Light Rail Transport Questionnaire Report Sample

Business Research Analysis

Glossary
Sample size – how many people are included in the survey.
Analysis – the type of statics that are used to learn more about the data.
Subgroups – Components of the data that are grouped together like under the group gender, female and male are subgroups
Part 1Developing a Questionnaire on LRT
Distribution
The most cost-efficient way of distributing the questionnaires is to randomly distribute them at the station when people are waiting a few minutes for their train. Questions can be answered and the finished questionnaires gathered at that time. Non-responses are always a problem in survey type research (Phellas, Bloch & Seale, 2011). The distributor can judge whether or not a rider shows a good chance of answering the questions and returning the questionnaire, since they will be on-site and in control of the questionnaires. Although having the distribution and pick-up of the questionnaires by a researcher is no guarantee that all the questionnaires distributed are answered, an assumption is made that fewer questionnaires will be ‘lost’ as non-responses. No incorrect or correct number of respondents exists and that is why statistical analysis is necessary. The way to decide on a sample size is to balance the cost and the best number for statistical analysis (Phelas et al., 2011). The research cannot survey the total population of riders on the LRT, so a statically practical number of respondents needs to be included; the closer the number of respondents is to the total number of population riding the LRT the less the sampling error will be (Phelas et al., 2011). Two hundred questionnaires were analysed. Each questionnaire was numbered from 1 to 200 and none of the names of the respondents were taken. An example of how the coding was carried out for the questions rated from one to ten is in Appendix B.
The developer of a questionnaire must stay focused on the goal of the questionnaire (Frary, n.d.) Close-ended questions were used to learn the gender of the respondent; male or female. Other close-ended questions are the time of day LRT is ridden, the type of ticket used by the respondent, and rated features. The opinions of the respondents on safety, value, cleanliness, reliability and overall opinion of the system were also close-ended. The respondents were asked for a rating from one to ten with one being the least attractive and ten being the most attractive of the choices. The open-ended questions were how much time in minutes the respondent spent on the system and number of people travelling together (including the respondent).

