Quantifying the Relation between Activity Pattern Complexity and Car Use Using a Partial Least Square Structural Equation Model
Round 1
Reviewer 1 Report
The title of the manuscript seems to be appropriate, and the content of the manuscript is suitable for the journal.
The study is about examining the relationship between the activity travel pattern and car use of the Luxembourg University staff members, using the Partial Least Squares SEM technique based on a multi-day activity pattern survey.
The abstract section is well-written and has covered the necessary information. However, a brief sentence about the need for the study should be included at the beginning of the abstract.
The introduction section satisfactorily describes the context of the study and clarifies the purpose of the study.
The literature review section needs to be strengthened by including the latest articles relating to modelling trip chaining behaviour concerning car use.
The methodology section gives an adequate level of detail on the methods employed for the modelling exercise. The results of the analysis have been explained adequately; however, a comparison between the results of this study and other similar studies should be provided. The pictorial presentation of the results makes it easier to follow the results. However, it must be made clear, which shortcomings of the previous studies have been overcome by this study.
My only concern is that the survey data used is relatively old, i.e., 8 years old. The authors have not justified the use of such old data, and what implications might it have for the recommended policies based on their analysis.
The manuscript is written excellently, but the research done is somewhat rudimentary with moderate interest likely to be drawn from the readers of this work.
There were a few grammatical errors noticed in the manuscript. Please re-read the manuscript and rectify those errors. Please see additional comments in the attached PDF.
Comments for author File: 
Comments.pdf
Author Response
Dear reviewer,
Thank you for your feedback and the positive and thorough assessment. We are glad that you find the work appropriate for the journal.
We have work on our manuscript to integrate all your remarks suggestion.
- The abstract has been modified to include the rationale of this paper and to better highlight the uniqueness of the data used.
- The literature review (p2,3 and 4) has been reworked to include recent references on modelling trip chaining.
Nicholson A.; Kingham S. The university of Canterbury transport strategy. 26th Australasian Transport Research Forum Wellington New Zealand 1-3 October, 2003.
Andrade K.; Kagaya S.; Uchida K.; Dantas A.; Nicholson A. A Study on the Temporal Transferability of Transport Modal Choice Models. Studies in Regional Science, 2007, 37(3), 887-899.
Daisy N.S.; Millward H.; Liu L. Trip chaining and tour mode choice of non-workers grouped by daily activity patterns. Journal of Transport Geography, 2014, 69: 150-162.
Bautista-Hernandez D. Urban structure and its influence on trip chaining complexity in the Mexico City Metropolitan Area. Urban Planning and Transport Research, 2020, 8(1): 71-97.
Thorhauge M.; Kassahun H.T.; Cherchi E.; Haustein S. Mobility needs, activity patterns and activity flexibility: How subjective and objective constraints influence mode choice. Transportation Research Part A, 2020, 139: 255-272.
Scheffer A., Connors R., Viti F. Trip chaining impact on within-day mode choice dynamics: Evidences from a multi-day travel survey. Transportation Research Procedia, 2021, 52: 684-691.
We also included additional references as response to Reviewer 3 comments, but these are not purely on trip chaining and mode choice.
- Regarding the methodology, PLS-SEM usage is not yet mainstream in travel behavior and the majority of the works have used CB-SEM. Hence, we stressed in the paper that the work is among the few to use this approach, and that differently from other works we show that the approach is robust towards contextual changes (workplace relocation). From an application perspective, we highlighted that the study demonstrated a strong relation between number of activities chained with commuting travels and car use, and in particular this relationship becomes stronger when non-work-related activities are performed.
- The data is indeed relatively old but we emphasized its uniqueness. We are not aware of any work that has collected and analysed two multi-day survey data sets from the same respondents who meanwhile relocated their workplace. Additionally, the behavior observed is not really affected by time or technological changes. An exception may be an increased use of the car due to the impact of Covid19, but unfortunately we cannot capture this aspect with our dataset. Due to the uniqueness of the data and the lack of similar works using PLS-SEM or other methods, it is really difficult to compare potential shortcomings of our paper with other publications. Nevertheless, we have emphasized how the results are in line or in discordance with similar works on trip chaining and mode choice.
- Although the work may be seen as exploratory, it contributes to the understanding and modelling of a complex relationship (trip chaining and car use), and it does so by using a novel methodology and by performing an extensive analysis of the results. The work can be an excellent starting point for behavioural modellers to develop predictive models that could estimate how mode choice can change in a region as result of land use characteristics and built environment decisions, such as developing activity zones that also offer opportunities for secondary activities (e.g. shopping, leisure).
- Thank you so much for the annotations and the additional comments. We have tried to address all comments. An exception is the reworking of Figure 1. Given the small number of respondents of the multi-day survey, and the precision of the home zip code, displaying their home location may generate privacy concerns. Hence, we preferred to keep their home location anonymous.
Reviewer 2 Report
I've found this paper very interesting, with stimulating results and usefull attempts to deal with the small sample size.
Author Response
Thank you very much for the positive assessment of our paper!
Reviewer 3 Report
This would be an interesting and valuable contribution to the literature on the complexity of car-based travel patterns and on the deployment of PLS-SEM in transport study.
I only have some methodological concerns. While you succeeded in partly persuading readers (at least for me) to agree on the use of PLS-SEM instead of CB-SEM, it would be better and needy to:
- show NFI to evaluate the model fit - SRMR is good but not enough.
- estimate and show Q2 estimated from the blindfolding process.
- cite some recent transport-specific papers using PLS-SEM, such as:
https://doi.org/10.1016/j.cities.2022.103691
https://doi.org/10.1016/j.jtrangeo.2022.103302
Author Response
Dear reviewer,
Thank you for positively assessing the paper and highlighting its methodological innovation.
Thank your for providing us reference on the use of recent PLS-SEM papers. Of course we have included these 2 papers, in addition to another reference we thought it is relevant to support the choice of SRMR and R-square as goodness-of-fit measures for PLS-SEM.
We see our paper as an explorative approach to extract as much information as possible from a very specific dataset. We do not intend to go beyond state of the art regarding such specific methodological aspect. This is why from our perspective the various indicators we have used (R2, SRMR, VIF and the MGA analysis) are enough for our explorative analysis. The first two are used for goodness-of-fit, whereas the other two are meant to assess the blindfolding procedure and prove robustness of the results.
