Designing Better Public Transport: Understanding Mode Choice Preferences Following the COVID-19 Pandemic

Round 1

Reviewer 1 Report

The aim of the article was to present the results of research on quantifying the impact of several service features on the choice of public transport.
In my opinion, the work is well written and doesn't require further corrections.

Author Response

Thank you for taking the time to review our work and happy that you found no need for further corrections. 

Reviewer 2 Report

This paper clearly delivers the implication for recent transport policy after COVID-19. However, overal structure of article could be more enhnaced from academic perspective. I recommend making new chapter for literature review and moving 1.1 to this chapter. Moreover, aim & objectives chapter needs to be included to chapter 1, and key points also needs to be included to conclusion chapter.  Lastly, I recommend more academic editing of the paper.

Author Response

Thanks for taking the time to review our paper. We have addressed all your comments below:

1. “I recommend making new chapter for literature review and moving 1.1 to this chapter. Moreover, aim & objectives chapter needs to be included to chapter 1 ”

 

We have now created a new Introduction chapter as requested and have moved the introductory text into it. We have also created a new literature review chapter and have added significantly more discussion around the area of mode choice in transport research. Finally, we have also moved the aims and objectives into the introduction chapter 1.

 

“1.1. Discrete choice methodology in transport research

Human behaivour can ultimately linked to decisions made regarding different choices of alternatives [18]. This could apply in many in different contexts- for example, which healthcare provider a person chooses, their choice of consumer products and also the modes of transport they choose to use. The fundamental assumption is that this choice is rational, hence, we can then attribute a value given by the person to the outcome- this is known as utility [19]. This leads to the Random Utility Theory (RUT) which assumes that an individual makes their decisions to maximise utility and that a consumer behaves rationally, by making their choice based on their preferences, which is represented as a utility function. We can take advantage of this fact to experimentally ascertain preferences towards various features and attributes of a particular product or service. This method is called a Discrete Choice Experiment (DCE) [20,21]. By understanding the priorities to-wards service/product attributes, this plays an important role in developing and setting priorities in policy creation and design [22].

The key advantage of using DCEs are the evident congruence with consumer theory, allowing the calculation of Willingness to Pay (WTP), which is a key output from the method [23]. WTP is a measure of how willing to pay a person is for a single unit change of a particular attribute of the product or service [24]. However, DCEs are not without their drawbacks, namely, the combinations of attributes and levels can increase the response complexity for participants [25]. Secondly, DCEs are based on hypothetical scenarios as opposed to real-world, observation based data collection [26]. However, these limitations can be mitigated through good DCE design. As will be demonstrated later, the DCE developed for this paper used careful construction of the attributes and levels to reduce the cognitive burden on participants; as well as pivot the questionnaire scenario around one that the respondents will be familiar with. 

DCEs have been used in many different contexts. For example, the method has been used in the development of health policy [27], employment decisions [21], and particu-larly in the transport research domain [18,28]. For example, the walking distance to a bus stop along with transport fare cost were found to have a negative impact on the assessment of utility of a service [29]. There have also been several studies that have addressed the UK in particular. For example, fare cost was found to have a negative impact on public transport utility, though this was dependant on factors such as the journey distance, type of traveller and income [13]. In a wider meta-analysis of literature, a significant, highly elastic relationship between income and value of travel time was established [30].

However, what appeared to be missing was a holistic understanding of mode choice and the corresponding influencing factors when multiple modes of transport are available for the same journey- particularly during the course of the COVID-19 pandemic.”

 

2. “key points also needs to be included to conclusion chapter”

We have moved the key points chapter into the conclusion

 

3. “Lastly, I recommend more academic editing of the paper.”

Thanks for noticing the opportunity to improve the academic language and editing of the paper. In response, we have now added a literature review section and have added more discussion around the literature of the area- in order to be more in-line with the academic writing style. We have also made edits across the entire paper to improve the academic editing of the paper.

