## Plotting SEPTA NTD time series data

This lab uses the National Transit Database to plot some SEPTA transit data. This uses an updated database that differs from the previous NTD lab in several important ways: (1) This dataset adds the years 2014 through 2017. (2) The TRS.ID has been replaced with and NTD.ID which contains one additional digit. (3) The data drops one small system (NTD.ID == 80188))

Here is the data

summary(subset(NTD.ts, NTD.ID == 30019 ))
##       Year          NTD.ID           Mode    Service     unique
##  Min.   :1991   Min.   :30019   DR     :54     :  0   Length:243
##  1st Qu.:1997   1st Qu.:30019   MB     :54   DO:189   Class :character
##  Median :2004   Median :30019   CR     :27   PT: 54   Mode  :character
##  Mean   :2004   Mean   :30019   HR     :27
##  3rd Qu.:2011   3rd Qu.:30019   LR     :27
##  Max.   :2017   Max.   :30019   SR     :27
##                                 (Other):27
##  Last.Report.Year Legacy.NTD.ID
##  Min.   :2017     3019   :243
##  1st Qu.:2017            :  0
##  Median :2017     0001   :  0
##  Mean   :2017     0002   :  0
##  3rd Qu.:2017     0003   :  0
##  Max.   :2017     0005   :  0
##                   (Other):  0
##                                                     Agency.Name
##  Southeastern Pennsylvania Transportation Authority(SEPTA):243
##                                                           :  0
##   Sistersville Ferry                                      :  0
##  10-15 Regional Transit Agency                            :  0
##  2Plus Partners in Transportation, Inc(2Plus)             :  0
##  A & A Transport                                          :  0
##  (Other)                                                  :  0
##   Agency.Status           Reporter.Type             City         State
##          :  0                    :  0   Philadelphia  :243   PA     :243
##  Active  :243   Building Reporter:  0                 :  0          :  0
##  Inactive:  0   Full Reporter    :243     Sistersville:  0   AK     :  0
##                 Planning Reporter:  0    Norman       :  0   AL     :  0
##                 Reduced Reporter :  0   Abbeville     :  0   AR     :  0
##                 Rural Reporter   :  0   Aberdeen      :  0   AS     :  0
##                 Separate Service :  0   (Other)       :  0   (Other):  0
##   Census.Year                                       UZA.Name        UZA
##  Min.   :2010   Philadelphia, PA-NJ-DE-MD               :243   Min.   :5
##  1st Qu.:2010                                           :  0   1st Qu.:5
##  Median :2010   Aberdeen-Bel Air South-Bel Air North, MD:  0   Median :5
##  Mean   :2010   Abilene, TX                             :  0   Mean   :5
##  3rd Qu.:2010   Aguadilla-Isabela-San Sebastián, PR     :  0   3rd Qu.:5
##  Max.   :2010   Akron, OH                               :  0   Max.   :5
##                 (Other)                                 :  0
##  UZA.Area.SQ.Miles UZA.Population     OPEXP_TOTAL
##  Min.   :1981      Min.   :5441567   Min.   :        0
##  1st Qu.:1981      1st Qu.:5441567   1st Qu.:        0
##  Median :1981      Median :5441567   Median : 38234876
##  Mean   :1981      Mean   :5441567   Mean   : 95788886
##  3rd Qu.:1981      3rd Qu.:5441567   3rd Qu.:143218152
##  Max.   :1981      Max.   :5441567   Max.   :628361295
##
##     OPEXP_VO            OPEXP_VM           OPEXP_NVM
##  Min.   :        0   Min.   :        0   Min.   :       0
##  1st Qu.:        0   1st Qu.:        0   1st Qu.:       0
##  Median : 19066965   Median :  8027121   Median : 1276522
##  Mean   : 51265069   Mean   : 17909043   Mean   :12596521
##  3rd Qu.: 71487635   3rd Qu.: 27655211   3rd Qu.:25401973
##  Max.   :358884108   Max.   :111383283   Max.   :76095578
##  NA's   :1           NA's   :6           NA's   :6
##     OPEXP_GA             FARES                VOMS
##  Min.   :        0   Min.   :        0   Min.   :   0.0
##  1st Qu.:        0   1st Qu.:        0   1st Qu.:   0.0
##  Median :  6252830   Median :  6148051   Median : 117.0
##  Mean   : 15044355   Mean   : 44545336   Mean   : 244.7
##  3rd Qu.: 18748079   3rd Qu.: 88624472   3rd Qu.: 297.0
##  Max.   :108531959   Max.   :180086911   Max.   :1222.0
##  NA's   :1           NA's   :99
##       VRM                VRH               DRM              UPT
##  Min.   :       0   Min.   :      0   Min.   :   0.0   Min.   :        0
##  1st Qu.:       0   1st Qu.:      0   1st Qu.:   0.0   1st Qu.:        0
##  Median : 3193515   Median : 351246   Median :  69.3   Median :  6696017
##  Mean   : 9121571   Mean   : 708592   Mean   : 402.9   Mean   : 36716365
##  3rd Qu.:15608592   3rd Qu.: 846898   3rd Qu.: 346.1   3rd Qu.: 37755689
##  Max.   :40879970   Max.   :4009611   Max.   :2622.7   Max.   :189040211
##                                       NA's   :29
##       PMT                 CPI
##  Min.   :        0   Min.   :0.950
##  1st Qu.:        0   1st Qu.:1.040
##  Median : 12854414   Median :1.230
##  Mean   :157823409   Mean   :1.257
##  3rd Qu.:390397448   3rd Qu.:1.450
##  Max.   :587747642   Max.   :1.710
## 

