harztraffic – gamlss2

Traffic Counts at Sonnenberg in the Harz Region

Description

This dataset contains daily traffic counts close to Sonnenberg, located in the Harz region in Germany. It covers a period of nearly three years, from 2021-01-01 to 2023-11-30.

Usage

data("HarzTraffic", package = "gamlss2")

Format

A data frame containing 1057 observations on 16 variables.

date: Date, the date of the record.
yday: Integer, the day of the year.
bikes: Integer, the number of motorcycles on that day.
cars: Integer, the number of cars on that day.
trucks: Integer, the number of trucks on that day.
others: Integer, the number of other vehicles on that day.
tempmin: Numeric, minimum temperature in \(^{\circ}C\).
tempmax: Numeric, maximum temperature in \(^{\circ}C\).
temp: Numeric, mean temperature in \(^{\circ}C\).
humidity: Numeric, mean relative humidity in percent.
tempdew: Numeric, average dewpoint temperature in \(^{\circ}C\).
cloudiness: Numeric, average cloud cover in percent.
rain: Numeric, amount of precipitation in mm (snow and rain).
sunshine: Numeric, sunshine duration in minutes.
wind: Numeric, mean wind speed in m/s.
windmax: Numeric, maximum wind speed in m/s.

Source

Weather Data:

Data Source:: Deutscher Wetterdienst (DWD), Climate Data Center (CDC).
Licence:: CC BY 4.0
URL:: https://opendata.dwd.de/climate_environment/CDC/
Station:: Wernigerode (5490; Sachsen-Anhalt)
Position:: 10.7686/51.8454/233 (lon, lat, alt, EPSG 4326)

Traffic Data:

Data Source:: Bundesanstalt für Strassenwesen (BASt)
Licence:: CC BY 4.0
URL:: https://www.bast.de, https://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Verkehrszaehlung.html

Examples

library("gamlss2")

## seasonal variation of motorcycle counts at Sonnenberg/Harz
data("HarzTraffic", package = "gamlss2")
plot(bikes ~ yday, data = HarzTraffic)

## count distribution
barplot(table(HarzTraffic$bikes))

## negative binomial seasonal model using cyclic splines
m <- gamlss2(bikes ~ s(yday, bs = "cc") | s(yday, bs = "cc"),
  data = HarzTraffic, family = NBI)

GAMLSS-RS iteration  1: Global Deviance = 10163.082 eps = 0.148402     
GAMLSS-RS iteration  2: Global Deviance = 10151.144 eps = 0.001174     
GAMLSS-RS iteration  3: Global Deviance = 10151.1409 eps = 0.000000

## visualize effects
plot(m)

## residual diagnostics
plot(m, which = "resid")

## fitted parameters for each day of the year
nd <- data.frame(yday = 1:365)
par <- predict(m, newdata = nd)

## corresponding quantiles
p <- sapply(c(0.05, 0.5, 0.95), function(q) family(m)$q(q, par))

## visualization
plot(bikes ~ yday, data = HarzTraffic, pch = 19, col = gray(0.1, alpha = 0.3))
matplot(nd$yday, p, type = "l", lty = c(2, 1, 2), lwd = 2, col = 4, add = TRUE)