 

Reviewer 3 Report

This paper tries to design better public transport service by performing an online discrete choice experiment, a multinomial logistic regression is adopted to analyze the experiment data. The study point is interesting and worth investigating, while there is not novelty methodology and conclusions. The comments that should be considered by the authors for improving the paper are as follow:

1. There are many minor issues, mostly related to language use, like ’the push towards more sustainable…has been increasing’ in page 1, the sentence pattern and the voice of increasing are not right; ‘a focus on service-related attributes was chosen’ is page 3, the grammar is wrong. I highly recommend checking the similar errors and doing careful editing of the manuscript.
2. Abstract is a brief summary of the whole paper, including the research issue, technical approaches, methodology, important conclusions and findings. However, the author doesn’t explain the research issue clearly at first, and more innovative conclusions and important findings need to be provided.
3. The literature review in introduction focuses on the fact of the transportation energy consumption, while there lacks of analyzing and summarizing the literature regarding the public transport mode choice and service system improvement, the research gaps corresponding to the innovation points of this paper is not prominent, which are suggested to supplement further.
4. Why a discrete choice experiment was used? Also, the introduction and characteristics of this method should be provide.
5. The average travel time on journey is different signally among several travel modes in Table 2. The results may be related to travel distances suitable for different modes of transport, and the travel times are not comparable. In addition, the data in Table 2 and Table 3 should be analyzed in detail.
6. The experimental is collected first, then processed and analyzed. Thus, the title of the section 3.4 and 3.5 should be exchanged.
7.  The process of the experiment especially under the COVID-19 pandemic condition should be provided, or there lacks the data understanding mode choice behavior during the COVID-19 pandemic. Then, the validity of the collected data should be analyzed. Please supplement the related contents.

Author Response

Thanks for taking the time to review our paper in detail. We have addressed all your concerns and they are detailed below: 

1. There are many minor issues, mostly related to language use, like ’the push towards more sustainable…has been increasing’ in page 1, the sentence pattern and the voice of increasing are not right; ‘a focus on service-related attributes was chosen’ is page 3, the grammar is wrong. I highly recommend checking the similar errors and doing careful editing of the manuscript.

Thanks for the detailed review of the language and grammar. This was also noted by another reviewer and we have improved the writing quality of the manuscript throughout the entire paper.

 

2. Abstract is a brief summary of the whole paper, including the research issue, technical approaches, methodology, important conclusions and findings. However, the author doesn’t explain the research issue clearly at first, and more innovative conclusions and important findings need to be provided.

Thanks, this is a fair point and we have addressed this by including a stronger introduction to the topic area and motivation behind the research issue of the paper; as well as rewrite the results in the abstract to more clearly identify the innovative and important findings:

 

“Transport behaviour has evidently changed following the COVID-19 pandemic, with lower usage across multiple modes of public transport and an increasing use of private vehicles. This is prob-lematic as private vehicle use has been linked to an increase in Traffic Related Air Pollutants, and consequently global warming and health related issues. Hence it is important to capture transport mode choice preferences following the pandemic, so that potential service changes can be made to address the lower usage. 1138 respondents took part in an online discrete choice experiment methodology to quantify the utility of public transport service attributes in decision making around the choice of public transport. The data resulted in the development of three models using a multinomial logistic regression in R. For respondents on personal or commuting journeys, the mode of transport had no effect on utility. Results found that fare cost was the most important factor driving transport mode preference, when a range of choices were available. Following this, keeping fare cost consistent, faster journey times were preferred to stronger access to transport (i.e. through the provision of more bus stops/stations). The provision of operational relevant infor-mation to the journey was only significantly valued by commuters and travellers who could claim their journey as a business expense. Finally, when cost became less relevant (i.e. for travel-lers on expensed journeys), there was a significantly strong preference for taxi and road-vehicle transport over all other transport modes. The results from this empirical research are discussed and the implications of recent transport policy are discussed, and recommendations of public transport service design are made.”

 

 

3. The literature review in introduction focuses on the fact of the transportation energy consumption, while there lacks of analyzing and summarizing the literature regarding the public transport mode choice and service system improvement, the research gaps corresponding to the innovation points of this paper is not prominent, which are suggested to supplement further.

In response, we have now added significantly more literature to the introduction and the new literature review chapter. In the introduction, we spend more time contextualising the research motivations with a more in-depth review of the literature surrounding the effects of COVID-19 on public transport usage.

