Homework 3

Homework 3, DACSS 601

#1. Identify the dataset you will be using for the final project.

I will be utilizing speed data from a roadway analytics company. INRIX can provide data from connected vehicles and deliver anonymized speed data aggregated to specific time intervals at predetermined locations.

This data is commonly used to monitor a transportation network to determine locations where congestion may be occuring in real-time, or is a reoccuring problem.

This data can be combined with TMC (Traffic Management Channel) locations to be mapped.

I have an archived copy of the TMC shapefile for mapping and determining roadway types (Interstate, US Route, State Route, Local). I also have access to a year of hourly speed data for each location.

tbl_IA_1HR_Speed_AVG_INRIX <- read_csv(unz('G:/My Drive/School/UMASS/DACSS/DACSS_601/Final Project/Data/Bottlenecks_05062020.zip','Bottlenecks_05062020.csv' ))

head(tbl_IA_1HR_Speed_AVG_INRIX)

# A tibble: 6 x 8
  tmc_code  measurement_tstamp  speed average_speed reference_speed
  <chr>     <dttm>              <dbl>         <dbl>           <dbl>
1 118+11507 2018-01-01 02:00:00  53.5            55              55
2 118+11507 2018-01-01 04:00:00  48.2            55              55
3 118+11507 2018-01-01 05:00:00  30              53              55
4 118+11507 2018-01-01 07:00:00  55.4            55              55
5 118+11507 2018-01-01 08:00:00  49.5            56              55
6 118+11507 2018-01-01 09:00:00  47.7            55              55
# ... with 3 more variables: travel_time_minutes <dbl>,
#   confidence_score <dbl>, cvalue <dbl>

TMC_CODE: This is the location where the speed is recorded

measurement_tstamp: the date and time that the measurement was recorded

speed: average 1-hour speed

average_speed: average speed within a 24 hr period

reference_speed: recorded speeds on TMC between 10PM and 6AM (uncongested)

travel_time_minutes: average time a vehicle spent traversing the TMC during the specified time period

confidence_score: indicates the source of a data record. 20(historical), 30(real-time)

C-value: the confidence of the reading

#2. Read in/clean the dataset.

A TMC can be said to be ‘bottlenecked’ when the travel speed is 60 percent of the free-flow speed.

tbl_IA_1HR_Speed_AVG_INRIX <- read_csv(unz('G:/My Drive/School/UMASS/DACSS/DACSS_601/Final Project/Data/Bottlenecks_05062020.zip','Bottlenecks_05062020.csv' )) %>%
  filter(confidence_score ==30 & cvalue >=75) %>%
  filter(speed <=.6*reference_speed | ( reference_speed >= 65 & speed <= .6*65))

We also must import the TMC shapefile:

shp_IA_TMC<-st_read('G:/My Drive/School/UMASS/DACSS/DACSS_601/Final Project/Data/Iowa_2018_TMC_shapefile/Iowa.shp')

Reading layer `Iowa' from data source 
  `G:\My Drive\School\UMASS\DACSS\DACSS_601\Final Project\Data\Iowa_2018_TMC_shapefile\Iowa.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 4862 features and 36 fields
Geometry type: MULTILINESTRING
Dimension:     XY
Bounding box:  xmin: -96.61288 ymin: 40.38535 xmax: -90.17986 ymax: 43.50076
Geodetic CRS:  WGS 84

plot(shp_IA_TMC,col = "blue", main="TMC Locations in Iowa",max.plot=1)

#3. Identify potential research questions that your dataset can help answer.

“Which routes in Iowa, when compared to similar routes of the same type (Interstate, US, IA, local), is congestion significantly worse?