of transport, favoured by over 45% of the population. Public transport is used by 12–19% of travellers. The most noteworthy difference is in the use of the bicycle. In Graz bicycles are used by 14%, while in Santander and Seville less than 1% uses this mode of transport. The modal distribution is similar when comparing alternative modes (on foot and bicycle) to motorised modes of transport: Santander 36%, Seville 31% and Graz 33% (Table 2). The modal distribution in 2008 for Santander, Seville and Graz is shown in Table 2. The table shows that the most common method for getting around Santander is the private car. This is followed by walking; Santander is a medium-sized town where the average journey distance is less than 5 km. The city’s bus service is the third most popular mode of transport. It is important to know what the public would like from their transport service in order to increase ridership through efficient, targeted policies. 2.2. Focus groups
Table 1 Summary of headway, average waiting time, average journey time and reply rate for each line. Line
Headway (min)
WT (min)
JT (min)
Reply rate (%)
L1 L2 L3 L4 L5C1 L5C2 L6C1 L6C2 L7C1 L7C2 L11 L12 L13 L14 L16 L17 L18 L19 Average
16 16 20 12 12 12 30 30 16 16 30 30 30 20 30 30 30 30 23
7 8 7 8 8 8 15 11 7 7 9 11 7 8 9 13 14 7 9
17 17 15 17 14 13 21 18 14 14 9 18 17 9 14 18 17 18 16
89 82 77 75 80 83 72 76 81 79 78 77 82 85 70 83 65 68 78
Focus groups are a qualitative investigation technique following a workshop-style methodology based on meetings using a predetermined group of individuals chosen for the purpose. The objective is to obtain information about their points of view, life experiences, expectations and knowledge about a specific subject matter (Ibeas et al., 2010). In this case the subject was public transport in the city of Santander. The focus group provides a type of ‘group opinion’ which encapsulates the personal perceptions of each individual, where all the answers are valid and which provides basic information for experimental design. The focus group is a tool used to assist in choosing the relevant variables and is an important starting point to complete and improve the design of the survey (see Fig. 1, points 3 and 4). This particular research work used a focus group of eight participants, four of whom were young people, two adults and two mature adults. The idea was to represent the users of public transport in Santander as closely as possible. The subjects dealt within the focus group allowed us to define the variables used in the pilot stated preferences survey, covering quality in general but particularly desired quality in public transport (Garrido and Ortu´zar, 1994; Powe et al., 2005; Rizzi and Ortu´zar, 2006).
220
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
The users considered the following variables to be the most sensitive when defining desired quality in a public transport service:
Waiting time at the stop Journey time on the bus Vehicle occupancy The cleanliness of the vehicle The driver’s kindness The comfort of the buses
The focus group followed guidelines to help the moderator organise and direct its development, thereby guaranteeing that all the subjects of interest to the research were dealt with. The guidelines followed are shown in Fig. 2. Only one focus group was held because of the ample knowledge already available to the research team on the quality of public transport in the city. This knowledge came from a revealed preferences survey held in the same city some months before the stated preferences survey covered in this article. The revealed preferences survey was used to characterise the users of public
Fig. 2. Guidelines followed in the focus group.
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
transport and the journeys made and covered perceived quality by bus users.
