DATA ON MODE CHOICE OF DANANG CITIZENS
Phương Anh
10/07/2019
1. Read Data
# Read Data directly
t = "F:\\NGHIEN CUU SINH\\NCS - PHUONG ANH\\Part 1-Mode choice\\SO LIEU R\\Mode choice in DN.csv"
MC = read.csv(t, header = T)
head(MC)## Travel.Mode Bus.Stop.Condition Central.Area Purpose Frequency Departure.Time
## 1 6 1 0 3 3 1
## 2 3 1 0 1 4 1
## 3 3 1 0 1 4 3
## 4 3 1 0 1 4 1
## 5 3 1 0 1 4 3
## 6 4 1 0 1 4 1
## Distance Travel.Period Sidewalk.Clearance Lane.Separate Temporary.Stop.Number
## 1 2 10 1 1 0
## 2 8 15 1 1 1
## 3 5 15 1 1 1
## 4 5 10 1 1 1
## 5 8 15 1 1 0
## 6 20 30 1 1 0
## Mode.Choice.Reason Weather Weekend Non.Bus.Reason Cost Bus.Stop.Present
## 1 4 1 0 1 12 1
## 2 2 3 0 1 8 2
## 3 4 1 0 2 5 1
## 4 2 1 0 4 5 1
## 5 2 3 0 2 8 2
## 6 5 3 0 2 40 2
## Gender Age Occupation Income Number.of.Children Motor.Certificate
## 1 0 5 4 2 2 0
## 2 0 3 6 3 1 1
## 3 1 3 2 4 0 1
## 4 1 3 2 3 0 1
## 5 0 4 6 3 1 1
## 6 1 4 3 4 2 1
## Car.Certificate Bicycle.Owning Motor.Owning Car.Owning Number.of.Bicycles
## 1 0 0 0 0 1
## 2 0 1 1 0 1
## 3 0 0 1 0 0
## 4 0 1 1 0 1
## 5 0 0 1 0 0
## 6 1 1 1 1 1
## Number.of.Motors Number.of.Car
## 1 2 1
## 2 3 0
## 3 3 0
## 4 3 0
## 5 2 0
## 6 2 1names(MC)## [1] "Travel.Mode" "Bus.Stop.Condition" "Central.Area"
## [4] "Purpose" "Frequency" "Departure.Time"
## [7] "Distance" "Travel.Period" "Sidewalk.Clearance"
## [10] "Lane.Separate" "Temporary.Stop.Number" "Mode.Choice.Reason"
## [13] "Weather" "Weekend" "Non.Bus.Reason"
## [16] "Cost" "Bus.Stop.Present" "Gender"
## [19] "Age" "Occupation" "Income"
## [22] "Number.of.Children" "Motor.Certificate" "Car.Certificate"
## [25] "Bicycle.Owning" "Motor.Owning" "Car.Owning"
## [28] "Number.of.Bicycles" "Number.of.Motors" "Number.of.Car"dim(MC)## [1] 847 302. Desscriptive statistic
# Data coding
str(MC)## 'data.frame': 847 obs. of 30 variables:
## $ Travel.Mode : int 6 3 3 3 3 4 4 3 3 3 ...
## $ Bus.Stop.Condition : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Central.Area : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Purpose : int 3 1 1 1 1 1 1 1 1 1 ...
## $ Frequency : int 3 4 4 4 4 4 4 4 4 4 ...
## $ Departure.Time : int 1 1 3 1 3 1 1 1 2 1 ...
## $ Distance : num 2 8 5 5 8 20 15 10 12 10 ...
## $ Travel.Period : num 10 15 15 10 15 30 20 25 30 25 ...
## $ Sidewalk.Clearance : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Lane.Separate : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Temporary.Stop.Number: int 0 1 1 1 0 0 0 0 1 1 ...
## $ Mode.Choice.Reason : int 4 2 4 2 2 5 5 2 2 2 ...
## $ Weather : int 1 3 1 1 3 3 3 3 1 3 ...
## $ Weekend : int 0 0 0 0 0 0 0 1 0 0 ...
## $ Non.Bus.Reason : int 1 1 2 4 2 2 4 2 1 4 ...
## $ Cost : int 12 8 5 5 8 40 30 10 12 10 ...
## $ Bus.Stop.Present : int 1 2 1 1 2 2 2 1 1 1 ...
## $ Gender : int 0 0 1 1 0 1 1 0 0 1 ...
## $ Age : int 5 3 3 3 4 4 5 4 3 4 ...
## $ Occupation : int 4 6 2 2 6 3 3 7 7 3 ...
## $ Income : int 2 3 4 3 3 4 4 2 3 3 ...
## $ Number.of.Children : int 2 1 0 0 1 2 2 0 1 0 ...
## $ Motor.Certificate : int 0 1 1 1 1 1 1 1 1 1 ...
## $ Car.Certificate : int 0 0 0 0 0 1 1 0 0 0 ...
## $ Bicycle.Owning : int 0 1 0 1 0 1 0 0 1 0 ...
## $ Motor.Owning : int 0 1 1 1 1 1 1 1 1 1 ...
## $ Car.Owning : int 0 0 0 0 0 1 1 0 0 0 ...
## $ Number.of.Bicycles : int 1 1 0 1 0 1 0 0 1 0 ...
## $ Number.of.Motors : int 2 3 3 3 2 2 2 2 3 3 ...
## $ Number.of.Car : int 1 0 0 0 0 1 1 0 0 0 ...attach(MC)
MC = within(MC, {
Travel.Mode = factor(Travel.Mode, labels = c("Walk", "Bicycle", "Motorbike", "Car", "Hichhiking", "App-based Motor/Car", "Bus"))
Bus.Stop.Condition = factor(Bus.Stop.Condition,labels = c("No", "Yes"))
Central.Area = factor(Central.Area, labels = c("No", "Yes"))
Purpose = factor(Purpose, labels = c("Work/Study", "Picking Children", "Entertainment", "Others"))
Frequency = factor(Frequency, labels = c("Once", "2 times", "3 times", "> 3 times"))
Departure.Time = factor(Departure.Time, labels = c("Morning", "Afternoon", "Evening", "Others"))
Sidewalk.Clearance = factor(Sidewalk.Clearance, labels = c("No", "Yes"))
Lane.Separate = factor(Lane.Separate, labels = c("No", "Yes"))
Temporary.Stop.Number = factor(Temporary.Stop.Number, labels = c("None", "1 stops", "2 stops", ">=3 stops"))
Mode.Choice.Reason = factor(Mode.Choice.Reason, labels = c("Safety", "Comfortable", "Low price", "Accessibility", "Reliability", "others"))
Weather = factor(Weather, labels = c("Sunny", "Rainny", "Cool"))
Weekend = factor(Weekend, labels = c("No", "Yes"))
Non.Bus.Reason = factor(Non.Bus.Reason, labels = c("No Route", "Uncomfortable", "Unsafety", "Long waiting time", "Unreliability", "others"))
Bus.Stop.Present = factor(Bus.Stop.Present, labels = c("No", "Yes", "Don't know"))
Gender = factor(Gender, labels = c("Female", "Male"))
Age = factor(Age, labels = c("<= 15", "16-18", "19-24", "25-45", "46-60", ">60"))
Occupation = factor(Occupation, labels = c("Pupils", "Students", "Officers", "Housewife", "Unemployed", "Workers", "Free labor", "Others"))
Number.of.Children = factor(Number.of.Children, labels = c("None", "1 child", "2 children", ">= 3 children"))
Motor.Certificate = factor(Motor.Certificate, labels = c("No", "Yes"))
Car.Certificate = factor(Car.Certificate, labels = c("No", "Yes"))
Bicycle.Owning = factor(Bicycle.Owning, labels = c("No", "Yes"))
Motor.Owning = factor(Motor.Owning, labels = c("No", "Yes"))
Car.Owning = factor(Car.Owning, labels = c("No", "Yes"))
Number.of.Bicycles = factor(Number.of.Bicycles, labels = c("None", "1", "2", ">=3"))
Number.of.Motors = factor(Number.of.Motors, labels = c("None", "1", "2", "3", ">3"))
Number.of.Car = factor(Number.of.Car, labels = c("None", "1", ">=2"))
Income = factor(Income, labels = c("<8 millions", "(8-15) millions", "(15-25) millions", ">25 millions"))
Distance = as.numeric(Distance)
Travel.Period = as.numeric(Travel.Period)
Cost = as.numeric(Cost)
} )
str(MC)## 'data.frame': 847 obs. of 30 variables:
## $ Travel.Mode : Factor w/ 7 levels "Walk","Bicycle",..: 6 3 3 3 3 4 4 3 3 3 ...
## $ Bus.Stop.Condition : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Central.Area : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ Purpose : Factor w/ 4 levels "Work/Study","Picking Children",..: 3 1 1 1 1 1 1 1 1 1 ...
## $ Frequency : Factor w/ 4 levels "Once","2 times",..: 3 4 4 4 4 4 4 4 4 4 ...
## $ Departure.Time : Factor w/ 4 levels "Morning","Afternoon",..: 1 1 3 1 3 1 1 1 2 1 ...
## $ Distance : num 2 8 5 5 8 20 15 10 12 10 ...
## $ Travel.Period : num 10 15 15 10 15 30 20 25 30 25 ...
## $ Sidewalk.Clearance : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Lane.Separate : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Temporary.Stop.Number: Factor w/ 4 levels "None","1 stops",..: 1 2 2 2 1 1 1 1 2 2 ...
## $ Mode.Choice.Reason : Factor w/ 6 levels "Safety","Comfortable",..: 4 2 4 2 2 5 5 2 2 2 ...
## $ Weather : Factor w/ 3 levels "Sunny","Rainny",..: 1 3 1 1 3 3 3 3 1 3 ...
## $ Weekend : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 2 1 1 ...
## $ Non.Bus.Reason : Factor w/ 6 levels "No Route","Uncomfortable",..: 1 1 2 4 2 2 4 2 1 4 ...
## $ Cost : num 12 8 5 5 8 40 30 10 12 10 ...
## $ Bus.Stop.Present : Factor w/ 3 levels "No","Yes","Don't know": 2 3 2 2 3 3 3 2 2 2 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 1 2 2 1 1 2 ...
## $ Age : Factor w/ 6 levels "<= 15","16-18",..: 5 3 3 3 4 4 5 4 3 4 ...
## $ Occupation : Factor w/ 8 levels "Pupils","Students",..: 4 6 2 2 6 3 3 7 7 3 ...
## $ Income : Factor w/ 4 levels "<8 millions",..: 2 3 4 3 3 4 4 2 3 3 ...
## $ Number.of.Children : Factor w/ 4 levels "None","1 child",..: 3 2 1 1 2 3 3 1 2 1 ...
## $ Motor.Certificate : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ Car.Certificate : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 2 1 1 1 ...
## $ Bicycle.Owning : Factor w/ 2 levels "No","Yes": 1 2 1 2 1 2 1 1 2 1 ...
## $ Motor.Owning : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ Car.Owning : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 2 1 1 1 ...
## $ Number.of.Bicycles : Factor w/ 4 levels "None","1","2",..: 2 2 1 2 1 2 1 1 2 1 ...
## $ Number.of.Motors : Factor w/ 5 levels "None","1","2",..: 3 4 4 4 3 3 3 3 4 4 ...
## $ Number.of.Car : Factor w/ 3 levels "None","1",">=2": 2 1 1 1 1 2 2 1 1 1 ...dim(MC)## [1] 847 30# Descritive Table
library(tableone)
require(tableone)
library(magrittr)
MCfactor = c("Travel.Mode", "Bus.Stop.Condition", "Central.Area", "Purpose", "Frequency", "Departure.Time", "Sidewalk.Clearance", "Lane.Separate", "Temporary.Stop.Number", "Mode.Choice.Reason", "Weather", "Weekend", "Non.Bus.Reason", "Bus.Stop.Present","Gender", "Age", "Occupation", "Income", "Number.of.Children", "Motor.Certificate", "Car.Certificate", "Bicycle.Owning", "Motor.Owning", "Car.Owning", "Number.of.Bicycles", "Number.of.Motors", "Number.of.Car")
MCfactor1 = c("Bus.Stop.Condition", "Central.Area", "Purpose", "Frequency")
MCfactor2 = c("Departure.Time", "Sidewalk.Clearance", "Lane.Separate", "Temporary.Stop.Number", "Mode.Choice.Reason", "Weather", "Weekend", "Non.Bus.Reason", "Bus.Stop.Present", "Gender", "Age", "Occupation", "Income", "Number.of.Children", "Motor.Certificate", "Car.Certificate", "Bicycle.Owning", "Motor.Owning", "Car.Owning", "Number.of.Bicycles", "Number.of.Motors", "Number.of.Car")
MCnumber = c("Distance", "Travel.Period", "Cost")
summary(MC)## Travel.Mode Bus.Stop.Condition Central.Area
## Walk : 35 No :416 No :443
## Bicycle : 44 Yes:431 Yes:404
## Motorbike :518
## Car : 82
## Hichhiking : 35
## App-based Motor/Car: 23
## Bus :110
## Purpose Frequency Departure.Time Distance
## Work/Study :571 Once : 58 Morning :514 Min. : 0.015
## Picking Children: 94 2 times : 68 Afternoon: 71 1st Qu.: 3.000
## Entertainment :159 3 times : 73 Evening :143 Median : 5.000
## Others : 23 > 3 times:648 Others :119 Mean : 6.492
## 3rd Qu.:10.000
## Max. :35.000
##
## Travel.Period Sidewalk.Clearance Lane.Separate Temporary.Stop.Number
## Min. : 0.00 No :113 No :178 None :447
## 1st Qu.: 10.00 Yes:734 Yes:669 1 stops :281
## Median : 15.00 2 stops : 60
## Mean : 17.42 >=3 stops: 59
## 3rd Qu.: 20.00
## Max. :180.00
##
## Mode.Choice.Reason Weather Weekend Non.Bus.Reason
## Safety :123 Sunny :564 No :640 No Route :164
## Comfortable :380 Rainny: 19 Yes:207 Uncomfortable :226
## Low price : 76 Cool :264 Unsafety : 19
## Accessibility:180 Long waiting time:213
## Reliability : 61 Unreliability : 69
## others : 27 others : 46
## NA's :110
## Cost Bus.Stop.Present Gender Age Occupation
## Min. : 0.000 No : 84 Female:353 <= 15: 38 Students :228
## 1st Qu.: 3.000 Yes :676 Male :494 16-18: 77 Free labor:184
## Median : 5.000 Don't know: 87 19-24:279 Officers :136
## Mean : 8.685 25-45:317 Pupils : 93
## 3rd Qu.: 10.000 46-60: 98 Workers : 83
## Max. :285.000 >60 : 38 Others : 69
## (Other) : 54
## Income Number.of.Children Motor.Certificate
## <8 millions :213 None :373 No :131
## (8-15) millions :336 1 child :323 Yes:716
## (15-25) millions:209 2 children :126
## >25 millions : 89 >= 3 children: 25
##
##
##
## Car.Certificate Bicycle.Owning Motor.Owning Car.Owning Number.of.Bicycles
## No :692 No :499 No :182 No :728 None:368
## Yes:155 Yes:348 Yes:665 Yes:119 1 :369
## 2 : 99
## >=3 : 11
##
##
##
## Number.of.Motors Number.of.Car
## None: 37 None:658
## 1 :133 1 :170
## 2 :423 >=2 : 19
## 3 :201
## >3 : 53
##
## 3. Describe Data by graph
library(magrittr)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x tidyr::extract() masks magrittr::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::set_names() masks magrittr::set_names()library(ggplot2)
library(car)## Warning: package 'car' was built under R version 4.0.4## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## some## Travel Mode ~ Bus Stop Condition
MC %>%
group_by(Travel.Mode, Bus.Stop.Condition) %>%
count() %>%
ggplot(aes(Bus.Stop.Condition, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Bus Stop Condition") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Bus Stop Condition")
MC %>%
group_by(Bus.Stop.Condition, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Bus.Stop.Condition)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Bus Stop Condition") +
ggtitle("Proportion of Travel Mode Choice ~ Bus Stop Condition")
## Travel Mode ~ Central AreaMC %>%
MC %>%
group_by(Travel.Mode, Central.Area) %>%
count() %>%
ggplot(aes(Central.Area, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Central Area") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Central Area")
MC %>%
group_by(Central.Area, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Central.Area)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Central Area") +
ggtitle("Proportion of Travel Mode Choice ~ Central Area")
## Travel Mode Choice ~ Purpose of Travelling
MC %>%
group_by(Travel.Mode, Purpose) %>%
count() %>%
ggplot(aes(Purpose, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Purpose of travelling") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Purpose of Travelling")
MC %>%
group_by(Purpose, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Purpose)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Purpose of travelling") +
ggtitle("Proportion of Travel Mode Choice ~ Purpose of Travelling")
MC %>%
group_by(Weekend, Purpose) %>%
count() %>%
ggplot(aes(Purpose, n, fill = Weekend)) +
geom_col(position = "fill") +
xlab("Purpose of travelling") +
ylab("Weekend") +
ggtitle("Proportion of Weekend ~ Purpose of Travelling")
## Travel Mode Choice ~ Frequency of Travelling
MC %>%
group_by(Travel.Mode, Frequency) %>%
count() %>%
ggplot(aes(Frequency, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Frequency of Travelling") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Frequency of Travelling")
MC %>%
group_by(Frequency, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Frequency)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Frequency of Travelling") +
ggtitle("Proportion of Travel Mode Choice ~ Frequency of Travelling")
## Travel Mode Choice ~ Departure Time of Travel
MC %>%
group_by(Travel.Mode, Departure.Time) %>%
count() %>%
ggplot(aes(Departure.Time, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Departure time of travel") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Departure Time of Travel")
MC %>%
group_by(Departure.Time, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Departure.Time)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Departure time of travel") +
ggtitle("Proportion of Travel Mode Choice ~ Departure Time of Travel")
## Travel Mode Choice ~ Sidewalk Clearance of Roads
MC %>%
group_by(Travel.Mode, Sidewalk.Clearance) %>%
count() %>%
ggplot(aes(Sidewalk.Clearance, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Sidewalk Clearance of Roads") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Sidewalk Clearance of Roads")
MC %>%
group_by(Sidewalk.Clearance, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Sidewalk.Clearance)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Sidewalk Clearance of Roads") +
ggtitle("Proportion of Travel Mode Choice ~ Sidewalk Clearance of Roads")
## Travel Mode Choice ~ Lane Separate of Roads
MC %>%
group_by(Travel.Mode, Lane.Separate) %>%
count() %>%
ggplot(aes(Lane.Separate, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Lane Separate") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Lane Separate of Roads")
MC %>%
group_by(Lane.Separate, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Lane.Separate)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Lane Separate") +
ggtitle("Proportion of Travel Mode Choice ~ Lane Separate of Roads")
## Travel Mode Choice ~ The number of temporary stops
MC %>%