library(ggplot2)

Make a smaller dataset that runs from 2002 when fares started getting reported. Also, only keep directly operated transit lines since reporting is better.

dat <- subset(NTD.ts, Year > 2001 & NTD.ID == 30019 & Service == "DO")
ggplot(dat,  aes(x = Year, y = PMT ,  colour = Mode)) +
geom_line()

Remove some modes to make plots easier to read.

dat <- subset(dat, Mode == "CR" | Mode == "HR" | Mode == "MB")
ggplot(dat,  aes(x = Year, y = PMT ,  colour = Mode)) +
geom_line()

Plot operating expenses per passenger mile

dat$op_PMT <- dat$OPEXP_TOTAL/dat$PMT ggplot(dat, aes(x = Year, y = op_PMT , colour = Mode)) + geom_line() Plot fare revenue per passenger mile dat$fare_PMT <- dat$FARES/dat$PMT
ggplot(dat,  aes(x = Year, y = fare_PMT ,  colour = Mode)) +
geom_line()

## 2018 Transit Trends

http://www.trapezegroup.com/blog-entry/2018-transit-trends-from-around-the-globe-north-america

## Where do bike lanes work best? A Bayesian spatial model of bicycle lanes and bicycle crashes

This link should work for 50 days: https://authors.elsevier.com/c/1WBX23IVV9Z7m9

## Working with the Philadelphia Region Household Travel Survey

The purpose of this lab is to learn to work with the Philadelphia region 2012 household travel survey. You will learn how to upload data into R, summarize information from the data, combine datasheets, and examine subsets of the data. By the end of the lab, you should feel comfortable looking at the travel survey and be able to complete the relevant questions from assignment 1.

Many large cities collect important individual-level data to understand and model regional travel behavior and predict the impacts of transportation investments and new commercial and housing developments. The United States also collects household travel data for the entire country. The data for surveys from 1983 to 2009 can be found here.

To work with the Philadelphia household travel survey, download the data from the Delaware Valley Regional Planning Commission website. You can find links to the 2002 data here as well. The 2012 files come as an Access database or in excel files. For this exercise, you need to open the excel files and save the sheets as a CSV. In Excel, click on File/Save as and then scroll down to CSV (comma delimited). Save or move the files to the folder where you want to work.

First, clear objects from the memory with rm() command, and set up the working directory to the folder containing your files. Note that if you are using a PC, you need to change all back slashes to forward slashes. For a brief tutorial, click here.

Next, upload the data into your work session with the read.csv() command. This will likely work with no additional options, but to be safe, use the following: Strip.white parameter gets rid of blank spaces in the table, sep parameter sets the separator, blank.lines.skip skips the blank lines, na.strings specifies the symbol for missing observations. Any of the parameters can be changed. Depending on how your data are saved to a CSV, you may need to change the seperator.

Note that you may have to change the names of the sub-folder and files, depending on how you saved the CSVs.

If you are having trouble reading the data into R, there is a handy interface in RStudio. Click on Tools in the top right, then Import Dataset, then From a Text File.

hh <- read.csv("DVRPC HTS Database Files/1_Household_Public.csv")

per <- read.csv("DVRPC HTS Database Files/2_Person_Public.csv")

veh <- read.csv("DVRPC HTS Database Files/3_Vehicle_Public.csv")

trip <- read.csv("DVRPC HTS Database Files/4_Trip_Public.csv")

Consult the documentation about the function parameters when you load the files.

?read.csv

You should now have four objects in the top right window of R Studio. You can also check that these loaded properly using the ls() command.

ls()
## [1] "hh"   "per"  "trip" "veh"

Again, type ?command to understand the function.

