In ahorawzy/Mreport: Monthly Report of traffic condition

knitr::opts_chunk$set(echo = F,message = F,warning = F)

options(stringsAsFactors = F)
options(digits = 3)
rm(list = ls())
source("D:\\R\\packages\\Mreport\\scripts\\caculate.R", encoding = "utf-8")
source("D:\\R\\packages\\Mreport\\scripts\\select.R", encoding = "utf-8")

library(Mreport)
library(plyr)
library(ggplot2)
library(reshape2)
library(knitr)

load_base()
load_sample_base()

jdnew <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_06_new.csv")
jdlast <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_05.csv")
jdprevious <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2017_06.csv")

jdnews <- handle_gather(jdnew)
jdlasts <- handle_gather(jdlast)
jdpreviouss <- handle_gather(jdprevious)

2. 全国公路网

2.1 机动车

totalnewcars <- ddply(jdnews,"level",summarise,Wmean = weighted.mean(cars,w=mileage))
totalpreviouscars <- ddply(jdpreviouss,"level",summarise,Wmean = weighted.mean(cars,w=mileage))
x <- caculate_increaseratio(totalnewcars,totalpreviouscars)
totallastcars <- ddply(jdlasts,"level",summarise,Wmean = weighted.mean(cars,w=mileage))
y <- caculate_increaseratio(totalnewcars,totallastcars)
z <- merge_outcome(totalnewcars,x,y,by="level")
temp <- z[3,]
z[3,] <- z[4,]
z[4,] <- temp
names(z) <- c("道路等级","本月","同比","环比")
kable(z)

2.2 客车

totalnewpasscars <- ddply(jdnews,"level",summarise,Wmean = weighted.mean(passenger_cars,w=mileage))
totalpreviouspasscars <- ddply(jdpreviouss,"level",summarise,Wmean = weighted.mean(passenger_cars,w=mileage))
x <- caculate_increaseratio(totalnewpasscars,totalpreviouspasscars)
totallastpasscars <- ddply(jdlasts,"level",summarise,Wmean = weighted.mean(passenger_cars,w=mileage))
y <- caculate_increaseratio(totalnewpasscars,totallastpasscars)
z <- merge_outcome(totalnewpasscars,x,y,by="level")
temp <- z[3,]
z[3,] <- z[4,]
z[4,] <- temp
names(z) <- c("道路等级","本月","同比","环比")
kable(z)

2.3 货车

totalnewfrecars <- ddply(jdnews,"level",summarise,Wmean = weighted.mean(freight_cars,w=mileage))
totalpreviousfrecars <- ddply(jdpreviouss,"level",summarise,Wmean = weighted.mean(freight_cars,w=mileage))
x <- caculate_increaseratio(totalnewfrecars,totalpreviousfrecars)
totallastfrecars <- ddply(jdlasts,"level",summarise,Wmean = weighted.mean(freight_cars,w=mileage))
y <- caculate_increaseratio(totalnewfrecars,totallastfrecars)
z <- merge_outcome(totalnewfrecars,x,y,by="level")
temp <- z[3,]
z[3,] <- z[4,]
z[4,] <- temp
names(z) <- c("道路等级","本月","同比","环比")
kable(z)

3. 通道分析

3.1 十横通道

机动车

horizonnew <- caculate_carsmean(jd = jdnews,attsname = "horizon10")
horizonprevious <- caculate_carsmean(jd = jdpreviouss,attsname = "horizon10")
x1 <- caculate_increaseratio(horizonnew,horizonprevious)
horizonlast <- caculate_carsmean(jd = jdlasts,attsname = "horizon10")
x2 <- caculate_increaseratio(horizonlast,horizonprevious)
t <- merge_outcome(horizonnew,x1,x2,bywhat = "horizon10")
names(t) <- c("十横通道","月平均日交通量","同比","环比")
kable(t)

客车

horizonnew <- caculate_passcarsmean(jd = jdnews,attsname = "horizon10")
horizonprevious <- caculate_passcarsmean(jd = jdpreviouss,attsname = "horizon10")
x1 <- caculate_increaseratio(horizonnew,horizonprevious)
horizonlast <- caculate_passcarsmean(jd = jdlasts,attsname = "horizon10")
x2 <- caculate_increaseratio(horizonlast,horizonprevious)
t <- merge_outcome(horizonnew,x1,x2,bywhat = "horizon10")
names(t) <- c("十横通道","月平均日交通量","同比","环比")
kable(t)

货车

horizonnew <- caculate_frecarsmean(jd = jdnews,attsname = "horizon10")
horizonprevious <- caculate_frecarsmean(jd = jdpreviouss,attsname = "horizon10")
x1 <- caculate_increaseratio(horizonnew,horizonprevious)
horizonlast <- caculate_frecarsmean(jd = jdlasts,attsname = "horizon10")
x2 <- caculate_increaseratio(horizonlast,horizonprevious)
t <- merge_outcome(horizonnew,x1,x2,bywhat = "horizon10")
names(t) <- c("十横通道","月平均日交通量","同比","环比")
kable(t)

