Carlsbad-Blvd
setwd("C:/Users/s-das/Syncplicity Folders/MY_Projects/NCHRP 17-76/04102017/Data Sources/INRIX/")
aa1 <- read.csv("Carlsbad-Blvd.csv")
library(lubridate)
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
aa1$Time <- format(as.POSIXct(strptime(aa1$measurement_tstamp, "%Y-%m-%d %H:%M:%S",tz="")) ,format = "%H:%M")
aa1$Time1 <- as.POSIXct(aa1$Time, format = "%H:%M")
aa1$Date <- format(as.POSIXct(strptime(aa1$measurement_tstamp, "%Y-%m-%d %H:%M:%S",tz="")) ,format = "%Y-%m-%d")
aa1$Time1 <- as.POSIXct(aa1$Time, format = "%H:%M")
aa1$Date1 <- as.Date(aa1$Date)
aa1$Day <- wday(aa1$Date1, label = TRUE)
str(aa1)
## 'data.frame': 108864 obs. of 13 variables:
## $ tmc_code : Factor w/ 54 levels "106-13652","106-13653",..: 3 41 49 10 45 40 27 47 36 42 ...
## $ measurement_tstamp : Factor w/ 2016 levels "2016-09-11 00:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ speed : num 21 25 22 30 22 35 32 44 40 33 ...
## $ average_speed : num 21 25 22 30 22 35 32 44 40 33 ...
## $ reference_speed : num 20 22 16 29 31 31 32 40 36 34 ...
## $ travel_time_minutes: num 1.3 0.04 0.04 1.6 0.13 0.01 3.74 0.28 0.5 0.03 ...
## $ confidence_score : num 20 20 20 20 20 20 20 20 20 20 ...
## $ cvalue : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Time : chr "00:00" "00:00" "00:00" "00:00" ...
## $ Time1 : POSIXct, format: "2017-04-10 00:00:00" "2017-04-10 00:00:00" ...
## $ Date : chr "2016-09-11" "2016-09-11" "2016-09-11" "2016-09-11" ...
## $ Date1 : Date, format: "2016-09-11" "2016-09-11" ...
## $ Day : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 1 1 1 1 1 1 1 1 1 1 ...
aa1_1 <- subset(aa1, tmc_code=="106-13652")
dim(aa1_1)
## [1] 2016 13
require(ggplot2)
## Loading required package: ggplot2
require(scales)
## Loading required package: scales
ggplot(aa1_1, aes(Time, speed)) + geom_point(size=0.5)+theme_bw()+labs(title="CA_CR001", x= " ")+
theme(axis.ticks = element_blank())+theme(axis.text = element_text(size=7))+
theme(legend.text = element_text(size=6))+ theme(axis.text.x=element_text(angle=90, vjust=0))
ggplot(aa1_1, aes(x=Time1, y=speed, group=Date)) +
geom_path(size=.5, alpha = 0.05, colour="blue") +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M"))+theme_bw()
ggplot(aa1, aes(x=Time1, y=speed, group=Date)) +
geom_path(size=.5, alpha = 0.05, colour="blue") +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M"))+theme_bw()
ggplot(aa1, aes(x=Time1, y=speed, colour=Day)) +
geom_point(cex=0.6) +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +theme_bw()
ggplot(aa1, aes(x=Time1, y=speed, group= tmc_code)) +
geom_point(size=.5, alpha = 0.1, colour="blue") +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M"))
ggplot(aa1, aes(x=Time1, y=speed, colour=Day)) +
geom_point(size=.5, alpha = 0.1) +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +
facet_grid(Day ~.) +
theme(legend.position="none") +theme_bw()
aa2 <- aggregate(speed ~ Time1 + Day, aa1, mean)
head(aa2)
