tiny typos

paper/PaperCEUS/PaperCEUS_rev2_word.Rmd

@@ -66,7 +66,7 @@ linkcolor: blue
 
 ```{r eval=FALSE, echo=FALSE}
 # setwd("paper/PaperCEUS/") # knit directory: Document
-system("latexdiff paper/PaperCEUS/PaperCEUS_rev1.tex paper/PaperCEUS/PaperCEUS_rev2.tex > paper/PaperCEUS/PaperCEUS_diff2.tex")
+system("latexdiff paper/PaperCEUS/PaperCEUS_rev1.tex paper/PaperCEUS/PaperCEUS_rev2_word.tex > paper/PaperCEUS/PaperCEUS_diff2.tex")
 #and change in tex the blue for another color (green? 0,0.5,0) and remove \uwave
 # \definecolor{green}{rgb}{0,0.5,0}
 # {\protect\color{green}{#1}}}
@@ -286,7 +286,7 @@ We used 'jittering' to disaggregate the OD data, resulting in a wide spatial dis
 The method works by sampling 'sub-points' (nodes on the transport network represented in OpenStreetMap in this case) and using these instead of a single point (typically the centroid) to represent trip origins and destinations for each zone.
 This method then distributes the trips to desire lines connecting the subpoints based on a 'disaggregation threshold' which determines the maximum number of trips that can be represented by a single desire line.
 
-Using the [`odjitter` R package](https://github.com/dabreegster/odjitter), we disaggregated the OD data into desire lines reprenting a maximum of 100 trips each.
+Using the [`odjitter` R package](https://github.com/dabreegster/odjitter), we disaggregated the OD data into desire lines representing a maximum of 100 trips each.
 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.
 As shown, the city of Lisbon (in the centre) is the main attractor of trips, with a high number of trips to and from the other municipalities. 
 
@@ -333,8 +333,8 @@ Even where such datasets exist, they cannot be shared for research due to data p
 
 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, which are suitable for cycling.
+Other constraints regarding the behaviour of potential cyclists determine the routing results.
+For example, such restrictions can favour 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. -->
 
@@ -452,7 +452,7 @@ Car routing was estimated for all the journeys with the potential to be replaced
 To estimate the car emissions, we used the EMEP/EEA's COPERT software v5 methods and reference values [@COPERT] for a Tier 3 detail level. 
 We used a family-size vehicle, EURO standard, and gasoline or diesel fuel.
 All trips were considered to be made under urban conditions and at an average speed of 15 km/h during rush hour periods.
-Since the average distance traveled per trip influences the overconsumption and emissions from cold-start engine operation, we estimated energy and emission factors for different ranges of trips at 500-meter intervals.  
+Since the average distance travelled per trip influences the overconsumption and emissions from cold-start engine operation, we estimated energy and emission factors for different ranges of trips at 500-meter intervals.  
 An equation was then used to calculate emission factors for the two types of fuel, for each type of pollutant, whose explanatory variables are driving speed ($speed$, in km/h) and average trip distance ($l_{trip}$, in km/trip). Thus, the emission factors ($EF_{fuel,l_{trip},speed}$, in g/km) can be calculated using equation \ref{eq:fatoremissaoauto}.
