Analysis for US Flights Data in Dec, 2015

NOTE:

1. The report is interactive. So move your cursor to the chart and get the reponses.
2. SOURCE　CODE is the source code aera, which you can skip if you stick more to my findings in BOLD.

This report contains three basic topics:

• Descriptive Statistics on three biggest concepts: Flights, Airlines and Cities.
• Connectedness Research of US flight network, via adjacency matrix from the directed graph. This can give you some holistic ideas about how convenient to travel in this network.
• Transfer route identification between two cities using the Dijkstra’s shortest path algorithm.

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Data Source

The dataset with selected columns was downloaded from http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time, under the assignment of Sportify Data Scientist position application, filtered by the interval of December of 2015.

The following table demonstrates the first twenty entries of the dataset:

Flights <- read.csv("C:/Users/weibo/Desktop/Data Scientist at Spotify/807104048_T_ONTIME.csv", stringsAsFactors=FALSE);
library(DT);
datatable(Flights[1:20,], options = list(pageLength = 5), fillContainer=T)

The dataset has 479230 rows and 23 columns.

Various Flights

Let’s focus on the flight data, especially, the CRS_DEP_TIME, ARR_TIME, ACTUAL_ELAPSED_TIME, DISTANCE and FL_DATE columns:

Flights by Departure and Arrival Time

library(htmlwidgets);
library(highcharter);
library(plyr);
library(dplyr);
options(digits=4);
FlightCountDep=count(Flights, CRS_DEP_TIME);
MorAftNigDep=data.frame(Period=c("Morning","Afternoon","Night"), 
                    FlightCount=c(nrow(subset(Flights, CRS_DEP_TIME>600 & CRS_DEP_TIME<=1200)),
                    nrow(subset(Flights, CRS_DEP_TIME>1200 & CRS_DEP_TIME<=1800)),
                    nrow(subset(Flights, (CRS_DEP_TIME>1800 & CRS_DEP_TIME<=2359) | (CRS_DEP_TIME>=0 & CRS_DEP_TIME<=600)))));

highchart() %>% 
  hc_title(text = "US Flight Count by Departure Time in Dec, 2015") %>%
  hc_add_series_labels_values(FlightCountDep$CRS_DEP_TIME, FlightCountDep$n, name = "Flight Count By Departure Time",
                              colorByPoint = F, type = "column") %>% 
  hc_add_series_labels_values(MorAftNigDep$Period, MorAftNigDep$FlightCount/nrow(Flights), type = "pie",
                              name = "Depature Ratio", colorByPoint = TRUE, center = c('75%', '25%'),
                              size = 200, dataLabels = list(enabled = FALSE)) %>% 
  hc_yAxis(title = list(text = "Flight Count by Departure Time")) %>% 
  hc_xAxis(categories = FlightCountDep$CRS_DEP_TIME) %>% 
  hc_tooltip(valueDecimals=4)%>%
  hc_legend(enabled = FALSE) 

This figure shows that:

• most flights departure as scheduled at the time point ending with 0 or 5, such as 1400, 1425, 2045 (high bars in the figure appear every five minutes.). From further summarization, flights departuring at those minutes take 0.7421 portion from all.
• flights departuring in the morning and afternoon are equivalently common. Flights departuring at night are relatively less common. Especially, flights departuring from 0 am to 5 am are fewest.

MorAftNigArr=data.frame(Period=c("Morning","Afternoon","Night"), 
                     FlightCount=c(nrow(subset(Flights, ARR_TIME>600 & ARR_TIME<=1200)),
                     nrow(subset(Flights, ARR_TIME>1200 & ARR_TIME<=1800)),
                     nrow(subset(Flights, (ARR_TIME>1800 & ARR_TIME<=2359) | (ARR_TIME>=0 & ARR_TIME<=600)))));
FlightCountArr=count(Flights, ARR_TIME);
highchart() %>% 
  hc_title(text = "US Flight Count by Arrival Time in Dec, 2015") %>%
  hc_add_series_labels_values(FlightCountArr$ARR_TIME, FlightCountArr$n, name = "Flight Count By Arrival Time",
                              colorByPoint = F, type = "column") %>% 
  hc_add_series_labels_values(MorAftNigArr$Period, MorAftNigArr$FlightCount/nrow(Flights), type = "pie",
                              name = "Arrival Ratio", colorByPoint = TRUE, center = c('75%', '25%'),
                              size = 200, dataLabels = list(enabled = FALSE)) %>% 
  hc_yAxis(title = list(text = "Flight Count by Arrival Time")) %>% 
  hc_xAxis(categories = FlightCountArr$ARR_TIME) %>% 
  hc_tooltip(valueDecimals=4)%>%
  hc_legend(enabled = FALSE) 

This figure shows that:

• actual arrival time of flights are evenly distributed from 6 am to midnight. That makes the count numbers relatively even (no high bars) in this period.
• flights arriving in the night and afternoon are equivalently common. While, flights arriving from 1 am to 6 am are fewest due to the elapsing time.

