Helsinki Region Travel Time Matrix 2018-2023
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<strong>Introduction</strong> This travel time matrix records <strong>travel times</strong> and travel distances for routes between all centroids (N = 13231) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by <strong>walking</strong>, <strong>cycling</strong>, <strong>public transportation</strong>, and <strong>private car</strong>. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), 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 <em>Yhdyskuntarakenteen seurantajärjestelmä</em> (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. <strong>Data formats</strong> 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. `Helsinki_Travel_Time_Matrix_2023.csv.zst`: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the Zstandard algorithm, that many data science libraries, for instance, pandas, support transparently, directly, and automatically. `Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip`: an OGC GeoPackage standard file containing all data columns <em>and</em> the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively. `Helsinki_Travel_Matrix_2023_travel_times.csv.zip`: a set of 13231 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern `Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv` where `5787545` is replaced by the `to_id` by which the rows in the file are grouped. Use the `from_id` column to join with the geometries from one of the files below. Geometry, only: `Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip`: an OGC GeoPackage standard file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the `from_id` and `to_id` columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively. `Helsinki_Travel_Time_Matrix_2023_grid.shp.zip`: an ESRI Shapefile archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the `from_id` and `to_id` columns of the data files. <strong>Table structure</strong> <pre> from_id</pre> ID number of the origin grid cell <pre> to_id</pre> ID number of the destination grid cell <pre> walk_avg</pre> Travel time in minutes from origin to destination by walking at an average speed <pre> walk_slo</pre> Travel time in minutes from origin to destination by walking slowly <pre> bike_avg </pre> Travel time in minutes from origin to destination by cycling at an average speed; incl. extra time (1 min) to unlock and lock bicycle <pre> bike_fst </pre> Travel time in minutes from origin to destination by cycling fast; incl. extra time (1 min) to unlock and lock bicycle <pre> bike_slo </pre> Travel time in minutes from origin to destination by cycling slowly; incl. extra time (1 min) to unlock and lock bicycle <pre> pt_r_avg</pre> Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at an average speed <pre> pt_r_slo</pre> Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at a slower speed <pre> pt_m_avg</pre> Travel time in minutes from origin to destination by public transportation in midday traffic, walking at an average speed <pre> pt_m_slo </pre> Travel time in minutes from origin to destination by public transportation in midday traffic, walking at a slower speed <pre> pt_n_avg </pre> Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at an average speed <pre> pt_n_slo</pre> Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at a lower speed <pre> car_r</pre> Travel time in minutes from origin to destination by private car in rush hour<br> traffic <pre> car_m</pre> Travel time in minutes from origin to destination by private car in midday<br> traffic <pre> car_n</pre> Travel time in minutes from origin to destination by private car in nighttime<br> traffic <pre> walk_d</pre> Distance from origin to destination, in metres, on foot <strong>Data for 2013, 2015, and 2018</strong> 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. <strong>Methodology</strong> 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 R<sup>5</sup> 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: <em>Walking</em> 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 the average measured walking speed in summer conditions for `walk_avg` (as well as the respective `pt_*_walk_avg`), and the slowest quintile of all measured walker across all conditions for `walk_slo` (and the respective `pt_*_walk_slo`). <em>Cycling</em> 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, slow, and average 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. Further, in line with the values observed by Jäppinen (2012), we add a flat 30 seconds each for unlocking and locking the bicycle at the origin and destination. <em>Public Transport</em> 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 <em>walking</em>. <em>Private motorcar</em> 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 Perola (2023) 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 R<sup>5</sup>. 