2.3. The definitive survey The pilot surveys were carried out just before preparing the questionnaire for the definitive surveys in order to check how much the interviewees understood the situations with which they were presented and their format. The data collected in the pilot survey also served as the basis for an efficient design, as explained below. The application presented in this work used 36 stated preferences interviews conducted on the buses as part of the pilot surveys in Santander and provided 864 observations. Each interviewee responded to eight scenarios and each scenario provided three observations (this was a hierarchical survey using three cards for each scenario). The team accepted the design of the cards as the public easily understood them and they provided the necessary data on which to base the efficient design of the definitive surveys. The design of the definitive stated preferences surveys followed the methodology proposed by Rose et al. (2008). They suggested a design for estimating parameters with minimum possible standard error by determining the variance–covariance matrix (VCM) based on previous experimentation and already known information, normally the parameters of the utility function. These parameters were obtained from information collected using the 864 observations in the pilot survey. The D-error was used as a measurement to check the efficiency of the design obtained by calculating O (VCM) and applying a scaling factor taking into account the number of parameters K to be estimated, or D-error ¼ detðWÞ1=K
ð1Þ
The experimental design used eight scenarios, considering the following attributes and their levels (varying with the survey performed):
Waiting time at the bus stop: 5, 10, 15 and 20 min Journey time on the bus: 15 and 30 min Vehicle occupancy: 50% and 80% Cleanliness of the vehicle: clean and dirty Driver’s kindness: good and bad Comfort of the buses: good and bad.
The design of these scenarios started from the data obtained in the revealed preferences survey mentioned earlier. This revealed preferences survey allowed us to characterise the journeys made on public transport (waiting timeE 10 min, journey timeE15 min, etc.). This data was then used to define the ‘typical journey’ in the city. The modifications made using this typical journey established the scenarios presented in the survey. The designs with the least D-error were obtained from 5000 iterations using an algorithm based on columns as suggested by Huber and Zwerina (1996). Table 3 shows the size of the D-error obtained and its corresponding iteration. The definitive surveys each had eight scenarios in which the interviewee had to choose from three alternatives in each case. This made it possible to study the six attributes organised as shown in Table 4. The working variables were defined to the interviewee before starting the survey. Furthermore, they were shown photographs which clarified the more complicated concepts or variables they would be asked about (cleanliness (clean or dirty), vehicle
221
Table 3 D-error obtained for each survey. Measure
Survey 1
Survey 2
Survey 3
D-error Iteration
0.202 382
0.05 531
0.07 332
Table 4 Summary of the values of the attributes of the stated preferences surveys on quality. Variables studied
Survey 1
Survey 2
Waiting Time (min) Vehicle Occupancy Cleanliness of the Bus Journey Time (min) Driver kindness Comfort on Journey
5 10 High Clean
5
15 20 Low Dirty
10
15 Good
15
30 Bad
Survey 3 20
5
10
15
15
30
Good
Bad
20
occupancy (half full or full) and comfort (agreeable conditions or not so comfortable)).
3. Collecting the information The three definitive stated preferences surveys were defined by eight situations in which the interviewee must choose from three hypothetical alternatives, each defined by a series of attributes with possible values. Each survey studied a group of three attributes; waiting time was used as a link between the results of the three surveys as it was the only common variable. Survey 1 looked at the relationship between waiting time at the bus stop, vehicle occupancy and its cleanliness, while Survey 2 checked the relationship between waiting time, journey time and driver kindness and Survey 3 studied the relationship between waiting time, journey time and comfort during the journey. Examples of the surveys performed are shown in Figs. 3–5. Three of each of the surveys have the three attributes available in Situation 1, the three cards on the left correspond to Survey 1, the three in the middle to Survey 2 and the three on the right to Survey 3 (Fig. 6). These surveys were asked to both users and potential users of public transport. As these were stated preferences surveys, the sample size is determined by the desired quality of data. The formula used to determine the sample size depends only on the parameters of the models obtained as a function of the data from the pilot survey and their standard deviation: !2 1:96 seðb^ k Þ N4 ð2Þ b^ k
where b^ k are the coefficients obtained from modelling the pilot survey data, seðb^ k Þis the standard deviation of each of the coefficients. The surveys were conducted at rush hour on working days, both on board the buses and at bus stops distributed along the bus routes of Santander. A total of 305 completed surveys were returned covering an ample spectrum of people with their respective opinions (this hierarchical survey provided 7320 observations from eight scenarios with three observations each). The information covered the needs of the ‘frequent’ or ‘very
222
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
Fig. 3. Example of the stated preference cards used in Survey 1.
