paper revs from FM

@filmoura

paper/PaperCEUS/PaperCEUS.Rmd

@@ -1,5 +1,5 @@
 ---
-title: "biclaR: Estimating the socio-environmental impacts of car substitution by bicycle and public transit using open tools"
+title: "XXX: Estimating the socio-environmental impacts of car substitution by bicycle and public transit using open tools"
 author:
   - name: Rosa Félix\corref{cor1}
     affiliation: CERIS
@@ -19,7 +19,7 @@ address:
     address: "Institute for Transport Studies, University of Leeds. 34-40 University Rd, Leeds LS2 9JT, UK"
 abstract: |
   A high proportion of car trips can be replaced by a combination of public transit and cycling for the first-and-last mile. This paper estimates the potential for cycling combined with public transit (PT) as a substitute for car trips in the Lisbon metropolitan area and assesses its socio-environmental impacts using open data and open source tools.
-  A decision support tool that facilitates the design and development of a metropolitan cycling network was developed (_biclaR_). The social and environmental impacts were assessed using the _HEAT for Cycling_ and the _HEAT as a Service_ tools. The impacts of shifting car trips to PT were also estimated and monetized.
+  A decision support tool that facilitates the design and development of a metropolitan cycling network was developed (_XXX_). The social and environmental impacts were assessed using the _HEAT for Cycling_ and the _HEAT as a Service_ tools. The impacts of shifting car trips to PT were also estimated and monetized.
   The results indicate that 10% of trips could be made by bicycle + PT combination. Shifting to cycling for the first-and-last mile stages can reduce annual CO~2~eq emissions from 3,000 to 7,500 tons/day, while for the PT leg, the transfer from car avoids of up to 20,500 tons of CO~2~eq emissions per year. The estimated socio-environmental benefits are of €125 million to €325 million over 10 years.
   This evidence can support policymakers to prioritize interventions that reduce the reliance on private motor vehicles.
   \
@@ -90,11 +90,13 @@ Cars and public transport are the most used modes for intercity trips, with cars
 
 <!-- Plot of  AML trips by mode and inter/intra %? -->
 
-To achieve the cycling targets set by the Portuguese national cycling strategy for 2025 and 2030 (4% and 10%, respectively) [@ENMAC], the Lisbon's Metropolitan Department of Transport introduced *biclaR*[^1], a decision support tool that facilitates the planning, design, and development of a metropolitan cycling network [@felix2023].
+To achieve the cycling targets set by the Portuguese national cycling strategy for 2025 and 2030 (4% and 10%, respectively) [@ENMAC], the Lisbon's Metropolitan Department of Transport introduced *XXX*[^1], a decision support tool that facilitates the planning, design, and development of a metropolitan cycling network (ref X).
+<!-- [@felix2023]. -->
 
-[^1]: See [biclar.tmlmobilidade.pt](https://biclar.tmlmobilidade.pt/).
+[^1]: See https://xxx.xxx. *For double blind peer-review process, the authors decided to truncate the name and references to this tool*.
+<!-- [^1]: See [biclar.tmlmobilidade.pt](https://biclar.tmlmobilidade.pt/). -->
 
-*biclaR* builds on the Propensity to Cycle Tool[^2] (PCT), a web application and research project funded by the UK's Department for Transport in 2015 which launched nationally in 2017 as part of the government's Cycling and Walking Investment Strategy.
+*XXX* builds on the Propensity to Cycle Tool[^2] (PCT), a web application and research project funded by the UK's Department for Transport in 2015 which launched nationally in 2017 as part of the government's Cycling and Walking Investment Strategy.
 The PCT initially used only origin-destination data for commuting trips as the basis of estimates of cycling potential at zone, route and route network levels [@lovelace2017].
 The PCT has been extended to include cycling potential for travel to school in England [@goodman2019] and other trip types in other countries.[^3]
 However, to the best of our knowledge, this is the first time that the method has been integrated with public transport data using multi-modal routing to estimate the potential and benefits of multi-stage cycling and PT trips.
@@ -116,14 +118,14 @@ The mobility survey data [@IMOB] is the basis for this project and defines the b
 Despite being conducted in the pre-pandemic period (2017), this dataset represents the most comprehensive and up-to-date information on urban mobility in Portuguese metropolitan areas (Lisbon and Porto).
 