Flights By Date

FlightCountDate=count(Flights, FL_DATE);
FlightCountDate=FlightCountDate[order(as.Date(FlightCountDate$FL_DATE)),];
highchart() %>%
  hc_title(text='Flights Count By Date in Dec, 2015')%>%
  hc_xAxis(categories=FlightCountDate$FL_DATE) %>%
  hc_add_series(data=FlightCountDate$n, type='column', colorByPoint=F, name="Flight Count By Date") 

This figure shows that

• The average count for each day is around 15k flights.
• The fluctuation is roughly periodic of one week. Flights in Saturday are fewest.
• Due to Christmas holiday, Flight count in 24th began to decrease. This may indicate that many people do not take airplanes in the Christmas holiday.

Flights By Distance

FlightCountDis=count(Flights, DISTANCE);
DistCount=data.frame(Distance=c("Shorter than 500 km","Between 500 and 1000 km", "Between 1000 and 1500 km", "Between 1500 and 2000 km", "Between 2000 and 2500 km", "Between 2500 and 3000 km","Longer than 3000 km"), 
                     FlightCount=c(nrow(subset(Flights, DISTANCE<=500)),
                       nrow(subset(Flights, DISTANCE>500 & DISTANCE<=1000)),
                       nrow(subset(Flights, DISTANCE>1000 & DISTANCE<=1500)),
                       nrow(subset(Flights, DISTANCE>1500 & DISTANCE<=2000)),
                       nrow(subset(Flights, DISTANCE>2000 & DISTANCE<=2500)),
                       nrow(subset(Flights, DISTANCE>2500 & DISTANCE<=3000)),
                       nrow(subset(Flights, DISTANCE>3000))));
highchart() %>%
  hc_title(text='US Flight Distance Distribution in Dec, 2015')%>%
  hc_xAxis(categories=FlightCountDis$DISTANCE) %>%
  hc_add_series(data=FlightCountDis$n, type='column',colorByPoint = F, name="Flight Count By Distance") %>%
  hc_add_series_labels_values(DistCount$Distance, DistCount$FlightCount/nrow(Flights), type = "pie",
                              name = "Distance Ratio", colorByPoint = TRUE, center = c('75%', '25%'),
                              size = 200, dataLabels = list(enabled = FALSE))%>% 
  hc_tooltip(valueDecimals=4)

This figure shows that :

• The most common flight distance is 337 km, totally 4364 times in December, 2015.
• Most flights’ distance is no longer than 1500 km. The short-distance aviation transportation is very popular in US.

Flights By Flying Time

FlightCountEla=count(Flights, ACTUAL_ELAPSED_TIME);
FlightCountEla=na.omit(FlightCountEla);
ElpTime=data.frame(Elaped=c("Less than 1 hour","Between 1 and 2 hours", "Between 2 and 3 hours","Between 3 and 4 hours","Between 4 and 5 hours", "More than 5 hours"), 
                     FlightCount=c(nrow(subset(Flights, ACTUAL_ELAPSED_TIME<=60)),
                       nrow(subset(Flights, ACTUAL_ELAPSED_TIME>60 & ACTUAL_ELAPSED_TIME<=120)),
                       nrow(subset(Flights, ACTUAL_ELAPSED_TIME>120 & ACTUAL_ELAPSED_TIME<=180)),
                       nrow(subset(Flights, ACTUAL_ELAPSED_TIME>180 & ACTUAL_ELAPSED_TIME<=240)),
                       nrow(subset(Flights, ACTUAL_ELAPSED_TIME>240 & ACTUAL_ELAPSED_TIME<=300)),
                       nrow(subset(Flights, ACTUAL_ELAPSED_TIME>300))));
highchart() %>%
  hc_title(text='US Flight Count by Time Elapsed in Dec, 2015')%>%
  hc_xAxis(categories=FlightCountEla$ACTUAL_ELAPSED_TIME) %>%
  hc_add_series(data=FlightCountEla$n, type='column', colorByPoint = F, name="Flight Count By Flying Time") %>%
  hc_add_series_labels_values(ElpTime$Elaped, ElpTime$FlightCount/nrow(Flights), type = "pie",
                              name = "Elapsing Time Ratio", colorByPoint = TRUE, center = c('75%', '25%'),
                              size = 200, dataLabels = list(enabled = FALSE))%>%
  hc_tooltip(valueDecimals=4)