?ls

Finally, check your dimension to make sure that you have the right number of observations and variables.

dim(hh)
## [1] 9235   38
dim(per)
## [1] 20216    72
dim(trip)
## [1] 81940    81
dim(veh)
## [1] 14903    13

Str() uncovers the structure of the data, as well as head(). Tail() takes the last 6 and is particularly useful for making sure that your data loaded properly. The summary() function provides descriptive statistics such as mean or range, depending on the data type (for example, factors and characters do not have mean values). The brackets used with the summary command (hh[,1:5]), indicate that you only want to look at the first five variables. If you move the comma after 1:5, then you will see a summary of the first five entries for all of the variables.

str(hh) 
## 'data.frame':    9235 obs. of  38 variables:
##  $ID : int 1 2 3 4 5 6 7 8 9 10 ... ##$ HH_ID         : int  100140 100206 100291 100355 100374 100449 100503 100601 100663 100677 ...
##  $HH_WEIGHT : num 133 204 288 222 365 ... ##$ HH_GEO_ACC    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $H_STATE : int 34 34 34 34 34 34 34 34 34 34 ... ##$ H_COUNTY      : int  34005 34005 34005 34005 34005 34005 34005 34005 34005 34005 ...
##  $H_CPA : Factor w/ 84 levels "005_701","005_702",..: 2 2 1 1 1 1 1 1 4 4 ... ##$ H_MCD         : num  3.4e+09 3.4e+09 3.4e+09 3.4e+09 3.4e+09 ...
##  $H_TRACT : num 3.4e+10 3.4e+10 3.4e+10 3.4e+10 3.4e+10 ... ##$ H_TAZ         : int  20225 20249 20048 20042 20002 20020 20026 20011 20637 20629 ...
##  $A_TYPE : int 4 4 4 4 4 4 4 5 5 5 ... ##$ HH_TOT_TRIPS  : int  8 11 9 5 24 9 9 18 4 5 ...
##  $HH_MO_TRIPS : int 8 5 9 4 24 9 7 16 4 5 ... ##$ HH_NM_TRIPS   : int  0 6 0 1 0 0 2 2 0 0 ...
##  $GPS_Factor : num 1.12 1.17 1.12 1.17 1.17 1.17 1.12 1.12 1.13 1.17 ... ##$ F_HH_TOT_TRIPS: num  8.96 12.87 10.08 5.85 28.08 ...
##  $F_HH_MO_TRIPS : num 8.96 5.85 10.08 4.68 28.08 ... ##$ F_HH_NM_TRIPS : num  0 7.02 0 1.17 0 0 2.24 2.24 0 0 ...
##  $HH_SIZE : int 2 3 5 1 4 2 2 4 1 2 ... ##$ HH_WORK       : int  2 2 2 1 1 0 1 2 0 2 ...
##  $TOT_VEH : int 2 2 2 1 2 2 2 2 1 1 ... ##$ OP_VEH        : int  2 2 2 1 2 2 2 2 1 1 ...
##  $VEH_OUT : int 20 9000 11 9000 3 20 50 15 1 30 ... ##$ TOT_BIKE      : int  1 3 7 0 4 0 2 4 0 0 ...
##  $TOLL_ACCNT : int 1 1 1 2 1 1 1 1 2 2 ... ##$ CAR_SHARE     : int  2 2 2 2 2 2 2 2 2 2 ...
##  $RES_TYPE : int 1 1 1 1 1 1 2 1 1 1 ... ##$ INCOME        : int  6 5 7 4 5 5 6 7 99 4 ...
##  $TRAV_DATE : Factor w/ 271 levels "1/1/2013","1/10/2013",..: 236 230 215 243 230 213 243 213 215 213 ... ##$ TRAV_DOW      : int  5 4 2 3 4 1 3 1 2 1 ...
##  $HOLIDAY : Factor w/ 21 levels "","Birthday of Martin Luther King, Jr.",..: 1 1 1 1 1 1 1 1 1 1 ... ##$ HOL_TYPE      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $OV_SAMPLE : int 1 0 0 0 1 0 1 0 0 0 ... ##$ STUDY         : int  1 1 1 1 1 1 1 1 1 1 ...
##  $RECRUIT : int 1 1 1 1 1 1 1 1 1 1 ... ##$ RETRIEVE      : int  3 3 3 2 3 3 2 3 3 2 ...
##  $R_LANG : int 1 1 1 1 1 1 1 1 1 1 ... ##$ SURV_LANG     : int  1 1 1 1 1 1 1 1 1 1 ...
head(hh)
##   ID  HH_ID HH_WEIGHT HH_GEO_ACC H_STATE H_COUNTY   H_CPA      H_MCD
## 1  1 100140 133.46032          1      34    34005 005_702 3400543740
## 2  2 100206 204.10617          1      34    34005 005_702 3400547880
## 3  3 100291 287.91055          1      34    34005 005_701 3400517440
## 4  4 100355 222.44414          1      34    34005 005_701 3400517080
## 5  5 100374 365.14646          1      34    34005 005_701 3400505740
## 6  6 100449  74.05932          1      34    34005 005_701 3400508950
##       H_TRACT H_TAZ A_TYPE HH_TOT_TRIPS HH_MO_TRIPS HH_NM_TRIPS GPS_Factor
## 1 34005700405 20225      4            8           8           0       1.12
## 2 34005700501 20249      4           11           5           6       1.17
## 3 34005700603 20048      4            9           9           0       1.12
## 4 34005700800 20042      4            5           4           1       1.17
## 5 34005700900 20002      4           24          24           0       1.17
## 6 34005701104 20020      4            9           9           0       1.17
##   F_HH_TOT_TRIPS F_HH_MO_TRIPS F_HH_NM_TRIPS HH_SIZE HH_WORK TOT_VEH
## 1           8.96          8.96          0.00       2       2       2
## 2          12.87          5.85          7.02       3       2       2
## 3          10.08         10.08          0.00       5       2       2
## 4           5.85          4.68          1.17       1       1       1
## 5          28.08         28.08          0.00       4       1       2
## 6          10.53         10.53          0.00       2       0       2
##   OP_VEH VEH_OUT TOT_BIKE TOLL_ACCNT CAR_SHARE RES_TYPE INCOME TRAV_DATE
## 1      2      20        1          1         2        1      6  8/3/2012
## 2      2    9000        3          1         2        1      5  8/2/2012
## 3      2      11        7          1         2        1      7 7/31/2012
## 4      1    9000        0          2         2        1      4  8/8/2012
## 5      2       3        4          1         2        1      5  8/2/2012
## 6      2      20        0          1         2        1      5 7/30/2012
##   TRAV_DOW HOLIDAY HOL_TYPE OV_SAMPLE STUDY RECRUIT RETRIEVE R_LANG
## 1        5                0         1     1       1        3      1
## 2        4                0         0     1       1        3      1
## 3        2                0         0     1       1        3      1
## 4        3                0         0     1       1        2      1
## 5        4                0         1     1       1        3      1
## 6        1                0         0     1       1        3      1
##   SURV_LANG
## 1         1
## 2         1
## 3         1
## 4         1
## 5         1
## 6         1
summary(hh[,1:5])
##        ID           HH_ID           HH_WEIGHT         HH_GEO_ACC
##  Min.   :   1   Min.   : 100140   Min.   :  40.32   Min.   :1
##  1st Qu.:2361   1st Qu.: 397484   1st Qu.: 109.56   1st Qu.:1
##  Median :4746   Median : 510518   Median : 165.11   Median :1
##  Mean   :4781   Mean   : 629041   Mean   : 227.09   Mean   :1
##  3rd Qu.:7198   3rd Qu.: 703038   3rd Qu.: 279.67   3rd Qu.:1
##  Max.   :9628   Max.   :1736693   Max.   :1086.64   Max.   :1
##     H_STATE
##  Min.   :34.00
##  1st Qu.:42.00
##  Median :42.00
##  Mean   :40.04
##  3rd Qu.:42.00
##  Max.   :42.00