group_by(Travel.Mode, Temporary.Stop.Number) %>%
count() %>%
ggplot(aes(Temporary.Stop.Number, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("The number of temporary stops") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ The number of temporary stops") 
MC %>%
group_by(Temporary.Stop.Number, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Temporary.Stop.Number)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("The number of temporary stops") +
ggtitle("Proportion of Travel Mode Choice ~ The number of temporary stops")
## Reason of mode choice ~ Travel Mode Choice
MC %>%
group_by(Travel.Mode, Mode.Choice.Reason) %>%
count() %>%
ggplot(aes(Mode.Choice.Reason, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("The reason of mode choice") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Mode Choice Reason")
MC %>%
group_by(Mode.Choice.Reason, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Mode.Choice.Reason)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("The reason of mode choice") +
ggtitle("Proportion of Reason of mode choice ~ Travel Mode Choice")
## Travel Mode Choice ~ Weather condition
MC %>%
group_by(Travel.Mode, Weather) %>%
count() %>%
ggplot(aes(Weather, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Weather condition") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Weather condition")
MC %>%
group_by(Weather, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Weather)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Weather condition") +
ggtitle("Proportion of Travel Mode Choice ~ Weather condition")
## Travel Mode Choice ~ Weekend
MC %>%
group_by(Travel.Mode, Weekend) %>%
count() %>%
ggplot(aes(Weekend, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Weekend") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Weekend")
MC %>%
group_by(Weekend, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Weekend)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Weekend") +
ggtitle("Proportion of Travel Mode Choice ~ Weekend")
## Travel Mode Choice ~ The presence of bus stop
MC %>%
group_by(Travel.Mode, Bus.Stop.Present) %>%
count() %>%
ggplot(aes(Bus.Stop.Present, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("The presence of bus stop") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ The presence of bus stop")
MC %>%
group_by(Bus.Stop.Present, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Bus.Stop.Present)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("The presence of bus stop") +
ggtitle("Proportion of Travel Mode Choice ~ The presence of bus stop")
## Travel Mode Choice ~ Gender
MC %>%
group_by(Travel.Mode, Gender) %>%
count() %>%
ggplot(aes(Gender, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Gender") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Gender") 
MC %>%
group_by(Gender, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Gender)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Gender") +
ggtitle("Proportion of Travel Mode Choice ~ Gender")
## Travel Mode Choice ~ Age
MC %>%
group_by(Travel.Mode, Age) %>%
count() %>%
ggplot(aes(Age, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Age") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Age")
MC %>%
group_by(Age, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Age)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Age") +
ggtitle("Proportion of Travel Mode Choice ~ Age")
## Travel Mode Choice ~ Occupation
MC %>%
group_by(Travel.Mode, Occupation) %>%
count() %>%
ggplot(aes(Occupation, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Occupation") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Occupation")
MC %>%
group_by(Occupation, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Occupation)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Occupation") +
ggtitle("Proportion of Travel Mode Choice ~ Occupation")
## Travel Mode Choice ~ Income
MC %>%
group_by(Travel.Mode, Income) %>%
count() %>%
ggplot(aes(Income, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Income") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Income")
MC %>%
group_by(Income, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Income)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Income") +
ggtitle("Proportion of Travel Mode Choice ~ Income")
## Travel Mode Choice ~ Number of Children in family
MC %>%
group_by(Travel.Mode, Number.of.Children) %>%
count() %>%
ggplot(aes(Number.of.Children, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Number of Children") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Number of Children in family")
MC %>%
group_by(Number.of.Children, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Number.of.Children)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Number of Children") +
ggtitle("Proportion of Travel Mode Choice ~ Number of Children in family")
## Travel Mode Choice ~ Motor certificate
MC %>%
group_by(Travel.Mode, Motor.Certificate) %>%
count() %>%
ggplot(aes(Motor.Certificate, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Motor Certificate") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Motor certificate")
MC %>%
group_by(Motor.Certificate, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Motor.Certificate)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Motor Certificate") +
ggtitle("Proportion of Travel Mode Choice ~ Motor certificate")
## Travel Mode Choice ~ Car certificate
MC %>%
group_by(Travel.Mode, Car.Certificate) %>%
count() %>%
ggplot(aes(Car.Certificate, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Car Certificate") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Car certificate")
MC %>%
group_by(Car.Certificate, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Car.Certificate)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Car Certificate") +
ggtitle("Proportion of Travel Mode Choice ~ Car certificate")
## Travel Mode Choice ~ Bicycle Owning
MC %>%
group_by(Travel.Mode, Bicycle.Owning) %>%
count() %>%
ggplot(aes(Bicycle.Owning, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Bicycle Owning") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Bicycle Owning")
MC %>%
group_by(Bicycle.Owning, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Bicycle.Owning)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Bicycle Owning") +
ggtitle("Proportion of Travel Mode Choice ~ Bicycle Owning")
## Travel Mode Choice ~ Motor Owning
MC %>%
group_by(Travel.Mode, Motor.Owning) %>%
count() %>%
ggplot(aes(Motor.Owning, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Motor Owning") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Motor Owning")
MC %>%
group_by(Motor.Owning, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Motor.Owning)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Motor Owning") +
ggtitle("Proportion of Travel Mode Choice ~ Motor Owning")
## Travel Mode Choice ~ Car Owning
MC %>%
group_by(Travel.Mode, Car.Owning) %>%
count() %>%
ggplot(aes(Car.Owning, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Car Owning") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Car Owning")
MC %>%
group_by(Car.Owning, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Car.Owning)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Car Owning") +
ggtitle("Proportion of Travel Mode Choice ~ Car Owning")
## Travel Mode Choice ~ Number of Bicycle
MC %>%
group_by(Travel.Mode, Number.of.Bicycles) %>%
count() %>%
ggplot(aes(Number.of.Bicycles, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Number of Bicycle") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Number of Bicycle")
MC %>%
group_by(Number.of.Bicycles, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Number.of.Bicycles)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Number of Bicycle") +
ggtitle("Proportion of Travel Mode Choice ~ Number of Bicycle")
## Travel Mode Choice ~ Number of Motors
MC %>%
group_by(Travel.Mode, Number.of.Motors) %>%
count() %>%
ggplot(aes(Number.of.Motors, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Number of Motors") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Number of Motors")
MC %>%
group_by(Number.of.Motors, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Number.of.Motors)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Number of Motors") +
ggtitle("Proportion of Travel Mode Choice ~ Number of Motors")
## Travel Mode Choice ~ Number of Cars
MC %>%
group_by(Travel.Mode, Number.of.Car) %>%
count() %>%
ggplot(aes(Number.of.Car, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Number of Cars") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Number of Cars")
MC %>%
group_by(Number.of.Car, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Number.of.Car)) +
geom_col(position = "fill") +
xlab("Travel Mode Choice") +
ylab("Number of Cars") +
ggtitle("Proportion of Travel Mode Choice ~ Number of Cars")
## Travel Mode Choice ~ Countinuous Variables (Distance, Travel Period and Cost)
### Travel Mode Choice ~ Distance
ggplot(MC, aes(x=Distance, fill = Travel.Mode, color = Travel.Mode)) +
geom_histogram (position = "dodge") +
xlab("Distance") +
ylab("Count") +
ggtitle("Histogram of Distance")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
MC %>%
group_by(Travel.Mode, Distance) %>%
count() %>%
ggplot(aes(Distance, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Distance") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Distance")
### Travel Mode Choice ~ Travel Period
ggplot(MC, aes(x=Travel.Period, fill = Travel.Mode, color = Travel.Mode)) +
geom_histogram (position = "dodge") +
xlab("Travel Period") +
ylab("Count") +
ggtitle("Histogram of Travel Period")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
MC %>%
group_by(Travel.Mode, Travel.Period) %>%
count() %>%
ggplot(aes(Travel.Period, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Travel Period") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Travel Period")
### Travel Mode Choice ~ Cost
ggplot(MC, aes(x=Cost, fill = Travel.Mode, color = Travel.Mode)) +
geom_histogram (position = "dodge") +
xlab("Cost") +
ylab("Count") +
ggtitle("Histogram of Cost")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
MC %>%
group_by(Travel.Mode, Cost) %>%
count() %>%
ggplot(aes(Cost, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Cost") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice ~ Cost")
## Correlation in continuous variables
library(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:car':
##
## logit## The following objects are masked from 'package:ggplot2':
##
## %+%, alphaCV = data.frame(MC$Travel.Mode, MC$Distance, MC$Travel.Period, MC$Cost)
pairs.panels(CV)
## Statistic Analysis by boxplot (continuous variales)
str(MC)## 'data.frame': 847 obs. of 30 variables:
## $ Travel.Mode : Factor w/ 7 levels "Walk","Bicycle",..: 6 3 3 3 3 4 4 3 3 3 ...
## $ Bus.Stop.Condition : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Central.Area : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ Purpose : Factor w/ 4 levels "Work/Study","Picking Children",..: 3 1 1 1 1 1 1 1 1 1 ...
## $ Frequency : Factor w/ 4 levels "Once","2 times",..: 3 4 4 4 4 4 4 4 4 4 ...
## $ Departure.Time : Factor w/ 4 levels "Morning","Afternoon",..: 1 1 3 1 3 1 1 1 2 1 ...
## $ Distance : num 2 8 5 5 8 20 15 10 12 10 ...
## $ Travel.Period : num 10 15 15 10 15 30 20 25 30 25 ...
## $ Sidewalk.Clearance : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Lane.Separate : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Temporary.Stop.Number: Factor w/ 4 levels "None","1 stops",..: 1 2 2 2 1 1 1 1 2 2 ...
## $ Mode.Choice.Reason : Factor w/ 6 levels "Safety","Comfortable",..: 4 2 4 2 2 5 5 2 2 2 ...
## $ Weather : Factor w/ 3 levels "Sunny","Rainny",..: 1 3 1 1 3 3 3 3 1 3 ...
## $ Weekend : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 2 1 1 ...
## $ Non.Bus.Reason : Factor w/ 6 levels "No Route","Uncomfortable",..: 1 1 2 4 2 2 4 2 1 4 ...
## $ Cost : num 12 8 5 5 8 40 30 10 12 10 ...
## $ Bus.Stop.Present : Factor w/ 3 levels "No","Yes","Don't know": 2 3 2 2 3 3 3 2 2 2 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 1 2 2 1 1 2 ...
## $ Age : Factor w/ 6 levels "<= 15","16-18",..: 5 3 3 3 4 4 5 4 3 4 ...
## $ Occupation : Factor w/ 8 levels "Pupils","Students",..: 4 6 2 2 6 3 3 7 7 3 ...
## $ Income : Factor w/ 4 levels "<8 millions",..: 2 3 4 3 3 4 4 2 3 3 ...
## $ Number.of.Children : Factor w/ 4 levels "None","1 child",..: 3 2 1 1 2 3 3 1 2 1 ...
## $ Motor.Certificate : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ Car.Certificate : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 2 1 1 1 ...
## $ Bicycle.Owning : Factor w/ 2 levels "No","Yes": 1 2 1 2 1 2 1 1 2 1 ...
## $ Motor.Owning : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ Car.Owning : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 2 1 1 1 ...
## $ Number.of.Bicycles : Factor w/ 4 levels "None","1","2",..: 2 2 1 2 1 2 1 1 2 1 ...
## $ Number.of.Motors : Factor w/ 5 levels "None","1","2",..: 3 4 4 4 3 3 3 3 4 4 ...
## $ Number.of.Car : Factor w/ 3 levels "None","1",">=2": 2 1 1 1 1 2 2 1 1 1 ...### Boxplot of Distance, Travel Period and Cost ~ Travel Mode Choice
MC %>%
group_by(Distance, Travel.Mode) %>%
count() %>%
ggplot(aes(x = Travel.Mode, y = Distance, fill = Travel.Mode)) +
geom_boxplot() +
xlab("Travel Mode Choice") +
ylab("Distance") +
ggtitle("Boxplot of Travel Mode Choice ~ Distance")
MC %>%
group_by(Cost, Travel.Mode) %>%
count() %>%
ggplot(aes(x = Travel.Mode, y = Cost, fill = Travel.Mode)) +
geom_boxplot() +
xlab("Travel Mode Choice") +
ylab("Cost") +
ggtitle("Boxplot of Travel Mode Choice ~ Cost")
MC %>%
group_by(Travel.Period, Travel.Mode) %>%
count() %>%
ggplot(aes(x = Travel.Mode, y = Travel.Period, fill = Travel.Mode)) +
geom_boxplot() +
xlab("Travel Mode Choice") +
ylab("Travel Period") +
ggtitle("Boxplot of Travel Mode Choice ~ Travel Period")
### Boxplot of Distance, Travel Period and Cost ~ Gender
MC %>%
group_by(Distance, Gender) %>%
count() %>%
ggplot(aes(x = Gender, y = Distance, fill = Gender)) +
geom_boxplot() +
xlab("Gender") +
ylab("Distance") +
ggtitle("Boxplot of Distance ~ Gender")
MC %>%
group_by(Cost, Gender) %>%
count() %>%
ggplot(aes(x = Gender, y = Cost, fill = Gender)) +
geom_boxplot() +
xlab("Gender") +
ylab("Cost") +
ggtitle("Boxplot of Cost ~ Gender")
MC %>%
group_by(Travel.Period, Gender) %>%
count() %>%
ggplot(aes(x = Gender, y = Travel.Period, fill = Gender)) +
geom_boxplot() +
xlab("Gender") +
ylab("Travel Period") +
ggtitle("Boxplot of Travel Period ~ Gender")
### Boxplot of Distance, Travel Period and Cost ~ Occupation
MC %>%
group_by(Distance, Occupation) %>%
count() %>%
ggplot(aes(x = Occupation, y = Distance, fill = Occupation)) +
geom_boxplot() +
xlab("Occupation") +
ylab("Distance") +
ggtitle("Boxplot of Distance ~ Occupation")
MC %>%
group_by(Cost, Occupation) %>%
count() %>%
ggplot(aes(x = Occupation, y = Cost, fill = Occupation)) +
geom_boxplot() +
xlab("Occupation") +
ylab("Cost") +
ggtitle("Boxplot of Cost ~ Occupation")
MC %>%
group_by(Travel.Period, Occupation) %>%
count() %>%
ggplot(aes(x = Occupation, y = Travel.Period, fill = Occupation)) +
geom_boxplot() +
xlab("Occupation") +
ylab("Travel Period") +
ggtitle("Boxplot of Travel Period ~ Occupation")
## Work with a part of Data MC - Non bus user (NBU)
MC$Travel.Mode = as.numeric(MC$Travel.Mode)
str(MC)## 'data.frame': 847 obs. of 30 variables:
## $ Travel.Mode : num 6 3 3 3 3 4 4 3 3 3 ...
## $ Bus.Stop.Condition : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Central.Area : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ Purpose : Factor w/ 4 levels "Work/Study","Picking Children",..: 3 1 1 1 1 1 1 1 1 1 ...