This figure shows that:

• The flying time distribution is right skewed. This distribution has long tails.
• Most flying time is between 1 and 4 hours. This may indicate that most people’s travels are between closed states (transfer routes are regarded as multiple flights.).

Busy Airlines

Thare are 13 airlines providing services in Dec, 2015.

Operating Carriers

CarrierCount=count(Flights, UNIQUE_CARRIER);
carriers <- read.csv("C:/Users/weibo/Desktop/Data Scientist at Spotify/carriers.csv", header=FALSE, stringsAsFactors=FALSE);
CarrierCountFN=merge(CarrierCount, carriers, by.x='UNIQUE_CARRIER', by.y='V1');
FlightsFN=merge(Flights, carriers, by.x='UNIQUE_CARRIER', by.y='V1');
DisCarrier=ddply(FlightsFN, .(V2), summarise, sumDis=sum(DISTANCE));
highchart() %>%
  hc_title(text='US Flight Distance By Carriers in Dec, 2015')%>%
  hc_xAxis(categories=FlightsFN$V2) %>%
  hc_add_series_boxplot(x=FlightsFN$DISTANCE, by=FlightsFN$V2, colorByPoint = T, name="Flight Distance By Carriers", outliers=F)%>%
  hc_add_series_labels_values(DisCarrier$V2, DisCarrier$sumDis, type='pie', name = "Total Distance in km", 
                              colorByPoint = TRUE, center = c('22%', '25%'), size = 150, dataLabels = list(enabled = FALSE))

This figure (outliers in the boxplot are actually skipped) reveals the diversity of each airline in terms of the flight distance. It can be seen that:

• Virgin America Airlines’ service covers both short distance and long distance flights, while Hawaiian Airlines is usually limited to the short distance flights. This may indicate that Hawaiian Airlines is only for in-state transportation.
• American Airline, Delta Airline, Southwest Airline and United Airline are the biggest four, with the obvious advantage on total distance.

How busy are these carriers? Let’s find out the flights of each day:

FlightsDaily=count(FlightsFN, V2, date=FL_DATE);
FlightsDaily$date=as.Date(FlightsDaily$date);
highchart() %>%
  hc_title(text='Daily US Flight Count of Each Carrier In Dec, 2015') %>%
  hc_xAxis(categories=FlightsDaily$date) %>%
  hc_add_series_df(FlightsDaily, y=n, type="line", group=V2) %>%
  hc_legend(layout = "vertical", align = "right")%>%
  hc_add_series_labels_values(CarrierCountFN$V2, CarrierCountFN$n, type = "pie", name = "Flight Count in Dec, 2015", 
                              center = c('125%', '25%'), size = 200, dataLabels = list(enabled = FALSE))

We can conclude that:

• all airlines can be categorised by daily flight count into four groups. Southwest Airlines is in top group, American Airlines/Delta Airlines are in the second group, Skywest Airlines/Atlantic Airlines/United Airlines are in the third group, and the remainings are in the last group.
• some carriers in the last group are not as highly periodic as top ones. Their passenger flows are much stabler than that of top airlines.

Distinctive Flights

Let’s see how many distinctive flight routes from all airlines. We assume that the flights operated by the same carrier, with the identical flight number, from the same origin city to the same destination, are regarded as the same flight. (This may violate the situtation that two carriers share the FL_NUM. Practically, two flights of this kind are the same flight. But under our assumption, they are two flights.)

Some flights are operated daily, but others weekly. The flight frequency is as shown in the following figure.