 

“1. Introduction- the importance of public transport usage

Following the COVID-19 pandemic, there have been significant changes in public perceptions. In response to the virus, countries around the world introduced lockdowns and social distancing guidance in a bid to contain its transmission [1,2]. As a result, public transport usage fell by as much as 80-90% worldwide [3,4]. Despite the regulations around social distancing and lockdowns being relaxed, public perceptions have changed with a greater focus on transport hygiene [5], particularly given the fact that public transport had  been identified as a potential vector for viral transmission [6].  As society begins to adjust to new normal redefined by the pandemic, it is evident that transport patterns have changed. As many as 52% of transport users say they will use public transport less in the future and considering access to a private vehicle more important than before the pan-demic [7]. This is illustrated by data from London, which found the rate of private vehicle use recover faster than public transport modes; and consequently, NOx levels have re-turned to their pre-pandemic levels. This is problematic for a number of reasons.”

 

            Second, we have now added subchapter 1.1, which puts more emphasis on the COVID-19 context in the UK, to further emphasise the gap in the knowledge around understanding how mode choice preferences changed. The timing of the questionnaire (October 2021) means the questionnaire was launched prior to the second national lockdown in the UK. This gives us a context of a population which had come out of lockdown and returned to a certain level of normality.

 

4. Why a discrete choice experiment was used? Also, the introduction and characteristics of this method should be provide.

Thanks for recognising this point. In response, we have now added significantly more literature, detailing the background of the DCE method, its advantages, disadvantages as well as more examples of how its been used, its value and its applicability to the aim of this research.

 

“ 1.1. Discrete choice methodology in transport research

Human behaivour can ultimately linked to decisions made regarding different choices of alternatives [23]. This could apply in many in different contexts- for example, which healthcare provider a person chooses, their choice of consumer products and also the modes of transport they choose to use. The fundamental assumption is that this choice is rational, hence, we can then attribute a value given by the person to the outcome- this is known as utility [24]. This leads to the Random Utility Theory (RUT) which assumes that an individual makes their decisions to maximise utility and that a consumer behaves rationally, by making their choice based on their preferences, which is represented as a utility function. We can take advantage of this fact to experimentally ascertain preferences towards various features and attributes of a particular product or service. This method is called a Discrete Choice Experiment (DCE) [25,26]. By understanding the priorities to-wards service/product attributes, this plays an important role in developing and setting priorities in policy creation and design [27].

The key advantage of using DCEs are the evident congruence with consumer theory, allowing the calculation of Willingness to Pay (WTP), which is a key output from the method [28]. WTP is a measure of how willing to pay a person is for a single unit change of a particular attribute of the product or service [29]. However, DCEs are not without their drawbacks, namely, the combinations of attributes and levels can increase the response complexity for participants [30]. Secondly, DCEs are based on hypothetical scenarios as opposed to real-world, observation based data collection [31]. However, these limitations can be mitigated through good DCE design. As will be demonstrated later, the DCE developed for this paper used careful construction of the attributes and levels to reduce the cognitive burden on participants; as well as pivot the questionnaire scenario around one that the respondents will be familiar with. 

DCEs have been used in many different contexts. For example, the method has been used in the development of health policy [32], employment decisions [26], and particu-larly in the transport research domain [23,33]. For example, the walking distance to a bus stop along with transport fare cost were found to have a negative impact on the assessment of utility of a service [34]. There have also been several studies that have addressed the UK in particular. For example, fare cost was found to have a negative impact on public transport utility, though this was dependant on factors such as the journey distance, type of traveller and income [20]. In a wider meta-analysis of literature, a significant, highly elastic relationship between income and value of travel time was established [35].

However, what appeared to be missing was a holistic understanding of mode choice and the corresponding influencing factors when multiple modes of transport are available for the same journey- particularly during the course of the COVID-19 pandemic.”

 

5. The average travel time on journey is different signally among several travel modes in Table 2. The results may be related to travel distances suitable for different modes of transport, and the travel times are not comparable. In addition, the data in Table 2 and Table 3 should be analyzed in detail.

Thanks for your comment. Apologies for it not being clear, we used the same journey length of 10 miles for the scenario. To make this clear, we’ve added the following to the paper:

 

“3.1. DCE Scenario

It is important that participants are presented with choices that can be considered to be real alternatives to each other. Hence, it was decided that journey distance would be consistent for all transport modes. The average commuting distance in 2015 was 8.8 miles [36]. We rounded this to 10 miles for simplicity. Furthermore, three scenarios of: personal travel, commuting and expensed travel were considered. These were based on analysis of most popular reasons for travel according to the UK Government’s national travel survey [37].         Hence, for each travel mode, the journey distance was kept consistent.”