For this purpose functions names() and colnames() are almost identical – they provide the names of all columns (variables) in the data-set. Rownames provides the list of names of rows 100 to 1005. Be sure to check the data dictionary provided with the data to understand each variable.

names(hh)
##  [1] "ID"             "HH_ID"          "HH_WEIGHT"      "HH_GEO_ACC"
##  [5] "H_STATE"        "H_COUNTY"       "H_CPA"          "H_MCD"
##  [9] "H_TRACT"        "H_TAZ"          "A_TYPE"         "HH_TOT_TRIPS"
## [13] "HH_MO_TRIPS"    "HH_NM_TRIPS"    "GPS_Factor"     "F_HH_TOT_TRIPS"
## [17] "F_HH_MO_TRIPS"  "F_HH_NM_TRIPS"  "HH_SIZE"        "HH_WORK"
## [21] "TOT_VEH"        "OP_VEH"         "VEH_OUT"        "TOT_BIKE"
## [25] "TOLL_ACCNT"     "CAR_SHARE"      "RES_TYPE"       "INCOME"
## [29] "TRAV_DATE"      "TRAV_DOW"       "HOLIDAY"        "HOL_TYPE"
## [33] "OV_SAMPLE"      "STUDY"          "RECRUIT"        "RETRIEVE"
## [37] "R_LANG"         "SURV_LANG"
colnames(hh)
##  [1] "ID"             "HH_ID"          "HH_WEIGHT"      "HH_GEO_ACC"
##  [5] "H_STATE"        "H_COUNTY"       "H_CPA"          "H_MCD"
##  [9] "H_TRACT"        "H_TAZ"          "A_TYPE"         "HH_TOT_TRIPS"
## [13] "HH_MO_TRIPS"    "HH_NM_TRIPS"    "GPS_Factor"     "F_HH_TOT_TRIPS"
## [17] "F_HH_MO_TRIPS"  "F_HH_NM_TRIPS"  "HH_SIZE"        "HH_WORK"
## [21] "TOT_VEH"        "OP_VEH"         "VEH_OUT"        "TOT_BIKE"
## [25] "TOLL_ACCNT"     "CAR_SHARE"      "RES_TYPE"       "INCOME"
## [29] "TRAV_DATE"      "TRAV_DOW"       "HOLIDAY"        "HOL_TYPE"
## [33] "OV_SAMPLE"      "STUDY"          "RECRUIT"        "RETRIEVE"
## [37] "R_LANG"         "SURV_LANG"
rownames(hh[100:105,])
## [1] "100" "101" "102" "103" "104" "105"