Critical Evaluation of the Questionnaire

The goal of gathering information to use for making a 10-year strategic plan must be remembered at all times so irrelevant questions are not included. The questionnaire used for the research did not ask irrelevant questions; in fact the biggest criticism is that not enough questions were asked. A second helpful step is to ask several people from a representative sample, such as asking informal open-ended questions or to ask the sample group to critique the questionnaire. Either way the questions that are most important will become evident. Another possibility is to take a field trial to learn response rate, question applicability, and question performance, but that can only be done if time is not short for developing the questionnaire(Presser, Couper, Lessler and Martin et al., 2004.).
Questionnaire development for the project was carried out to collect factual information and rider opinions. Since time and cost are an issue the survey method of using questionnaires was chosen instead of face-to-face interviews. Face-to-face interviews are valuable for learning more detailed information about what makes a rider satisfied or dissatisfied with riding the LRT.
The questionnaire was developed in order to learn rider-opinions about light rail transport (LRT) in the UK. (See appendix A) The reason the questionnaire was used is because a LRT system in an urban region in the UK is developing plans strategic plans and need the input of consumers. The strategic plan is being designed for the next ten years. The decisions the planners need to make are whether to extend the system a further distance from the centre, to run more trains during peak commuting hours or to run the trains later into the night. Another option under consideration is to run more trains during off peak hours such as in the evenings or on weekends. A third option to add into the strategic plan is to replace older trains with new, modern trains. The costs of including all the options in the strategic plan are too high so the city wants to prioritize the most practical developments. The number of riders at specific times of the day and days of the week are necessary to understand the number of trains needed and the times the trains are needed. The town also wants to learn the factors that have the most influence on particular findings such as gender or the number of people travelling together. As many of the above factors that the town wanted to use as input for the strategic plan were included in the questionnaire.
A more complete questionnaire for more inputs would have added several other topics for questions. Suggestions for more questions are discussed below. The age of the travellers is good to know and to compare to the days and times travelled throughout the week (week days plus weekends). The comparison of age and gender could give more information when evaluating the data for overall satisfaction and for the four factors of safety, value, reliability and cleanliness. For example the analysis might show that the younger the rider is, the less they are interested in cleanliness or safety. On the other hand older riders may base their riding habits on whether they are satisfied with the cleanliness of the LRT and safety.
Another good question to ask the respondents is where they are going, for example, if the destination is school, work, shopping or entertainment. If the disruption in travel for construction will be great, then it would be good to learn about whether or not they know an alternate route for travelling to their destination ( Kattan et al., 2013). If room and time had permitted, another question that would add knowledge to the strategic planning would be about choices of types of travel. For example, do the riders prefer LRT, buses, or using their own car to travel. A study based on the English census showed that LRT did not result in less private cars used to travel; instead riders were choosing LRT instead of taking busses (Lee & Senior, 2013).
The research used a rating scale from One to Ten to understand the importance of safety, reliability, cleanliness and value. One is the least satisfactory and Ten represents the most satisfaction. The rating system is very vague and it is difficult to code each number in the range of one to ten for how much the traveller likes or dislikes the feature under consideration.
A decision about expanding the LRT to outer lying areas was being considered as a possible addition to the strategic plan. Ambarwati, Verhaeghe, Pel and van Arem (2014) have demonstrated that urban sprawl can be discouraged by applying knowledge about the population density and the time of travel to destinations.
Knowles (2007) raises interesting issues from his research that predicts a bleak future for light rail transit in the UK. He discusses the reasons for the plans for many light rail transit lines were cancelled due to investment and dependence on government funds (Knowles, 2007). On the other hand the light rail system for Greater Manchesters’ Metrolink extension was readopted (Knowles, 2007). The decisions to shutdown strategic plans in the past were mainly due to funding, and the riders were not part of the decisions to stop building LRT. Questions on the questionnaire or better yet interviews to learn more about transit riders’ opinion on the topic would be very helpful for strategic planning.
An academic article in a journal for policy and practice in transportation research discussed the decision-making challenges for light rail. The article discusses the reason the LRT is being used by city planners. For example in most of Europe and in the USA, the reason is to help travellers move from the centre of urban areas to other areas; or to enhance the mobility of travellers into urban centres (Bruijn and Veeneman, 2009). If the results of the questionnaire were to be used for one of these reasons was not known, but a question that would be good to ask under any scenario is what direction from or to the urban centre were travellers trying to reach. Additionally the questionnaire could ask if they were travelling from home to their work or other destination in the urban centre or from home to work located away from the urban centre.
The questionnaire did not address the amount of space in the LRT and that is an issue that can determine the amount of satisfaction a rider has about using LRT. Learning more about space means, are the conditions crowded or not when the rider is travelling. Mahudin, Cox and Griffiths (2012) found that psychological factors impact a rider’s experience if the LRT is crowded. For example, learning how commuter’s evaluated psychosocial aspects of crowding could lead to feelings of stress and exhaustion (Mahudin et al., 2012).
The results of the questionnaire were graphed in bar charts and with multiple regression graphs, but pie graphs and bar graphs in different combinations could have been used in order to make the presentation more interesting.
Research by Mackett and Sutcliffe (2003) found that the answers to written questionnaires helped to validate a framework for making LRT systems more successful. The questionnaire developed here, was also to aid in strategic planning to make LRT more successful by incorporating feedback from riders into the strategic planning stage. The success of their research adds credibility and confidence to the use of written questionnaires for the purpose of LRT use planning.

Part 2 Data analysis, findings and managerial implications

A decision tree for aiding in choosing the type of statistical analysis shows the correct analysis to use when carrying out qualitative and quantitative research. (See Appendix C)
One hundred fifteen male (57.5 percent) and 85 females (42.5 percent) participated in the sampling. (See fig. 1) The number of each of the gender becomes particularly relevant when the analysis is done to understand how gender influences certain factors. The majority of the riders are using the LRT in the evening. (See fig. 2) The summary of the travel time showed that the three times of 10 to 10 minutes, 20 to 30 minutes, and 30 to 40 minutes are similar and measure approximately 30 percent each; while only a few riders ride for up to ten minutes. (See fig. 3) Most of the riders carry a monthly pass (about 32 percent), followed by the weekly pass (26 percent) and the annual pass (24.5 percent). The distribution is displayed in figure 4. Most of the riders are travelling alone (about 43 percent). About 25.5 percent of the people travel with another person. (See fig. 4)
The perception of safety distribution of the riders is shown in figure 5. The overall statistics on safety perception has an average of 4.7 and a standard deviation of 2.0. Therefore on the average the people do not feel very safe or in very much danger. Table 1 compares the overall statistics as a function of the gender. Both genders demonstrate average value and median value for safety perception. (See table 1) The standard deviation is similar for the genders showing that both men and women have similar perceptions on safety. The safety perception mean ranges from 4.5 to 4.8 for the four time periods of morning, afternoon, evening and night. The standard deviation ranged from 1.8 to 2.5, indicating that there is no significant difference of the safety perception between travel ties.