## $ Frequency : Factor w/ 4 levels "Once","2 times",..: 3 4 4 4 4 4 4 4 4 4 ...
## $ Departure.Time : Factor w/ 4 levels "Morning","Afternoon",..: 1 1 3 1 3 1 1 1 2 1 ...
## $ Distance : num 2 8 5 5 8 20 15 10 12 10 ...
## $ Travel.Period : num 10 15 15 10 15 30 20 25 30 25 ...
## $ Sidewalk.Clearance : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Lane.Separate : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Temporary.Stop.Number: Factor w/ 4 levels "None","1 stops",..: 1 2 2 2 1 1 1 1 2 2 ...
## $ Mode.Choice.Reason : Factor w/ 6 levels "Safety","Comfortable",..: 4 2 4 2 2 5 5 2 2 2 ...
## $ Weather : Factor w/ 3 levels "Sunny","Rainny",..: 1 3 1 1 3 3 3 3 1 3 ...
## $ Weekend : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 2 1 1 ...
## $ Non.Bus.Reason : Factor w/ 6 levels "No Route","Uncomfortable",..: 1 1 2 4 2 2 4 2 1 4 ...
## $ Cost : num 12 8 5 5 8 40 30 10 12 10 ...
## $ Bus.Stop.Present : Factor w/ 3 levels "No","Yes","Don't know": 2 3 2 2 3 3 3 2 2 2 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 1 2 2 1 1 2 ...
## $ Age : Factor w/ 6 levels "<= 15","16-18",..: 5 3 3 3 4 4 5 4 3 4 ...
## $ Occupation : Factor w/ 8 levels "Pupils","Students",..: 4 6 2 2 6 3 3 7 7 3 ...
## $ Income : Factor w/ 4 levels "<8 millions",..: 2 3 4 3 3 4 4 2 3 3 ...
## $ Number.of.Children : Factor w/ 4 levels "None","1 child",..: 3 2 1 1 2 3 3 1 2 1 ...
## $ Motor.Certificate : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ Car.Certificate : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 2 1 1 1 ...
## $ Bicycle.Owning : Factor w/ 2 levels "No","Yes": 1 2 1 2 1 2 1 1 2 1 ...
## $ Motor.Owning : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ Car.Owning : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 2 1 1 1 ...
## $ Number.of.Bicycles : Factor w/ 4 levels "None","1","2",..: 2 2 1 2 1 2 1 1 2 1 ...
## $ Number.of.Motors : Factor w/ 5 levels "None","1","2",..: 3 4 4 4 3 3 3 3 4 4 ...
## $ Number.of.Car : Factor w/ 3 levels "None","1",">=2": 2 1 1 1 1 2 2 1 1 1 ...NBU = subset(MC, Travel.Mode <= 6)
NBU$Travel.Mode = as.factor(NBU$Travel.Mode)
str(NBU)## 'data.frame': 737 obs. of 30 variables:
## $ Travel.Mode : Factor w/ 6 levels "1","2","3","4",..: 6 3 3 3 3 4 4 3 3 3 ...
## $ Bus.Stop.Condition : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Central.Area : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ Purpose : Factor w/ 4 levels "Work/Study","Picking Children",..: 3 1 1 1 1 1 1 1 1 1 ...
## $ Frequency : Factor w/ 4 levels "Once","2 times",..: 3 4 4 4 4 4 4 4 4 4 ...
## $ Departure.Time : Factor w/ 4 levels "Morning","Afternoon",..: 1 1 3 1 3 1 1 1 2 1 ...
## $ Distance : num 2 8 5 5 8 20 15 10 12 10 ...
## $ Travel.Period : num 10 15 15 10 15 30 20 25 30 25 ...
## $ Sidewalk.Clearance : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Lane.Separate : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Temporary.Stop.Number: Factor w/ 4 levels "None","1 stops",..: 1 2 2 2 1 1 1 1 2 2 ...
## $ Mode.Choice.Reason : Factor w/ 6 levels "Safety","Comfortable",..: 4 2 4 2 2 5 5 2 2 2 ...
## $ Weather : Factor w/ 3 levels "Sunny","Rainny",..: 1 3 1 1 3 3 3 3 1 3 ...
## $ Weekend : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 2 1 1 ...
## $ Non.Bus.Reason : Factor w/ 6 levels "No Route","Uncomfortable",..: 1 1 2 4 2 2 4 2 1 4 ...
## $ Cost : num 12 8 5 5 8 40 30 10 12 10 ...
## $ Bus.Stop.Present : Factor w/ 3 levels "No","Yes","Don't know": 2 3 2 2 3 3 3 2 2 2 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 1 2 2 1 1 2 ...
## $ Age : Factor w/ 6 levels "<= 15","16-18",..: 5 3 3 3 4 4 5 4 3 4 ...
## $ Occupation : Factor w/ 8 levels "Pupils","Students",..: 4 6 2 2 6 3 3 7 7 3 ...
## $ Income : Factor w/ 4 levels "<8 millions",..: 2 3 4 3 3 4 4 2 3 3 ...
## $ Number.of.Children : Factor w/ 4 levels "None","1 child",..: 3 2 1 1 2 3 3 1 2 1 ...
## $ Motor.Certificate : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ Car.Certificate : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 2 1 1 1 ...
## $ Bicycle.Owning : Factor w/ 2 levels "No","Yes": 1 2 1 2 1 2 1 1 2 1 ...
## $ Motor.Owning : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ Car.Owning : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 2 1 1 1 ...
## $ Number.of.Bicycles : Factor w/ 4 levels "None","1","2",..: 2 2 1 2 1 2 1 1 2 1 ...
## $ Number.of.Motors : Factor w/ 5 levels "None","1","2",..: 3 4 4 4 3 3 3 3 4 4 ...
## $ Number.of.Car : Factor w/ 3 levels "None","1",">=2": 2 1 1 1 1 2 2 1 1 1 ...attach(NBU)## The following objects are masked from MC:
##
## Age, Bicycle.Owning, Bus.Stop.Condition, Bus.Stop.Present,
## Car.Certificate, Car.Owning, Central.Area, Cost, Departure.Time,
## Distance, Frequency, Gender, Income, Lane.Separate,
## Mode.Choice.Reason, Motor.Certificate, Motor.Owning,
## Non.Bus.Reason, Number.of.Bicycles, Number.of.Car,
## Number.of.Children, Number.of.Motors, Occupation, Purpose,
## Sidewalk.Clearance, Temporary.Stop.Number, Travel.Mode,
## Travel.Period, Weather, WeekendNBU %>%
group_by(Travel.Mode, Non.Bus.Reason) %>%
count() %>%
ggplot(aes(Non.Bus.Reason, n, fill = Travel.Mode)) +
geom_col(position = "fill") +
xlab("Non Bus Reason") +
ylab("Travel Mode Choice") +
ggtitle("Proportion of Travel Mode Choice of Non bus user ~ Reasons not choosing bus for travelling") 
NBU %>%
group_by(Non.Bus.Reason, Travel.Mode) %>%
count() %>%
ggplot(aes(Travel.Mode, n, fill = Non.Bus.Reason)) +
geom_col(position = "fill") +
xlab("Travel mode of non bus users") +
ylab("Reasons not choosing bus for travelling") +
ggtitle("Proportion of non bus reasons ~ Travel mode") 
4. Descriptive statistical analysis
summary(MC)## Travel.Mode Bus.Stop.Condition Central.Area Purpose
## Min. :1.000 No :416 No :443 Work/Study :571
## 1st Qu.:3.000 Yes:431 Yes:404 Picking Children: 94
## Median :3.000 Entertainment :159
## Mean :3.646 Others : 23
## 3rd Qu.:4.000
## Max. :7.000
##
## Frequency Departure.Time Distance Travel.Period
## Once : 58 Morning :514 Min. : 0.015 Min. : 0.00
## 2 times : 68 Afternoon: 71 1st Qu.: 3.000 1st Qu.: 10.00
## 3 times : 73 Evening :143 Median : 5.000 Median : 15.00
## > 3 times:648 Others :119 Mean : 6.492 Mean : 17.42
## 3rd Qu.:10.000 3rd Qu.: 20.00
## Max. :35.000 Max. :180.00
##
## Sidewalk.Clearance Lane.Separate Temporary.Stop.Number Mode.Choice.Reason
## No :113 No :178 None :447 Safety :123
## Yes:734 Yes:669 1 stops :281 Comfortable :380
## 2 stops : 60 Low price : 76
## >=3 stops: 59 Accessibility:180
## Reliability : 61
## others : 27
##
## Weather Weekend Non.Bus.Reason Cost
## Sunny :564 No :640 No Route :164 Min. : 0.000
## Rainny: 19 Yes:207 Uncomfortable :226 1st Qu.: 3.000
## Cool :264 Unsafety : 19 Median : 5.000
## Long waiting time:213 Mean : 8.685
## Unreliability : 69 3rd Qu.: 10.000
## others : 46 Max. :285.000
## NA's :110
## Bus.Stop.Present Gender Age Occupation
## No : 84 Female:353 <= 15: 38 Students :228
## Yes :676 Male :494 16-18: 77 Free labor:184
## Don't know: 87 19-24:279 Officers :136
## 25-45:317 Pupils : 93
## 46-60: 98 Workers : 83
## >60 : 38 Others : 69
## (Other) : 54
## Income Number.of.Children Motor.Certificate
## <8 millions :213 None :373 No :131
## (8-15) millions :336 1 child :323 Yes:716
## (15-25) millions:209 2 children :126
## >25 millions : 89 >= 3 children: 25
##
##
##
## Car.Certificate Bicycle.Owning Motor.Owning Car.Owning Number.of.Bicycles
## No :692 No :499 No :182 No :728 None:368
## Yes:155 Yes:348 Yes:665 Yes:119 1 :369
## 2 : 99
## >=3 : 11
##
##
##
## Number.of.Motors Number.of.Car
## None: 37 None:658
## 1 :133 1 :170
## 2 :423 >=2 : 19
## 3 :201
## >3 : 53
##
## library(pastecs)## Warning: package 'pastecs' was built under R version 4.0.4##
## Attaching package: 'pastecs'## The following objects are masked from 'package:dplyr':
##
## first, last## The following object is masked from 'package:tidyr':
##
## extract## The following object is masked from 'package:magrittr':
##
## extract# With continuous variables
stat.desc(MC) ## Travel.Mode Bus.Stop.Condition Central.Area Purpose Frequency
## nbr.val 8.470000e+02 NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA
## min 1.000000e+00 NA NA NA NA
## max 7.000000e+00 NA NA NA NA
## range 6.000000e+00 NA NA NA NA
## sum 3.088000e+03 NA NA NA NA
## median 3.000000e+00 NA NA NA NA
## mean 3.645809e+00 NA NA NA NA
## SE.mean 5.309103e-02 NA NA NA NA
## CI.mean.0.95 1.042056e-01 NA NA NA NA
## var 2.387403e+00 NA NA NA NA
## std.dev 1.545122e+00 NA NA NA NA
## coef.var 4.238078e-01 NA NA NA NA
## Departure.Time Distance Travel.Period Sidewalk.Clearance
## nbr.val NA 847.0000000 8.470000e+02 NA
## nbr.null NA 0.0000000 2.000000e+00 NA
## nbr.na NA 0.0000000 0.000000e+00 NA
## min NA 0.0150000 0.000000e+00 NA
## max NA 35.0000000 1.800000e+02 NA
## range NA 34.9850000 1.800000e+02 NA
## sum NA 5498.7550000 1.475400e+04 NA
## median NA 5.0000000 1.500000e+01 NA
## mean NA 6.4920366 1.741913e+01 NA
## SE.mean NA 0.1871930 5.225879e-01 NA
## CI.mean.0.95 NA 0.3674173 1.025721e+00 NA
## var NA 29.6799259 2.313141e+02 NA
## std.dev NA 5.4479286 1.520901e+01 NA
## coef.var NA 0.8391710 8.731215e-01 NA
## Lane.Separate Temporary.Stop.Number Mode.Choice.Reason Weather
## nbr.val NA NA NA NA
## nbr.null NA NA NA NA
## nbr.na NA NA NA NA
## min NA NA NA NA
## max NA NA NA NA
## range NA NA NA NA
## sum NA NA NA NA
## median NA NA NA NA
## mean NA NA NA NA
## SE.mean NA NA NA NA
## CI.mean.0.95 NA NA NA NA
## var NA NA NA NA
## std.dev NA NA NA NA
## coef.var NA NA NA NA
## Weekend Non.Bus.Reason Cost Bus.Stop.Present Gender Age
## nbr.val NA NA 847.000000 NA NA NA
## nbr.null NA NA 83.000000 NA NA NA
## nbr.na NA NA 0.000000 NA NA NA
## min NA NA 0.000000 NA NA NA
## max NA NA 285.000000 NA NA NA
## range NA NA 285.000000 NA NA NA
## sum NA NA 7356.000000 NA NA NA
## median NA NA 5.000000 NA NA NA
## mean NA NA 8.684770 NA NA NA
## SE.mean NA NA 0.559535 NA NA NA
## CI.mean.0.95 NA NA 1.098240 NA NA NA
## var NA NA 265.178290 NA NA NA
## std.dev NA NA 16.284296 NA NA NA
## coef.var NA NA 1.875041 NA NA NA
## Occupation Income Number.of.Children Motor.Certificate
## nbr.val NA NA NA NA
## nbr.null NA NA NA NA
## nbr.na NA NA NA NA
## min NA NA NA NA
## max NA NA NA NA
## range NA NA NA NA
## sum NA NA NA NA
## median NA NA NA NA
## mean NA NA NA NA
## SE.mean NA NA NA NA
## CI.mean.0.95 NA NA NA NA
## var NA NA NA NA
## std.dev NA NA NA NA
## coef.var NA NA NA NA
## Car.Certificate Bicycle.Owning Motor.Owning Car.Owning
## nbr.val NA NA NA NA
## nbr.null NA NA NA NA
## nbr.na NA NA NA NA
## min NA NA NA NA
## max NA NA NA NA
## range NA NA NA NA
## sum NA NA NA NA
## median NA NA NA NA
## mean NA NA NA NA
## SE.mean NA NA NA NA
## CI.mean.0.95 NA NA NA NA
## var NA NA NA NA
## std.dev NA NA NA NA
## coef.var NA NA NA NA
## Number.of.Bicycles Number.of.Motors Number.of.Car
## nbr.val NA NA NA
## nbr.null NA NA NA
## nbr.na NA NA NA
## min NA NA NA
## max NA NA NA
## range NA NA NA
## sum NA NA NA
## median NA NA NA
## mean NA NA NA
## SE.mean NA NA NA
## CI.mean.0.95 NA NA NA
## var NA NA NA
## std.dev NA NA NA
## coef.var NA NA NA# Caculate SD (do lech chuan) and SE (sai so chuan)
desc <- function (Cost)
{
av <- mean(Cost)
sd <- sd(Cost)
se <- sd/sqrt(length(Cost))
c(mean = av, SD = sd, SE = se)
}
table(Travel.Mode) ## Travel.Mode