Fig. 4. Example of the stated preference cards used in Survey 2.
Fig. 5. Example of the stated preference cards used in Survey 3.
Fig. 6. User characterisation survey.
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
frequent’ user when defining the bus service as efficient and the requirements of the ‘sporadic or infrequent user’ with the idea of thinking about a modal change towards using the bus as their normal mode of tranport, i.e. what they hoped to receive from the service in order to guarantee the change of mode. The survey on potential bus users began by asking people on the street (parking their car or walking) if they currently used public transport. If the reply was negative a characterisation survey was carried out along with exactly the same stated preferences survey asked to the users of public transport. Overall city coverage was obtained by conducting the surveys on potential users both in the city centre and in peripheral zones. The total sample was distributed into the socio-economic categories shown in Table 5. The surveys were designed with unlabelled alternatives, i.e. the interviewee was only presented with the situations defined in function of certain journey characteristics. They were asked to give an order of preference in each situation, and the three situations were placed in order from higher to lower preference. 3.1. Data analysis Table 5 shows the socio-economic characterisation of the users and potential users from data obtained in the characterisation survey performed before the stated preferences surveys. The public transport user characterisation form is shown below. The form used to characterise the potential users is very similar and only differs in two questions:
Why did they not use the bus? What mode of transport did they use for their journey? Several important observations appear from studying the sample’s socio-economic characteristics (presented in Table 5) before even starting the modelling phase. These could serve as general guides when planning policies for promoting the use of public transport. Table 5 Distribution of the sample into different socio-economic groups. Category
Sub-category
Total sample Users (%) Potential users (%)
Sex
Man Woman Frequency of use Very frequent Frequent Casual Age Under 30 Between 30 and 60 Over 60 Income level Low household income Medium household income High household income
32.13 67.87 62.62 12.46 24.92 75.74 17.38 6.89 22.3 23.61 54.1
223
One of the first observations was that the population using public transport in Santander is mainly composed of people under 25 years old. The next important group by number was the age range from 25 to 34. The age distribution was practically the same for the potential users of public transport. This classification by age range is important as it helps the authorities know where to direct possible future policy changes to promote transit use. The sex variable shows that most users of public transport are women, approximately seven out of ten, meaning that they should be an important consideration when making decisions on service improvement. There are also more women than men among the potential bus users, but with a rather less important gender difference (56% women against 44% men). Working categories must be defined before proposing the types of users found on public transport. They are defined according to the frequency they use the bus: very frequent users use the bus on a daily basis; frequent users use the bus on a weekly basis, at least twice a week; casual users only occasionally, under certain conditions, use public transport. For the group of potential bus users, frequency of journey referred to the frequency they made the journey they had just finished or were making at the time they were asked the survey. The bus users were also asked to give their overall evaluation of the service, rated by marks from one to five. Table 6 shows how most users give a pass rating to the service provided, the average mark from the three surveys was 3.45 (out of five). The potential users were more critical of the city’s transport system than the users were, with an average score of 2.88 out of five. Quite a high percentage, almost 20% of interviewees, felt they did not have enough knowledge to be able to score the public transport service.
4. Modelling and discussion of the results Once all the possible information from the stated preferences surveys was sorted and placed into a unique database, different models were looked at to explain what the users of public transport expected from the service provided (quality desired or expected). These were reflected in a series of representative journey variables defined beforehand. Multinomial discrete choice models were used in estimating the relative bearing of each variable on the desired service quality. These models are based on random utility theory. It is assumed that this utility can be represented by two components:
44.11 55.89 37.74 27.67 34.59 68.55 25.79 4.4 8.18 31.45 60.37
A systematic or representative component Viq, which is a function of measured attributes (x).
