Helsinki Region Travel Time Matrix contains travel time and distance information for routes between all 250 m x 250 m grid cell centroids (n = 13231) in the Helsinki Region, Finland by walking, cycling, public transportation and car. The grid cells are compatible with the statistical grid cells used by Statistics Finland and the YKR (yhdyskuntarakenteen seurantajärjestelmä) data set. The Helsinki Region Travel Time Matrix is available for three different years:

2018

2015

2013

The data consists of travel time and distance information of the routes that have been calculated between all statistical grid cell centroids (n = 13231) by walking, cycling, public transportation and car.

The data have been calculated for two different times of the day: 1) midday and 2) rush hour.

The data may be used freely (under Creative Commons 4.0 licence). We do not take any responsibility for any mistakes, errors or other deficiencies in the data.

Organization of data

The data have been divided into 13231 text files according to destinations of the routes. The data files have been organized into sub-folders that contain multiple (approx. 4-150) Travel Time Matrix result files. Individual folders consist of all the Travel Time Matrices that have same first four digits in their filename (e.g. 5785xxx).

In order to visualize the data on a map, the result tables can be joined with the MetropAccess YKR-grid shapefile (attached here). The data can be joined by using the field ‘from_id’ in the text files and the field ‘YKR_ID’ in MetropAccess-YKR-grid shapefile as a common key.

Data structure

The data have been divided into 13231 text files according to destinations of the routes. One file includes the routes from all statistical grid cells to a particular destination grid cell. All files have been named according to the destination grid cell code and each file includes 13231 rows.

NODATA values have been stored as value -1.

Each file consists of 17 attribute fields: 1) from_id, 2) to_id, 3) walk_t, 4) walk_d, 5) bike_f_t, 6) bike_s_t, 7) bike_d, 8) pt_r_tt, 9) pt_r_t, 10) pt_r_d, 11) pt_m_tt, 12) pt_m_t, 13) pt_m_d, 14) car_r_t, 15) car_r_d, 16) car_m_t, 17) car_m_d, 18) car_sl_t

The fields are separated by semicolon in the text files.

Attributes

from_id: ID number of the origin grid cell

to_id: ID number of the destination grid cell

walk_t: Travel time in minutes from origin to destination by walking

walk_d: Distance in meters of the walking route

bike_f_t: Total travel time in minutes from origin to destination by fast cycling; Includes extra time (1 min) that it takes to take/return bike

bike_s_t: Total travel time in minutes from origin to destination by slow cycling; Includes extra time (1 min) that it takes to take/return bike

bike_d:Distance in meters of the cycling route

pt_r_tt: Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account including the waiting time at home

pt_r_t: Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account excluding the waiting time at home

pt_r_d: Distance in meters of the public transportation route in rush hour traffic

pt_m_tt: Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account including the waiting time at home

pt_m_t: Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account excluding the waiting time at home

pt_m_d: Distance in meters of the public transportation route in midday traffic

car_r_t: Travel time in minutes from origin to destination by private car in rush hour traffic; the whole travel chain has been taken into account

car_r_d: Distance in meters of the private car route in rush hour traffic

car_m_t: Travel time in minutes from origin to destination by private car in midday traffic; the whole travel chain has been taken into account

car_m_d: Distance in meters of the private car route in midday traffic

car_sl_t: Travel time from origin to destination by private car following speed limits without any additional impedances; the whole travel chain has been taken into account

METHODS

For detailed documentation and how to reproduce the data, see HelsinkiRegionTravelTimeMatrix2018 GitHub repository.

THE ROUTE BY CAR have been calculated with a dedicated open source tool called DORA (DOor-to-door Routing Analyst) developed for this project. DORA uses PostgreSQL database with PostGIS extension and is based on the pgRouting toolkit. MetropAccess-Digiroad (modified from the original Digiroad data provided by Finnish Transport Agency) has been used as a street network in which the travel times of the road segments are made more realistic by adding crossroad impedances for different road classes.

The calculations have been repeated for two times of the day using 1) the “midday impedance” (i.e. travel times outside rush hour) and 2) the “rush hour impendance” as impedance in the calculations. Moreover, there is 3) the “speed limit impedance” calculated in the matrix (i.e. using speed limit without any additional impedances).

