In ahorawzy/Mreport: Monthly Report of traffic condition

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

options(stringsAsFactors = F)
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.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")

jdnew <- caculate_equivalent(jdnew)
jdnew <- select_atts(jdnew)
jdnew <- handle_mergeline(jdnew,station_line)
jdnew <- handle_mergesample(jdnew,sample_base)
jdnew <- merge(jdnew,roadlevel,by="index",all.x = T)
jdnews <- subset(jdnew,index %in% station_use)

jdlast <- caculate_equivalent(jdlast)
jdlast <- select_atts(jdlast)
jdlast <- handle_mergeline(jdlast,station_line)
jdlast <- handle_mergesample(jdlast,sample_base)
jdlast <- merge(jdlast,roadlevel,by="index",all.x = T)
jdlasts <- subset(jdlast,index %in% station_use)

jdprevious <- caculate_equivalent(jdprevious)
jdprevious <- select_atts(jdprevious)
jdprevious <- handle_mergeline(jdprevious,station_line)
jdprevious <- handle_mergesample(jdprevious,sample_base)
jdprevious <- merge(jdprevious,roadlevel,by="index",all.x = T)
jdpreviouss <- subset(jdprevious,index %in% station_use)

2. 总体运行态势分析

2.1 机动车交通量情况

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 客运车辆交通量情况

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 货运车辆交通量情况

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. 省级行政区路网运行情况

3.1 各省机动车交通量情况

本月

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

t <- caculate_carsmean(jdnews,"province")
names(t) <- c("省级行政区","月平均日交通量")
kable(t)

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)

同比

t <- caculate_increaseratio(caculate_carsmean(jdnews,"province"),
                       caculate_carsmean(jdpreviouss,"province"))
names(t) <- c("省级行政区","月平均日交通量")
kable(t)

环比

t <- caculate_increaseratio(caculate_carsmean(jdnews,"province"),
                       caculate_carsmean(jdlasts,"province"))
names(t) <- c("省级行政区","月平均日交通量")
kable(t)

3.2 各省分道路等级交通量情况

本月

provincenewcars <- caculate_level_carsmean(jdnews,"province")
kable(provincenewcars)

同比

provincepreviouscars <- caculate_level_carsmean(jdpreviouss,"province")
caculate_increaseratio(provincenewcars,provincepreviouscars) %>% kable()

环比

provincelastcars <- caculate_level_carsmean(jdlasts,"province")
caculate_increaseratio(provincenewcars,provincelastcars) %>% kable()

3.3 各省客运车辆

本月

t <- provincepassnew <- caculate_passcarsmean(jdnews,"province")
names(t) <- c("省级行政区","月平均日交通量")
kable(t)

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

同比

provincepassprevious <- caculate_passcarsmean(jdpreviouss,"province")
t <- caculate_increaseratio(provincepassnew,provincepassprevious)
names(t) <- c("省级行政区","月平均日交通量")
kable(t)

环比

provincepasslast <- caculate_passcarsmean(jdlasts,"province")
t <- caculate_increaseratio(provincepassnew,provincepasslast)
names(t) <- c("省级行政区","月平均日交通量")
kable(t)

3.4 各省货运车辆

本月

t <- provincefrenew <- caculate_frecarsmean(jdnews,"province")
names(t) <- c("省级行政区","月平均日交通量")
kable(t)

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

同比

provincefreprevious <- caculate_frecarsmean(jdpreviouss,"province")
t <- caculate_increaseratio(provincefrenew,provincefreprevious)
names(t) <- c("省级行政区","月平均日交通量")
kable(t)

环比

provincefrelast <- caculate_frecarsmean(jdlasts,"province")
t <- caculate_increaseratio(provincefrenew,provincefrelast)
names(t) <- c("省级行政区","月平均日交通量")
kable(t)

4. 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)

4.1 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)

4.2 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)

4.3 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)

5. 通道分析

5.1 十横通道

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)

5.2 十横重点关注通道交通量情况

5.2.1 沪昆通道

x <- subset(jdnews,horizon10 == "上海-瑞丽")
x <- ddply(x,c("horizon10","province"),
                     summarise,Wmean = weighted.mean(cars,w=mileage))
x <- na.omit(x)
names(x) <- c("沪昆通道","省份","月平均日交通量")
x %>% kable()

5.2.2 陆桥通道

x <- subset(jdnews,horizon10 == "连云港-霍尔果斯")
x <- ddply(x,c("horizon10","province"),
                     summarise,Wmean = weighted.mean(cars,w=mileage))
x <- na.omit(x)
names(x) <- c("路桥通道","省份","月平均日交通量")
x %>% kable()

5.2.3 沿江通道

x <- subset(jdnews,horizon10 == "上海-樟木")
x <- ddply(x,c("horizon10","province"),
                     summarise,Wmean = weighted.mean(cars,w=mileage))
x <- na.omit(x)
names(x) <- c("沿江通道","省份","月平均日交通量")
x %>% kable()

5.3 十纵通道交通量情况

十纵通道

verticalnew <- caculate_carsmean(jd = jdnews,attsname = "vertical10")

gg_boxplot(verticalnew,xangle = 15,xlabname = "十横通道",ylabname = "平均机动车当量")

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)

5.4 十纵重点通道

5.4.1 京沪通道

x <- subset(jdnews,vertical10 == "北京-上海")
x <- ddply(x,c("vertical10","province"),
                     summarise,Wmean = weighted.mean(cars,w=mileage))
x <- na.omit(x)
x %>% kable()

5.4.2 沿海通道

x <- subset(jdnews,vertical10 == "同江-三亚")
x <- ddply(x,c("vertical10","province"),
                     summarise,Wmean = weighted.mean(cars,w=mileage))
x <- na.omit(x)
x %>% kable()

5.4.3 黑河至港澳台通道

x <- subset(jdnews,vertical10 == "黑河-港澳台")
x <- ddply(x,c("vertical10","province"),
                     summarise,Wmean = weighted.mean(cars,w=mileage))
x <- na.omit(x)
x %>% kable()

6. 疏港公路分析

portroadnew <- caculate_carsmean(jdnews,attsname = "portroad")

gg_boxplot(portroadnew,xangle = 20,xlabname = "疏港公路",ylabname = "平均机动车当量")

portroadprevious <- caculate_carsmean(jdprevious,attsname = "portroad")
x1 <- caculate_increaseratio(portroadnew,portroadprevious)

portroadlast <- caculate_carsmean(jdlasts,attsname = "portroad")
x2 <- caculate_increaseratio(portroadnew,portroadlast)

merge_outcome(portroadnew,x1,x2,bywhat = "portroad") %>% kable()

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