Functions View() and fix() provide a useful tabular view of the data. View() opens a separate tab in RStudio. It is important to remember to capitalize the first letter of this function, otherwise it will not run. Fix() (not capitalized) opens a new window and allows for editing, just like Excel. In order to reuse the edits the R object has to be saved as a new file.

View(hh)
fix(hh)

Function table() creates useful summary table and multidimensional cross-tables. In this case, passing the TOT_VEH variable to the function creates a one-dimensional table of the number of households by the number of vehicles. Dividing it by the length of the variable produces the percentages.

table(hh$TOT_VEH) ## ## 0 1 2 3 4 5 6 7 8 10 15 ## 833 3109 3726 1120 318 93 22 8 4 1 1 table(hh$TOT_VEH)/length(hh$TOT_VEH) ## ## 0 1 2 3 4 ## 0.0902003249 0.3366540336 0.4034650785 0.1212777477 0.0344342177 ## 5 6 7 8 10 ## 0.0100703844 0.0023822415 0.0008662696 0.0004331348 0.0001082837 ## 15 ## 0.0001082837 Using the round() function we rounded the values and multiplied them by 100 to make them more understandable. 40.4% of the households have two cars. round(table(hh$TOT_VEH)/length(hh$TOT_VEH)*100, digits=3) ## ## 0 1 2 3 4 5 6 7 8 10 ## 9.020 33.665 40.347 12.128 3.443 1.007 0.238 0.087 0.043 0.011 ## 15 ## 0.011 Passing two arguments creates a two-dimensional table of the number of households by the number of cars and the household income. Note that the brackets are removing data where the income is unknown (98) or not given (99). You will also need to consult the data dictionary to understand the income levels represented by the numbers 1 through 10. table(hh$TOT_VEH[hh$INCOME < 97], hh$INCOME[hh$INCOME < 97]) ## ## 1 2 3 4 5 6 7 8 9 10 ## 0 179 240 93 90 95 33 30 17 15 0 ## 1 114 504 405 510 587 335 236 84 82 27 ## 2 19 90 140 333 646 685 812 343 210 121 ## 3 3 14 25 57 147 187 295 152 78 64 ## 4 2 4 6 15 47 53 70 47 23 19 ## 5 1 1 2 3 10 10 23 16 7 6 ## 6 0 0 0 0 3 5 5 5 1 3 ## 7 0 0 0 0 1 4 1 0 1 0 ## 8 0 0 0 0 2 1 0 0 0 1 ## 10 0 0 0 0 0 0 0 1 0 0 ## 15 0 0 0 0 1 0 0 0 0 0 Functions hist() and plot() are used for plotting and let us see how the data is distributed. Hist() creates a histogram, embedding the function density() in plot() will plot the density of the observed distribution. In this case the density plot is not particularly useful, but it frequently is quite helpful with larger, more continuous data. hist(hh$TOT_VEH)

plot(density(hh$TOT_VEH)) More on functions table() and hist() ?table ?hist You see that many of the observations have a value of 98 or 99. This is the value used for those households that refused to report their income. Using the square brackets or the function subset() allows us to only select valid observations. In general, use brackets for a single variable, but subset for multiple variables (including the entire data frame). table(hh$INCOME)
##
##    1    2    3    4    5    6    7    8    9   10   98   99
##  318  853  671 1008 1539 1313 1472  665  417  241   13  725
hist(hh$INCOM[hh$INCOME < 98])

table(hh$TOT_VEH[hh$INCOME < 98])
##
##    0    1    2    3    4    5    6    7    8   10   15
##  792 2884 3399 1022  286   79   22    7    4    1    1
summary(subset(hh[,1:5], hh$INCOME < 98)) ## ID HH_ID HH_WEIGHT HH_GEO_ACC ## Min. : 1 Min. : 100140 Min. : 40.32 Min. :1 ## 1st Qu.:2366 1st Qu.: 397693 1st Qu.: 113.42 1st Qu.:1 ## Median :4783 Median : 512032 Median : 169.94 Median :1 ## Mean :4815 Mean : 636321 Mean : 233.20 Mean :1 ## 3rd Qu.:7288 3rd Qu.: 708975 3rd Qu.: 291.01 3rd Qu.:1 ## Max. :9628 Max. :1736693 Max. :1086.64 Max. :1 ## H_STATE ## Min. :34.00 ## 1st Qu.:42.00 ## Median :42.00 ## Mean :40.03 ## 3rd Qu.:42.00 ## Max. :42.00 More on function subset() ?subset You can also recode the missing observations, so that they can be displayed nicely in the histogram. hh$inc <- hh$INCOM #same variable hh$inc[hh$inc==98]<- 11 #recoding missing from 98 to 11 hh$inc[hh$inc==99]<- 12 #recoding refused from 99 to 12 hist(hh$inc) #looks much better now