 We used a method for disaggregating the origins and destinations of trips between the centroids of two districts (same as "parish") to ensure that a district is not solely characterized by a single point of origin and destination for its trips.
-Aggregating all trips into centroids renders the exercise less realistic, as it excludes a significant portion of short-distance trips, a prevalent characteristic of active mode travel [@Lovelace2022Jittering].
+Aggregating all trips into centroids renders the exercise less realistic, as it excludes a significant portion of short-distance trips, a prevalent characteristic of active travel [@Lovelace2022Jittering].
 The OD Jittering method breaks down a single point (i.e., the centroid of an area) into multiple random points on the existing and neighboring road network, using OpenStreetMap as a reference.
 This method then distributes the volume of trips within the district among the randomly generated origin-destination pairs.
 
 Using the [`odjitter` R package](https://github.com/dabreegster/odjitter), we employed a maximum disaggregation level of 100 trips per O-D pair for this project.
 Figure \ref{fig:jitter} illustrates the contrast between trip representation through the traditional method, which connects a single desire line between each district, and the presentation achieved through the randomization and disaggregation of trips between districts, specifically for the Lisbon metropolitan area.
 
-```{r jitter, fig.align='center', fig.cap="Representation of OD pairs in the Lisbon metropolitan area between districts, without jittering (left) and with jittering (right).", out.width="100%"}
+```{r jitter, fig.align='center', fig.cap="Representation of desire lines in the Lisbon metropolitan area between districts, without jittering (left) and with jittering (right).", out.width="100%"}
 
 # od_all = readRDS("paper/paperTRA/load/od_all.Rds")
 # od_jittered_filter = readRDS("paper/paperTRA/load/od_jittered_filter.Rds")
@@ -143,7 +145,7 @@ Although this method provides a more realistic representation of the trips under
 The mobility survey collects the origin and destination of trips but does not include the respective routes.
 Modeling the realistic cycling + PT routes between OD pairs depends on assumptions regarding the characteristics of the cycling and road networks and the location of public transport interfaces.
 Other constraints regarding the behavior of potential cyclists determine the routing results.
-For example, such restrictions can favor low speed, low traffic streets, more direct routes, and less steep paths, among others, suitable for cycling.
+For example, such restrictions can favor low speed, low traffic streets, more direct routes, and less steep paths, among others, which are suitable for cycling.
 
 <!-- When comparing ground-truth dataset from 67 count locations distributed throughout the city of Lisbon with differemt routing providers and options, @Lovelace2022exploring found that jittering and disaggregating OD data, combined with routing using low level of traffic stress (quieter) preferences resulted in the most accurate route networks. -->
 
@@ -165,25 +167,26 @@ The routes were then overlaid and aggregated by segments, using [`stplanr overli
 ## Modeling intermodality
 
 The intermodality scenario considers trips combining PT and cycling for the first and last legs.
-In a conservative approach, we have restricted our analysis to the first and last legs with a combined length of up to 5 km (for instance: 1 km from origin to interface _A_ plus 4 km from interface _B_ to destination) or up to 25 minutes on bike.
-Furthermore, we have imposed restrictions on PT usage to include only trips with no PT transfers, and up to 2 hours (120 min).
+In a conservative approach, we have restricted our analysis to the first and last legs with a combined length of up to 5 km (for example: 1 km from origin to interface _A_ plus 4 km from interface _B_ to destination) or up to 25 minutes on bike travel-time.
+Furthermore, we have imposed restrictions on PT usage, limiting it to trips without PT transfers, and within a duration of up to 2 hours (120 minutes).
 Additionally, we have only included PT modes that can easily accommodate bicycles, such as trains, ferries, trams, and inter-municipal bus lines equipped with bike racks (Figure \ref{fig:map1}).
+These restrictions can be eased in the future when testing more developed policy interventions to enhance intermodality between cycling and PT, considering both the vehicle and infrastructure perspectives.
 
 ```{r map1, out.width="60%", fig.cap="Interfaces and lines considered, by transport mode, in the Lisbon metropolitan area.", fig.align='center'}
 knitr::include_graphics("img/map1.png", error = FALSE)
 ```
 
 Figure \ref{fig:map2} illustrates the resulting bicycle routes to access the main PT interfaces in the LMA.
 