 

For the consistent journey distance, this then meant the travel times had to vary. This is an important point of the study design. You are correct in pointing out that travel time values are different for each mode of transport. However, as illustrated in the paper by Merkert and Beck (2017), published in Transportation Research Part A, it is important that the attributes are in fact reflective of real-world travel options. If we had the same times across all travel modes, this could not be considered representative.

 

The times we have used are based on extant market values. This means we considered several journeys around the UK where multiple travel modes were possible. Then, we carefully looked at the times each mode would take. We also took into account average delays. This was how we came to the conclusion that it was the case that on average, different transport modes had different journey times. A train that takes 20 minutes would be considered relatively slow for the type of journey we analysed. In comparison, a bus that takes 20 minutes would be considered realtively fast. Hence, this was why we went with extant market values, along with the fact that there was evidence this was a standard procedure in the DCE literature (see Merkert and Beck 2017).

 

Furthermore, it is for this reason we also added the ‘additional travel time’ attribute. We recognised that yes, while on average the journey time on trains for the type of journey we analysed is relatively short (10-20 minutes), train stations are less accessible than bus or metro/underground stops. Hence, the additional travel time reflects this and is also brought into the participant’s consideration. Then, when considering the ‘total’ travel time of journey time + additional travel time (which the participants likely would have done as both attributes were presented simultaneously as part of their decision making), then the differences in time between the attributes are less dramatic.

 

We have added more details in section 3.2.3 to help explain the reasoning behind the varying travel times and we hope this gives you a satisfactory answer as to why we chose to construct the DCE this way.

 

“ 3.2.3. Travel Time (on journey)

Travel time described the length of time the journey would take on the specific travel mode. This was important to calculate the willingness to pay (WTP), which as previously discussed is an important aspect for transportation research. Furthermore, there is con-sensus across the literature that journey time is an important factor in the appraisal of public transport services [43,44].

Travel times that were appropriate to each travel mode were calculated, based on extant market values. This results in travel times that are different for each mode of transport, however, this is important for the creation of choice sets that are congruent to real-world scenarios. As also illustrated in [41], there are differences in how people perceive a ‘fast’ train compared to a ‘fast’ bus. Hence it was important that for a consistent travel distance, the times were appropriately adjusted for each mode to ensure that the choice sets reviewed could be considered representative of a potential real-world journey.”

 

“3.2.4. Additional Travel Time

Additional travel time was found to be a key aspect of public transport use [46,47], describing the time required to, for example, travel to the train or bus station. It also accounted for average delays, based on data from UK government data [45,48,49]. Values were adapted appropriately to their corresponding transport type, to create realistic times and comparisons for the DCE. This was an important consideration as while trains are relatively fast in terms of travel time, the fact that there are fewer train stations than bus stops means there is, on average, a greater journey time to reach the mode of transport. Hence, it was important that this was reflected, so that a representative illustration of the total journey time could be considered by participants in the DCE.”

 

 

 

6. The experimental is collected first, then processed and analyzed. Thus, the title of the section 3.4 and 3.5 should be exchanged.

Thanks for noticing this, we have corrected it as recommended.

 

7. The process of the experiment especially under the COVID-19 pandemic condition should be provided, or there lacks the data understanding mode choice behavior during the COVID-19 pandemic. Then, the validity of the collected data should be analyzed. Please supplement the related contents.

 

More details have been added about the questionnaire design:

 

“3.4. Data Collection and Sample

Data collection was facilitated using the Qualtrics Panels service. Hence, the DCE was presented as an online questionnaire. Particularly considering the context of the COVID-19 pandemic, this format allowed participants to complete the questionnaire in their own environment and device. The questionnaire was designed to be accessible and legible on both desktop and mobile devices. All respondents were checked for validity against their complete time. The sample size after validation was N=1136.”

Given that the questionnaire was online and we took time in ensuring the format worked across both desktop and mobile devices, it was considered that the data was still valid. Participants were free to complete the questionnaire at their own convenience, in the safety of their own environment, avoiding issues of social distancing etc. of conducting the study in person. The online format has been used by numerous other DCEs as the preferred method of engaging with users.

Round 2

Reviewer 2 Report

The organization has been well revised following the recommendations.

Author Response

Thank you for taking the time to provide comments on the original manuscript and review the revisions we made. We are pleased that we've been able to address your comments. 

Reviewer 3 Report

The authors have supplemented and modified the points and concerns in the last review, and generally responded to the previous related questions. 