Travel Use Week Days and Weekend

One hundred thirty two of the respondents travelled during the week. (See table 2) Figure 7 shows the distribution of travel use during the week days is clearly weighted towards the evening hours. Sixty eight of the respondents travelled during the weekend. (See table 2) Figure 8 shows how the travel use was during the weekend and once again the bar graphs demonstrate travel use is weighted towards evening and late evening. On the weekend none of the respondents reported travelling in the morning hours.
During the week, 51 percent of the travel use is during the evening periods and 34 percent is during the morning. During the weekend, 81 percent of the travel use is in the evening and late evening periods. The mean (average) travel time during the week was reported to be 24.3 minutes with a standard deviation of 9.5 while the mean (average) travel time during the week was 23 minutes almost the same as during the week, and with the same standard deviation .The statistics show there is no significant difference in travel time during the week and weekend. The average number of people travelling during the week is 2.1 with a standard deviation of 1.2; while the average travelling weekend was 2 with a standard deviation of 1.1. The results suggest that there is no difference for the average number of people travelling during the week and during the weekend.

Satisfaction

The overall satisfaction is presented in figure 9. The number of respondents choosing the least satisfaction is almost zero percent. The distribution shown by the bar graphs peaks at the 5-5to 6 rating and is fairly evenly distributed between 3 to 4, 7 to 8 and 9 to 10. (See fig. 9) Therefore the calculation for average overall satisfaction of 6.3 is reasonable from a visual inspection of the bar graph.
Figure 10 shows the average satisfaction as a function of the travel time of day. The average satisfaction in this category ranged from 5.6 with a std. dev. 2.3 for the late evening; the least satisfaction therefore was for the late evening. The highest satisfaction was 6.6 for both the afternoon and evening time periods; the standard deviations ranged from 1.9 to 2.3. When the highest standard deviation of 2.3 was added to the each of the calculated means, the average satisfaction for the time periods were shown to be sadistically the same. Similar results were found when the average satisfaction was examined as a ticket type. (See fig. 11) The highest satisfaction was for the monthly pass and the lowest for the day ticket.
Relationships between safety, value, cleanliness and reliability as of a function of overall satisfaction are presented in figures 12 through 15. The figures include the regression line and the regression coefficient. From the plots and the regression coefficients on the graphs, we observe the following.
no relationship between safety and overall satisfaction (R2 = 0.0047)
no relationship between cleanliness and overall satisfaction (R2=0.0687)
a good relationship between value and overall satisfaction (R2=0.4515)
a good relationship between reliability and overall satisfaction (R2=0.4417)
The good relationship with overall satisfaction is indicated with the slope is equal to 1 or close to 1. R2 is the result that represents the slope. Comparing the four R2 results shows that value and reliability have slopes much closer to 1 than do safety and cleanliness when all four are graphed versus overall satisfaction.

Based on these results the best model to predict overall satisfaction is the one provided by the score for value.

For Case 1 if we use the regression equations obtained for safety, value , cleanliness, and reliability & we assign the respective scores of 4, 5,7,10 then we predict the overall satisfaction 6.3, 6.0, 6.6 and 9.4 respectively. Based on the best model, the value model, the overall satisfaction is predicted to be 6.0.
For Case 2 if we use the regression equations obtained for safety, value, cleanliness, and reliability & we assign the respective scores of 1,2,3,1 then we predict the overall satisfaction 6.1, 63.5, 4.7 and 2.3 respectively. Based on the best model, the value model, the overall satisfaction is predicted to be 3.5.
The evaluation of Case 1 and Case 2 suggests that if we use the value model, it can provide consistent results regarding the overall satisfaction of the riders. The model can be used only as an indication of overall satisfaction, because the variability of the riders overall satisfaction with respect to value is very significant. (See fig. 13) The variability is clear because the points of data are scattered across the graph instead of grouped together in a common location or along a common line.
Figure 1 Gender
Figure 2 Summary of Travel Time of Day
Figure 3 Travel Time in Minutes
Figure 4 Travel Type of Tickets
Figure 5 Number of people travelling
Figure 6 Safety Perception Distribution