-```{r map2, out.width="80%", fig.cap="Bike routes with highest potential to serve as first and last leg when replacing cycling and PT from car trips (screenshot of the interactive online tool).", fig.align='center'}
+```{r map2, out.width="80%", fig.cap="Bike routes with the highest potential to serve as first and last leg when replacing cycling and PT from car trips (screenshot of the interactive online tool).", fig.align='center'}
 knitr::include_graphics("img/map2.png", error = FALSE)
 ```
 
 ## Assessing socio-environmental benefits
 
 <!-- The impacts were assessed for the total metro area. -->
 
-For the *cycling legs of the journey* (first and last legs), socio-environmental impacts were estimated, using the HEAT for Cycling tool v5.2 [@HEAT] from the World Health Organization, and the [`HEATaaS` R package](https://github.com/HEAT-WHO/HEAT_heatr_api)[^5]. 
+For the *cycling legs of the journey* (first and last legs), socio-environmental impacts were estimated, using the Health Economic Assessment Tool (HEAT) for Cycling v5.2 [@HEAT], from the World Health Organization, and the [`HEATaaS` R package](https://github.com/HEAT-WHO/HEAT_heatr_api)[^5]. 
 The use of this package made it possible to run multiple scenarios with few changes in input values, making the interaction with HEAT more reliable when reproducing runs.    
 The HEAT tool provided estimates on the shifting from car to cycling for a short term time horizon (i.e., one year) and the long term (i.e., ten years). It estimates the differences between two considered scenarios. In this case: one baseline scenario, with data from the mobility survey, and one cycling potential scenario in which targets of 4% and 10% of cycling levels were achieved, transferred from car trips.
 We considered two dimensions: *social* — including the physical activity, air pollution exposure, and road casualties; and *environmental* — including CO~2~eq emissions and other pollutants. 
@@ -220,7 +223,7 @@ We updated all the monetary reference values of the literature based on the annu
 # Results and Discussion
 
 Table \ref{tab:summary1} presents the LMA total daily trips that can be made with cycling + TP combination (with the aforementioned restrictions), the trips in the baseline scenario and corresponding new trips to achieve the national strategy targets (4% and 10%), for different route profiles. 
-For the cycling legs of the journey (first and last legs), the environmental avoided emissions and monetized socio-environment (SE) benefits are presented in \ref{tab:summary1b}, resulting from replacing car trips with cycling.
+For the cycling legs of the journey (first and last legs), the environmental avoided emissions and monetized socio-environment (SE) benefits are presented in Table \ref{tab:summary1b}, resulting from replacing car trips with cycling.
 
 
 ```{r summary0}
@@ -279,7 +282,7 @@ For both *direct* and *safe* route profiles, 10% of the daily trips have the pot
 
 Table \ref{tab:summary21} shows the potential trips by PT mode to replace the second leg of the journey, in combination with cycling.
 Train offers the greatest potential for substitution (88%).
-When comparing the existing PT interfaces (Figure \ref{fig:map1}) with the bike routes with highest potential to serve as first and last legs (Figure \ref{fig:map2}) it becomes clear that the Train interfaces are the ones that have the highest potential to attract car-to-PT substituting trips, if their accessibility by bicycle is improved to be safe. 
+When comparing the existing PT interfaces (Figure \ref{fig:map1}) with the bike routes with highest potential to serve as first and last legs (Figure \ref{fig:map2}) it becomes clear that the Train interfaces are the ones that have the highest potential to attract car-to-PT substituting trips, if their accessibility by bicycle is improved to become safer. 
 
 ```{r summary21}
 summary2 = readRDS("load/summary2.Rds")
@@ -329,7 +332,7 @@ knitr::kable(summary22,
   kableExtra::column_spec(column = c(3:7), width = "3.5em")
 ```
 