There are several suggestions for the author to improve: 

 

1) The title of the section of Literature Review is wrong, please check it. At the same time, the review and comments on relevant studies are not complete enough, and the deficiencies and research gap of existing studies should be clarified in more detail, rather than just in general terms. 

2) The third part of the research method, it is suggested to further expand, the current content is more focused on how to determine the parameters. There is insufficient exposition and discussion about the adopted method n, it is suggested to further expand. 

3) The conclusion of the paper, 6.1 Key Points, is not recommended to be listed here. It is suitable for Highlights in the manuscript submission.

Author Response

Thanks for taking the time to review our paper in detail. We have addressed all your concerns and they are detailed below:

 

 

1. The title of the section of Literature Review is wrong, please check it. At the same time, the review and comments on relevant studies are not complete enough, and the deficiencies and research gap of existing studies should be clarified in more detail, rather than just in general terms.

 

We have corrected the error in the numbering in the literature review section. In response to the second point, we have now added the chapter 2.2 DCEs in the transport context. This chapter expands in much more detail around the existing literature. In particular, we highlight the strength of the DCE methodology in investigating issues around service and non-market good design and the subsequent policies required to support these public goods. Secondly, the review of papers highlights the fact that none of the current literature has looked particularly at the context of the COVID-19 pandemic. We draw attention to the fact that the study was carried out in October 2021, which potentially means that the data is relevant particularly in a post-pandemic world, as that current period of time was 5 months after the end of the first lockdown in the UK.

 

We have also added more detail throughout chapter 2.1.

 

The final paragraph now explicitly states the gap in the literature, and what exactly doing the study will enable us to understand.

 

 2.2 DCEs in the transport context

Primarily, the DCE allows for a better understanding of user needs in order to drive decision making around service design in situations where there are limited resources [35]. There have also been several studies that have addressed the UK in particular. For example, fare cost was found to have a negative impact on public transport utility, though this was dependant on factors such as the journey distance, type of traveller and income [20]. The results from this DCE would suggest that fare cost reductions should take priority over other service improvements with regards to user preferences. In a wider me-ta-analysis of literature, a significant, highly elastic relationship between income and value of travel time was established [36]. In the wider transport context, DCEs have been used to investigate a variety of aspects. For example, preferences towards car-free city centres [23], understanding the benefits and preferences towards ride-pooling services [37], understanding preferences for clean-fuel vehicles [34], and in assessing future au-tomated vehicle preferences [38]. In all these papers, the recurring theme is around making policy or service changes based on user preferences. For instance, in their paper, around the appraisal of ride-pooling services, [37] was able to develop a set of re-quirements for future services, making DCEs a strong choice for user-centred design.

For this reason, DCEs are an ideal choice to explore the public transport preferences in the UK following the COVID-19 pandemic. In this case, the study was interested in evaluating a complex non-market good and the subsequent policies required to support it- for which there is a consensus in the literature that DCEs an ideal methodological choice for this aim [23]. 

To date, no other paper has explicitly used the DCE methodology to understand public transport preferences during this period of moving out of the pandemic, with all of the papers reviewed considering transport only in a pre-pandemic context. Hence, given the need to address the negative impact of the pandemic on the use of public transport, there needs to be an investigation as to how preferences have changed given the pan-demic. There can then be a greater understanding as to whether current transport lit-erature can still be relied upon given this new post-pandemic world.

 

 

2. The third part of the research method, it is suggested to further expand, the current content is more focused on how to determine the parameters. There is insufficient exposition and discussion about the adopted method n, it is suggested to further expand.

We have now added more to the literature section which goes into detail around the choice of the DCE method. We have now summarised this and added it to the beginning of chapter 3. Next, throughout chapter 3, we have added additional details as to how the questionnaire was implemented in Qualtrics and about the pilot test that was run on the questionnaire. We have also added a new Figure 1 to illustrate exactly how the questions were presented to the participants.

 

We believe the focus on the determination of the parameters is a crucial point. As (Kløjgaard et al., 2012) highlight, this is often a part of DCE reporting that is underreported and plays a crucial role in the validity of the work. Hence, this is the reason we dedicated a significant portion of the chapter 3 to this. However, we hope that the new additions across the methodology satisfy your comment.

 

 

 

3. The conclusion of the paper, 6.1 Key Points, is not recommended to be listed here. It is suitable for Highlights in the manuscript submission.

The key points section has been removed