A random component eiq, representing individual idiosyncrasies and tastes, as well as any measurement or observational errors made by the modeller. Therefore, Uiq ¼ Viq þ eiq
ð3Þ
Table 6 Distribution by EVALUATION of the service, differentiating between users and potential users of public transport. % Distribution by evaluation of the service
Users Potential users
1
2
3
4
5
Not applicable
Average score
2.67 4.4
11.00 23.27
37.33 35.22
36.67 13.21
12.33 4.4
– 19.5
3.45 2.88
224
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
The first part of the sum can be expressed as Viq ¼
k X
Table 9 Multinomial logit considering income-level interactions.
yik xikq
ð4Þ
Income level
k¼1
where the parameter q is assumed to be constant for all individuals, though they can vary between different alternatives. These parameters are usually estimated by using the maximum likelihood method. As this is an unlabelled design the intercept has not been considered when designing the models and no socio-economic variables have been introduced (Hensher et al., 2005). It was decided to work with categories of users defined by income level, sex, age, and frequency of use (defined as type of user: very frequent, frequent and casual). Two other categories of users were also studied independently and comparisons made between the results of their corresponding models. These categories were:
Potential users: people who do not currently use the bus. Bus-dependent users: people who depend on the bus to get around. After very exhaustive work, these were the only categories found to give significant results. The results of these model runs are shown in Tables 7–12, which specify the variables used and the interactions with a certain statistical significance within each user characteristic. Other models were run beforehand using all the variables affected by the interactions and the respective socio-economic ranges. Although all of them had the correct signs, some were rejected because of their low statistical significance and these were aggregated.
Log likelihood function Variable Waiting time Waiting time low income Waiting time medium income Vehicle occupancy Cleanliness Journey time Journey time (low income +medium income) Driver kindness Comfort on journey
Coefficient (b)
b/St.Er.
3905.562 P[9Z94 z]
0.092 0.038 0.012 0.102 0.948 0.004 0.004
18.244 3.144 1.630 1.198 13.882 1.598 1.035
0.000 0.002 0.103 0.231 0.000 0.110 0.301
0.257 1.035
3.340 12.652
0.001 0.000
Table 10 Multinomial logit considering user type interactions. Type of User Log likelihood function Variable Waiting time Waiting time very frequent Waiting time frequent Vehicle occupancy Cleanliness Journey time Journey time frequent Driver kindness Comfort on journey Comfort on journey (very frequent + frequent)
Coefficient (b)
b/St.Er.
3901.86 P[9Z94 z]
0.104 0.027 0.025 0.099 0.932 0.008 0.015 0.256 1.259 0.268
13.952 3.188 2.077 1.158 13.829 4.366 2.649 3.318 6.542 1.261
0.000 0.001 0.038 0.247 0.000 0.000 0.008 0.001 0.000 0.207
Table 7 Multinomial logit considering sex interactions. Table 11 Multinomial logit model estimated for the bus-dependent users.
Sex Log likelihood function Variable
Coefficient (b)
b/St.Er.
3903.69 P[9Z94z]
Waiting time Waiting time sex Vehicle occupancy Cleanliness Cleanliness sex Journey time Journey time sex Driver kindness Comfort on journey Comfort on journey sex
0.089 0.009 0.093 0.745 0.288 0.013 0.009 0.257 0.910 0.181
13.656 1.126 1.098 6.975 2.113 4.108 2.501 3.337 6.037 1.010
0.000 0.260 0.272 0.000 0.035 0.000 0.012 0.001 0.000 0.313
Bus dependent users Log likelihood function Variable
Coefficient (b)
b/St.Er.
1017.642 P[9Z94 z]
Waiting time Vehicle occupancy Cleanliness Journey time Driver kindness Comfort on journey
0.094 0.219 1.124 0.006 0.231 1.061
12.80 1.42 9.02 1.66 1.40 6.40
0 0.16 0 0.10 0.16 0
Table 12 Multinomial logit model estimated for potential users of public transport.