-The sum of CO~2~eq avoided emissions from the potential car trips shifted to bike (first-and-last legs) in combination with PT (second leg) in the LMA is presented on Table \ref{tab:summaryall}, for both national cycling strategy targets and routing profiles, and the socio-environmental benefits monetized in €, for a 1-year and 10-year time periods.
+The sum of CO~2~eq avoided emissions from the potential car trips shifted to bike (first-and-last legs) in combination with PT (second leg) in the LMA is presented in Table \ref{tab:summaryall}, for both national cycling strategy targets and routing profiles, and the socio-environmental benefits monetized in €, for a 1-year and 10-year time periods.
 
 ```{r summaryall, message=FALSE, warning=FALSE}
 
@@ -367,16 +370,16 @@ The 10-year socio-environmental benefits account for €125 million to €325 mi
 The environmental impacts represent less than 2% of the socio-environmental benefits (in value) from replacing car trips to bicycle in first-and-last legs. 
 For the PT segment, we did not estimate the social impacts from substituting car trips. 
 One of the main socio-environmetal benefits, valued after monetization, comes from the increase in physical activity [@Felix2023ES].
-Although are also social benefits form shifting car trips to PT, its health benefits would not be as high as shifting to cycling. 
+Although there are also social benefits form shifting car trips to PT, its health benefits would not be as high as shifting to cycling. 
 The literature shows that the Metabolic Equivalent Tasks (MET) for "riding in a bus or a train" is 1.3 plus the "walking for transportation" as 3.5, while "driving a car" is 2.5 [@MET2011].
-The difference between these activities is not very obvious when compared to cycling, whose equivalent MET is between 6.8 to 7.5.
+The difference between these activities - shifting from car to PT - is not very obvious when compared to shifting from car to cycling, whose MET is about 6.8.
 Nevertheless, future works should also encompass the estimation of the social impacts for the PT leg of the journey, shifting from car.
  <!--Overall there was very little evidence available on mode switch to public transport. -->
 
 The emissions of CO~2~eq that are avoided during both the initial and final journey segments account for about 74% of the emissions avoided during the PT segment. This finding, while expected -- due the zero cycling emissions, should not be overlooked when promoting the PT use.
 Improving the safe accessibility to PT interfaces to cyclists and providing bicycle-friendly amenities such as parking facilities can potentially lead to a higher reduction in CO~2~eq emissions, compared to a scenario where individuals shift from car travel to car + PT combination.
 
-Our findings suggest that cycling and PT *in combination* could viably replace 10% of current LMA trips, with an additional 6% of PT journeys prone to further substitution. 
+Our findings suggest that cycling *in combination* with PT could potentially replace 10% of current LMA trips, with an additional 6% of PT journeys prone to further substitution. 
 <!-- repeated? -->
 
 
@@ -386,13 +389,17 @@ The information on socio-economic benefits can support policy-makers in prioriti
 
 The information available at *biclaR* tool -- an open access website -- can be downloaded and used with any GIS software. This allows practitioners to, for example, gain insights into which potential cycling connections have the highest socio-environmental impacts, quantified in tons of avoided CO~2~eq emissions, or in long term social benefits. <!-- move to introduction? -->
 
-By making the research process publicly accessible in a code repository, it enables the replication of similar estimates for socio-environmental impacts, resulting from a modal shift from car to bicycle in combination with PT, in other metropolitan areas.
+By making the research process publicly accessible in a code repository (see [Research Data](#research-data)), it enables the replication of similar estimates for socio-environmental impacts, resulting from a modal shift from car to bicycle in combination with PT, in other metropolitan areas.
 
 
-### Acknowledgements. {.unnumbered}
+## Acknowledgements {.unnumbered}
 [*blind*]
 <!-- This research was funded by the Lisbon's Metropolitan Department of Transport (TML - Transportes Metropolitanos de Lisboa, E.M.T., S.A.), under the *biclaR* Project. -->
 <!-- This work is part of the research activity carried out at Civil Engineering Research and Innovation for Sustainability (CERIS) and has been funded by Fundação para a Ciência e a Tecnologia (FCT), Portugal in the framework of project UIDB/04625/2020.  -->
 <!-- The authors thank Thomas Götschi (HEAT for Cycling) for providing access to HaaS tool, which is under development. -->
 
+
+## Research data {.unnumbered}
+The data and the code to produce XXX and this paper are available at: https://github.com/[*blind*].
+
 # References {.unnumbered}