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Abstract

Traffic and transport are one of the major causes of environmental problems in urban regions, and there is growing concern over carbon dioxide (CO2) emissions resulting from daily travel. Decision making and urban planning are expected to be supported with research based information about the current processes and patterns. Thus, the need for comparable and timely information about the functioning and impacts of the transport system (e.g. CO2 emissions, travel times, distances etc.) is higher than ever as our society is getting increasingly dynamic and mobile. Here, we present a dataset called Helsinki Region Travel CO2 Matrix 2015 that is available via Helsinki Region Infoshare service. The dataset presents modelled comparable CO2 emissions and trip lengths (meters) for the capital region of Finland by car and public transport (PT). CO2 emissions are presented as grams per passenger (PT) or grams per vehicle (private car with 1 passenger). In addition, the dataset presents fuel consumption of a car (liters / vehicle) and the number of PT lines that it requires to take on a specific route. CO2 emissions, distances and other variables have been calculated between ~13 000 gridded (250 m) data points over the study area at two different times of the day (rush hour/midday). In total, approximately 1 billion individual routings were calculated for creating the dataset. The modelling is made with in-house tools and based on various open data sources, including
Dataset: Helsinki Region Travel CO2 Matrix 2015
TUULI TOIVONEN1, HENRIKKI TENKANEN1, VUOKKO HEIKINHEIMO1, TIMO JAAKKOLA1, JUHA JÄRVI2 & MARIA SALONEN1
1University of Helsinki, Department of Geosciences and Geography, 2BusFaster Oy
Traffic and transport are one of the major causes of environmental problems in urban regions, and there
is growing concern over carbon dioxide (CO2) emissions resulting from daily travel. Decision making
and urban planning are expected to be supported with research based information about the current
processes and patterns. Thus, the need for comparable and timely information about the functioning and
impacts of the transport system (e.g. CO2 emissions, travel times, distances etc.) is higher than ever as
our society is getting increasingly dynamic and mobile.
Here, we present a dataset called Helsinki Region Travel CO2 Matrix 2015 that is available via
Helsinki Region Infoshare service. The dataset presents modelled comparable CO2 emissions and trip
lengths (meters) for the capital region of Finland by car and public transport (PT). CO2 emissions are
presented as grams per passenger (PT) or grams per vehicle (private car with 1 passenger). In addition,
the dataset presents fuel consumption of a car (liters / vehicle) and the number of PT lines that it requires
to take on a specific route. CO2 emissions, distances and other variables have been calculated between
~13 000 gridded (250 m) data points over the study area at two different times of the day (rush
hour/midday). In total, approximately 1 billion individual routings were calculated for creating the
dataset. The modelling is made with in-house tools and based on various open data sources, including
Digiroad, Kalkati.net XML (similar to GTFS data), and OpenStreetMap, accompanied with ancillary
data. Calculations were done using CSC-IT Center for Science cosmputing resources.
More information and the dataset described in this paper is freely available in:
http://www.helsinki.fi/science/accessibility/data/helsinki-region-travel-co2-matrix/
Chapter
Full-text available
As new datasets and methods become more available and comparable for analyzing accessibility, and especially on multi-modal travel effects on different contour catchments, there is a growing need to further test and advance these studies. In this study, we used centrality measure calculations, more accurately closeness and degree centralities, to create and analyze working population catchments in the Helsinki metropolitan area. For this purpose, we utilized Python with the comprehensive Helsinki Region Travel Time Matrix 2015 (Toivonen et al., 2015) by Digital Geography Lab (University of Helsinki) as our base dataset. We present (I) the most accessible places for the working population by public transportation and private car, and (II) the effects of travel time and amount of transfers in analyzing population centrality by public transportation. Although we conclude that private car grants far better accessibility over public transportation given our parameters, it is evident that adding accurate population details into analysis can illuminate otherwise hidden constructs of accessibility differences between modes of transport and social structures.
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