In ahorawzy/Mreport: Monthly Report of traffic condition

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

本实验主要任务是：

1. 导入2017年6月数据，做出同比比较。
2. 研究分日该怎么分析。

环境准备

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)

load_base()
load_sample_base()

数据准备

2018年6月

jd201806 <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_06.csv")
dim(jd201806)

jd201806 <- caculate_equivalent(jd201806)
jd201806 <- select_atts(jd201806)
jd201806 <- handle_mergeline(jd201806,station_line)
jd201806 <- handle_mergesample(jd201806,sample_base)
jd201806 <- merge(jd201806,roadlevel,by="index",all.x = T)
dim(jd201806)

jd201806s <- subset(jd201806,index %in% station_use)
dim(jd201806s)

2018年5月

jd201805 <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_05.csv")
dim(jd201805)

jd201805 <- caculate_equivalent(jd201805)
jd201805 <- select_atts(jd201805)
jd201805 <- handle_mergeline(jd201805,station_line)
jd201805 <- handle_mergesample(jd201805,sample_base)
jd201805 <- merge(jd201805,roadlevel,by="index",all.x = T)
dim(jd201805)

jd201805s <- subset(jd201805,index %in% station_use)
dim(jd201805s)

2017年6月

jd201706 <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2017_06.csv")
dim(jd201706)

jd201706 <- caculate_equivalent(jd201706)
jd201706 <- select_atts(jd201706)
jd201706 <- handle_mergeline(jd201706,station_line)
jd201706 <- handle_mergesample(jd201706,sample_base)
jd201706 <- merge(jd201706,roadlevel,by="index",all.x = T)
dim(jd201706)

jd201706s <- subset(jd201706,index %in% station_use)
dim(jd201706s)

1. 基础数据解释

所使用交调站原则：

1. 本月有数据（并在交调系统中可下载）的站点；
2. 连续式站点；
3. 道路等级为国家级高速路、省级高速路，普通国道，普通省道

所使用的各省分等级交调站数目如下：

table(jd201806s$province,jd201806s$level)

2. 总体分析

2.1 机动车当量

本月

(total1806cars <- ddply(jd201806s,"level",summarise,Wmean = weighted.mean(cars,w=mileage)))

同比

total1706cars <- ddply(jd201706s,"level",summarise,Wmean = weighted.mean(cars,w=mileage))
caculate_increaseratio(total1806cars,total1706cars)

环比

total1805cars <- ddply(jd201805s,"level",summarise,Wmean = weighted.mean(cars,w=mileage))
caculate_increaseratio(total1806cars,total1805cars)

2.2 分等级客车当量

本月

(total1806passcars <- ddply(jd201806s,"level",summarise,Wmean = weighted.mean(passenger_cars,w=mileage)))

同比

total1706passcars <- ddply(jd201706s,"level",summarise,Wmean = weighted.mean(passenger_cars,w=mileage))
caculate_increaseratio(total1806passcars,total1706passcars)

环比

total1805passcars <- ddply(jd201805s,"level",summarise,Wmean = weighted.mean(passenger_cars,w=mileage))
caculate_increaseratio(total1806passcars,total1805passcars)

2.3 分等级货车当量

本月

(total1806frecars <- ddply(jd201806s,"level",summarise,Wmean = weighted.mean(freight_cars,w=mileage)))

同比

total1706frecars <- ddply(jd201706s,"level",summarise,Wmean = weighted.mean(freight_cars,w=mileage))
caculate_increaseratio(total1806frecars,total1706frecars)

环比

total1805frecars <- ddply(jd201805s,"level",summarise,Wmean = weighted.mean(freight_cars,w=mileage))
caculate_increaseratio(total1806frecars,total1805frecars)

3. 分省分析

3.1 分等级机动车

本月

(province1806cars <- caculate_level_carsmean(jd201806s,"province"))

```r caculate_carsmean(jd201806s,"province") %>% geojsonMap(mapName = "China") ```

同比

province1706cars <- caculate_level_carsmean(jd201706s,"province")
caculate_increaseratio(province1806cars,province1706cars)

环比

province1805cars <- caculate_level_carsmean(jd201805s,"province")
caculate_increaseratio(province1806cars,province1805cars)

4. 城市群分析

4.1 分等级机动车

本月

(citygroup1806 <- caculate_level_carsmean(jd201806s,"citygroup2"))

同比

citygroup1706 <- caculate_level_carsmean(jd201706s,"citygroup2")
caculate_increaseratio(citygroup1806,citygroup1706)

环比

citygroup1805 <- caculate_level_carsmean(jd201805s,"citygroup2")
caculate_increaseratio(citygroup1806,citygroup1805)

5. 国家公路运输枢纽分析

5.1 分等级机动车

本月

(roadhub1806 <- caculate_level_carsmean(jd = jd201806s,attsname = "roadhub"))

同比

roadhub1706 <- caculate_level_carsmean(jd = jd201706s,attsname = "roadhub")
caculate_increaseratio(roadhub1806,roadhub1706)

环比

roadhub1805 <- caculate_level_carsmean(jd = jd201805s,attsname = "roadhub")
caculate_increaseratio(roadhub1806,roadhub1805)

6. 通道分析

6.1 十横通道

本月

(horizon1806 <- caculate_carsmean(jd = jd201806s,attsname = "horizon10"))

同比

horizon1706 <- caculate_carsmean(jd = jd201706s,attsname = "horizon10")
caculate_increaseratio(horizon1806,horizon1706)

环比

horizon1805 <- caculate_carsmean(jd = jd201805s,attsname = "horizon10")
caculate_increaseratio(horizon1805,horizon1706)

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