There are also 3 other data-frames from DVRPC. Often you will want to combine information. In order to combine the data, you first need to understand the different data values. Notice that the person-level file has a column called HH_ID. This will be the variable to use to join the person data and the household data

names(per)
##  [1] "ID"            "HH_ID"         "PERSON_NUM"    "PERSON_ID"
##  [5] "HH_WEIGHT"     "P_WEIGHT"      "P_TOT_TRIPS"   "P_MO_TRIPS"
##  [9] "P_NM_TRIPS"    "GPS_Factor"    "F_P_TOT_TRIPS" "F_P_MO_TRIPS"
## [13] "F_P_NM_TRIPS"  "GEND"          "AGECAT"        "RACE"
## [17] "EDUCA"         "RELAT"         "LIC"           "EMPLY"
## [21] "WK_STAT"       "SEMPLY"        "JOBS"          "WK_LOC"
## [25] "W_GEO_ACC"     "W_STATE"       "W_REGION"      "W_COUNTY"
## [29] "W_CPA"         "W_MCD"         "W_TRACT"       "W_TAZ"
## [33] "HOURS"         "OCCUP"         "WK_MODE"       "WK_MODETMS"
## [37] "ARRV_WRK"      "ARRV_TMS"      "LV_WRK"        "LV_TMS"
## [41] "TCOMM"         "PARK_SUB"      "PARK_SUB_AMT"  "TRAN_SUB"
## [45] "TRAN_SUB_AMT"  "WDAY_1"        "WDAY_2"        "WDAY_3"
## [49] "WDAY_4"        "WDAY_5"        "WDAY_6"        "WDAY_7"
## [53] "STUDE"         "SCHOL"         "S_HOME"        "S_ONLN"
## [57] "S_GEO_ACC"     "S_STATE"       "S_REGION"      "S_COUNTY"
## [61] "S_CPA"         "S_MCD"         "S_TRACT"       "S_TAZ"
## [65] "PRESCH_1"      "PRESCH_2"      "PRESCH_3"      "T_TRIP"
## [69] "PAY_TYPE1"     "PAY_TYPE2"     "PAY_TYPE3"     "TAP"

Use the length() and unique() commands to check how well the data will combine. The function length() allows you to extract the length of any vector; unique() creates a vector of all unique observations within another vector or a variable. Note that this should be equal to the number of observations in the household data. By contrast, the length of the person id should be equal to the number of observations in the person data.

length(unique(per$HH_ID)) ## [1] 9235 length(unique(per$PERSON_ID))
## [1] 20216

You should also notice that the person id is the same as the household id with the person number added to it.

head(per[1:4])
##    ID  HH_ID PERSON_NUM PERSON_ID
## 1 279 100140          1  10014001
## 2 280 100140          2  10014002
## 3 281 100206          1  10020601
## 4 282 100206          2  10020602
## 5 283 100206          3  10020603
## 6 284 100291          1  10029101

Now that you have found the correct identification numbers, you can merge the files using the merge command. Add all of the household data to the person file and create a new data-frame. In this case, there is a perfect merger, but if there were some people with missing household data, you could keep all entries by setting all.x = True instead of False.

per_hh <- merge(per, hh, by = "HH_ID", all.x = FALSE, all.y=FALSE, sort = FALSE)

Always be careful to check your data before and after a merge to make sure that you are getting what you wants. In this case, we want the dimensions of the data-frame to have the same number of observations as the person data.

The next data-set contains data about individual trips that people made during one day. Note that you made the head() command show 10 entries instead of the default of 6. The trips data-frame has an id to match both the household and person data.

head(trip[,1:12], 10)

Try making a subset of all trips made by members of one household using the subset() function. The output of the function is a data frame, which can be checked using class() function. Some functions return objects in matrix form, which makes the data processing a little bit different.

test <- subset(trip, trip$HH_ID == 100374 ) class(test) ## [1] "data.frame" Now look at two variables of this subset. The variable MODE contains information about the mode that was used in each trip. MODE_AGG combines some of the modes into larger categories. Again refer to the data dictionary. test$MODE
##  [1]  5  5  5  5 NA  5  5  5  5  5  5  5  5  5  5 NA  5  5  5  5 NA  5  5
## [24]  5  5  5  5 NA
test$MODE_AGG ## [1] 3 3 3 3 NA 3 3 3 3 3 3 3 3 3 3 NA 3 3 3 3 NA 3 3 ## [24] 3 3 3 3 NA You can find out how many trips do not have any information about mode using is.na function. It produces a vector of the same length as trip$mode, and only contain TRUE or FALSE values. Summary() function counts how many different values are contained in the logical vector. The number of TRUEs is about 20,000, therefore there are 20,000 trips with no information about mode.

