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)
library(leaflet)
library(leafletCN)

load_base()
load_sample_base()

jdnew <- read.csv("D:\\data\\sx_raw\\交调数据\\jd201811.csv")
jdlast <- read.csv("D:\\data\\sx_raw\\交调数据\\jd201810.csv")
jdprevious <- read.csv("D:\\data\\sx_raw\\交调数据\\jd201711.csv")

jdnews <- handle_gather(jdnew)
jdlasts <- handle_gather(jdlast)
jdpreviouss <- handle_gather(jdprevious)
usefulstation <- intersect(jdnews$index,jdlasts$index)
usefulstation <- intersect(usefulstation,jdpreviouss$index)
jdnews <- jdnews[jdnews$index %in% usefulstation,]
jdlasts <- jdlasts[jdlasts$index %in% usefulstation,]
jdpreviouss <- jdpreviouss[jdpreviouss$index %in% usefulstation,]

2. 全国公路网

2.1 机动车

z <- result_present3(jdnews,jdpreviouss,jdlasts,"level","cars")
names(z) <- c("道路等级","本月","同比","环比")
f <- factor(c("国家高速","普通国道","省级高速","普通省道"),ordered=T)
z <- z[order(f),]
kable(z)

2.2 客车

z <- result_present3(jdnews,jdpreviouss,jdlasts,"level","passcars")
names(z) <- c("道路等级","本月","同比","环比")
f <- factor(c("国家高速","普通国道","省级高速","普通省道"),ordered=T)
z <- z[order(f),]
kable(z)

2.3 货车

z <- result_present3(jdnews,jdpreviouss,jdlasts,"level","frecars")
names(z) <- c("道路等级","本月","同比","环比")
f <- factor(c("国家高速","普通国道","省级高速","普通省道"),ordered=T)
z <- z[order(f),]
kable(z)

3. 通道分析

3.1 十横通道

机动车

t <- result_present3(jdnews,jdpreviouss,jdlasts,"horizon10","cars")
names(t) <- c("十横通道","月平均日交通量","同比","环比")
kable(t)

客车

t <- result_present3(jdnews,jdpreviouss,jdlasts,"horizon10","passcars")
names(t) <- c("十横通道","月平均日交通量","同比","环比")
kable(t)

货车

t <- result_present3(jdnews,jdpreviouss,jdlasts,"horizon10","frecars")
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

t <- result_present3(jdnews,jdpreviouss,jdlasts,"vertical10","cars")
names(t) <- c("十横通道","月平均日交通量","同比","环比")
kable(t)

客车

t <- result_present3(jdnews,jdpreviouss,jdlasts,"vertical10","passcars")
names(t) <- c("十横通道","月平均日交通量","同比","环比")
kable(t)

货车

t <- result_present3(jdnews,jdpreviouss,jdlasts,"vertical10","frecars")
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. 集疏运通道分析

4.1 疏港公路分析

t <- result_present3(jdnews,jdpreviouss,jdlasts,"portroad","frecars")
t$portroad <- substr(t$portroad,1,nchar(t$portroad)-1)
t <- t[order(t$now,decreasing = T),]
rownames(t) <- NULL
names(t) <- c("港口","日均交通量","同比","环比")
kable(t)

t <- merge(station_plot,sample_base$portroad,by.x = "popup",by.y = "index",all.y = T)
p <- jdnews[,c(1,10,11,12)]
tt <- merge(t,p,by.x = "popup",by.y="index")
tt <- tt[,-c(1,2)]
names(tt)[3] <- "label"
tt <- tt[,c(1,2,3,6)]
tt <- aggregate(tt,by=list(tt$label),FUN=mean)
tt$label <- tt$Group.1

leaflet(tt) %>% addTiles() %>% 
  # addProviderTiles("CartoDB.PositronNoLabels") %>% 
  addCircles(label = ~label,lng = ~lng,lat = ~lat,
             radius = ~cars/3,stroke = F, fillOpacity = 1, fillColor ="red",
             labelOptions = labelOptions(noHide = T,draggable=T,textsize=25))

4.2 机场连接公路分析

t <- result_present3(jdnews,jdpreviouss,jdlasts,"airport","cars")
t <- t[order(t$now,decreasing = T),]
names(t) <- c("机场","日均交通量","同比","环比")
rownames(t) <- NULL
kable(t)

t <- merge(station_plot,sample_base$airport,by.x = "popup",by.y = "index",all.y = T)
p <- jdnews[,c(1,10,11,12)]
tt <- merge(t,p,by.x = "popup",by.y="index")
tt <- tt[,-2]
names(tt)[4] <- "label"
leaflet(tt) %>% addTiles() %>% 
  addCircles(label = ~label,lng = ~lng,lat = ~lat,popup = ~popup,
             radius = ~cars/5,stroke = F, fillOpacity = 1, fillColor ="red")

5. 城市群分析

t <- result_present3(jdnews,jdpreviouss,jdlasts,"citygroup2","cars")
names(t) <- c("城市群","月平均日交通量","同比","环比")
kable(t)

5.2 客车

t <- result_present3(jdnews,jdpreviouss,jdlasts,"citygroup2","passcars")
names(t) <- c("城市群","月平均日交通量","同比","环比")
kable(t)

5.3 货车

t <- result_present3(jdnews,jdpreviouss,jdlasts,"citygroup2","frecars")
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")[-31,] %>% 
  geojsonMap(mapName = "China",palette = "Reds",legendTitle = "交通量图例")

t <- result_present3(jdnews,jdpreviouss,jdlasts,"province","cars")
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)
t <- t[order(t$省级行政区),]
rownames(t) <- NULL
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\\11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")]
rownames(provincenewcars) <- NULL
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)
t <- t[order(t$province),]
rownames(t) <- NULL
kable(t)

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)
t <- t[order(t$province),]
rownames(t) <- NULL
kable(t)

6.3 客车交通量情况

caculate_passcarsmean(jdnews,"province") %>% 
  geojsonMap(mapName = "China",palette = "Reds",legendTitle = "交通量图例")

t <- result_present3(jdnews,jdpreviouss,jdlasts,"province","passcars")
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)
t <- t[order(t$省级行政区),]
rownames(t) <- NULL
kable(t)

6.4 货车交通量情况

caculate_frecarsmean(jdnews,"province")[-31,] %>% 
  geojsonMap(mapName = "China",palette = "Reds",legendTitle = "交通量图例")

t <- result_present3(jdnews,jdpreviouss,jdlasts,"province","frecars")
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)
t <- t[order(t$省级行政区),]
rownames(t) <- NULL
kable(t)

8. 数据使用情况

本月，上月，去年同月交调站数量分别为：

nrow(jdnews)
nrow(jdlasts)
nrow(jdpreviouss)

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)

分道路等级分别占比

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

东中西部数量合计

t$合计 <- rowSums(t)
t$省份 <- rownames(t)
t2 <- merge(t,province_region,by="省份")
tapply(t2$合计, t2$地域, sum)

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