DistinctFlight=count(Flights, UNIQUE_CARRIER, FL_NUM, ORIGIN_CITY_NAME, DEST_CITY_NAME);
FlightsByCarriers=count(DistinctFlight[1], UNIQUE_CARRIER);
DistinctFlightCount=count(DistinctFlight[5], n);
highchart() %>%
  hc_title(text='Distinctive US Flight Count Distribution in Dec, 2015')%>%
  hc_add_series_labels_values(DistinctFlightCount$n, DistinctFlightCount$nn, name = "Flight Count",
                              colorByPoint =F, type = "column") %>% 
  hc_add_series_labels_values(FlightsByCarriers$UNIQUE_CARRIER, FlightsByCarriers$n, type = "pie",
                             name = "Distinctive Flights Count", colorByPoint = TRUE, center = c('75%', '25%'),
                              size = 200, dataLabels = list(enabled = FALSE))

There are 44416 distinctive flights operated in Dec, 2015, of which:

• 0.5347 of all are only operated no more than four times in Dec, 2015.
• there are 0.0886 flights operated daily. And 0.0499 flights are operated in weekdays (or similarly five days a week).
• Southwest Airlines owns the most distinctive flight routes amongst all.

Cancellation Rate

FlightsCancelled=ddply(FlightsFN, .(V2,FL_DATE), summarise,cancelledNo=sum(CANCELLED));
FlightsCancelled$FL_DATE=as.Date(FlightsCancelled$FL_DATE);
FlightsCancelledData=merge(FlightsCancelled, FlightsDaily, by.x=c('V2', 'FL_DATE'), by.y=c('V2','date'));
FlightsCancelledData=mutate(FlightsCancelledData, CancelRate=cancelledNo/n);
CancelContribution=ddply(FlightsCancelledData, .(V2), summarise,EachSum=sum(cancelledNo), Total=sum(n));
highchart() %>%
  hc_xAxis(categories=FlightsCancelledData$FL_DATE) %>%
  hc_add_series_df(data=FlightsCancelledData, y=CancelRate, type="line", group = V2) %>%
  hc_add_series_labels_values(CancelContribution$V2, CancelContribution$EachSum, type = "pie",
                              name = "Cancelled Flights Contribution", center = c('125%', '25%'),
                              size = 200, dataLabels = list(enabled = FALSE)) %>%
  hc_legend(layout = "vertical", align = "right") %>%
  hc_title(text='Flight Cancellation of Each Carrier in Dec, 2015')

From this figure, we can see:

• daily cancellation rate is generally under 0.02, except 15th, 24th and 28th.
• Southwest, Skywest, Atlantic, American, American Eagle,and United contributed most cancellation flights. There are 7403 flights of them were cancelled in this month.
• there was a cancellation peak of many airlines in 28th. This may be caused by harsh weather condition on that day.

Linked Cities

Inter-City Conveniency

To see which combination of two cities are provided with most flights, we find out all cities linked by distinctive flights.

CityFlight=count(DistinctFlight[3:4], ORIGIN_CITY_NAME, DEST_CITY_NAME);

There are 3803 city combinations served by all flights. These flights cover 303 cities as origins and 303 cities as destinations. Their airports can be located as below:

library(httr);
library(geojsonio);
map <- "https://raw.githubusercontent.com/johan/world.geo.json/master/countries/USA.geo.json" %>% 
  GET() %>% 
  content() %>% 
  jsonlite::fromJSON(simplifyVector = FALSE);
airports <- read.csv("C:/Users/weibo/Desktop/Data Scientist at Spotify/airports.csv", stringsAsFactors=FALSE);
OriCity=count(Flights, ORIGIN);
OriCity=merge(OriCity, airports[c(1,6,7)], by.x='ORIGIN', by.y='iata');
OriCity=unique(merge(OriCity,Flights[8:9], by='ORIGIN'));
airpjsonOri <- geojson_json(OriCity, lat = "lat", lon = "long");
highchart(type='map') %>% 
  hc_title(text = "Airports Scattered in US") %>% 
  hc_add_series(mapData = map, showInLegend = F, nullColor = "#A9CF54", borderColor="#A9CF54") %>% 
  hc_add_series(data=airpjsonOri, type="mappoint", dataLabels = list(enabled = F), name = "Airports", color = '#4F4F4F',
                tooltip = list(pointFormat = "{point.properties.ORIGIN_CITY_NAME}:{point.properties.n} depature flights")) %>% 
  hc_mapNavigation(enabled = T)

These 307 airports scatter in the 303 cities of mainland, Alaska, Hawaii and oversea territories, including Guam, Virgin Islands and Puerto Rico.