## 1 2 3 4 5 6
## 35 44 518 82 35 23# Descriptive Statistics of categorical variables
with(MC, table(Bus.Stop.Condition, Travel.Mode))## Travel.Mode
## Bus.Stop.Condition 1 2 3 4 5 6 7
## No 15 19 253 39 16 11 63
## Yes 20 25 265 43 19 12 47with(MC, table(Central.Area, Travel.Mode))## Travel.Mode
## Central.Area 1 2 3 4 5 6 7
## No 18 29 284 45 16 7 44
## Yes 17 15 234 37 19 16 66with(MC, table(Purpose, Travel.Mode))## Travel.Mode
## Purpose 1 2 3 4 5 6 7
## Work/Study 23 32 351 54 17 14 80
## Picking Children 3 1 69 10 0 2 9
## Entertainment 7 8 89 17 18 6 14
## Others 2 3 9 1 0 1 7with(MC, table(Frequency, Travel.Mode))## Travel.Mode
## Frequency 1 2 3 4 5 6 7
## Once 2 2 23 9 3 6 13
## 2 times 1 6 18 11 7 6 19
## 3 times 3 2 33 7 4 4 20
## > 3 times 29 34 444 55 21 7 58with(MC, table(Departure.Time, Travel.Mode))## Travel.Mode
## Departure.Time 1 2 3 4 5 6 7
## Morning 19 25 328 44 15 12 71
## Afternoon 4 7 34 7 5 2 12
## Evening 10 6 85 13 7 2 20
## Others 2 6 71 18 8 7 7with(MC, table(Sidewalk.Clearance, Travel.Mode))## Travel.Mode
## Sidewalk.Clearance 1 2 3 4 5 6 7
## No 3 6 79 4 3 3 15
## Yes 32 38 439 78 32 20 95with(MC, table(Lane.Separate, Travel.Mode))## Travel.Mode
## Lane.Separate 1 2 3 4 5 6 7
## No 14 12 111 13 6 2 20
## Yes 21 32 407 69 29 21 90with(MC, table(Temporary.Stop.Number, Travel.Mode))## Travel.Mode
## Temporary.Stop.Number 1 2 3 4 5 6 7
## None 22 20 247 46 17 14 81
## 1 stops 9 22 197 25 12 6 10
## 2 stops 1 1 35 7 4 1 11
## >=3 stops 3 1 39 4 2 2 8with(MC, table(Mode.Choice.Reason, Travel.Mode))## Travel.Mode
## Mode.Choice.Reason 1 2 3 4 5 6 7
## Safety 7 10 27 37 7 3 32
## Comfortable 15 14 256 35 14 5 41
## Low price 7 10 24 0 0 1 34
## Accessibility 1 3 161 2 4 8 1
## Reliability 0 4 42 6 6 3 0
## others 5 3 8 2 4 3 2with(MC, table(Weather, Travel.Mode))## Travel.Mode
## Weather 1 2 3 4 5 6 7
## Sunny 21 25 357 54 18 14 75
## Rainny 0 0 2 4 2 2 9
## Cool 14 19 159 24 15 7 26with(MC, table(Weekend, Travel.Mode))## Travel.Mode
## Weekend 1 2 3 4 5 6 7
## No 29 33 403 53 22 13 87
## Yes 6 11 115 29 13 10 23with(MC, table(Bus.Stop.Present, Travel.Mode))## Travel.Mode
## Bus.Stop.Present 1 2 3 4 5 6 7
## No 3 4 48 5 5 0 19
## Yes 31 37 415 58 28 21 86
## Don't know 1 3 55 19 2 2 5with(MC, table(Gender, Travel.Mode))## Travel.Mode
## Gender 1 2 3 4 5 6 7
## Female 15 14 223 20 24 13 44
## Male 20 30 295 62 11 10 66with(MC, table(Age, Travel.Mode))## Travel.Mode
## Age 1 2 3 4 5 6 7
## <= 15 5 17 3 1 6 2 4
## 16-18 7 12 32 1 11 0 14
## 19-24 12 10 180 8 12 11 46
## 25-45 5 3 232 59 2 3 13
## 46-60 4 0 62 13 2 4 13
## >60 2 2 9 0 2 3 20with(MC, table(Occupation, Travel.Mode))## Travel.Mode
## Occupation 1 2 3 4 5 6 7
## Pupils 13 26 21 1 18 2 12
## Students 11 12 144 6 7 6 42
## Officers 2 1 81 36 2 2 12
## Housewife 1 0 27 2 2 5 11
## Unemployed 0 1 4 0 0 0 1
## Workers 1 2 72 5 0 1 2
## Free labor 4 0 139 20 3 6 12
## Others 3 2 30 12 3 1 18with(MC, table(Income, Travel.Mode))## Travel.Mode
## Income 1 2 3 4 5 6 7
## <8 millions 18 11 114 3 7 8 52
## (8-15) millions 7 20 236 22 14 8 29
## (15-25) millions 6 8 129 25 10 5 26
## >25 millions 4 5 39 32 4 2 3with(MC, table(Number.of.Children, Travel.Mode))## Travel.Mode
## Number.of.Children 1 2 3 4 5 6 7
## None 18 21 224 20 21 5 64
## 1 child 10 18 226 26 10 8 25
## 2 children 3 4 57 33 4 6 19
## >= 3 children 4 1 11 3 0 4 2with(MC, table(Motor.Certificate, Travel.Mode))## Travel.Mode
## Motor.Certificate 1 2 3 4 5 6 7
## No 14 27 25 2 19 5 39
## Yes 21 17 493 80 16 18 71with(MC, table(Car.Certificate, Travel.Mode))## Travel.Mode
## Car.Certificate 1 2 3 4 5 6 7
## No 32 44 447 11 35 19 104
## Yes 3 0 71 71 0 4 6with(MC, table(Bicycle.Owning, Travel.Mode))## Travel.Mode
## Bicycle.Owning 1 2 3 4 5 6 7
## No 19 2 336 51 17 12 62
## Yes 16 42 182 31 18 11 48with(MC, table(Motor.Owning, Travel.Mode))## Travel.Mode
## Motor.Owning 1 2 3 4 5 6 7
## No 20 33 42 6 25 5 51
## Yes 15 11 476 76 10 18 59with(MC, table(Car.Owning, Travel.Mode))## Travel.Mode
## Car.Owning 1 2 3 4 5 6 7
## No 32 44 477 15 35 19 106
## Yes 3 0 41 67 0 4 4with(MC, table(Number.of.Bicycles, Travel.Mode))## Travel.Mode
## Number.of.Bicycles 1 2 3 4 5 6 7
## None 15 0 254 41 13 7 38
## 1 14 30 198 36 19 12 60
## 2 4 14 59 4 3 4 11
## >=3 2 0 7 1 0 0 1with(MC, table(Number.of.Motors, Travel.Mode))## Travel.Mode
## Number.of.Motors 1 2 3 4 5 6 7
## None 6 6 5 2 4 1 13
## 1 6 6 68 17 7 2 27
## 2 15 23 262 49 15 9 50
## 3 4 6 147 9 9 9 17
## >3 4 3 36 5 0 2 3with(MC, table(Number.of.Car, Travel.Mode))## Travel.Mode
## Number.of.Car 1 2 3 4 5 6 7
## None 27 38 436 10 30 16 101
## 1 7 5 75 66 5 4 8
## >=2 1 1 7 6 0 3 1# Descriptive Statistics of categorical variables
with(MC, do.call(rbind, tapply(Distance, Travel.Mode, function(x) c(M = mean(x), SD = sd(x)))))## M SD
## 1 0.6481429 0.6188225
## 2 1.9879545 1.4531963
## 3 6.3102317 5.0049213
## 4 10.2926829 7.6650365
## 5 4.9142857 4.3139407
## 6 7.1043478 4.1292172
## 7 8.5500000 4.7283791with(MC, do.call(rbind, tapply(Travel.Period, Travel.Mode, function(x) c(M = mean(x), SD = sd(x)))))## M SD
## 1 10.65714 11.85437
## 2 15.93182 14.43896
## 3 15.79923 12.75120
## 4 25.86585 27.07272
## 5 15.94286 12.08534
## 6 19.47826 16.35923
## 7 21.53636 12.38575with(MC, do.call(rbind, tapply(Cost, Travel.Mode, function(x) c(M = mean(x), SD = sd(x)))))## M SD
## 1 0.000000 0.000000
## 2 0.000000 0.000000
## 3 6.332046 4.984868
## 4 20.585366 15.330073
## 5 6.800000 8.259754
## 6 68.478261 62.489904
## 7 5.227273 1.0462615. Estimate Multinominal Logit Regression Model
# Multinominal Logit Model
## Way 1 - Use mlogit (in mlogit package)
## Way 2 - Use multinom (in nnet package)
library(nnet)
attach(MC)## The following objects are masked from NBU:
##
## Age, Bicycle.Owning, Bus.Stop.Condition, Bus.Stop.Present,
## Car.Certificate, Car.Owning, Central.Area, Cost, Departure.Time,
## Distance, Frequency, Gender, Income, Lane.Separate,
## Mode.Choice.Reason, Motor.Certificate, Motor.Owning,
## Non.Bus.Reason, Number.of.Bicycles, Number.of.Car,
## Number.of.Children, Number.of.Motors, Occupation, Purpose,
## Sidewalk.Clearance, Temporary.Stop.Number, Travel.Mode,
## Travel.Period, Weather, Weekend## The following objects are masked from MC (pos = 20):
##
## Age, Bicycle.Owning, Bus.Stop.Condition, Bus.Stop.Present,
## Car.Certificate, Car.Owning, Central.Area, Cost, Departure.Time,
## Distance, Frequency, Gender, Income, Lane.Separate,
## Mode.Choice.Reason, Motor.Certificate, Motor.Owning,
## Non.Bus.Reason, Number.of.Bicycles, Number.of.Car,
## Number.of.Children, Number.of.Motors, Occupation, Purpose,
## Sidewalk.Clearance, Temporary.Stop.Number, Travel.Mode,
## Travel.Period, Weather, Weekend#MC$Travel.Mode <- relevel (MC$Travel.Mode, ref = "Motorbike")
mlm <- multinom(Travel.Mode ~ Bus.Stop.Condition + Central.Area + Purpose + Frequency + Departure.Time + Sidewalk.Clearance + Lane.Separate + Temporary.Stop.Number + Mode.Choice.Reason + Weather + Weekend + Bus.Stop.Present + Gender + Age + Occupation + Income + Number.of.Children + Motor.Certificate + Car.Certificate + Bicycle.Owning + Motor.Owning + Car.Owning + Number.of.Bicycles + Number.of.Motors + Number.of.Car + Distance + Travel.Period + Cost, data = MC)## # weights: 448 (378 variable)
## initial value 1648.185896
## iter 10 value 837.316762
## iter 20 value 515.614078
## iter 30 value 318.969973
## iter 40 value 220.338239
## iter 50 value 138.434422
## iter 60 value 87.547025
## iter 70 value 67.982714
## iter 80 value 58.842117
## iter 90 value 51.464154
## iter 100 value 47.921917
## final value 47.921917
## stopped after 100 iterationssummary(mlm)## Warning in sqrt(diag(vc)): NaNs produced## Call:
## multinom(formula = Travel.Mode ~ Bus.Stop.Condition + Central.Area +
## Purpose + Frequency + Departure.Time + Sidewalk.Clearance +
## Lane.Separate + Temporary.Stop.Number + Mode.Choice.Reason +
## Weather + Weekend + Bus.Stop.Present + Gender + Age + Occupation +
## Income + Number.of.Children + Motor.Certificate + Car.Certificate +
## Bicycle.Owning + Motor.Owning + Car.Owning + Number.of.Bicycles +
## Number.of.Motors + Number.of.Car + Distance + Travel.Period +
## Cost, data = MC)
##
## Coefficients:
## (Intercept) Bus.Stop.ConditionYes Central.AreaYes PurposePicking Children
## 2 -65.88309 8.5050215 -15.6747067 -10.928156
## 3 -31.94959 -3.7987439 5.3127500 5.587730
## 4 -114.47512 6.5603347 11.3154862 2.835531
## 5 -48.28852 -0.6748395 0.6297604 -31.456326
## 6 -44.23307 9.8094331 0.9310058 -8.899826
## 7 -20.00151 -2.9692153 1.2990007 -2.105245
## PurposeEntertainment PurposeOthers Frequency2 times Frequency3 times
## 2 -3.544378 33.62123 9.640538 0.4212961
## 3 1.016287 5.55503 -16.960394 -17.3869516
## 4 -17.926037 30.54329 -6.434457 -19.9447904
## 5 9.148288 -32.55171 14.238415 7.0487005
## 6 -12.713104 -31.38836 -20.017056 -13.2641194
## 7 -23.397109 13.38565 24.032834 -6.9497566
## Frequency> 3 times Departure.TimeAfternoon Departure.TimeEvening
## 2 -2.224911 16.052091 -22.708855
## 3 -18.688299 4.069003 -12.161995
## 4 -25.762874 5.847510 -7.696991
## 5 -14.902881 -12.376268 -4.550587
## 6 -36.583856 -17.820829 -12.168660
## 7 -14.950601 -5.842983 -2.503386
## Departure.TimeOthers Sidewalk.ClearanceYes Lane.SeparateYes
## 2 -11.3817448 21.647411 -8.618778
## 3 0.3103967 3.175385 -3.715576
## 4 8.6188563 10.852905 -18.756181
## 5 -1.9513499 32.717271 3.064914
## 6 -3.4973866 -2.626050 -1.082702
## 7 0.3679199 6.132839 1.026910
## Temporary.Stop.Number1 stops Temporary.Stop.Number2 stops
## 2 3.1713327 5.8788669
## 3 -0.9782363 14.5478088
## 4 1.8845265 0.5172116
## 5 5.9001998 17.5915053
## 6 -9.1305072 11.7022206
## 7 -9.5643889 17.4381549
## Temporary.Stop.Number>=3 stops Mode.Choice.ReasonComfortable
## 2 -21.2218043 -16.987098
## 3 5.2750069 18.208647
## 4 -13.8169940 0.553613
## 5 15.1926416 4.342323
## 6 0.6472311 29.193941
## 7 -11.4080209 7.240924
## Mode.Choice.ReasonLow price Mode.Choice.ReasonAccessibility
## 2 1.82971227 -0.4813187
## 3 -2.21977403 34.0881582
## 4 -44.82466069 -14.0394393
## 5 -65.99930654 11.3950467
## 6 24.27642217 62.7348134
## 7 -0.01377295 -2.5862467
## Mode.Choice.ReasonReliability Mode.Choice.Reasonothers WeatherRainny
## 2 25.61237 -32.170189 -31.716264
## 3 51.88824 3.599263 -6.035592
## 4 29.09767 -5.422621 30.400870
## 5 55.86081 -17.239671 22.526836
## 6 45.30641 7.104049 30.672508
## 7 -70.95640 -73.352631 4.231033
## WeatherCool WeekendYes Bus.Stop.PresentYes Bus.Stop.PresentDon't know
## 2 11.614129 33.192640 9.673060 12.375397
## 3 -5.015971 5.816160 5.506442 24.247159
## 4 12.127513 10.351254 -10.582221 8.669239
## 5 -7.124760 5.373836 3.807179 6.673898
## 6 11.339220 -7.229816 -8.997566 14.798245
## 7 -2.588744 4.356638 -13.059774 6.781345
## GenderMale Age16-18 Age19-24 Age25-45 Age46-60 Age>60
## 2 14.556269 -30.621305 -33.035497 -18.526833 -37.136120 -0.3770594
## 3 4.842833 -8.834645 -28.807808 -27.972520 -20.163157 -36.5320478
## 4 4.427165 -15.061870 -18.620761 5.212277 3.423268 -68.1424256
## 5 -4.784744 6.127076 24.845222 -22.803082 3.819504 -11.7336534
## 6 -3.920150 -27.199070 5.347109 -11.868310 -9.383458 -4.4667963
## 7 4.272923 -2.005498 -13.925199 -5.634598 4.911796 17.5236200
## OccupationStudents OccupationOfficers OccupationHousewife
## 2 34.71662 -7.512362 -33.17564
## 3 23.36027 23.171679 43.66380
## 4 25.39870 3.697737 18.57300
## 5 -13.78764 7.903832 29.62351
## 6 -27.43480 -11.091747 31.32912
## 7 36.30393 33.813194 33.09436