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 R<sup>5</sup> or r5py. Other modifications required more profound changes to the source code of the R<sup>5</sup> engine. You can find a fully patched fork of the R<sup>5</sup> 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.
<strong>数据集简介</strong> 本出行时间矩阵记录了赫尔辛基都会区人口聚居区内250×250米网格的所有质心（共13231个）之间，以步行、骑行、公共交通及私家车为出行方式的路径出行时间与出行距离。若有需要，已针对不同时段（早高峰、午间、平峰）及不同身体能力水平（如步行与骑行速度）计算了对应路径，详见下文。该网格遵循芬兰多领域应用中广泛使用的<em>通用城市结构监测系统（Yhdyskuntarakenteen seurantajärjestelmä，简称YKR）</em>的几何属性与编号规则，覆盖芬兰首都地区的赫尔辛基、埃斯波、考尼艾宁与万塔四座城市。<strong>数据格式</strong> 本数据集提供多种适配不同软件环境需求的格式，所有格式共享一套通用数据列（详见下文），可互换使用。`Helsinki_Travel_Time_Matrix_2023.csv.zst`: 逗号分隔值（CSV）格式的全量数据列文件，不含几何信息。该数据集将所有路径整合至单个文件中，可根据分析需求按起点或终点进行筛选。数据记录可与下文提供的几何信息进行关联。该文件采用Zstandard压缩算法，可被pandas等多数数据科学库直接、透明地自动读取。`Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip`: 符合开放地理空间联盟（OGC）GeoPackage标准的文件，包含全量数据列及对应终点网格单元格的几何信息。该数据集以ZIP压缩包形式分发，可被GDAL/OGR、QGIS、geopandas等多数GIS系统与库原生支持。`Helsinki_Travel_Matrix_2023_travel_times.csv.zip`: 包含13231个逗号分隔值文件的压缩包，每个文件对应单个终点网格单元格的出行路径。所有文件均包含全量数据列，不含几何信息，可与下文提供的几何信息进行关联。压缩包内单个文件的命名格式为`Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv`，其中`5787545`将替换为该文件分组依据的`to_id`字段值。可通过`from_id`字段与下文任一文件的几何信息进行关联。仅含几何信息的文件：`Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip`: 符合OGC GeoPackage标准的文件，包含本次分析所用网格的几何信息与标识编号。该文件可与数据文件的`from_id`及`to_id`字段进行关联。数据集以ZIP压缩包形式分发，可被多数GIS系统与库原生支持。`Helsinki_Travel_Time_Matrix_2023_grid.shp.zip`: 环境系统研究所（ESRI）Shapefile格式的压缩包，包含本次分析所用网格的几何信息与标识编号。该文件可与数据文件的`from_id`及`to_id`字段进行关联。<strong>表结构</strong> <pre> from_id</pre> 起点网格单元格的唯一标识编号<pre> to_id</pre> 终点网格单元格的唯一标识编号<pre> walk_avg</pre> 以平均步行速度从起点到终点的出行时间（单位：分钟）<pre> walk_slo</pre> 以慢速步行从起点到终点的出行时间（单位：分钟）<pre> bike_avg </pre> 以平均骑行速度从起点到终点的出行时间（单位：分钟），包含解锁与锁车的额外耗时（1分钟）<pre> bike_fst </pre> 以快速骑行速度从起点到终点的出行时间（单位：分钟），包含解锁与锁车的额外耗时（1分钟）<pre> bike_slo </pre> 以慢速骑行速度从起点到终点的出行时间（单位：分钟），包含解锁与锁车的额外耗时（1分钟）<pre> pt_r_avg</pre> 早高峰时段公共交通出行时间（单位：分钟，接驳步行按平均速度计算）<pre> pt_r_slo</pre> 早高峰时段公共交通出行时间（单位：分钟，接驳步行按慢速计算）<pre> pt_m_avg</pre> 午间时段公共交通出行时间（单位：分钟，接驳步行按平均速度计算）<pre> pt_m_slo </pre> 午间时段公共交通出行时间（单位：分钟，接驳步行按慢速计算）<pre> pt_n_avg </pre> 夜间时段公共交通出行时间（单位：分钟，接驳步行按平均速度计算）<pre> pt_n_slo</pre> 夜间时段公共交通出行时间（单位：分钟，接驳步行按慢速计算）<pre> car_r</pre> 早高峰时段私家车出行时间（单位：分钟）<pre> car_m</pre> 午间时段私家车出行时间（单位：分钟）<pre> car_n</pre> 夜间时段私家车出行时间（单位：分钟）<pre> walk_d</pre> 起点至终点的步行距离（单位：米）<strong>2013、2015及2018年数据集</strong> 数字地理实验室（Digital Geography Lab）于2013年首次开展出行时间矩阵的计算工作。后续迭代中我们更新了计算方法，不同年份的结果存在系统性差异。部分历史数据集无法使用新方法重新计算，但我们已采用与2023年数据集一致的方法重新完成了2018年的计算，相关结果将以相同格式提供。如需获取2013、2015年及采用旧方法计算的2018年出行时间矩阵，请参阅DOI:10.5281/zenodo.3247563。<strong>计算方法</strong> 本次计算分别基于2023年2月15日（周三）与2018年1月29日（周一）的交通数据。其中“早高峰”指当日8:00-9:00的1小时时段，“午间”指12:00-13:00时段，“夜间”指2:00-3:00时段。所有路径均通过r5py（一款基于Conveyal公司R⁵引擎的Python库）计算完成，我们针对赫尔辛基地区的应用场景对该库进行了适配修改，并基于本地真实数据集优化了计算模型。具体修改内容如下：<em>步行</em> 步行速度及对应的步行时间基于Willberg等人（2023）的研究成果，该研究实测了赫尔辛基不同年龄群体在不同路面条件下的步行速度。我们针对`walk_avg`（及对应的`pt_*_walk_avg`字段）选用了夏季环境下实测的平均步行速度；针对`walk_slo`（及对应的`pt_*_walk_slo`字段）选用了所有测试条件下实测步行速度最慢的20%群体的速度。<em>骑行</em> 骑行速度基于两份输入数据集推导得出：首先，我们基于Strava数据计算了各道路路段的平均骑行速度，并得到各路段骑行速度与整体平均速度的比值；随后基于Strava整体平均速度、赫尔辛基城市共享单车系统的平均骑行速度以及二者的平均值，计算得到各路段的快速、慢速及平均骑行速度。此外，参考Jäppinen（2012）的观测结果，我们在计算中为起点与终点的解锁与锁车操作各添加了30秒的固定耗时。<em>公共交通</em> 我们使用了赫尔辛基区域交通局发布的通用公交时刻表规范（General Transit Feed Specification，简称GTFS）格式的公共交通时刻表，并采用与上文“步行”部分一致的方法调整了接驳步行速度（包括换乘步行及往返公交站点的步行速度）。<em>私家车</em> 为还原赫尔辛基都会区的实际道路行驶速度，我们基于区域内道路样本的浮动车数据，推导了不同道路等级及限速条件下，实际行驶速度与法定限速的差值，具体方法详见Perola（2023）。由于上述路段速度已包含路口等待耗时，我们移除了R⁵引擎原有的转弯耗时惩罚项。我们的修改通过两种方式实现：部分修改可通过预处理输入数据集或在R⁵或r5py外部设置模型参数完成；其余修改则需要对R⁵引擎的源代码进行深度调整。你可在数字地理实验室的GitHub仓库中获取经过完整补丁的R⁵引擎分支：github.com/DigitalGeographyLab/r5。用于处理输入数据预处理、模型参数估计，以及读取输入参数与整理输出数据的代码，可在仓库github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices中获取。