 
 \begin{equation}\label{eq:fatoremissaoauto}
@@ -515,8 +515,8 @@ knitr::kable(summary1,
       digits = 0,
       format.args = list(big.mark = " "),
       col.names = c("Target", "Routing", "Total trips",
-                    "Baseline Cycling + PT", "Potencial Cycling + PT"),
-      caption = "\\label{summary1}Summary of the cycling potencial of the intermodality scenario. Values in 'trips/day'.",
+                    "Baseline Cycling + PT", "Potential Cycling + PT"),
+      caption = "\\label{summary1}Summary of the cycling potential of the intermodality scenario. Values in 'trips/day'.",
       format="latex",
       booktabs = TRUE) |> 
   kableExtra::column_spec(column = c(4:5), width = "7em")
@@ -536,7 +536,7 @@ knitr::kable(summary1b,
       col.names = c("Target", "Routing",
                     "Avoided Mortality (deaths/yr)", "Social benefits (k€/yr)", 
                     "Avoided CO2eq (ton/yr)", "Environmental benefits (k€/yr)"),
-      caption = "\\label{summary1b}Summary of the cycling potencial of intermodality scenario and its socio-environmental benefits for the cycling legs.",
+      caption = "\\label{summary1b}Summary of the cycling potential of intermodality scenario and its socio-environmental benefits for the cycling legs.",
       format="latex",
       booktabs = TRUE) |> 
   kableExtra::column_spec(column = c(3:6), width = "6em")
@@ -802,7 +802,7 @@ knitr::kable(summary22,
       format.args = list(big.mark = " "),
       col.names = c("Target", "Routing", "CO2eq",
                     "CO", "PM10", "NOx", "VOC", "Value (k€)"),
-      caption = "\\label{summary22}Summary of the avoided emmissions (ton/year) and corresponding monetization (thousand €) by replacing car trips with PT, in the second leg.",
+      caption = "\\label{summary22}Summary of the avoided emissions (ton/year) and corresponding monetization (thousand €) by replacing car trips with PT, in the second leg.",
       format="latex",
       booktabs = TRUE) |> 
   kableExtra::column_spec(column = c(3:7), width = "3.5em")
@@ -832,7 +832,7 @@ knitr::kable(summaryall,
       format.args = list(big.mark = " "),
       col.names = c("Target", "Routing", "Avoided CO2eq (tons)",
                     "SE Benefits 1yr (k€)", "SE Benefits 10yrs (k€)"),
-      caption = "\\label{summaryall}Summary of the avoided CO2eq emmissions (ton/year) and the estimated social and environmental benefits (monetized in thousand €) by replacing car trips with cycling in combination with PT.",
+      caption = "\\label{summaryall}Summary of the avoided CO2eq emissions (ton/year) and the estimated social and environmental benefits (monetized in thousand €) by replacing car trips with cycling in combination with PT.",
       format="latex",
       booktabs = TRUE) |> 
   kableExtra::column_spec(column = c(3:5), width = "8em")
@@ -846,7 +846,7 @@ 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].
+One of the main socio-environmental benefits, valued after monetization, comes from the increase in physical activity [@Felix2023ES].
 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 - shifting from car to PT - is not very obvious when compared to shifting from car to cycling, whose MET is about 6.8.
@@ -898,7 +898,7 @@ The case study of the Lisbon metropolitan area demonstrates that cycling-PT inte
 
 This research also quantifies the socio-environmental benefits of achieving such targets, exploring an intermodality scenario, where car trips are potentially substituted by bicycle in combination with PT.
 
-The findings indicate that cycling combined with public transport has the potential to account for 10% of total trips. Transitioning to cycling for the first and last segments could lead to a decrease in annual CO~2~eq emissions by a substantial margin, ranging from 3,000 to 7,500 tons per day, depending on the cycling target and routing profile. Moreover, for the second trip segment, the shift from car to public transport contributes to avoiding up to 20,500 tons of CO~2~eq emissions annually. These changes are estimated to yield socio-environmental benefits totaling €125 million to €325 million over a decade.
+The findings indicate that cycling combined with public transport has the potential to account for 10% of total trips. Transitioning to cycling for the first and last segments could lead to a decrease in annual CO~2~eq emissions by a substantial margin, ranging from 3,000 to 7,500 tons per day, depending on the cycling target and routing profile. Moreover, for the second trip segment, the shift from car to public transport contributes to avoiding up to 20,500 tons of CO~2~eq emissions annually. These changes are estimated to yield socio-environmental benefits totalling €125 million to €325 million over a decade.
 The quantification of such benefits can support policy-makers in prioritizing interventions to reduce the reliance on private motorized modes of transportation.
 
 Additionally, the results suggest that the potential for cycling-PT intermodality is spatially unevenly distributed across the LMA. The highest potential occurs in the municipalities with the highest number of trips and PT interfaces with connecting modes to Lisbon that allow for carrying bicycles on board, such as Lisbon, Sintra, Amadora, and Almada.