## Time1 Day speed
## 1 2017-04-10 00:00:00 Sun 31.04407
## 2 2017-04-10 00:05:00 Sun 30.97222
## 3 2017-04-10 00:10:00 Sun 31.10630
## 4 2017-04-10 00:15:00 Sun 31.37944
## 5 2017-04-10 00:20:00 Sun 31.45537
## 6 2017-04-10 00:25:00 Sun 31.39444
ggplot(aa2, aes(x=Time1, y=speed, colour=Day)) +
geom_path() +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +
facet_grid(Day ~.) +
theme(legend.position="none")
ggplot(aa2, aes(x=Time1, y=speed, colour=Day)) +
geom_path(cex=1.1) +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +theme_bw()
Kearny-Villa-Rd
setwd("C:/Users/s-das/Syncplicity Folders/MY_Projects/NCHRP 17-76/04102017/Data Sources/INRIX/")
aa1 <- read.csv("Kearny-Villa-Rd.csv")
library(lubridate)
aa1$Time <- format(as.POSIXct(strptime(aa1$measurement_tstamp, "%Y-%m-%d %H:%M:%S",tz="")) ,format = "%H:%M")
aa1$Time1 <- as.POSIXct(aa1$Time, format = "%H:%M")
aa1$Date <- format(as.POSIXct(strptime(aa1$measurement_tstamp, "%Y-%m-%d %H:%M:%S",tz="")) ,format = "%Y-%m-%d")
aa1$Time1 <- as.POSIXct(aa1$Time, format = "%H:%M")
aa1$Date1 <- as.Date(aa1$Date)
aa1$Day <- wday(aa1$Date1, label = TRUE)
str(aa1)
## 'data.frame': 64512 obs. of 13 variables:
## $ tmc_code : Factor w/ 32 levels "106-11852","106-11853",..: 11 5 20 30 26 12 14 1 16 18 ...
## $ measurement_tstamp : Factor w/ 2016 levels "2016-09-11 00:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ speed : num 25 56 35 56 28 52 61 32 21 29 ...
## $ average_speed : num 25 56 35 56 28 52 61 38 21 29 ...
## $ reference_speed : num 27 56 33 57 22 47 59 34 21 23 ...
## $ travel_time_minutes: num 2.49 0.08 0.27 0.45 0.56 0.95 0.91 2.26 1.1 0.55 ...
## $ confidence_score : num 20 20 20 20 20 20 20 30 20 20 ...
## $ cvalue : num 0 0 0 0 0 0 0 100 0 0 ...
## $ Time : chr "00:00" "00:00" "00:00" "00:00" ...
## $ Time1 : POSIXct, format: "2017-04-10 00:00:00" "2017-04-10 00:00:00" ...
## $ Date : chr "2016-09-11" "2016-09-11" "2016-09-11" "2016-09-11" ...
## $ Date1 : Date, format: "2016-09-11" "2016-09-11" ...
## $ Day : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 1 1 1 1 1 1 1 1 1 1 ...
ggplot(aa1, aes(x=Time1, y=speed, group=Date)) +
geom_path(size=.5, alpha = 0.05, colour="blue") +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M"))+theme_bw()
ggplot(aa1, aes(x=Time1, y=speed, colour=Day)) +
geom_point(cex=0.6) +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +theme_bw()
ggplot(aa1, aes(x=Time1, y=speed, group= tmc_code)) +
geom_point(size=.5, alpha = 0.1, colour="blue") +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M"))
ggplot(aa1, aes(x=Time1, y=speed, colour=Day)) +
geom_point(size=.5, alpha = 0.1) +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +
facet_grid(Day ~.) +
theme(legend.position="none") +theme_bw()
aa2 <- aggregate(speed ~ Time1 + Day, aa1, mean)
head(aa2)
## Time1 Day speed
## 1 2017-04-10 00:00:00 Sun 37.46875
## 2 2017-04-10 00:05:00 Sun 37.46875
## 3 2017-04-10 00:10:00 Sun 37.65625
## 4 2017-04-10 00:15:00 Sun 37.65625
## 5 2017-04-10 00:20:00 Sun 37.65625
## 6 2017-04-10 00:25:00 Sun 37.65625
ggplot(aa2, aes(x=Time1, y=speed, colour=Day)) +