Table 8 Multinomial logit considering age interactions.
Potential users of public transport Age Log likelihood function Variable
Coefficient (b)
b/St.Er.
3910.4 P[9Z94z]
Waiting time Vehicle occupancy Cleanliness Journey time Driver kindness Comfort on journey Comfort on journey ageo 30 Comfort on journey age 30–60
0.083 0.094 0.915 0.006 0.254 1.351 0.331 0.428
22.856 1.109 13.689 3.701 3.305 5.194 1.192 1.336
0.000 0.268 0.000 0.000 0.001 0.000 0.233 0.182
Log likelihood function Variable
Coefficient (b)
b/St.Er.
815.5148 P[9Z94z]
Waiting time Vehicle occupancy Journey time
0.077 1.583 0.038
5.4 6.64 7.71
0 0 0
In the particular case of the SEX category (Table 7), two variables had very little influence whether or not the user was a man or a woman: vehicle occupancy (boccup: 0.093) and driver
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
kindness (bkind: 0.257). In both cases the low statistical relevance meant the interaction had to be removed in order to study this variable globally. Cleanliness (bman: 0.745; bwoman: 0.745+ 0.288) and comfort during the journey (bman: 0.910; bwoman: 0.910 +0.181) have much more weight for women than for men, who appear to be much less demanding about what they hope for from these two variables within the overall quality of service. However, the opposite occurs with the variables covering the overall journey times: journey time (bman: 0.013; bwoman: 0.013 +0.009) and waiting time, though the values for the two sexes are very similar for the latter variable (bman: 0.089; bwoman: 0.089 + 0.009). Three variables in the SEX category with coefficients not significant to 95% have been allowed to form part of the model: vehicle occupancy (test t: 1.098) and the interactions of waiting time with gender (test t: 1.126) and comfort with gender (test t: 1.010). In all three cases the sign of the parameters is as expected. Vehicle occupancy has been left because of its importance within this research. The cases of the two interactions of gender with waiting time and comfort have also been left because they could provide relevant information for policy proposals. If the model is looked at as a function of the AGE of the interviewee (Table 8) (separating young users, adults and older adults (over 60s)), only one variable justifies studying the interactions separately (based on the statistical significance of the variable associated with these interactions); comfort during the journey. In this case, the associated weight is lower for younger users than older adults and even less for adults (b 4 60: 1.351; b o 30:1.351–0.331; b30–60:1.351–0.428). This was to be expected because older adults are physically less adaptable to travel and usually have greater difficulties in staying in uncomfortable positions for long periods; therefore, they place greater value on comfort than the younger age groups do. Three variables in the AGE category have coefficients not significant to 95%: vehicle occupancy (test t: 1.019), the interactions of people under 30 (test t: 1.192) and comfort for those in the age range between 30 and 60 (test t: 1.336). Vehicle occupancy, as in the sex and user-type categories, has been left in the model because it has the correct sign and is relevant to this study. The interactions of the age ranges with comfort, apart from having the expected signs, show how the concept of comfort varies with age. Income level (Table 9), separated into low income, medium income and high income, showed that it was only beneficial to separate the effect of the different categories of income in the case of waiting time. It appeared that the weight given by users to waiting time at the bus stop as part of the quality hoped for from an efficient service increased with income (bh.incom: 0.092; bm.incom: 0.092 +0.012; bl.incom: 0.092 +0.038). It was evident that people with more resources valued their time more and saw time spent waiting at the bus stop as lost, useless time. There are four variables in the Income level category with coefficients that are not significant at a 95% confidence level: vehicle occupancy (test t: 1.198), journey time (test t: 1.598), the interactions of journey time with the low-medium income level (test t: 1.035) and waiting time with medium income level (test t: 1.630). Vehicle occupancy and journey time have been left because they are fundamental variables within this research and also have the correct sign. Income level is an important socioeconomic variable when evaluating the different times used for journeys and also had the expected signs. People did not value the times equally, independently of their income level. These are important variables when establishing future policies for efficient action. An analysis according to type of user (Table 10) shows that vehicle occupancy (boccup: 0.099), cleanliness (bclean: 0.932) and