3.2 十纵通道

机动车

verticalnew <- caculate_carsmean(jd = jdnews,attsname = "vertical10")
verticalprevious <- caculate_carsmean(jd = jdpreviouss,attsname = "vertical10")
x1 <- caculate_increaseratio(verticalnew,verticalprevious)
verticallast <- caculate_carsmean(jd = jdlasts,attsname = "vertical10")
x2 <- caculate_increaseratio(verticallast,verticalprevious)
t <- merge_outcome(verticalnew,x1,x2,bywhat = "vertical10")
names(t) <- c("十横通道","月平均日交通量","同比","环比")
kable(t)

客车

verticalnew <- caculate_passcarsmean(jd = jdnews,attsname = "vertical10")
verticalprevious <- caculate_passcarsmean(jd = jdpreviouss,attsname = "vertical10")
x1 <- caculate_increaseratio(verticalnew,verticalprevious)
verticallast <- caculate_passcarsmean(jd = jdlasts,attsname = "vertical10")
x2 <- caculate_increaseratio(verticallast,verticalprevious)
t <- merge_outcome(verticalnew,x1,x2,bywhat = "vertical10")
names(t) <- c("十横通道","月平均日交通量","同比","环比")
kable(t)

货车

verticalnew <- caculate_frecarsmean(jd = jdnews,attsname = "vertical10")
verticalprevious <- caculate_frecarsmean(jd = jdpreviouss,attsname = "vertical10")
x1 <- caculate_increaseratio(verticalnew,verticalprevious)
verticallast <- caculate_frecarsmean(jd = jdlasts,attsname = "vertical10")
x2 <- caculate_increaseratio(verticallast,verticalprevious)
t <- merge_outcome(verticalnew,x1,x2,bywhat = "vertical10")
names(t) <- c("十横通道","月平均日交通量","同比","环比")
kable(t)

3.3 典型通道分省交通情况

3.3.1 沪昆通道

t <- typicalroute_horizon(jdnews,jdpreviouss,"上海-瑞丽")
names(t) <- c("省份","月平均日交通量","同比")
t %>% kable()

3.3.2 陆桥通道

t <- typicalroute_horizon(jdnews,jdpreviouss,"连云港-霍尔果斯")
names(t) <- c("省份","月平均日交通量","同比")
t %>% kable()

3.3.3 沿江通道

t <- typicalroute_horizon(jdnews,jdpreviouss,"上海-樟木")
names(t) <- c("省份","月平均日交通量","同比")
t %>% kable()

3.3.4 天津至红其拉甫通道

t <- typicalroute_horizon(jdnews,jdpreviouss,"天津-红其拉甫")
names(t) <- c("省份","月平均日交通量","同比")
t %>% kable()

3.3.5 京沪通道

t <- typicalroute_vertical(jdnews,jdpreviouss,"北京-上海")
names(t) <- c("省份","月平均日交通量","同比")
t %>% kable()

3.3.6 沿海通道

t <- typicalroute_vertical(jdnews,jdpreviouss,"同江-三亚")
names(t) <- c("省份","月平均日交通量","同比")
t %>% kable()

3.3.7 黑河至港澳台通道

t <- typicalroute_vertical(jdnews,jdpreviouss,"黑河-港澳台")
names(t) <- c("省份","月平均日交通量","同比")
t %>% kable()

4. 疏港公路分析

portroadnew <- caculate_frecarsmean(jdnews,attsname = "portroad")
portroadprevious <- caculate_frecarsmean(jdpreviouss,attsname = "portroad")
x1 <- caculate_increaseratio(portroadnew,portroadprevious)
portroadlast <- caculate_frecarsmean(jdlasts,attsname = "portroad")
x2 <- caculate_increaseratio(portroadnew,portroadlast)
t <- merge_outcome(portroadnew,x1,x2,bywhat = "portroad")
t$portroad <- substr(t$portroad,1,nchar(t$portroad)-1)
t <- t[order(t$now,decreasing = T),]
kable(t)

5. 城市群分析

站点在各个城市群的分布如下：

t <- merge(station_plot,sample_base$citygroup2,by.x = "popup",by.y = "index",all.y = T)
names(t)[5] <- "type"
geo_pointplot(t,na.rm=T,type = T)

5.1 总体

x <- caculate_carsmean(jdnews,"citygroup2")

x1 <- caculate_increaseratio(caculate_carsmean(jdnews,"citygroup2"),
                       caculate_carsmean(jdpreviouss,"citygroup2"))

x2 <- caculate_increaseratio(caculate_carsmean(jdnews,"citygroup2"),
                       caculate_carsmean(jdlasts,"citygroup2"))

t <- merge_outcome(x,x1,x2,bywhat = "citygroup2")
names(t) <- c("城市群","月平均日交通量","同比","环比")
kable(t)