geom_path() +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +
facet_grid(Day ~.) +
theme(legend.position="none")
ggplot(aa2, aes(x=Time1, y=speed, colour=Day)) +
geom_path(cex=1.1) +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +theme_bw()
Nimitz-Blvd
setwd("C:/Users/s-das/Syncplicity Folders/MY_Projects/NCHRP 17-76/04102017/Data Sources/INRIX/")
aa1 <- read.csv("Nimitz-Blvd.csv")
library(lubridate)
aa1$Time <- format(as.POSIXct(strptime(aa1$measurement_tstamp, "%Y-%m-%d %H:%M:%S",tz="")) ,format = "%H:%M")
aa1$Time1 <- as.POSIXct(aa1$Time, format = "%H:%M")
aa1$Date <- format(as.POSIXct(strptime(aa1$measurement_tstamp, "%Y-%m-%d %H:%M:%S",tz="")) ,format = "%Y-%m-%d")
aa1$Time1 <- as.POSIXct(aa1$Time, format = "%H:%M")
aa1$Date1 <- as.Date(aa1$Date)
aa1$Day <- wday(aa1$Date1, label = TRUE)
str(aa1)
## 'data.frame': 32256 obs. of 13 variables:
## $ tmc_code : Factor w/ 16 levels "106-14016","106-14017",..: 16 7 13 4 9 1 2 3 5 14 ...
## $ measurement_tstamp : Factor w/ 2016 levels "2016-09-11 00:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ speed : num 19 36.1 36 29 9 ...
## $ average_speed : num 19 35 36 22 9 24 31 38 27 12 ...
## $ reference_speed : num 18 40 36 19 13 24 29 37 25 14 ...
## $ travel_time_minutes: num 0.28 1.54 0.01 0.44 0.06 0.8 1.31 1.45 0.72 0.04 ...
## $ confidence_score : num 20 22 10 30 20 20 20 30 20 20 ...
## $ cvalue : num 0 19.2 0 22.6 0 0 0 95.8 0 0 ...
## $ Time : chr "00:00" "00:00" "00:00" "00:00" ...
## $ Time1 : POSIXct, format: "2017-04-10 00:00:00" "2017-04-10 00:00:00" ...
## $ Date : chr "2016-09-11" "2016-09-11" "2016-09-11" "2016-09-11" ...
## $ Date1 : Date, format: "2016-09-11" "2016-09-11" ...
## $ Day : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 1 1 1 1 1 1 1 1 1 1 ...
ggplot(aa1, aes(x=Time1, y=speed, group=Date)) +
geom_path(size=.5, alpha = 0.05, colour="blue") +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M"))+theme_bw()
ggplot(aa1, aes(x=Time1, y=speed, colour=Day)) +
geom_point(cex=0.6) +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +theme_bw()
ggplot(aa1, aes(x=Time1, y=speed, group= tmc_code)) +
geom_point(size=.5, alpha = 0.1, colour="blue") +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M"))
ggplot(aa1, aes(x=Time1, y=speed, colour=Day)) +
geom_point(size=.5, alpha = 0.1) +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +
facet_grid(Day ~.) +
theme(legend.position="none") +theme_bw()
aa2 <- aggregate(speed ~ Time1 + Day, aa1, mean)
head(aa2)
## Time1 Day speed
## 1 2017-04-10 00:00:00 Sun 26.11188
## 2 2017-04-10 00:05:00 Sun 25.69125
## 3 2017-04-10 00:10:00 Sun 25.22812
## 4 2017-04-10 00:15:00 Sun 24.59375
## 5 2017-04-10 00:20:00 Sun 23.50000
## 6 2017-04-10 00:25:00 Sun 23.50000
ggplot(aa2, aes(x=Time1, y=speed, colour=Day)) +
geom_path() +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +
facet_grid(Day ~.) +
theme(legend.position="none")
ggplot(aa2, aes(x=Time1, y=speed, colour=Day)) +
geom_path(cex=1.1) +
scale_x_datetime(breaks=date_breaks("2 hour"), labels=date_format("%H:%M")) +theme_bw()