225
driver kindness (bkind: 0.256) are not worth separating as a function of user journey frequency because the statistical significance of the interactions studied is hardly relevant. It is worth pointing out that very frequent and frequent users are less impressed by comfort during their journey than the other users are (bcasual: 1.259; bfrec, v.frec: 1.259–0.268). This could be due to the fact that the company running the bus fleet in Santander has recently been named as Exemplary Company of the Year in Spain in 2005), boasting vehicles which are comfortable, safe and adapted to the needs of all the routes. The weight given to waiting time at the bus stop within the quality of service desired by users diminishes as use of public transport grows (bcasual: 0.104; bfrec: 0.104+ 0.025; bv.frec: 0.104+ 0.027). Passengers use public transport more efficiently when they have more knowledge of the timetables and spend less waiting time at the bus stop. This variable is highly valued when defining a service as efficient. There are two variables in the type of user category with coefficients not significant at 95% which have been allowed to form part of the definitive model: vehicle occupancy (test t: 1.158) and the interaction of comfort with the group of users covering very frequent and frequent (test t: 1.261). As in the case of the sex category, vehicle occupancy has been left because of its relevance within this study and its correct sign. The interaction of comfort with very frequent and frequent users, apart from having the expected sign, is important for evaluating comfort. People who least use the bus demands the most comfort which is important information when deciding policies to attract potential bus users. The model obtained for the bus-dependent users shows that the variables with most weight are similar to the rest of the categories studied up to now: waiting time (bwt: 0.094), cleanliness (bclean: 1.124) and comfort (bcomfort: 1.124). The variable with least weight for this group is journey time (bjt: 0.006). Perhaps their dependence on this mode of transport to get around means they accept the time is necessary for the bus journey and have accounted for it in their plans. However, the cost associated with waiting at the bus stop is one of the variables they value most when defining efficiency in a public transport system. The potential users of public transport do not value the variables being studied in the same way as the different types of actual users. They do not consider the variables defining cleanliness, driver kindness and comfort as important in defining expected quality from public transport. The multinomial logit model run for the potential users showed that these three variables had a t-test lower than 1, meaning they were barely statistically significant and had incorrect signs (prohibiting their inclusion in the model). The incorrect sign of these variables means the potential users do not consider them when making their choice, they only consider journey time, waiting time and level of occupancy. These results appear to show that potential users only value variables directly related to the journey, the times (both for waiting (bwt: 0.077) and journey (bjt: 0.038)) and the level of occupancy (boccup: 1.583), i.e. the space available to them when travelling together with other people. At this point it is of interest to see the information all together and graphically represent the contribution each of the variables makes to the utility function according to the socio-economic characteristics used in this study. Having knowledge of what users and potential users desire from an efficient and goodquality service makes it easier to establish lines of action or personalised marketing policies orientated at improving the quality of public transport. Fig. 7 shows that the contribution each variable makes to the utility functions estimated for the different categories follows a very similar line in all cases except for the potential users.
226
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
Waiting Time Vehicle Occupancy Cleanliness Journey Time Driver Kindness Comfort
Contribution of each variable to the Utility Function
1.6 1.4 1.2
Contribution to the Utility Function
1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -1.2 -1.4 -1.6 -1.8 Woman
Man
Very Frequent Casual Age<30 Age frequent 30-60
Age>60
Low Medium High Dependt Potentioal Income Income Income user user
Fig. 7. Contribution of each variable to the utility function.