So obviously, some cities has more than one airports. See this table:

airpC=count(FlightsFN, city=ORIGIN_CITY_NAME, airport=ORIGIN);
datatable(airpC, options = list(pageLength = 5))

From this table, we can find that:

• Chicago, Houston, New York and Washington, DC have two airports.
• Atlanta airport is the busiest one with 31019 departure flights in Dec, 2015.

Transfering Capability

We can explore the departure and arrival flights for each airport. And also, D/A imbalance rate can be defined as the result of departure flight count devided by arrival flight count.

OriCity=count(Flights, ORIGIN);
OriCity=unique(merge(OriCity,Flights[8:9], by='ORIGIN'));
DesCity=count(Flights, DEST);
DesCity=unique(merge(DesCity,Flights[13:14], by='DEST'));
EndCity=merge(OriCity,DesCity, by.x='ORIGIN', by.y='DEST');
highchart() %>%
  hc_xAxis(categories=EndCity$ORIGIN)%>%
  hc_yAxis_multiples(
    list(title = list(text = "D/A Imbalance Rate"), align = "right", showFirstLabel = FALSE),
    list(title = list(text = "Distinctive Flight Count"), align = "left",showFirstLabel = FALSE,opposite = TRUE)) %>% 
  hc_add_series(data=EndCity$n.x, colorByPoint = F, name = "Departure Flights", type='column', yAxis=1)%>%
  hc_add_series(data=EndCity$n.y, colorByPoint = F, name = "Arrival Flights", type='column', yAxis=1) %>%
  hc_add_series(data=EndCity$n.x/EndCity$n.y, name = "Imbalance Rate", type='line') %>%
  hc_title(text='Distinctive Flights Count and Imbalance Rate for Each US Airport')%>%
  hc_tooltip(valueDecimals=4)

As shown above:

• ATL in Atlanta, ORD in Chicago and DFW in Dallas are three biggest airport for transfer service, due to their high throughput of inward and outward directions.
• most airports maintain the imbalance rate between 0.99 and 1.01. But SWF in Newburgh/Poughkeepsie has more departure flights. And BGR in Bangor, ITH in Ithaca/Cortland and ORH in Worcester have more arrival flights.

Daily Flights of Each Airport

library(viridisLite);
AirpOriDate=count(Flights, FL_DATE, ORIGIN);
AirpDesDate=count(Flights, FL_DATE, DEST);
AirpDate=merge(AirpOriDate, AirpDesDate, by.x=c('ORIGIN', 'FL_DATE'), by.y=c('DEST', 'FL_DATE'));
AirpDate=mutate(AirpDate, throughput=n.x+n.y);
stpscol <- color_stops(10, viridis(20));
fntltp <- JS("function(){return this.point.ORIGIN + ' in ' +  this.series.yAxis.categories[this.point.y] + ':<br>' +      Highcharts.numberFormat(this.point.value, 0) + ' flights';}");
hchart(AirpDate, "heatmap", y = FL_DATE, x = ORIGIN, value = throughput, name='Airport Flight Throughput') %>%
  hc_colorAxis(stops = stpscol, type = "logarithmic") %>% 
  hc_title(text = "Daily Throughput of Each Airport in Dec, 2015") %>% 
  hc_tooltip(formatter = fntltp) %>% 
  hc_legend(layout = "vertical", verticalAlign = "bottom", align = "right") 

This figure gives the holistic idea about how many flights are operated by each airport everyday.

• Most airports provide services everyday. While some airports only function on specific days with some white blanks in this figure.
• Weekends are less busy than usual. There are five dark horizontal ribbons across the figure. They are generated by 5th, 12th, 19th, 24th and 25th.

Connectedness of Two Cities

We can construct the adjacency matrix for each pair of two cities. It shoud be a 303 * 303 matrix. Hence in the matrix, 0 value indicates no flight from ORIGIN to DEST, and 1 value for at least one flight. Due to the flight route is directed, the matrix is not necessarily symmetric.