References

Ambarwati,L. Robert Verhaeghe, Adam J. Pel, Bart van Arem, Controlling Urban Sprawl with Integrated Approach of Space-transport Development Strategies, Procedia - Social and Behavioral Sciences, Volume 138, 14 July 2014, Pages 679-694, ISSN 1877-0428, http://dx.doi.org/10.1016/j.sbspro.2014.07.261.
Bruijn, H.D., Wijnand Veeneman, Decision-making for light rail, Transportation Research Part A: Policy and Practice, Volume 43, Issue 4, May 2009, Pages 349-359, ISSN 0965-8564, http://dx.doi.org/10.1016/j.tra.2008.11.003.
Frary, Robert B. (n.d.) ‘A Brief Guide to Questionnaire Development’ Math 20, Introduction to Statistics, Office of Measurement and Research Service, Virginia Polytechnic Institute and Research Service, http://www.ericae.net/ft/tamu/vpiques3.htm
Kattan, Lina, Alexandre G. de Barros, Hina Saleemi, Travel behavior changes and responses to advanced traveler information in prolonged and large-scale network disruptions: A case study of west LRT line construction in the city of Calgary, Transportation Research Part F: Traffic Psychology and Behaviour, Volume 21, November 2013, Pages 90-102, ISSN 1369-8478,.http://dx.doi.org/10.1016/j.trf.2013.08.005
Knowles,Richard D. What future for light rail in the UK after Ten Year Transport Plan targets are scrapped?, Transport Policy, Volume 14, Issue 1, January 2007, Pages 81-93, ISSN 0967-070X, http://dx.doi.org/10.1016/j.tranpol.2006.10.001
Roger Mackett, Roger, Ela Babalik Sutcliffe, New urban rail systems: a policy-based technique to make them more successful, Journal of Transport Geography, Volume 11, Issue 2, June 2003, Pages 151-164, ISSN 0966-6923, http://dx.doi.org/10.1016/S0966-6923(03)00003-6.
Mahudin, Nor Diana Mohd, Tom Cox, Amanda Griffiths, Measuring rail passenger crowding: Scale development and psychometric properties, Transportation Research Part F: Traffic Psychology and Behaviour, Volume 15, Issue 1, January 2012, Pages 38-51, ISSN 1369-8478,.
Shin S. Lee, Martyn L. Senior, Do light rail services discourage car ownership and use? Evidence from Census data for four English cities, Journal of Transport Geography, Volume 29, May 2013, Pages 11-23, ISSN 0966-6923, http://dx.doi.org/10.1016/j.jtrangeo.2012.12.002.
Phellas, Constantinos N., Bloch, Alice & Seale, Clive. (2011) Sturctured methods: Interviews, Questionanaires and observation. Chapter 11. pp. 25. http://www.sagepub.com/upm-data/47370_Seale_Chapter_11.pdf
Presser, S., Couper, M. P.,Messler, J.T., Martin, E., Martin, J., Rothgeb,J.M., and Singer, E. (2004). Methods for testing and evaluating survey questions. Public Opinion Quarterly, 68(1),l 109-130.http://isites.harvard.edu/fs/docs/icb.topic1352376.files/Presser%20et%20al%20Cognitive%20Testing.pdf
Appendices
Appendix A Questionnaire
Dear Participant,
Thank you for agreeing to take this survey about the addition of a Light Rail Transit (LRT) to our urban area. Part I requests some demographic information including gender, age, and employment situation.
Confidentiality: Your entire survey will be coded so your name will be kept absolutely confidential. None of the pages of any survey will be seen by anyone except for the researcher.
Thank you!
A. Gender: □ Female □ Male
B. Time: What time of day will you most often travel on the LRT?
□ morning: 6 am-12noon □ afternoon: 12 noon-4:30pm □evening:4:30pm-9pm
□ late evening: after 9pm
C. Travel time
Please indicate the approximate time you spend on the system _______________
D. Ticket: What type of ticket do you use?
□ single or return day □ weekly pass □ monthly pass
□ annual pass □ free pass for a retired person
E. Number of people travelling together including yourself ______________
F. Safety: How do you rate the Safety on the system?
1□ not safe
2□
3□
4□
5□
6 □
7□
8□
9□
10□ very safe
G. Value: Is the value of the system equal to the money spent?
1□ not at all a good value
2□
3□
4□
5□
6 □
7□
8□
9□
10□ a very good value
H. Cleanliness: Rate the cleanliness of the system
1□ very dirty
2□
3□
4□
5□
6 □
7□
8□
9□
10□ very clean
I. Reliability: Rate the reliability of the system
1□ not at all reliable
2□
3□
4□
5□
6 □
7□
8□
9□
10□ very reliable
J. Opinion of your overall experience
I. Reliability: Rate the reliability of the system
1□ very poor
2□
3□
4□
5□
6 □
7□
8□
9□
10□ very good
Appendix B Coding examples for 1 to 10 ratings
3. Employment Status
Employed □ Unemployed □
1□ not safe
2□highly unsafe
3□ medium unsafe
4□ somewhat unsafe
5□ a little bit unsafe
6 □ a little bit safe
7□ somewhat safe
8□ medium safe
9□ highly safe
10□ very safe
G. Value: Is the value of the system equal to the money spent?
1□ not at all a good value
2□ some value but very little
3□ very poor value
4□ poor value
5□ medium value
6 □ medium to fair value
7□ fair to more value
8□ reasonable value
9□ good value
10□ a very good value
Appendix C Decision Flowchart