Certain variables contribute greater weight to the utility function for all the defined categories of current bus users, independently of socio-economic characteristics. These variables are waiting time (with a negative sign), comfort on board and cleanliness in the bus (both with positive signs). However, the contribution of comfort and cleanliness to the utility functions can be seen to have no weight for potential bus users. These variables have not been considered when defining what they expect from a high-quality public transport service. Other variables make scarcely any contribution to the utility function by category of actual user. These are mainly vehicle occupancy and journey time, both being variables whose value is taken away from the overall utility. Nevertheless, these two variables carry more weight in the estimated utility functions for the potential users, being the only ones they value along with waiting time at the bus stop. Driver kindness stays practically constant for each type of actual bus users. The potential bus users do not place much importance on this variable. Fig. 7 provides a simple idea of the variables with greatest weight within the utility function defining the quality desired from public transport (bus mode, in the particular case of Santander).
The end goal of this research is to provide information for future policy making leading to the creation of an efficient transport service which lives up to the quality standards that the users expect or desire and which motivates the potential users (those who do not actually use it) to make that modal change to public transport. Once the authorities know the variables that actual users value most they can define and direct more efficient lines of investment at those specific points. Knowledge of the variables more highly valued by the potential users of public transport means policies can be designed to create the conditions that would encourage them to change over to using public transport. Knowing which variables are most important to both actual and potential users is useful information for public transport operating companies as they provide guidelines to follow when asked to improve the service. The three variables that stand out independently of the grouping criteria and define the quality desired from an efficient and safe public transport service are:
5. Conclusions
Cleanliness turns out to have practically the same weight in all the cases studied so all the interactions needed to be aggregated. The only case where it was worth studying separately was in the case of sex, as women value this variable more than men. Comfort during the journey turns out to have very similar values in all the models. The users who gave most weight to this were people over 65 years old and those passengers that only used the service sporadically or circumstantially. Both results
The objective of this research was to study the quality desired by users and potential users of public transport when defining an efficient and reliable system. The municipal bus service in the city of Santander was used as a practical example. A pilot stated preferences survey was performed and efficient design was used to prepare the definitive version.
1. Waiting time 2. Cleanliness 3. Comfort
L. dell’Olio et al. / Transport Policy 18 (2011) 217–227
have a certain logic, as older people do not like to suffer long periods of discomfort in uncomfortable or unstable postures; and those customers that use the service casually expect that once they have to depend on the timetables and routes of public transport their journey should be as least annoying as possible, as compensation for their loss of independence. Waiting time is always one of the variables that usually gets the most weight in the utility functions of a transport mode, because it represents time that the user sees as lost and the loss of time is irritating (Lirman, 2008). In this study it stands out as one of the most important factors when defining what people want from an efficient or high-quality service. The users that give most weight to waiting time are those who use the service casually or circumstantially. This could be explained because their infrequent use of the service gives them little knowledge of timetables and therefore they cannot optimise their waiting times, which consequently turn out to be longer, more irritating and of greater importance to them when defining an efficient system. The least important variable was found to be vehicle occupancy. The resulting weight turned out to be almost equal in all the cases studied, to such an extent that it became necessary to aggregate them all, as the different interactions studied (with the socio-economic characteristics) are hardly statistically significant. This could be due to the company distributing the fleet in such a way that it provides a service to all parts of the city and allocates more buses where there is greater demand, therefore the users do not see it as a problem that needs solving to improve the quality of service. The variables which are most valued by the potential users are different from those valued by the various types of actual users. It appears that not being a bus user is very