CityList=intersect(CityFlight$ORIGIN_CITY_NAME,CityFlight$DEST_CITY_NAME);
AdjMatrix=matrix(0, nrow=length(unique(CityFlight$ORIGIN_CITY_NAME)), ncol=length(unique(CityFlight$DEST_CITY_NAME)));
colnames(AdjMatrix)=c(CityList);
rownames(AdjMatrix)=c(CityList);
for (i in 1 : nrow(AdjMatrix)){
  for (j in 1: ncol(AdjMatrix)){
    if (is.element(CityList[j], CityFlight$DEST_CITY_NAME[CityFlight[,1]==CityList[i]])==T) {AdjMatrix[i, j]=1}
    else {AdjMatrix[i, j]=0}}};
fntltp <- JS("function(){return  'DEST: ' +  this.point.name + ' as ORIGIN: ' + this.point.value}");
hchart(AdjMatrix) %>%
  hc_legend(enabled=F) %>%
  hc_tooltip(formatter=fntltp) %>%
  hc_title(text='Connectedness of Two US Cities by Flight')

This data presents the possibility of travelling between two US cities with no-transfer flights. As shown above, existing flights covers 0.0414 of all no-stop flight requirements. If one transfer is allowed, let’s see how many cities will be connected. To do this, we employ the reachability theory.

AdjMatrix1Trans=AdjMatrix%*%AdjMatrix;
AdjMatrix1Transfer=AdjMatrix+AdjMatrix1Trans;
BooleanM=function(x){
  for (i in 1 : nrow(x)){
    for (j in 1 : ncol(x)){
      if (x[i,j]>0){x[i,j]=1}
      else {x[i,j]=x[i,j]}}}
  return(x)};
AdjMatrix1Transfer=BooleanM(AdjMatrix1Transfer);
hchart(AdjMatrix1Transfer) %>%
  hc_legend(enabled=F) %>%
  hc_tooltip(formatter=fntltp) %>%
  hc_title(text='Connectedness of Two US　Cities by One-Transfer Flight')

These flights cover 0.6 of all flights requirements between two arbitrary cities with at most one transfer. Let’s make two transfers.

AdjMatrix2Trans=AdjMatrix1Trans%*%AdjMatrix;
AdjMatrix2Transfer=AdjMatrix1Transfer+AdjMatrix2Trans;
AdjMatrix2Transfer=BooleanM(AdjMatrix2Transfer);
hchart(AdjMatrix2Transfer) %>%
  hc_legend(enabled=F) %>%
  hc_tooltip(formatter=fntltp) %>%
  hc_title(text='Connectedness of Two US Cities by Two-Transfer Flight')

88211 fligts with at most two transfers are provided. 0.9608 of all requirements are met. There are still some cities out of flight reach. Three transfers may make all cities connected in dual directions.

AdjMatrix3Trans=AdjMatrix2Trans%*%AdjMatrix;
AdjMatrix3Transfer=AdjMatrix2Transfer+AdjMatrix3Trans;
AdjMatrix3Transfer=BooleanM(AdjMatrix3Transfer);
hchart(AdjMatrix3Transfer) %>%
  hc_legend(enabled=F) %>%
  hc_tooltip(formatter=fntltp) %>%
  hc_title(text='Connectedness of Two Cities by Three-Transfer Flight')

0.9997 of all requirements are met. Four-transfer flight are for these black naughty combinations.

AdjMatrix4Trans=AdjMatrix3Trans%*%AdjMatrix;
AdjMatrix4Transfer=AdjMatrix3Transfer+AdjMatrix4Trans;
AdjMatrix4Transfer=BooleanM(AdjMatrix4Transfer);

The new matrix provides 91809 flights which covers all flights requirements between two cities. The figure has been omitted. Finally,

• with at most four transfers, we can travel between two arbitrary US cities with airports.

Fewest-Transfer Route

We can also find the fewest-transfer flight route for any two cities by applying Dijkstra Algorithm on the adjacency matrix.

library(edgebundleR);
library(igraph);
edgeVector=NULL;
for(i in 1 : nrow(CityFlight)){edgeVector=c(edgeVector, CityFlight[i,1:2])};
CityLink=make_graph(unlist(edgeVector), directed = T);
TransferCount=shortest.paths(CityLink, V(CityLink),V(CityLink), algorithm='dijkstra')

From TransferCount matrix, we can quickly find out the fewest transfer times between two cities. Furthermore, we can use get.shortest.paths to obtain one shortest path of two cities. For example,

get.shortest.paths(CityLink, "Hattiesburg/Laurel, MS", "Wrangell, AK")$vpath

## [[1]]
## + 5/303 vertices, named:
## [1] Hattiesburg/Laurel, MS Dallas/Fort Worth, TX  Seattle, WA           
## [4] Ketchikan, AK          Wrangell, AK

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