## OccupationUnemployed OccupationWorkers OccupationFree labor OccupationOthers
## 2 36.3319071 26.4028664 18.226109 9.521910
## 3 7.1152758 19.4165214 32.183391 11.500174
## 4 0.8762254 0.9296973 8.321882 -31.250752
## 5 -30.3032091 -22.8548871 22.516461 30.802064
## 6 9.5651744 -18.5458682 -4.528603 -4.507908
## 7 26.3794067 -28.9111082 31.250915 27.417192
## Income(8-15) millions Income(15-25) millions Income>25 millions
## 2 17.81753 27.352420 13.298759
## 3 17.92829 12.524375 15.778952
## 4 24.91385 7.075959 14.280702
## 5 27.89878 51.392576 33.996373
## 6 17.60108 10.750648 3.646807
## 7 6.30491 5.636736 -7.321030
## Number.of.Children1 child Number.of.Children2 children
## 2 18.376210 9.487571
## 3 14.776101 11.546773
## 4 11.334344 28.173885
## 5 3.421037 27.710355
## 6 12.176367 15.420497
## 7 13.169499 3.652937
## Number.of.Children>= 3 children Motor.CertificateYes Car.CertificateYes
## 2 -7.33467 -5.6392209 13.691566
## 3 -18.95306 0.6552896 1.710987
## 4 -46.28468 10.0332017 10.579014
## 5 -87.34465 -24.3200742 -12.693989
## 6 -54.62553 -5.4515155 -5.777335
## 7 -24.82050 -15.4352514 -6.176104
## Bicycle.OwningYes Motor.OwningYes Car.OwningYes Number.of.Bicycles1
## 2 13.2138518 -22.846153 -17.845881 21.618004
## 3 1.0656901 7.180406 3.984878 -8.143055
## 4 7.9710301 13.692934 29.664021 7.708493
## 5 -0.6828263 -9.124028 -1.147317 1.138528
## 6 3.2999676 3.012221 16.936406 3.081916
## 7 5.0871164 -6.664886 10.346634 -11.186013
## Number.of.Bicycles2 Number.of.Bicycles>=3 Number.of.Motors1 Number.of.Motors2
## 2 30.266971 -34.837993 -19.002691 -5.114798
## 3 -5.084783 7.942406 -5.674482 7.777190
## 4 -6.225399 11.590058 14.319384 19.438750
## 5 -14.022375 -24.819800 -18.394167 -8.183637
## 6 -4.907710 4.848789 -9.800646 8.319197
## 7 -19.276988 -31.740016 9.537736 22.656786
## Number.of.Motors3 Number.of.Motors>3 Number.of.Car1 Number.of.Car>=2
## 2 -13.120710 -6.500748 3.292208 16.950101
## 3 2.937216 -6.594606 -8.712119 -19.326856
## 4 8.659924 23.866019 1.802742 -22.930814
## 5 -24.962545 -58.414429 -25.697651 -14.110599
## 6 10.176784 3.024154 1.173069 -2.411943
## 7 11.816824 7.375892 -25.109911 -10.202022
## Distance Travel.Period Cost
## 2 4.9017500 0.18936772 0.1437458
## 3 0.1652592 -0.11044200 11.4393980
## 4 -10.4118911 0.16455717 21.2721360
## 5 -9.9858088 0.01570071 18.9511235
## 6 -13.5316780 0.20933864 21.7440584
## 7 5.5803532 -0.28718800 5.9982307
##
## Std. Errors:
## (Intercept) Bus.Stop.ConditionYes Central.AreaYes PurposePicking Children
## 2 20.29336 17.37617 14.655121 25.282880
## 3 22.03849 13.17371 9.179734 16.753564
## 4 11.79982 17.46420 24.940349 8.115991
## 5 30.25081 14.56282 10.679512 2.104509
## 6 12.00263 13.47516 1.180054 7.188219
## 7 22.16710 13.00126 9.260512 16.352543
## PurposeEntertainment PurposeOthers Frequency2 times Frequency3 times
## 2 22.033554 23.82017294 21.601037 31.818088
## 3 12.594042 18.29806484 16.205713 15.807078
## 4 5.922091 0.04261744 4.263298 8.047264
## 5 13.416169 43.86380572 15.952738 17.209124
## 6 13.006217 0.04366229 3.864937 13.304819
## 7 14.170597 18.19783491 17.342419 15.705317
## Frequency> 3 times Departure.TimeAfternoon Departure.TimeEvening
## 2 20.91433 26.238874 20.713054
## 3 12.30076 12.264698 15.422456
## 4 14.33475 6.566297 11.535940
## 5 13.50220 14.709455 16.510372
## 6 11.41922 3.995918 9.677194
## 7 12.35492 12.917008 14.856463
## Departure.TimeOthers Sidewalk.ClearanceYes Lane.SeparateYes
## 2 26.7616458 25.267606 14.72445
## 3 18.9812714 15.471749 13.74630
## 4 0.7367377 9.366865 25.69159
## 5 18.7980023 21.130370 15.55975
## 6 0.7534031 11.340327 11.50480
## 7 19.0421903 15.605120 13.75238
## Temporary.Stop.Number1 stops Temporary.Stop.Number2 stops
## 2 16.204835 28.266972
## 3 11.389309 13.829101
## 4 25.326816 11.434405
## 5 12.656817 18.959527
## 6 8.049242 9.577741
## 7 11.773772 13.840515
## Temporary.Stop.Number>=3 stops Mode.Choice.ReasonComfortable
## 2 3.373588e+01 18.52794
## 3 2.469182e+01 14.53093
## 4 1.056424e-02 23.19962
## 5 2.871289e+01 15.42538
## 6 1.995455e-04 14.48898
## 7 2.615565e+01 13.58816
## Mode.Choice.ReasonLow price Mode.Choice.ReasonAccessibility
## 2 2.132557e+01 29.65431666
## 3 2.010788e+01 14.87571974
## 4 1.597884e-12 0.04361645
## 5 6.420828e+01 17.15912280
## 6 1.250917e+00 13.77209115
## 7 2.006744e+01 16.86151652
## Mode.Choice.ReasonReliability Mode.Choice.Reasonothers WeatherRainny
## 2 1.791744e+01 21.9476738 9.666821e-09
## 3 1.272922e+01 15.7208939 2.648567e+00
## 4 2.623777e+01 6.7205628 4.263717e+00
## 5 1.291856e+01 19.1092091 1.055209e-02
## 6 1.630065e+01 6.7233532 4.260593e+00
## 7 3.429618e-25 0.9737488 2.648517e+00
## WeatherCool WeekendYes Bus.Stop.PresentYes Bus.Stop.PresentDon't know
## 2 17.465762 16.03649 22.182147 33.245715
## 3 11.499973 14.05133 14.740253 15.920203
## 4 4.712808 25.83584 9.615579 8.120043
## 5 14.106593 15.52646 17.161846 20.481558
## 6 7.771390 21.55537 19.156013 7.732225
## 7 11.635133 14.09497 14.687178 15.029555
## GenderMale Age16-18 Age19-24 Age25-45 Age46-60 Age>60
## 2 17.51950 21.470996 22.38564 21.45037 27.621945 3.382908e+01
## 3 12.37504 12.977128 16.80777 14.12205 19.864961 2.254060e+01
## 4 18.40252 6.562133 12.32213 19.04402 6.655124 1.365172e-09
## 5 14.61407 15.963229 26.55727 19.17632 26.838238 3.345748e+01
## 6 12.38995 4.063099 13.95182 18.14476 15.459355 6.197404e+00
## 7 12.47847 13.713903 17.42705 14.90973 22.050522 2.761891e+01
## OccupationStudents OccupationOfficers OccupationHousewife
## 2 21.84364 37.15857 1.104176e-10
## 3 16.72176 17.52789 2.482050e+01
## 4 11.91335 17.34669 1.818930e-04
## 5 22.81467 25.12426 2.695013e+01
## 6 13.39194 16.30094 1.221307e+01
## 7 17.70150 17.87876 2.418951e+01
## OccupationUnemployed OccupationWorkers OccupationFree labor OccupationOthers
## 2 2.724435e+01 30.2334001 33.958599 23.5183047
## 3 1.788479e+01 21.8005572 14.160145 16.9261074
## 4 NaN 8.0959200 16.470101 0.2696104
## 5 1.017261e-12 35.5140132 20.361814 31.9812775
## 6 4.012988e-11 0.6957329 7.021386 10.2518708
## 7 2.012132e+01 28.2096217 14.762331 17.0630769
## Income(8-15) millions Income(15-25) millions Income>25 millions
## 2 18.36123 21.330921 25.763086
## 3 12.41022 12.125418 13.553864
## 4 15.89782 10.644056 12.327606
## 5 12.20178 17.309336 29.618526
## 6 24.90504 7.437589 4.093651
## 7 12.17699 12.484369 15.526578
## Number.of.Children1 child Number.of.Children2 children
## 2 19.45649 24.21193
## 3 10.15147 17.81406
## 4 22.71941 12.71257
## 5 16.92511 32.83700
## 6 14.91813 13.36824
## 7 10.12130 18.19826
## Number.of.Children>= 3 children Motor.CertificateYes Car.CertificateYes
## 2 3.553993e+01 16.43805 33.084540
## 3 1.503777e+01 12.56269 30.339570
## 4 3.585120e-08 11.79921 11.019520
## 5 5.212488e-17 20.12292 57.542865
## 6 2.141673e-04 12.59996 8.894594
## 7 1.628098e+01 12.64654 30.569417
## Bicycle.OwningYes Motor.OwningYes Car.OwningYes Number.of.Bicycles1
## 2 16.410961 22.030949 29.236685 17.74670
## 3 15.787687 12.110133 26.447941 11.66336
## 4 13.867284 14.405233 11.023593 12.07309
## 5 15.974220 13.598001 53.590076 12.93687
## 6 9.981325 6.930657 8.889264 12.99069
## 7 15.778778 13.164790 26.560783 11.75292
## Number.of.Bicycles2 Number.of.Bicycles>=3 Number.of.Motors1 Number.of.Motors2
## 2 26.069542 NaN 22.90090 15.10541
## 3 16.590297 9.409492e+00 14.68317 12.21702
## 4 6.693222 1.395329e-06 26.96749 25.75150
## 5 19.863239 4.177149e+00 21.46507 16.11014
## 6 6.697362 1.250898e+00 10.07368 15.84779
## 7 16.327584 1.024671e+01 15.17003 14.09406
## Number.of.Motors3 Number.of.Motors>3 Number.of.Car1 Number.of.Car>=2
## 2 18.693519 2.354881e+01 20.885856 0.000926459
## 3 19.148662 1.482611e+01 13.976937 19.454778375
## 4 6.144533 3.057191e-02 8.918476 6.772808630
## 5 23.261402 7.157342e-04 22.781101 0.004097429
## 6 7.541138 7.985084e-03 12.268160 6.695992664
## 7 19.787966 1.501720e+01 14.529932 21.627861883
## Distance Travel.Period Cost
## 2 7.831015 0.8939013 7.861235
## 3 8.569957 0.6299910 7.703074
## 4 29.299647 6.1185902 14.678352
## 5 9.812395 0.7314050 9.700458
## 6 24.880453 1.3234314 14.635195
## 7 8.392133 0.6475349 6.951994
##
## Residual Deviance: 95.84383
## AIC: 851.8438# Calculate OR and CI
exp(coef(mlm))## (Intercept) Bus.Stop.ConditionYes Central.AreaYes PurposePicking Children
## 2 2.439697e-29 4.939511e+03 1.557978e-07 1.794578e-05
## 3 1.331889e-14 2.239889e-02 2.029075e+02 2.671287e+02
## 4 1.923476e-50 7.065081e+02 8.208300e+04 1.703944e+01
## 5 1.067983e-21 5.092381e-01 1.877161e+00 2.181179e-14
## 6 6.163448e-20 1.820466e+04 2.537060e+00 1.364126e-04
## 7 2.058050e-09 5.134359e-02 3.665632e+00 1.218158e-01
## PurposeEntertainment PurposeOthers Frequency2 times Frequency3 times
## 2 2.888657e-02 3.994967e+14 1.537561e+04 1.523935e+00
## 3 2.762918e+00 2.585347e+02 4.307195e-08 2.811531e-08
## 4 1.639915e-08 1.839844e+13 1.605280e-03 2.178149e-09
## 5 9.398334e+03 7.294076e-15 1.526389e+06 1.151362e+03
## 6 3.011404e-06 2.334577e-14 2.026296e-09 1.735666e-06
## 7 6.898658e-11 6.505974e+05 2.737330e+10 9.588685e-04
## Frequency> 3 times Departure.TimeAfternoon Departure.TimeEvening
## 2 1.080771e-01 9.361261e+06 1.372997e-10
## 3 7.651998e-09 5.849858e+01 5.225319e-06
## 4 6.476288e-12 3.463708e+02 4.541918e-04
## 5 3.371018e-07 4.217501e-06 1.056100e-02
## 6 1.293699e-16 1.821850e-08 5.190604e-06
## 7 3.213930e-07 2.900178e-03 8.180749e-02
## Departure.TimeOthers Sidewalk.ClearanceYes Lane.SeparateYes
## 2 1.140174e-05 2.519712e+09 1.806810e-04
## 3 1.363966e+00 2.393604e+01 2.434141e-02
## 4 5.535052e+03 5.168408e+04 7.149805e-09
## 5 1.420821e-01 1.617821e+14 2.143261e+01
## 6 3.027640e-02 7.236376e-02 3.386792e-01
## 7 1.444726e+00 4.607422e+02 2.792423e+00
## Temporary.Stop.Number1 stops Temporary.Stop.Number2 stops
## 2 2.383923e+01 3.574041e+02
## 3 3.759736e-01 2.079855e+06
## 4 6.583236e+00 1.677344e+00
## 5 3.651104e+02 4.364090e+07
## 6 1.083106e-04 1.208398e+05
## 7 7.018409e-05 3.743643e+07
## Temporary.Stop.Number>=3 stops Mode.Choice.ReasonComfortable
## 2 6.074177e-10 4.193696e-08
## 3 1.953918e+02 8.089376e+07
## 4 9.985176e-07 1.739527e+00
## 5 3.963514e+06 7.688592e+01
## 6 1.910244e+00 4.772734e+12
## 7 1.110605e-05 1.395382e+03
## Mode.Choice.ReasonLow price Mode.Choice.ReasonAccessibility
## 2 6.232093e+00 6.179680e-01
## 3 1.086337e-01 6.372341e+14
## 4 3.411122e-20 7.993721e-07
## 5 2.172028e-29 8.888038e+04
## 6 3.492337e+10 1.759476e+27
## 7 9.863215e-01 7.530214e-02
## Mode.Choice.ReasonReliability Mode.Choice.Reasonothers WeatherRainny
## 2 1.328350e+11 1.068229e-14 1.681905e-14
## 3 3.425897e+22 3.657128e+01 2.392080e-03
## 4 4.334698e+12 4.415558e-03 1.595622e+13
## 5 1.819867e+24 3.257661e-08 6.071284e+09
## 6 4.745964e+19 1.216884e+03 2.093635e+13
## 7 1.527666e-31 1.391097e-32 6.878823e+01
## WeatherCool WeekendYes Bus.Stop.PresentYes Bus.Stop.PresentDon't know
## 2 1.106502e+05 2.602437e+14 1.588388e+04 2.369008e+05
## 3 6.631192e-03 3.356805e+02 2.462734e+02 3.391620e+10
## 4 1.848894e+05 3.129628e+04 2.536296e-05 5.821069e+03
## 5 8.049258e-04 2.156886e+02 4.502327e+01 7.914746e+02
## 6 8.405440e+04 7.246540e-04 1.237106e-04 2.671751e+06
## 7 7.511435e-02 7.799449e+01 2.129178e-06 8.812534e+02
## GenderMale Age16-18 Age19-24 Age25-45 Age46-60 Age>60
## 2 2.097526e+06 5.027317e-14 4.496409e-15 8.992875e-09 7.447111e-17 6.858753e-01
## 3 1.268282e+02 1.456003e-04 3.082677e-13 