5.2 客车

x <- caculate_passcarsmean(jdnews,"citygroup2")

x1 <- caculate_increaseratio(caculate_passcarsmean(jdnews,"citygroup2"),
                       caculate_passcarsmean(jdpreviouss,"citygroup2"))

x2 <- caculate_increaseratio(caculate_passcarsmean(jdnews,"citygroup2"),
                       caculate_passcarsmean(jdlasts,"citygroup2"))

t <- merge_outcome(x,x1,x2,bywhat = "citygroup2")
names(t) <- c("城市群","月平均日交通量","同比","环比")
kable(t)

5.3 货车

x <- caculate_frecarsmean(jdnews,"citygroup2")

x1 <- caculate_increaseratio(caculate_frecarsmean(jdnews,"citygroup2"),
                       caculate_frecarsmean(jdpreviouss,"citygroup2"))

x2 <- caculate_increaseratio(caculate_frecarsmean(jdnews,"citygroup2"),
                       caculate_frecarsmean(jdlasts,"citygroup2"))

t <- merge_outcome(x,x1,x2,bywhat = "citygroup2")
names(t) <- c("城市群","月平均日交通量","同比","环比")
kable(t)

6. 分省情况

6.1 总体情况

caculate_carsmean(jdnews,"province") %>% 
geojsonMap(mapName = "China")

x <- caculate_carsmean(jdnews,"province")
y <- caculate_increaseratio(caculate_carsmean(jdnews,"province"),
                       caculate_carsmean(jdpreviouss,"province"))
z <- caculate_increaseratio(caculate_carsmean(jdnews,"province"),
                       caculate_carsmean(jdlasts,"province"))
t <- merge_outcome(x,y,z,bywhat = "province")
names(t) <- c("省级行政区","月平均日交通量","同比","环比")
t$省级行政区 <- factor(t$省级行政区,ordered=T,levels = province_level)
kable(t[order(t$省级行政区),])

caculate_carsmean(jdnews,"province") %>% gg_boxplot(xangle = 90,xlabname = "省级行政区",
                                                    ylabname="月平均日机动车交通量")
ggsave(filename = "D:\\交大云同步\\实习\\06_月度分析报告\\6月分析\\绘图\\省级机动车.jpg",dpi=600)

6.2 分道路等级情况

本月

provincenewcars <- caculate_level_carsmean(jdnews,"province")
provincenewcars$province <- factor(provincenewcars$province,ordered=T,levels = province_level)
provincenewcars <- provincenewcars[order(provincenewcars$province),c("province","国家高速","普通国道","省级高速","普通省道")]
kable(provincenewcars)

同比

provincepreviouscars <- caculate_level_carsmean(jdpreviouss,"province")
provincepreviouscars <- provincepreviouscars[,c("province","国家高速","普通国道","省级高速","普通省道")]
t <- caculate_increaseratio(provincenewcars,provincepreviouscars)
t$province <- factor(t$province,ordered=T,levels = province_level)
kable(t[order(t$province),])

环比

provincelastcars <- caculate_level_carsmean(jdlasts,"province")
provincelastcars <- provincelastcars[,c("province","国家高速","普通国道","省级高速","普通省道")]
t <- caculate_increaseratio(provincenewcars,provincelastcars)
t$province <- factor(t$province,ordered=T,levels = province_level)
kable(t[order(t$province),])

6.3 客车交通量情况

caculate_passcarsmean(jdnews,"province") %>% 
geojsonMap(mapName = "China")

x <- caculate_passcarsmean(jdnews,"province")
provincepassprevious <- caculate_passcarsmean(jdpreviouss,"province")
y <- caculate_increaseratio(x,provincepassprevious)
provincepasslast <- caculate_passcarsmean(jdlasts,"province")
z <- caculate_increaseratio(x,provincepasslast)
t <- merge_outcome(x,y,z,bywhat = "province")
names(t) <- c("省级行政区","月平均日交通量","同比","环比")
t$省级行政区 <- factor(t$省级行政区,ordered=T,levels = province_level)
kable(t[order(t$省级行政区),])

6.4 货车交通量情况

caculate_frecarsmean(jdnews,"province") %>% 
geojsonMap(mapName = "China")

x <- caculate_frecarsmean(jdnews,"province")
provincefreprevious <- caculate_frecarsmean(jdpreviouss,"province")
y <- caculate_increaseratio(x,provincefreprevious)
provincefrelast <- caculate_frecarsmean(jdlasts,"province")
z <- caculate_increaseratio(x,provincefrelast)
t <- merge_outcome(x,y,z,bywhat = "province")
names(t) <- c("省级行政区","月平均日交通量","同比","环比")
t$省级行政区 <- factor(t$省级行政区,ordered=T,levels = province_level)
kable(t[order(t$省级行政区),])

8. 数据使用情况

t <- data_use(jdnews)[[1]]
rownames(t)[32] <- c("合计")
t <- t[,c("国家高速","普通国道","省级高速","普通省道")]
kable(t)

p <- data_use(jdnews)[[2]]
p

ahorawzy/Mreport documentation built on May 3, 2019, 3:40 p.m.