In ahorawzy/TFTSA: Traffic Flow Time Series Analysis

library(tidyverse)
library(TFTSA)

Analysis of patterns of traffic volume.

Temporal Analysis

tml <- jdzzl[[10]]
tml_loess <- jdzzl_loess[[10]]

tml_long <- reshape2::melt(tml)
names(tml_long) <- c("Date","Timestamp","Traffic_volume")
tml_long <- arrange(tml_long,Date,Timestamp)
head(tml_long)

holiday <- unique(tml_long$Date)[9:16]

tml_long$Holiday <- ifelse(tml_long$Date %in% holiday, TRUE, FALSE)
head(tml_long)

ggplot(tml_long,aes(x=Timestamp,y=Traffic_volume,group=Date,color=Holiday))+
  geom_line()

by_holiday <- group_by(tml_long,Holiday,Timestamp)
summarise(by_holiday,Mean_traffic_volume=mean(Traffic_volume)) %>% 
  ggplot(aes(x=Timestamp,y=Mean_traffic_volume,group=Holiday,color=Holiday))+
  geom_line(size=1)+
  scale_x_continuous(breaks = seq(0,288,24))+
  scale_y_continuous(breaks = seq(0,120,20))+
  theme_bw()

ggsave(file="plot/tml_holidayandnot.jpg",width=7.29,height=4.5,dpi=600)

Holiday

filter(tml_long,Date=="Oct-01") %>% 
  ggplot(aes(x=Timestamp,y=Traffic_volume))+
  geom_point(color="black")+
  geom_line(color="steelblue",size=0.8)+
  geom_smooth(span=0.1,size=1.5)+
  scale_x_continuous(breaks = seq(0,288,24))+
  scale_y_continuous(breaks = seq(0,120,20))+
  theme_bw()

ggsave(file="plot/03_tml_Oct-01.jpg",width=7.29,height=4.5,dpi=600)

filter(tml_long,Holiday == T) %>% 
  ggplot(aes(x=Timestamp,y=Traffic_volume,group=Date,color=Date))+
  geom_line(alpha=2/5)+
  geom_smooth(method = "loess",span=0.1)+
  scale_x_continuous(breaks = seq(0,288,24))+
  scale_y_continuous(breaks = seq(0,120,20))+
  theme_bw()

ggsave(file="plot/tml_holiday.jpg",width=7.29,height=4.5,dpi=600)

Not-holiday

filter(tml_long,Holiday == F) %>% 
  ggplot(aes(x=Timestamp,y=Traffic_volume,group=Date,color=Date))+
  geom_line(alpha=2/5)+
  geom_smooth(method = "loess",span=0.1)+
  scale_x_continuous(breaks = seq(0,288,24))+
  scale_y_continuous(breaks = seq(0,120,20))+
  theme_bw()

ggsave(file="plot/tml_notholiday.jpg",width=7.29,height=4.5,dpi=600)

Spatio-temporal Analysis

Melt every data frame in list.

jdzzl_long <- lapply(jdzzl, function(x){
  x <- reshape2::melt(x)
  names(x) <- c("Date","Timestamp","Traffic_volume")
  x <- arrange(x,Date,Timestamp)
  x$Holiday <- ifelse(x$Date %in% holiday, T, F)
  by_holiday <- group_by(x,Holiday,Timestamp)
  x <- summarise(by_holiday,Mean_traffic_volume=mean(Traffic_volume))
  return(x)
})

Holiday

merge_list <- function(bywhat){
  function(df1,df2){
    merge(df1,df2,by=bywhat)
  }
}
tep_list <- lapply(jdzzl_long,filter,Holiday == T)
tep_list <- lapply(tep_list,`[`,c(2,3))
merged_list <- Reduce(merge_list(bywhat = "Timestamp"),tep_list)
names(merged_list)[2:11] <- names(jdzzl_long)
merged_df <- merged_list[,c(1,2,3,5,6,9,10,11)]
names(merged_df)[c(5:6,8)] <- c("平关","松坎","桐木岭")
reshape2::melt(merged_df, id.vars="Timestamp", variable.name="Station", value.name="Mean_traffic_volume") %>% 
  ggplot(aes(x=Timestamp,y=Mean_traffic_volume,group=Station,color=Station))+
  geom_line(size=1)+
  scale_x_continuous(breaks = seq(0,288,24))+
  scale_y_continuous(breaks = seq(0,200,20))+
  theme_bw()

ggsave(file="plot/holiday_station.jpg",width=7.29,height=4.5,dpi=600)

rm(tep_list,merged_list,merged_df)

Not-holiday

tep_list <- lapply(jdzzl_long,filter,Holiday == F)
tep_list <- lapply(tep_list,`[`,c(2,3))
merged_list <- Reduce(merge_list(bywhat = "Timestamp"),tep_list)
names(merged_list)[2:11] <- names(jdzzl_long)
merged_df <- merged_list[,c(1,2,3,5,6,9,10,11)]
names(merged_df)[c(5:6,8)] <- c("平关","松坎","桐木岭")
reshape2::melt(merged_df, id.vars="Timestamp", variable.name="Station", value.name="Mean_traffic_volume") %>% 
  ggplot(aes(x=Timestamp,y=Mean_traffic_volume,group=Station,color=Station))+
  geom_line(size=1)+
  scale_x_continuous(breaks = seq(0,288,24))+
  scale_y_continuous(breaks = seq(0,120,20))+
  theme_bw()

ggsave(file="plot/notholiday_station.jpg",width=7.29,height=4.5,dpi=600)

selectedstation <- c("丙妹","黑石","南宁","平关","松坎","台盘","桐木岭")

merged <- Reduce(merge_list(bywhat = c("Timestamp","Holiday")),jdzzl_long)
names(merged)[3:12] <- names(jdzzl_long)
merged <- arrange(merged,Holiday,Timestamp)
melted <- reshape2::melt(merged,id.vars=c("Timestamp","Holiday"),variable.name="Station",value.name="Mean_traffic_volume")
melted <- filter(melted,Station %in% selectedstation)
ggplot(melted,aes(x=Timestamp,y=Mean_traffic_volume,Group=Holiday,color=Holiday))+
  geom_line(alpha=0.8)+
  facet_wrap(~ Station, nrow = 2, scales = "free")+
  theme_bw()

ggsave(file="plot/holiday_station_facet.jpg",width=7.29,height=4.5,dpi=600)

ahorawzy/TFTSA documentation built on May 13, 2019, 12:18 p.m.