This travel time matrix records travel times and travel distances for routes between all centroids (N = 13231) of a 250 × 250 m grid over the Helsinki metropolitan area by walking, cycling, public transportation, and private car. If applicable, the routes have been calculated for different times of the day (rush hour, midday, nighttime), and assuming different physical abilities (such as walking and cycling speeds), see details below.
The grid follows the geometric properties and enumeration of the versatile Yhdyskuntarakenteen seurantajärjestelmä (YKR) grid used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region.
The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably.
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from_id |
ID number of the origin grid cell |
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to_id |
ID number of the destination grid cell |
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walk_avg |
Travel time in minutes from origin to destination by walking at an average speed |
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walk_slo |
Travel time in minutes from origin to destination by walking slowly |
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bike_avg
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Travel time in minutes from origin to destination by cycling at an average speed; incl. extra time (1 min) to unlock and lock bicycle |
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bike_fst |
Travel time in minutes from origin to destination by cycling fast; incl. extra time (1 min) to unlock and lock bicycle |
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bike_slo |
Travel time in minutes from origin to destination by cycling slowly; incl. extra time (1 min) to unlock and lock bicycle |
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pt_r_avg |
Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at an average speed |
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pt_r_slo |
Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at a slower speed |
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pt_m_avg |
Travel time in minutes from origin to destination by public transportation in midday traffic, walking at an average speed |
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pt_m_slo |
Travel time in minutes from origin to destination by public transportation in midday traffic, walking at a slower speed |
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pt_n_avg |
Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at an average speed |
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pt_n_slo |
Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at a lower speed |
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car_r |
Travel time in minutes from origin to destination by private car in rush hour traffic |
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car_m |
Travel time in minutes from origin to destination by private car in midday |
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car_n |
Travel time in minutes from origin to destination by private car in nighttime traffic |
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walk_d |
Distance from origin to destination by walking |
At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iterations’ results. Not all input data sets are available to recompute the historical matrices with new methods, however, we were able to repeat the 2018 calculation using the same methods as the 2023 data set, please find the results below, in the same format.
For the travel time matrices for 2013 and 2015, as well as for 2018 using an older methodology, please refer to DOI:10.5281/zenodo.3247563.
Computations were carried out for Wednesday, 15 February, 2023, and Monday, 29 January, 2018, respectively. ‘Rush hour’ refers to an 1-hour window between 8 and 9 am, ‘midday’ to 12 noon to 1 pm, and ‘nighttime’ to 2-3 am.
All routes have been calculated using r5py, a Python library making use of the R5 engine by Conveyal, with modifications to consider local characteristics of the Helsinki use case and to inform the computation models from local real-world data sets. In particular, we made the following modifications:
Walking speeds, and in turn walking times, are based on the findings of Willberg et al., 2023, in which we measured walking speeds of people of different age groups in varying road surface conditions in Helsinki. Specifically, we chose to use a typical walking speed in summer conditions for walk_avg (as well as the respective pt_*_walk_avg) (4.7 km/h), and the slowest quintile of all measured walker across all conditions for walk_slo (and the respective pt_*_walk_slo) (3.43 km/h).
Cycling speeds are derived from two input data sets. First, we averaged cycling speeds per network segment from Strava data, and computed a ratio between the speed ridden in each segment and the overall average speed. We then use these ratios to compute fast (19 km/h), slow (12 km/h), and average (15.5 km/h) cycling speeds for each segment, based on the mean overall Strava speed, the mean speeds cycled in the Helsinki City Bike bike-share system, and the mean between the two.
We used public transport schedules in General Transit Feed Specification (GTFS) format published by the Helsinki Regional Transport Authority, and adjusted the walking speeds (for connections between vehicles, as well as for access and egress to and from public transport stops) using the same methods as described above for walking.
To represent road speeds actually driven in the Helsinki metropolitan region, we used floating car data of a representative sample of the roads in the region to derive the differences between the speed limit and the driven speed on different road classes, and by speed limit, see Eerola (in preparation) and Jaakkola (2013) for a detailed description of the methodology. Because these per-segment speeds factor in potential waiting times at road crossings, we eliminated turn penalties from R5.
Our modifications were carried out in two ways: some changes can be controlled by preparing input data sets in a certain way, or by setting model parameters outside of R5 or r5py. Other modifications required more profound changes to the source code of the R5 engine.
You can find a fully patched fork of the R5 engine in the Digital Geography Lab’s GitHub repositories at github.com/DigitalGeographyLab/r5. The code that handles input data mangling and model parameter estimations is kept together with the logic to read input parameters and to collate output data, in the repository at github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices.
Download the Helsinki metropolitan area travel time matrix from the Zenodo repository for open scientific data: doi.org/10.5281/zenodo.7907548
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Recording and presentations from the launch event of the new travel time matrix 2023 and GREENTRAVEL project on 17 May 2023