significant when evaluating the different variables influencing quality in public transport. The potential users hardly take into account cleanliness, employee attitude and comfort during the journey. In fact, these variables showed incorrect signs in the modelling process, implying that these variables were ignored in most cases when they were choosing between the different scenarios. The only variables valued by the potential users are journey time, much more important than for the actual bus users, waiting time and the level of bus occupancy. This latter variable had the most weight for the potential bus users. The results and conclusions show that any policies aimed at increasing the number of users of public transport in Santander should be orientated at: 1. Reducing waiting times: perhaps with more information panels, more publicity campaigns aimed at getting closer to the people (so they know the routes, timetables and fares); by improving bus frequency; by improving the traffic management system which includes public transport; or by improving the location of bus stops and bus-only lanes. 2. Improving comfort during the journey: keeping the vehicles in perfect condition; cleaning them regularly and maintaining their current good condition; providing courses for drivers on calm driving, consequently increasing the perception of safety during the journey; introducing systems that provide constant good ventilation and an agreeable temperature inside the bus throughout its journey. 3. Introducing information campaigns aimed at potential bus users which outline, for example, the real journey times by bus
227
between significant city landmarks, thereby countering the erroneous belief of the non-users that it is a slow mode of transport. 4. Strengthening the busiest lines at rush hour by providing more services which would allow the public to make their journey in greater comfort. This action would also counter the non-bus users’ perception of overcrowding as they observe the running of the public transport system. Finally, it is worth pointing out that the analysis done for this work has clearly defined the population strata that use the public transport service in Santander as well as those that do not use it. Based on the information and characteristics studied in this work, the corresponding public authorities and bus operating companies can set out policies aimed at capturing specific types of users. The model designed here provides the variables most valued by the users for a desired quality of service and the results can help in the design of a greater range of measures to improve the service.
Acknowledgement The authors would like to thank the Spanish Ministry of Science and Innovation for financing the project TRA2006-14663/ MODAL, which has allowed this research to take place. References dell’Olio, L., et al., 2010. Modelling user perception of bus transit quality. Transport Policy, doi:10.1016/j.tranpol.2010.04.006. Foote, P.J., Stuart, D.G., 1998. Customer satisfaction contrasts express versus local bus service in Chicago’s North Corridor. Transportation Research Record (1618), 143–152. Garrido, R.A., Ortu´zar, J.D., 1994. Deriving public transport level of service weights from a multiple comparison of latent and observable variables. The Journal of the Operational Research Society 45. Glascock, J., 1997. Research on customer requirements for transit service design and delivery. Transportation Research Record (1604), 121–127. Hensher, D.A., 1994. Stated preference analysis of travel choices: the state of the practice. Transportation 21, 107–133. Hensher, D.A., Houghton, E., 2004. Performance-based quality contracts for the bus sector: delivering social and commercial value for money. Transportation Research Part B 38, 123–146. Hensher, D.A., Rose, J.M., Greene, W.H., 2005. Applied Choice Analysis: A Primer. Cambridge University Press. Hensher, D.A., Stanley, J., 2003. Performance-based quality contracts in bus service provision. Transportation Research Part A (37), 519–538. 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 37, 499–517. Huber, J., Zwerina, K., 1996. The importance of utility balance and efficient choice designs. Journal of Marketing Research (33), 307–317. Ibeas, A., dell’Olio, L., Barreda, R., 2010. Citizen involvement in promoting sustainable mobility. Journal of Transport Geography. doi:10.1016/j.jtrangeo. 2010.01.005. Lirman, T., 2008. Valuing transit service quality improvements. Journal of Public Transportation 11 (2), 43–63. ˜ o, operacio´n y Molinero, A., Sanchez, L., 1997. Transporte pu´blico: Planeacio´n, disen administracio´n. Publicaciones UAEM. Ortu´zar, J.D., Willumsen, L., 1994. Modeling Transport. John Wiley and Sons. Powe, N.A., Garrod, G.D., McMahon, P.L., 2005. Mixing methods within stated preference environmental valuation: choice experiments and post-questionnaire qualitative analysis. Ecological Economics. Rizzi, L.I., Ortu´zar, J.D., 2006. Road safety valuation under a state choice framework. Journal of Transport Economics and Policy. Rose, J.M., Bliemer, M., Hensher, D., Collins, A., 2008. Designing efficient stated choice experiments in the presence of reference alternatives. Transportation Research Part B 42, 395–406.