summary(is.na(trip$MODE)) ## Mode FALSE TRUE NA's ## logical 61739 20201 0 Then you can select only the observations that have information about mode. head(subset(trip,is.na(trip$MODE)==FALSE))[,5:10]
##   HH_WEIGHT P_WEIGHT TOUR_NUM WKSUB_NUM   WKSUB_ID TRIP_NUM
## 1  133.4603 132.5431        1        NA 1.0014e+11        1
## 2  133.4603 132.5431        1        NA 1.0014e+11        2
## 3  133.4603 132.5431        1        NA 1.0014e+11        3
## 4  133.4603 132.5431        1        NA 1.0014e+11        4
## 5  133.4603 132.5431        1        NA 1.0014e+11        5
## 7  133.4603 115.1988        1        NA 1.0014e+11        1

You may also wish to have a better idea of what is happening with the data with no reported mode. Notice that the trip number is equal to 0 (no trips) or 97 (final destination).

table(trip$TRIP_NUM[is.na(trip$MODE)==TRUE])
##
##     0    97
##  3611 16590

Finally, look at the vehicle data-set.

head(veh)
##   ID  HH_ID HH_WEIGHT TOT_VEH OP_VEH VEH_NUM   VEH_ID VEH_OWN YEAR MAKE
## 1 79 100140  133.4603       2      2       1 10014001       1 2009   15
## 2 80 100140  133.4603       2      2       2 10014002       1 2010    8
## 3 81 100206  204.1062       2      2       1 10020601       1 1999   14
## 4 82 100206  204.1062       2      2       2 10020602       1 2009   37
## 5 83 100291  287.9105       2      2       1 10029101       2 2010   39
## 6 84 100291  287.9105       2      2       2 10029102       2 2011   39
##           MODEL BODY VEH_TYPE
## 1        SONATA    1        2
## 2 GRAND CARAVAN    8        2
## 3           CRV    2        2
## 4     FORRESTER    2        2
## 5         PRIUS    1        1
## 6       SEIANNA    8        2

Notice that there are more vehicles than households. And that there are fewer unique household ids in the vehicle data than there are households. This is because not all households own cars, but some own more than 1.

dim(veh)
## [1] 14903    13
length(unique(veh$HH_ID)) ## [1] 8307 Now that you have looked at all the data, try to answer these two questions: How many trips do people take on average? And how does this vary by income? The first should be relatively straightforward, since the person data already contains a variable on each persons total trips. table(per$P_TOT_TRIPS)
##
##    0    1    2    3    4    5    6    7    8    9   10   11   12   13   14
## 3625  629 6025 2467 2880 1544 1251  698  478  292  182   76   33   21    6
##   15   16   19   20
##    4    2    1    2
mean(per$P_TOT_TRIPS) ## [1] 3.053225 This, however, is not entirely correct. The data come from a sample of households that vary systematically from households in Philadelphia. This is represented by weights. Look at the value of weights and then use the weighted.mean() function to apply them. summary(per$P_WEIGHT)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##   73.82   88.67  117.50  278.30  233.10 3665.00
weighted.mean(per$P_TOT_TRIPS, per$P_WEIGHT)
## [1] 2.902891

It is often interesting to look at who gets under-sampled in these types of surveys. First, plot the density of person weights. Notice how there is a long tail of people with very high weights.