7.107043e-13 1.750863e-09 1.362490e-16
## 4 8.369378e+01 2.875497e-07 8.186647e-09 1.835114e+02 3.066947e+01 2.547537e-30
## 5 8.356260e-03 4.580948e+02 6.167980e+10 1.249532e-10 4.558161e+01 8.019348e-06
## 6 1.983811e-02 1.540260e-12 2.100003e+02 7.009038e-06 8.410383e-05 1.148405e-02
## 7 7.173097e+01 1.345932e-01 8.961134e-07 3.572111e-03 1.358832e+02 4.077664e+07
## OccupationStudents OccupationOfficers OccupationHousewife
## 2 1.194636e+15 5.462891e-04 3.908445e-15
## 3 1.397124e+10 1.156998e+10 9.182175e+18
## 4 1.072787e+11 4.035587e+01 1.164534e+08
## 5 1.028260e-06 2.707638e+03 7.333755e+12
## 6 1.216799e-12 1.523756e-05 4.037051e+13
## 7 5.842451e+15 4.840426e+14 2.358843e+14
## OccupationUnemployed OccupationWorkers OccupationFree labor OccupationOthers
## 2 6.008233e+15 2.928324e+11 8.231867e+07 1.365567e+04
## 3 1.230623e+03 2.706999e+08 9.485694e+13 9.873291e+04
## 4 2.401817e+00 2.533742e+00 4.112894e+03 2.678989e-14
## 5 6.910087e-14 1.186448e-10 6.008621e+09 2.383232e+13
## 6 1.425944e+04 8.823315e-09 1.079574e-02 1.102149e-02
## 7 2.860426e+11 2.780131e-13 3.733361e+13 8.074864e+11
## Income(8-15) millions Income(15-25) millions Income>25 millions
## 2 5.470875e+07 7.568421e+11 5.964548e+05
## 3 6.111628e+07 2.749584e+05 7.123799e+06
## 4 6.606121e+10 1.183178e+03 1.592319e+06
## 5 1.307033e+12 2.086950e+22 5.813494e+14
## 6 4.406089e+07 4.666024e+04 3.835202e+01
## 7 5.472524e+02 2.805454e+02 6.614802e-04
## Number.of.Children1 child Number.of.Children2 children
## 2 9.565032e+07 1.319470e+04
## 3 2.613239e+06 1.034426e+05
## 4 8.364559e+04 1.720929e+12
## 5 3.060114e+01 1.082566e+12
## 6 1.941463e+05 4.977791e+06
## 7 5.241321e+05 3.858784e+01
## Number.of.Children>= 3 children Motor.CertificateYes Car.CertificateYes
## 2 6.525193e-04 3.555637e-03 8.834287e+05
## 3 5.872074e-09 1.925700e+00 5.534421e+00
## 4 7.921747e-21 2.277006e+04 3.930133e+04
## 5 1.166000e-38 2.741107e-11 3.069522e-06
## 6 1.889873e-24 4.289799e-03 3.096958e-03
## 7 1.661854e-11 1.979500e-07 2.078510e-03
## Bicycle.OwningYes Motor.OwningYes Car.OwningYes Number.of.Bicycles1
## 2 5.479021e+05 1.196855e-10 1.776775e-08 2.446696e+09
## 3 2.902842e+00 1.313442e+03 5.377871e+01 2.907477e-04
## 4 2.895839e+03 8.846379e+05 7.636962e+12 2.227182e+03
## 5 5.051872e-01 1.090147e-04 3.174876e-01 3.122170e+00
## 6 2.711176e+01 2.033252e+01 2.266667e+07 2.180014e+01
## 7 1.619223e+02 1.274902e-03 3.115202e+04 1.386680e-05
## Number.of.Bicycles2 Number.of.Bicycles>=3 Number.of.Motors1 Number.of.Motors2
## 2 1.395656e+13 7.413988e-16 5.587742e-09 6.007192e-03
## 3 6.190233e-03 2.814122e+03 3.432447e-03 2.385561e+03
## 4 1.978534e-03 1.080185e+05 1.655121e+06 2.767845e+08
## 5 8.131297e-07 1.663021e-11 1.026868e-08 2.791848e-04
## 6 7.389389e-03 1.275858e+02 5.541580e-05 4.101864e+03
## 7 4.247274e-09 1.642427e-14 1.387351e+04 6.913808e+09
## Number.of.Motors3 Number.of.Motors>3 Number.of.Car1 Number.of.Car>=2
## 2 2.003309e-06 1.502315e-03 2.690219e+01 2.297922e+07
## 3 1.886325e+01 1.367725e-03 1.645792e-04 4.040668e-09
## 4 5.767098e+03 2.316755e+10 6.066261e+00 1.099699e-10
## 5 1.441798e-11 4.274998e-26 6.912771e-12 7.444654e-07
## 6 2.628579e+04 2.057659e+01 3.231897e+00 8.964094e-02
## 7 1.355131e+05 1.597016e+03 1.244240e-11 3.709525e-05
## Distance Travel.Period Cost
## 2 1.345250e+02 1.2084852 1.154591e+00
## 3 1.179699e+00 0.8954383 9.291106e+04
## 4 3.007275e-05 1.1788710 1.731296e+09
## 5 4.604880e-05 1.0158246 1.699685e+08
## 6 1.328210e-06 1.2328624 2.775394e+09
## 7 2.651653e+02 0.7503706 4.027156e+02exp(confint(mlm))## Warning in sqrt(diag(vcov(object))): NaNs produced## , , 2
##
## 2.5 % 97.5 %
## (Intercept) 1.298951e-46 4.582252e-12
## Bus.Stop.ConditionYes 7.999474e-12 3.050046e+18
## Central.AreaYes 5.225157e-20 4.645403e+05
## PurposePicking Children 5.409235e-27 5.953725e+16
## PurposeEntertainment 5.078098e-21 1.643202e+17
## PurposeOthers 2.117119e-06 7.538434e+34
## Frequency2 times 6.309515e-15 3.746869e+22
## Frequency3 times 1.257075e-27 1.847446e+27
## Frequency> 3 times 1.703829e-19 6.855533e+16
## Departure.TimeAfternoon 4.332758e-16 2.022573e+29
## Departure.TimeEvening 3.211337e-28 5.870205e+07
## Departure.TimeOthers 1.894161e-28 6.863177e+17
## Sidewalk.ClearanceYes 7.825744e-13 8.112903e+30
## Lane.SeparateYes 5.289788e-17 6.171441e+08
## Temporary.Stop.Number1 stops 3.834524e-13 1.482085e+15
## Temporary.Stop.Number2 stops 3.106484e-22 4.111968e+26
## Temporary.Stop.Number>=3 stops 1.168022e-38 3.158812e+19
## Mode.Choice.ReasonComfortable 7.105354e-24 2.475188e+08
## Mode.Choice.ReasonLow price 4.388106e-18 8.850968e+18
## Mode.Choice.ReasonAccessibility 3.541326e-26 1.078366e+25
## Mode.Choice.ReasonReliability 7.446567e-05 2.369568e+26
## Mode.Choice.Reasonothers 2.222135e-33 5.135208e+04
## WeatherRainny 1.681905e-14 1.681905e-14
## WeatherCool 1.503379e-10 8.143969e+19
## WeekendYes 5.822284e+00 1.163234e+28
## Bus.Stop.PresentYes 2.086790e-15 1.209023e+23
## Bus.Stop.PresentDon't know 1.190591e-23 4.713791e+33
## GenderMale 2.564964e-09 1.715274e+21
## Age16-18 2.661897e-32 9.494701e+04
## Age19-24 3.964402e-34 5.099809e+04
## Age25-45 4.958063e-27 1.631117e+10
## Age46-60 2.291634e-40 2.420084e+07
## Age>60 1.098703e-29 4.281639e+28
## OccupationStudents 3.047149e-04 4.683579e+33
## OccupationOfficers 1.282298e-35 2.327320e+28
## OccupationHousewife 3.908445e-15 3.908445e-15
## OccupationUnemployed 3.875377e-08 9.314928e+38
## OccupationWorkers 5.393894e-15 1.589776e+37
## OccupationFree labor 1.023026e-21 6.623844e+36
## OccupationOthers 1.307663e-16 1.426035e+24
## Income(8-15) millions 1.285148e-08 2.328952e+23
## Income(15-25) millions 5.273425e-07 1.086220e+30
## Income>25 millions 7.014541e-17 5.071727e+27
## Number.of.Children1 child 2.625990e-09 3.484013e+24
## Number.of.Children2 children 3.244660e-17 5.365743e+24
## Number.of.Children>= 3 children 3.655562e-34 1.164750e+27
## Motor.CertificateYes 3.620934e-17 3.491519e+11
## Car.CertificateYes 6.089194e-23 1.281691e+34
## Bicycle.OwningYes 5.883936e-09 5.101971e+19
## Motor.OwningYes 2.114774e-29 6.773595e+08
## Car.OwningYes 2.308451e-33 1.367553e+17
## Number.of.Bicycles1 1.916740e-06 3.123178e+24
## Number.of.Bicycles2 9.002097e-10 2.163781e+35
## Number.of.Bicycles>=3 NaN NaN
## Number.of.Motors1 1.794559e-28 1.739863e+11
## Number.of.Motors2 8.335403e-16 4.329288e+10
## Number.of.Motors3 2.453555e-22 1.635687e+10
## Number.of.Motors>3 1.355107e-23 1.665514e+17
## Number.of.Car1 4.484511e-17 1.613839e+19
## Number.of.Car>=2 2.293753e+07 2.302099e+07
## Distance 2.904230e-05 6.231248e+08
## Travel.Period 2.095807e-01 6.968375e+00
## Cost 2.349271e-07 5.674440e+06
##
## , , 3
##
## 2.5 % 97.5 %
## (Intercept) 2.318859e-33 7.650000e+04
## Bus.Stop.ConditionYes 1.370083e-13 3.661897e+09
## Central.AreaYes 3.115263e-06 1.321604e+10
## PurposePicking Children 1.465753e-12 4.868333e+16
## PurposeEntertainment 5.263803e-11 1.450228e+11
## PurposeOthers 6.873497e-14 9.724332e+17
## Frequency2 times 6.916188e-22 2.682393e+06
## Frequency3 times 9.861288e-22 8.015897e+05
## Frequency> 3 times 2.590286e-19 2.260487e+02
## Departure.TimeAfternoon 2.125277e-09 1.610183e+12
## Departure.TimeEvening 3.894886e-19 7.010209e+07
## Departure.TimeOthers 9.504212e-17 1.957452e+16
## Sidewalk.ClearanceYes 1.619852e-12 3.536953e+14
## Lane.SeparateYes 4.846993e-14 1.222416e+10
## Temporary.Stop.Number1 stops 7.595570e-11 1.861034e+09
## Temporary.Stop.Number2 stops 3.521119e-06 1.228529e+18
## Temporary.Stop.Number>=3 stops 1.875823e-19 2.035265e+23
## Mode.Choice.ReasonComfortable 3.460722e-05 1.890877e+20
## Mode.Choice.ReasonLow price 8.319712e-19 1.418471e+16
## Mode.Choice.ReasonAccessibility 1.386958e+02 2.927754e+27
## Mode.Choice.ReasonReliability 5.007798e+11 2.343699e+33
## Mode.Choice.Reasonothers 1.518767e-12 8.806217e+14
## WeatherRainny 1.331410e-05 4.297736e-01
## WeatherCool 1.078442e-12 4.077428e+07
## WeekendYes 3.676332e-10 3.065049e+14
## Bus.Stop.PresentYes 6.990234e-11 8.676478e+14
## Bus.Stop.PresentDon't know 9.530291e-04 1.207003e+24
## GenderMale 3.711608e-09 4.333807e+12
## Age16-18 1.309215e-15 1.619249e+07
## Age19-24 1.521009e-27 6.247759e+01
## Age25-45 6.776085e-25 7.454165e-01
## Age46-60 2.158575e-26 1.420160e+08
## Age>60 8.866301e-36 2.093746e+03
## OccupationStudents 8.159114e-05 2.392361e+24
## OccupationOfficers 1.391774e-05 9.618256e+24
## OccupationHousewife 6.850084e-03 1.230822e+40
## OccupationUnemployed 7.354671e-13 2.059144e+18
## OccupationWorkers 7.513118e-11 9.753400e+26
## OccupationFree labor 8.393278e+01 1.072029e+26
## OccupationOthers 3.863075e-10 2.523427e+19
## Income(8-15) millions 1.669394e-03 2.237458e+18
## Income(15-25) millions 1.312478e-05 5.760255e+15
## Income>25 millions 2.068433e-05 2.453476e+18
## Number.of.Children1 child 5.973565e-03 1.143206e+15
## Number.of.Children2 children 7.101401e-11 1.506799e+20
## Number.of.Children>= 3 children 9.302965e-22 3.706480e+04
## Motor.CertificateYes 3.901259e-11 9.505446e+10
## Car.CertificateYes 8.279113e-26 3.699650e+26
## Bicycle.OwningYes 1.057596e-13 7.967588e+13
## Motor.OwningYes 6.460217e-08 2.670390e+13
## Car.OwningYes 1.652274e-21 1.750405e+24
## Number.of.Bicycles1 3.432762e-14 2.462572e+06
## Number.of.Bicycles2 4.677571e-17 8.192070e+11
## Number.of.Bicycles>=3 2.754032e-05 2.875523e+11
## Number.of.Motors1 1.089594e-15 1.081292e+10
## Number.of.Motors2 9.515778e-08 5.980492e+13
## Number.of.Motors3 9.467723e-16 3.758266e+17
## Number.of.Motors>3 3.280913e-16 5.701684e+09
## Number.of.Car1 2.085378e-16 1.298868e+08
## Number.of.Car>=2 1.113053e-25 1.466866e+08
## Distance 5.984259e-08 2.325584e+07
## Travel.Period 2.604872e-01 3.078116e+00
## Cost 2.577503e-02 3.349158e+11
##
## , , 4
##
## 2.5 % 97.5 %
## (Intercept) 1.738055e-60 2.128680e-40
## Bus.Stop.ConditionYes 9.628673e-13 5.184034e+17
## Central.AreaYes 4.841608e-17 1.391608e+26
## PurposePicking Children 2.104316e-06 1.379748e+08
## PurposeEntertainment 1.492548e-13 1.801831e-03
## PurposeOthers 1.692407e+13 2.000124e+13
## Frequency2 times 3.772462e-07 6.830878e+00
## Frequency3 times 3.077825e-16 1.541457e-02
## Frequency> 3 times 4.069758e-24 1.030585e+01
## Departure.TimeAfternoon 8.918648e-04 1.345190e+08
## Departure.TimeEvening 6.883812e-14 2.996743e+06
## Departure.TimeOthers 1.306200e+03 2.345492e+04
## Sidewalk.ClearanceYes 5.498785e-04 4.857881e+12
## Lane.SeparateYes 9.673292e-31 5.284624e+13
## Temporary.Stop.Number1 stops 1.820599e-21 2.380480e+22
## Temporary.Stop.Number2 stops 3.101978e-10 9.069964e+09
## Temporary.Stop.Number>=3 stops 9.780554e-07 1.019408e-06
## Mode.Choice.ReasonComfortable 3.110867e-20 9.727039e+19
## Mode.Choice.ReasonLow price 3.411122e-20 3.411122e-20
## Mode.Choice.ReasonAccessibility 7.338758e-07 8.707137e-07
## Mode.Choice.ReasonReliability 2.010600e-10 9.345276e+34
## Mode.Choice.Reasonothers 8.402958e-09 2.320273e+03
## WeatherRainny 3.746687e+09 6.795366e+16
## WeatherCool 1.800372e+01 1.898724e+09
## WeekendYes 3.191454e-18 3.069000e+26
## Bus.Stop.PresentYes 1.657311e-13 3.881466e+03
## Bus.Stop.PresentDon't know 7.131970e-04 4.751120e+10
## GenderMale 1.813169e-14 3.863209e+17
## Age16-18 7.464757e-13 1.107670e-01
## Age19-24 2.657581e-19 2.521887e+02
## Age25-45 1.130742e-14 2.978259e+18
## Age46-60 6.635208e-05 1.417614e+07
## Age>60 2.547537e-30 2.547538e-30
## OccupationStudents 