The whole travel chain (“door-to-door approach”) is taken into account in the calculations: 1) walking time from the real origin to the nearest network location (based on Euclidean distance), 2) average walking time from the origin to the parking lot, 3) travel time from parking lot to destination, 4) average time for searching a parking lot, 5) walking time from parking lot to nearest network location of the destination and 6) walking time from network location to the real destination (based on Euclidean distance).

THE ROUTES BY PUBLIC TRANSPORTATION have been calculated by using the MetropAccess-Reititin tool which also takes into account the whole travel chains from the origin to the destination: 1) possible waiting at home before leaving, 2) walking from home to the transit stop, 3) waiting at the transit stop, 4) travel time to next transit stop, 5) transport mode change, 6) travel time to next transit stop and 7) walking to the destination.

Travel times by public transportation have been optimized using 10 different departure times within the calculation hour using so called Golomb ruler. The fastest route from these calculations are selected for the final travel time matrix.

THE ROUTES BY CYCLING are also calculated using the DORA tool. The network dataset underneath is MetropAccess-CyclingNetwork, which is a modified version from the original Digiroad data provided by Finnish Transport Agency. In the dataset the travel times for the road segments have been modified to be more realistic based on Strava sports application data from the Helsinki region from 2016 and the bike sharing system data from Helsinki from 2017.

For each road segment a separate speed value was calculated for slow and fast cycling. The value for fast cycling is based on a percentual difference between segment specific Strava speed value and the average speed value for the whole Strava data. This same percentual difference has been applied to calculate the slower speed value for each road segment. The speed value is then the average speed value of bike sharing system users multiplied by the percentual difference value.

The reference value for faster cycling has been 19km/h, which is based on the average speed of Strava sports application users in the Helsinki region. The reference value for slower cycling has been 12km/, which has been the average travel speed of bike sharing system users in Helsinki. Additional 1 minute have been added to the travel time to consider the time for taking (30s) and returning (30s) bike on the origin/destination.

More information of the Strava dataset that was used can be found from the Cycling routes and fluency report, which was published by us and the city of Helsinki.

THE ROUTES BY WALKING were also calculated using the MetropAccess-Reititin by disabling all motorized transport modesin the calculation. Thus, all routes are based on the Open Street Map geometry.

The walking speed has been adjusted to 70 meters per minute, which is the default speed in the HSL Journey Planner (also in the calculations by public transportation).

All calculations were done using the computing resources of CSC-IT Center for Science (https://www.csc.fi/home).

Our dataset contains 2 weeks of approx. 8-9 hours of acceleration data per day from 11 participants wearing a Bangle.js Version 1 smartwatch with our firmware installed.

The dataset contains annotations from 4 different commonly used annotation methods utilized in user studies that focus on in-the-wild data. These methods can be grouped in user-driven, in situ annotations - which are performed before or during the activity is recorded - and recall methods - where participants annotate their data in hindsight at the end of the day.

The participants had the task to label their activities using (1) a button located on the smartwatch, (2) the activity tracking app Strava, (3) a (hand)written diary and (4) a tool to visually inspect and label activity data, called MAD-GUI. Methods (1)-(3) are used in both weeks, however method (4) is introduced in the beginning of the second study week.

The accelerometer data is recorded with 25 Hz, a sensitivity of ±8g and is stored in a csv format. Labels and raw data are not yet combined. You can either write your own script to label the data or follow the instructions in our corresponding Github repository.

The following unique classes are included in our dataset:

laying, sitting, walking, running, cycling, bus_driving, car_driving, vacuum_cleaning, laundry, cooking, eating, shopping, showering, yoga, sport, playing_games, desk_work, guitar_playing, gardening, table_tennis, badminton, horse_riding.

However, many activities are very participant specific and therefore only performed by one of the participants.

The labels are also stored as a .csv file and have the following columns:

week_day, start, stop, activity, layer

Example:

week2_day2,10:30:00,11:00:00,vacuum_cleaning,d

The layer columns specifies which annotation method was used to set this label.

The following identifiers can be found in the column:

b: in situ button

a: in situ app

d: self-recall diary

g: time-series recall labelled with a the MAD-GUI

The corresponding publication is currently under review.