plot(density(per$P_WEIGHT)) plot(density(per$P_WEIGHT[per$P_WEIGHT<500])) Now compare the racial differences across the high and low weights. Notice how the under-sampled population (those given a high weight) is less white than the over-sampled population. In general, these types of surveys tend to over-sample white households, households that own there own homes, large households, and households in single-family homes. Without applying the weights, the summaries are more likely to reflect the sample than the population of the Philadelphia region. round(table(per$RACE[per$P_WEIGHT<=278.3])/length(per$RACE[per$P_WEIGHT<=278.3]),3) ## ## 1 2 3 4 5 6 97 98 99 100 988 ## 0.872 0.052 0.008 0.001 0.020 0.000 0.002 0.001 0.011 0.008 0.024 round(table(per$RACE[per$P_WEIGHT>278.3])/length(per$RACE[per$P_WEIGHT>278.3]),3) ## ## 1 2 3 4 5 97 98 99 100 988 ## 0.712 0.157 0.039 0.003 0.027 0.004 0.001 0.011 0.018 0.027 Note that each weight is equal to the number of people or households that the observation is supposed to represent. So that you can sum them for an estimate of the number of people, households, or vehicles in the region. sum(per$P_WEIGHT)
## [1] 5627010
sum(hh$HH_WEIGHT) ## [1] 2097203 sum(hh$HH_WEIGHT*hh$TOT_VEH) ## [1] 3330849 Now look at how the number of trips vary by income. This is a little bit trickier. First, you have to have merge the person and household data (which we already did). Second, you need to make the estimate for each income group. There are a few ways to automate this (if you are interested look at the apply functions and the doBy package), but for now do it manually for the highest and lowest income groups. weighted.mean(per_hh$P_TOT_TRIPS[per_hh$INCOME==1], per_hh$P_WEIGHT[per_hh$INCOME==1])  ## [1] 2.489796 weighted.mean(per_hh$P_TOT_TRIPS[per_hh$INCOME==10], per_hh$P_WEIGHT[per_hh$INCOME==10]) ## [1] 3.339281 Finally, you may want to create and save a new data-frame that only contains what you want. First create a new data-frame that contains the variables you want to keep. dat <- per_hh[c("HH_ID", "PERSON_ID", "P_TOT_TRIPS", "GEND" , "AGECAT", "RACE", "INCOME", "TOT_VEH")] You can save it as a CSV or an R data file. A CSV is particularly useful if you have created a summary file that you want to open to make a nicely formatted table in Excel or send your file to someone who does not use R. The first argument specifies the data frame that needs to be saved, argument specifies the file name. By default, R will write the files to your current working directory. write.csv(dat, file="per_hh.csv") save(dat, file="per_hh.Rda") EXERCISE 1. Describe in detail the daily activities and trips of the first person in the trips data. 2. What is the mode share for the Philadelphia region? (Hint: Start with MODE_AGG instead of MODE) 3. For Philadelphia? 4. Give the weighted value of the percent of trips by car. (Hint: use the trip files with the person weights). 5. Compare the regional unweighted mode share for work and non-work trips. (For convenience, use the TOUR_TYPE variable instead of the activities.) 6. Compare the unweighted mode share for households with income under$35,000 with those above or equal to \$35,000.
7. Plot the number of person trips against income and describe any patterns that you see.
8. What percent of driving trips had to pay for parking?
9. What was the average hourly parking rate? (Hint: not all prices are presented in hours, so convert these)

Posted in Transportation Planning | Comments Off on Working with the Philadelphia Region Household Travel Survey

## Transit user perceptions of driverless buses

PhD student Xiaoxia (Summer) Dong presents our paper with Matt DiScenna on Transit user perceptions of driverless buses at the 2017 PA Automated Vehicle Summit in State College, PA.

The paper reports the results of a stated preference survey of regular transit users’ willingness to ride and concerns about driverless buses in the Philadelphia region. As automated technologies advance, driverless buses may offer significant efficiency, safety, and operational improvements over traditional bus services. However, unfamiliarity with automated vehicle technology may challenge its acceptance among the general public and slow the adoption of new technologies. Using a mixed logit modeling framework, this research examines which types of transit users are most willing to ride in driverless buses and whether having a transit employee on board to monitor the vehicle operations and/or provide customer service matters. Of the 891 surveyed members of University of Pennsylvania’s transit pass benefit program, two-thirds express a willingness to ride in a driverless bus when a transit employee is on board to monitor vehicle operations and provide customer service. By contrast, only 13% would agree to ride a bus without an employee on board. Males and those in younger age groups (18–34) are more willing to ride in driverless buses than females and those in older age groups. Findings suggest that, so long as a transit employee is onboard, many transit passengers will willingly board early generation automated buses. An abrupt shift to buses without employees on board, by contrast, will likely alienate many transit users.

This lab provides some tips for using American Factfinder to complete a neighborhood analysis over time in Philadelphia.

First, you will need to identify the location of your neighborhood and identify the appropriate Census Tracts in 2000 and 2010. Note that these numbers can change over time, as can the basic geographies. It is extremely important that the boundaries used in 2000 match the boundaries used in 2010. Otherwise, you are not comparing the same neighborhoods.
This website is a helpful starting point, but there are no official neighborhood designations in Philadelphia. To find your Census boundaries, refer to these or other resources:
Tiger Web Apps
2000 Census Tract Maps (note that you can change the location of the map based on the number at the end of the URL and the map key in the bottom right.)
2010 Maps (See note above about moving the map)

Once you have written down the appropriate Census Tract numbers, get started with American Factfinder. In the left drag-down menu, start with Advanced Search. I recommend starting with the dataset in the Topics section and entering one of the data sets you plan to use (e.g., 2000 SF3, the 2000 long form data.) After you click on the dataset, you will see it gets added to the selection criteria in the top left.

Next, click Geographies, manually enter Census Tracts, Pennsylvania, Philadelphia, and the desired Census Tract numbers.