7.759892e+00 1.483104e+21
## OccupationOfficers 6.924265e-14 2.352014e+16
## OccupationHousewife 1.164119e+08 1.164949e+08
## OccupationUnemployed NaN NaN
## OccupationWorkers 3.254639e-07 1.972523e+07
## OccupationFree labor 3.933434e-11 4.300543e+17
## OccupationOthers 1.579349e-14 4.544266e-14
## Income(8-15) millions 1.939517e-03 2.250088e+24
## Income(15-25) millions 1.029943e-06 1.359211e+12
## Income>25 millions 5.113911e-05 4.958005e+16
## Number.of.Children1 child 3.833958e-15 1.824899e+24
## Number.of.Children2 children 2.598983e+01 1.139522e+23
## Number.of.Children>= 3 children 7.921747e-21 7.921748e-21
## Motor.CertificateYes 2.059972e-06 2.516906e+14
## Car.CertificateYes 1.638984e-05 9.424099e+13
## Bicycle.OwningYes 4.549050e-09 1.843436e+15
## Motor.OwningYes 4.841821e-07 1.616301e+18
## Car.OwningYes 3.159521e+03 1.845950e+22
## Number.of.Bicycles1 1.177942e-07 4.211023e+13
## Number.of.Bicycles2 3.972491e-09 9.854263e+02
## Number.of.Bicycles>=3 1.080182e+05 1.080188e+05
## Number.of.Motors1 1.836804e-17 1.491408e+29
## Number.of.Motors2 3.329875e-14 2.300677e+30
## Number.of.Motors3 3.394077e-02 9.799253e+08
## Number.of.Motors>3 2.182013e+10 2.459818e+10
## Number.of.Car1 1.554178e-07 2.367780e+08
## Number.of.Car>=2 1.889073e-16 6.401754e-05
## Distance 3.453573e-30 2.618652e+20
## Travel.Period 7.299831e-06 1.903793e+05
## Cost 5.547981e-04 5.402659e+21
##
## , , 5
##
## 2.5 % 97.5 %
## (Intercept) 1.901190e-47 5.999334e+04
## Bus.Stop.ConditionYes 2.046565e-13 1.267116e+12
## Central.AreaYes 1.524348e-09 2.311632e+09
## PurposePicking Children 3.526385e-16 1.349127e-12
## PurposeEntertainment 3.574269e-08 2.471238e+15
## PurposeOthers 3.357617e-52 1.584563e+23
## Frequency2 times 4.024110e-08 5.789761e+19
## Frequency3 times 2.586896e-12 5.124417e+17
## Frequency> 3 times 1.083103e-18 1.049186e+05
## Departure.TimeAfternoon 1.271582e-18 1.398834e+07
## Departure.TimeEvening 9.333637e-17 1.194976e+12
## Departure.TimeOthers 1.417917e-17 1.423732e+15
## Sidewalk.ClearanceYes 1.670040e-04 1.567234e+32
## Lane.SeparateYes 1.220660e-12 3.763184e+14
## Temporary.Stop.Number1 stops 6.150659e-09 2.167339e+13
## Temporary.Stop.Number2 stops 3.173326e-09 6.001678e+23
## Temporary.Stop.Number>=3 stops 1.437551e-18 1.092792e+31
## Mode.Choice.ReasonComfortable 5.698277e-12 1.037409e+15
## Mode.Choice.ReasonLow price 4.815877e-84 9.796149e+25
## Mode.Choice.ReasonAccessibility 2.202592e-10 3.586557e+19
## Mode.Choice.ReasonReliability 1.835463e+13 1.804403e+35
## Mode.Choice.Reasonothers 1.766516e-24 6.007504e+08
## WeatherRainny 5.947009e+09 6.198156e+09
## WeatherCool 7.910469e-16 8.190482e+08
## WeekendYes 1.311241e-11 3.547904e+15
## Bus.Stop.PresentYes 1.109806e-13 1.826530e+16
## Bus.Stop.PresentDon't know 2.914092e-15 2.149665e+20
## GenderMale 3.037362e-15 2.298939e+10
## Age16-18 1.183123e-11 1.773703e+16
## Age19-24 1.529525e-12 2.487306e+33
## Age25-45 5.940632e-27 2.628224e+06
## Age46-60 6.516884e-22 3.188154e+24
## Age>60 2.661200e-34 2.416577e+23
## OccupationStudents 3.910393e-26 2.703870e+13
## OccupationOfficers 1.113725e-18 6.582689e+24
## OccupationHousewife 8.420445e-11 6.387306e+35
## OccupationUnemployed 6.910087e-14 6.910087e-14
## OccupationWorkers 6.993036e-41 2.012943e+20
## OccupationFree labor 2.797480e-08 1.290573e+27
## OccupationOthers 1.427754e-14 3.978131e+40
## Income(8-15) millions 5.371755e+01 3.180218e+22
## Income(15-25) millions 3.852816e+07 1.130436e+37
## Income>25 millions 3.573572e-11 9.457403e+39
## Number.of.Children1 child 1.199653e-13 7.805837e+15
## Number.of.Children2 children 1.212126e-16 9.668546e+39
## Number.of.Children>= 3 children 1.166000e-38 1.166000e-38
## Motor.CertificateYes 2.038297e-28 3.686247e+06
## Car.CertificateYes 3.209926e-55 2.935258e+43
## Bicycle.OwningYes 1.276944e-14 1.998632e+13
## Motor.OwningYes 2.902992e-16 4.093779e+07
## Car.OwningYes 7.687261e-47 1.311239e+45
## Number.of.Bicycles1 3.037917e-11 3.208759e+11
## Number.of.Bicycles2 1.005865e-23 6.573246e+10
## Number.of.Bicycles>=3 4.627028e-15 5.977139e-08
## Number.of.Motors1 5.500680e-27 1.916961e+10
## Number.of.Motors2 5.406531e-18 1.441666e+10
## Number.of.Motors3 2.284353e-31 9.100088e+08
## Number.of.Motors>3 4.269005e-26 4.280999e-26
## Number.of.Car1 2.807656e-31 1.702004e+08
## Number.of.Car>=2 7.385107e-07 7.504682e-07
## Distance 2.045940e-13 1.036439e+04
## Travel.Period 2.422399e-01 4.259825e+00
## Cost 9.404229e-01 3.071945e+16
##
## , , 6
##
## 2.5 % 97.5 %
## (Intercept) 3.742558e-30 1.015030e-09
## Bus.Stop.ConditionYes 6.167443e-08 5.373537e+15
## Central.AreaYes 2.511116e-01 2.563272e+01
## PurposePicking Children 1.038082e-10 1.792577e+02
## PurposeEntertainment 2.557744e-17 3.545528e+05
## PurposeOthers 2.143102e-14 2.543159e-14
## Frequency2 times 1.039592e-12 3.949506e-06
## Frequency3 times 8.210764e-18 3.669009e+05
## Frequency> 3 times 2.464784e-26 6.790280e-07
## Departure.TimeAfternoon 7.230712e-12 4.590330e-05
## Departure.TimeEvening 3.005904e-14 8.963153e+02
## Departure.TimeOthers 6.915226e-03 1.325569e-01
## Sidewalk.ClearanceYes 1.609228e-11 3.254053e+08
## Lane.SeparateYes 5.456136e-11 2.102286e+09
## Temporary.Stop.Number1 stops 1.524557e-11 7.694821e+02
## Temporary.Stop.Number2 stops 8.503979e-04 1.717107e+13
## Temporary.Stop.Number>=3 stops 1.909497e+00 1.910991e+00
## Mode.Choice.ReasonComfortable 2.216780e+00 1.027571e+25
## Mode.Choice.ReasonLow price 3.008386e+09 4.054141e+11
## Mode.Choice.ReasonAccessibility 3.330873e+15 9.294125e+38
## Mode.Choice.ReasonReliability 6.326865e+05 3.560084e+33
## Mode.Choice.Reasonothers 2.303141e-03 6.429509e+08
## WeatherRainny 4.946269e+09 8.861848e+16
## WeatherCool 2.039584e-02 3.464012e+11
## WeekendYes 3.252138e-22 1.614702e+15
## Bus.Stop.PresentYes 6.120394e-21 2.500543e+12
## Bus.Stop.PresentDon't know 7.000266e-01 1.019712e+13
## GenderMale 5.638407e-13 6.979820e+08
## Age16-18 5.358930e-16 4.427007e-09
## Age19-24 2.795158e-10 1.577733e+14
## Age25-45 2.516569e-21 1.952127e+10
## Age46-60 5.831615e-18 1.212950e+09
## Age>60 6.093340e-08 2.164385e+03
## OccupationStudents 4.852673e-24 3.051102e-01
## OccupationOfficers 2.030172e-19 1.143663e+09
## OccupationHousewife 1.622842e+03 1.004274e+24
## OccupationUnemployed 1.425944e+04 1.425944e+04
## OccupationWorkers 2.256435e-09 3.450172e-08
## OccupationFree labor 1.139299e-08 1.022981e+04
## OccupationOthers 2.069341e-11 5.870143e+06
## Income(8-15) millions 2.785141e-14 6.970426e+28
## Income(15-25) millions 2.178011e-02 9.996175e+10
## Income>25 millions 1.256801e-02 1.170334e+05
## Number.of.Children1 child 3.888587e-08 9.693184e+17
## Number.of.Children2 children 2.079538e-05 1.191534e+18
## Number.of.Children>= 3 children 1.889080e-24 1.890666e-24
## Motor.CertificateYes 8.078543e-14 2.277932e+08
## Car.CertificateYes 8.314638e-11 1.153526e+05
## Bicycle.OwningYes 8.650441e-08 8.497227e+09
## Motor.OwningYes 2.563336e-05 1.612786e+07
## Car.OwningYes 6.149399e-01 8.354933e+14
## Number.of.Bicycles1 1.908827e-10 2.489728e+12
## Number.of.Bicycles2 1.471648e-08 3.710335e+03
## Number.of.Bicycles>=3 1.099097e+01 1.481046e+03
## Number.of.Motors1 1.475378e-13 2.081440e+04
## Number.of.Motors2 1.328369e-10 1.266613e+17
## Number.of.Motors3 1.001595e-02 6.898419e+10
## Number.of.Motors>3 2.025706e+01 2.090116e+01
## Number.of.Car1 1.166220e-10 8.956422e+10
## Number.of.Car>=2 1.790058e-07 4.488959e+04
## Distance 8.810221e-28 2.002382e+15
## Travel.Period 9.213141e-02 1.649763e+01
## Cost 9.678848e-04 7.958395e+21
##
## , , 7
##
## 2.5 % 97.5 %
## (Intercept) 2.784726e-28 1.521001e+10
## Bus.Stop.ConditionYes 4.403494e-13 5.986527e+09
## Central.AreaYes 4.803817e-08 2.797121e+08
## PurposePicking Children 1.466877e-15 1.011611e+13
## PurposeEntertainment 5.980400e-23 7.957911e+01
## PurposeOthers 2.105170e-10 2.010655e+21
## Frequency2 times 4.736234e-05 1.582053e+25
## Frequency3 times 4.105527e-17 2.239491e+10
## Frequency> 3 times 9.783909e-18 1.055749e+04
## Departure.TimeAfternoon 2.933914e-14 2.866831e+08
## Departure.TimeEvening 1.849052e-14 3.619403e+11
## Departure.TimeOthers 8.933955e-17 2.336293e+16
## Sidewalk.ClearanceYes 2.400801e-11 8.842191e+15
## Lane.SeparateYes 5.494563e-12 1.419154e+12
## Temporary.Stop.Number1 stops 6.673997e-15 7.380594e+05
## Temporary.Stop.Number2 stops 6.197646e-05 2.261320e+19
## Temporary.Stop.Number>=3 stops 6.051069e-28 2.038388e+17
## Mode.Choice.ReasonComfortable 3.788169e-09 5.139929e+14
## Mode.Choice.ReasonLow price 8.176727e-18 1.189755e+17
## Mode.Choice.ReasonAccessibility 3.343940e-16 1.695728e+13
## Mode.Choice.ReasonReliability 1.527666e-31 1.527666e-31
## Mode.Choice.Reasonothers 2.063008e-33 9.380237e-32
## WeatherRainny 3.829064e-01 1.235764e+04
## WeatherCool 9.373024e-12 6.019579e+08
## WeekendYes 7.841615e-11 7.757510e+13
## Bus.Stop.PresentYes 6.706000e-19 6.760216e+06
## Bus.Stop.PresentDon't know 1.418798e-10 5.473701e+15
## GenderMale 1.714013e-09 3.001921e+12
## Age16-18 2.855801e-13 6.343349e+10
## Age19-24 1.313506e-21 6.113556e+08
## Age25-45 7.273446e-16 1.754324e+10
## Age46-60 2.310613e-17 7.991062e+20
## Age>60 1.262274e-16 1.317254e+31
## OccupationStudents 5.000970e+00 6.825523e+30
## OccupationOfficers 2.927211e-01 8.004112e+29
## OccupationHousewife 6.061058e-07 9.180148e+34
## OccupationUnemployed 2.133678e-06 3.834711e+28
## OccupationWorkers 2.703907e-37 2.858504e+11
## OccupationFree labor 1.014801e+01 1.373469e+26
## OccupationOthers 2.415560e-03 2.699309e+26
## Income(8-15) millions 2.361098e-08 1.268415e+13
## Income(15-25) millions 6.626579e-09 1.187728e+13
## Income>25 millions 4.020408e-17 1.088338e+10
## Number.of.Children1 child 1.271082e-03 2.161265e+14
## Number.of.Children2 children 1.247573e-14 1.193535e+17
## Number.of.Children>= 3 children 2.302507e-25 1.199457e+03
## Motor.CertificateYes 3.402538e-18 1.151617e+04
## Car.CertificateYes 1.981603e-29 2.180155e+23
## Bicycle.OwningYes 6.003245e-12 4.367441e+15
## Motor.OwningYes 7.935737e-15 2.048173e+08
## Car.OwningYes 7.671973e-19 1.264926e+27
## Number.of.Bicycles1 1.373635e-15 1.399850e+05
## Number.of.Bicycles2 5.370877e-23 3.358732e+05
## Number.of.Bicycles>=3 3.115118e-23 8.659602e-06
## Number.of.Motors1 1.696023e-09 1.134857e+17
## Number.of.Motors2 6.963579e-03 6.864393e+21
## Number.of.Motors3 1.942826e-12 9.452113e+21
## Number.of.Motors>3 2.634191e-10 9.682137e+15
## Number.of.Car1 5.333391e-24 2.902720e+01
## Number.of.Car>=2 1.444271e-23 9.527691e+13
## Distance 1.905989e-05 3.689036e+09
## Travel.Period 2.109080e-01 2.669676e+00
## Cost 4.869120e-04 3.330784e+08z <- summary(mlm)$coefficients/summary(mlm)$standard.errors## Warning in sqrt(diag(vc)): NaNs produced
## Warning in sqrt(diag(vc)): NaNs producedz## (Intercept) Bus.Stop.ConditionYes Central.AreaYes PurposePicking Children
## 2 -3.2465338 0.48946469 -1.06957194 -0.4322354
## 3 -1.4497181 -0.28835801 0.57874774 0.3335249
## 4 -9.7014309 0.37564481 0.45370200 0.3493758
## 5 -1.5962716 -0.04633989 0.05896902 -14.9471068
## 6 -3.6852818 0.72796409 0.78895178 -1.2381128
## 7 -0.9023058 -0.22837912 0.14027309 -0.1287411
## PurposeEntertainment PurposeOthers Frequency2 times Frequency3 times
## 2 -0.16086277 1.4114602 0.4462997 0.01324077
## 3 0.08069588 0.3035856 -1.0465688 -1.09994722
## 4 -3.02697761 716.6852262 -1.5092675 -2.47845603
## 5 0.68188525 -0.7421087 0.8925374 0.40959090
## 6 -0.97746364 -718.8894906 -5.1791423 -0.99694098
## 7 -1.65110251 0.7355626 1.3857832 -0.44250979
## Frequency> 3 times Departure.TimeAfternoon Departure.TimeEvening
## 2 -0.1063821 0.6117675 -1.0963547
## 3 -1.5192799 0.3317654 -0.7885900
## 4 -1.7972327 0.8905339 -0.6672183
## 5 -1.1037374 -0.8413818 -0.2756199
## 6 -3.2037099 -4.4597585 -1.2574575
## 7 -1.2100932 -0.4523480 -0.1685049
## Departure.TimeOthers Sidewalk.ClearanceYes Lane.SeparateYes
## 2 -0.42530063 0.8567258 -0.58533785
## 3 0.01635279 0.2052376 -0.27029643
## 4 11.69867770 1.1586486 -0.73005142
## 5 -0.10380624 1.5483530 0.19697709
## 6 -4.64211853 -0.2315674 -0.09410873
## 7 0.01932130 0.3930017 0.07467142
## Temporary.Stop.Number1 stops Temporary.Stop.Number2 stops
## 2 0.19570287 0.20797654
## 3 -0.08589075 1.05197066
## 4 0.07440834 0.04523292
## 5 0.46616772 0.92784513
## 6 -1.13433128 1.22181431
## 7 -0.81234707 1.25993541
## Temporary.Stop.Number>=3 stops Mode.Choice.ReasonComfortable
## 2 -0.6290573 -0.91683685
## 3 0.2136338 1.25309597
## 4 -1307.9021121 0.02386302
## 5 0.5291227 0.28150516
## 6 3243.5264704 2.01490613
## 7 -0.4361590 0.53288473
## Mode.Choice.ReasonLow price Mode.Choice.ReasonAccessibility
## 2 8.579899e-02 -0.01623098
## 3 -1.103933e-01 2.29153001
## 4 -2.805251e+13 -321.88402644
## 5 -1.027894e+00 0.66408096
## 6 1.940690e+01 4.55521334
## 7 -6.863332e-04 -0.15338162
## Mode.Choice.ReasonReliability Mode.Choice.Reasonothers WeatherRainny
## 2 1.429466e+00 -1.4657676 -3.280940e+09
## 3 4.076311e+00 0.2289478 -2.278814e+00
## 4 1.108999e+00 -0.8068701 7.130133e+00
## 5 4.324075e+00 -0.9021656 2.134822e+03
## 6 2.779424e+00 1.0566229 7.199117e+00
## 7 -2.068930e+26 -75.3301360 1.597510e+00
## WeatherCool WeekendYes Bus.Stop.PresentYes Bus.Stop.PresentDon't know
## 2 0.6649655 2.0698192 0.4360741 0.3722404
## 3 -0.4361724 0.4139225 0.3735650 1.5230433
## 4 2.5733092 0.4006549 -1.1005287 1.0676346
## 5 -0.5050660 0.3461083 0.2218397 0.3258491
## 6 1.4590980 -0.3354068 -0.4696993 1.9138405
## 7 -0.2224937 0.3090917 -0.8891956 0.4512007
## GenderMale Age16-18 Age19-24 Age25-45 Age46-60 Age>60
## 2 0.8308610 -1.4261707 -1.4757453 -0.8637070 -1.3444426 -1.114602e-02
## 3 0.3913388 -0.6807859 -1.7139582 -1.9807692 -1.0150112 -1.620722e+00
## 4 0.2405738 -2.2952706 -1.5111637 0.2736962 0.5143808 -4.991491e+10
## 5 -0.3274068 0.3838244 0.9355339 -1.1891270 0.1423158 -3.507034e-01
## 6 -0.3163976 -6.6941688 0.3832552 -0.6540901 -0.6069761 -7.207528e-01
## 7 0.3424236 -0.1462383 -0.7990566 -0.3779142 0.2227519 6.344791e-01
## OccupationStudents OccupationOfficers OccupationHousewife
## 2 1.5893237 -0.2021704 -3.004561e+11
## 3 1.3969977 1.3219894 1.759183e+00
## 4 2.1319533 0.2131667 1.021095e+05
## 5 -0.6043323 0.3145896 1.099197e+00
## 6 -2.0486055 -0.6804362 2.565212e+00
## 7 2.0508960 1.8912492 1.368129e+00
## OccupationUnemployed OccupationWorkers OccupationFree labor OccupationOthers
## 2 1.333557e+00 0.8733013 0.5367156 0.4048723
## 3 3.978395e-01 0.8906434 2.2728150 0.6794340
## 4 NaN 0.1148353 0.5052721 -115.9107833
## 5 -2.978903e+13 -0.6435456 1.1058180 0.9631280
## 6 2.383554e+11 -26.6565908 -0.6449728 -0.4397157
## 7 1.311017e+00 -1.0248669 2.1169363 1.6068141
## Income(8-15) millions Income(15-25) millions Income>25 millions
## 2 0.9703891 1.2822897 0.5161943
## 3 1.4446391 1.0329025 1.1641663
## 4 1.5671230 0.6647804 1.1584327
## 5 2.2864525 2.9690668 1.1478077
## 6 0.7067278 1.4454480 0.8908448
## 7 0.5177724 0.4515035 -0.4715160
## Number.of.Children1 child Number.of.Children2 children
## 2 0.9444772 0.3918552
## 3 1.4555629 0.6481831
## 4 0.4988838 2.2162226
## 5 0.2021279 0.8438759
## 6 0.8162126 1.1535170
## 7 1.3011666 0.2007301
## Number.of.Children>= 3 children Motor.CertificateYes Car.CertificateYes
## 2 -2.063783e-01 -0.34305891 0.41383576
## 3 -1.260364e+00 0.05216156 0.05639457
## 4 -1.291022e+09 0.85032851 0.96002490
## 5 -1.675681e+18 -1.20857595 -0.22060057
## 6 -2.550601e+05 -0.43266139 -0.64953330
## 7 -1.524509e+00 -1.22051202 -0.20203539
## Bicycle.OwningYes Motor.OwningYes Car.OwningYes Number.of.Bicycles1
## 2 0.80518454 -1.0370027 -0.61039344 1.2181423
## 3 0.06750134 0.5929255 0.15066873 -0.6981738
## 4 0.57480830 0.9505527 2.69095755 0.6384855
## 5 -0.04274552 -0.6709830 -0.02140912 0.0880065
## 6 0.33061417 0.4346228 1.90526533 0.2372404
## 7 0.32240242 -0.5062660 0.38954554 -0.9517644
## Number.of.Bicycles2 Number.of.Bicycles>=3 Number.of.Motors1 Number.of.Motors2
## 2 1.1610089 NaN -0.8297793 -0.3386071
## 3 -0.3064913 8.440844e-01 -0.3864616 0.6365865
## 4 -0.9301050 8.306328e+06 0.5309870 0.7548590
## 5 -0.7059460 -5.941804e+00 -0.8569350 -0.5079806
## 6 -0.7327826 3.876247e+00 -0.9728965 0.5249437
## 7 -1.1806393 -3.097582e+00 0.6287222 1.6075416
## Number.of.Motors3 Number.of.Motors>3 Number.of.Car1 Number.of.Car>=2
## 2 -0.7018855 -2.760542e-01 0.1576286 18295.5759350
## 3 0.1533901 -4.447969e-01 -0.6233210 -0.9934246
## 4 1.4093707 7.806519e+02 0.2021357 -3.3857172
## 5 -1.0731316 -8.161470e+04 -1.1280250 -3443.7693409
## 6 1.3495023 3.787254e+02 0.0956190 -0.3602070
## 7 0.5971722 4.911630e-01 -1.7281506 -0.4717074
## Distance Travel.Period Cost
## 2 0.62594058 0.21184411 0.0182854
## 3 0.01928356 -0.17530727 1.4850433
## 4 -0.35535893 0.02689462 1.4492182
## 5 -1.01767296 0.02146650 1.9536318
## 6 -0.54386783 0.15817868 1.4857375
## 7 0.66495056 -0.44350967 0.8628072pvalue <- (1-pnorm(abs(z), 0, 1))*2
pvalue## (Intercept) Bus.Stop.ConditionYes Central.AreaYes PurposePicking Children
## 2 0.0011681957 0.6245127 0.2848120 0.6655703
## 3 0.1471371345 0.7730727 0.5627594 0.7387381
## 4 0.0000000000 0.7071810 0.6500433 0.7268072
## 5 0.1104281569 0.9630393 0.9529768 0.0000000
## 6 0.0002284497 0.4666356 0.4301402 0.2156742
## 7 0.3668944476 0.8193515 0.8884442 0.8975625
## PurposeEntertainment PurposeOthers Frequency2 times Frequency3 times
## 2 0.872201485 0.1581090 6.553807e-01 0.98943570
## 3 0.935683816 0.7614436 2.952985e-01 0.27135512
## 4 0.002470123 0.0000000 1.312304e-01 0.01319524
## 5 0.495311512 0.4580215 3.721050e-01 0.68210608
## 6 0.328339671 0.0000000 2.229084e-07 0.31879316
## 7 0.098717644 0.4619969 1.658131e-01 0.65812035
## Frequency> 3 times Departure.TimeAfternoon Departure.TimeEvening
## 2 0.915279174 5.406916e-01 0.2729236
## 3 0.128692067 7.400664e-01 0.4303517
## 4 0.072298685 3.731793e-01 0.5046327
## 5 0.269707055 4.001341e-01 0.7828400
## 6 0.001356691 8.205206e-06 0.2085880
## 7 0.226243118 6.510183e-01 0.8661861
## Departure.TimeOthers Sidewalk.ClearanceYes Lane.SeparateYes
## 2 6.706175e-01 0.3915964 0.5583206
## 3 9.869529e-01 0.8373865 0.7869322
## 4 0.000000e+00 0.2465995 0.4653588
## 5 9.173231e-01 0.1215373 0.8438455
## 6 3.448549e-06 0.8168741 0.9250228
## 7 9.845848e-01 0.6943182 0.9404761
## Temporary.Stop.Number1 stops Temporary.Stop.Number2 stops
## 2 0.8448427 0.8352473
## 3 0.9315533 0.2928130
## 4 0.9406855 0.9639217
## 5 0.6410955 0.3534879
## 6 0.2566556 0.2217779
## 7 0.4165925 0.2076927
## Temporary.Stop.Number>=3 stops Mode.Choice.ReasonComfortable
## 2 0.5293115 0.35922814
## 3 0.8308326 0.21017078
## 4 0.0000000 0.98096187
## 5 0.5967204 0.77832297
## 6 0.0000000 0.04391449
## 7 0.6627214 0.59411337
## Mode.Choice.ReasonLow price Mode.Choice.ReasonAccessibility
## 2 0.9316262 9.870501e-01
## 3 0.9120975 2.193278e-02
## 4 0.0000000 0.000000e+00
## 5 0.3039997 5.066385e-01
## 6 0.0000000 5.233244e-06
## 7 0.9994524 8.780973e-01
## Mode.Choice.ReasonReliability Mode.Choice.Reasonothers WeatherRainny
## 2 1.528704e-01 0.1427116 0.000000e+00
## 3 4.575588e-05 0.8189095 2.267810e-02
## 4 2.674305e-01 0.4197413 1.002753e-12
## 5 1.531729e-05 0.3669689 0.000000e+00
## 6 5.445538e-03 0.2906837 6.059597e-13
## 7 0.000000e+00 0.0000000 1.101520e-01
## WeatherCool WeekendYes Bus.Stop.PresentYes Bus.Stop.PresentDon't know
## 2 0.50607256 0.03846928 0.6627830 0.70971390
## 3 0.66271166 0.67893090 0.7087280 0.12774786
## 4 0.01007312 0.68867422 0.2711018 0.28568536
## 5 0.61351248 0.72926134 0.8244386 0.74453851
## 6 0.14453813 0.73731825 0.6385699 0.05564054
## 7 0.82392958 0.75725174 0.3738980 0.65184493
## GenderMale Age16-18 Age19-24 Age25-45 Age46-60 Age>60
## 2 0.4060522 1.538191e-01 0.14001228 0.38774887 0.1788053 0.9911070
## 3 0.6955468 4.960070e-01 0.08653641 0.04761716 0.3101005 0.1050772
## 4 0.8098855 2.171762e-02 0.13074675 0.78431807 0.6069858 0.0000000
## 5 0.7433602 7.011086e-01 0.34951324 0.23438971 0.8868306 0.7258109
## 6 0.7517007 2.169021e-11 0.70153055 0.51305373 0.5438668 0.4710616
## 7 0.7320321 8.837333e-01 0.42425758 0.70549434 0.8237286 0.5257682
## OccupationStudents OccupationOfficers OccupationHousewife
## 2 0.11198733 0.83978354 0.00000000
## 3 0.16241427 0.18617169 0.07854650
## 4 0.03301068 0.83119697 0.00000000
## 5 0.54562274 0.75307328 0.27168202
## 6 0.04050070 0.49622832 0.01031129
## 7 0.04027708 0.05859108 0.17127185
## OccupationUnemployed OccupationWorkers OccupationFree labor OccupationOthers
## 2 0.1823489 0.3824989 0.59146410 0.6855714
## 3 0.6907485 0.3731205 0.02303733 0.4968629
## 4 NaN 0.9085757 0.61336775 0.0000000
## 5 0.0000000 0.5198701 0.26880530 0.3354833
## 6 0.0000000 0.0000000 0.51894481 0.6601431
## 7 0.1898519 0.3054260 0.03426525 0.1080952
## Income(8-15) millions Income(15-25) millions Income>25 millions
## 2 0.3318526 0.199741027 0.6057187
## 3 0.1485593 0.301649524 0.2443566
## 4 0.1170859 0.506190985 0.2466875
## 5 0.0222278 0.002987057 0.2510479
## 6 0.4797356 0.148332084 0.3730124
## 7 0.6046171 0.651626730 0.6372723
## Number.of.Children1 child Number.of.Children2 children
## 2 0.3449259 0.69516518
## 3 0.1455135 0.51686654
## 4 0.6178613 0.02667626
## 5 0.8398167 0.39873875
## 6 0.4143786 0.24869824
## 7 0.1932014 0.84090965
## Number.of.Children>= 3 children Motor.CertificateYes Car.CertificateYes
## 2 0.8364954 0.7315541 0.6789944
## 3 0.2075381 0.9584000 0.9550275
## 4 0.0000000 0.3951425 0.3370427
## 5 0.0000000 0.2268258 0.8254035
## 6 0.0000000 0.6652608 0.5159937
## 7 0.1273815 0.2222708 0.8398891
## Bicycle.OwningYes Motor.OwningYes Car.OwningYes Number.of.Bicycles1
## 2 0.4207132 0.2997346 0.541601215 0.2231699
## 3 0.9461826 0.5532310 0.880237042 0.4850685
## 4 0.5654210 0.3418315 0.007124726 0.5231577
## 5 0.9659044 0.5022313 0.982919296 0.9298715
## 6 0.7409359 0.6638362 0.056745590 0.8124703
## 7 0.7471479 0.6126699 0.696872633 0.3412165
## Number.of.Bicycles2 Number.of.Bicycles>=3 Number.of.Motors1 Number.of.Motors2
## 2 0.2456383 NaN 0.4066636 0.7349057
## 3 0.7592306 3.986222e-01 0.6991549 0.5243942
## 4 0.3523167 0.000000e+00 0.5954278 0.4503336
## 5 0.4802217 2.819024e-09 0.3914808 0.6114669
## 6 0.4636910 1.060800e-04 0.3306048 0.5996223
## 7 0.2377460 1.951061e-03 0.5295309 0.1079356
## Number.of.Motors3 Number.of.Motors>3 Number.of.Car1 Number.of.Car>=2
## 2 0.4827506 0.7825065 0.87474948 0.0000000000
## 3 0.8780906 0.6564665 0.53307358 0.3205030700
## 4 0.1587256 0.0000000 0.83981063 0.0007099251
## 5 0.2832121 0.0000000 0.25930938 0.0000000000
## 6 0.1771757 0.0000000 0.92382317 0.7186923687
## 7 0.5503924 0.6233112 0.08396123 0.6371356793
## Distance Travel.Period Cost
## 2 0.5313539 0.8322287 0.98541117
## 3 0.9846149 0.8608382 0.13753235
## 4 0.7223207 0.9785438 0.14727665
## 5 0.3088334 0.9828735 0.05074478
## 6 0.5865324 0.8743160 0.13734857
## 7 0.5060821 0.6573971 0.38824346