Christina Ludwig

Time dependent traffic speed information on a street level is important for routing services to estimate accurate arrival times and to recommend routes which avoid traffic congestion. Still, most open-source routing machines that use OpenStreetMap (OSM) as the primary data source rely on static driving speeds for different highway types, since comprehensive traffic speed data is not openly available. In this talk, we will present a method to model traffic speed by hour of day for the street network of ten different cities worldwide and its integration in route planning using the open-source routing engine openrouteservice. Current datasets on traffic speed are either not openly available (e.g. Google traffic layer may be viewed but not downloaded), have very limited spatial coverage or do not follow a consistent data format (e.g. data published by municipalities). In addition, these datasets are often not based on the OSM street network, which means it would require extensive map matching procedures to transfer the traffic speed information to the OSM features. The most promising data set is currently provided by Uber Movement containing hourly traffic speed data along OSM street segments in 51 cities worldwide from 2015 until 2020. Still, this data only covers roads for which enough Uber user data is available. In recent years, several studies have proposed methods and evaluated different data sources for traffic speed modelling. Most of them model traffic speed using machine learning methods and different indicators such as OSM tags (e.g. highway=*), points-of-interest (Camargo et al., 2020), centrality indicators (Zhao et al., 2017) or social media data (Pandhare & Shah, 2017). All of these indicators proved to be suitable for modelling traffic flow, but none of these studies has evaluated the effect of the modelled traffic speed on route planning and arrival time estimation. In this study, we modelled traffic speed by hour of day on a street level for 10 cities worldwide based on OSM tags, an adapted betweenness centrality indicator and Twitter data. Uber traffic speed data was used as reference data to train and evaluate a gradient boosting regression model with different combinations of features. The simplest baseline model only used the OSM tags highway=* and maxspeed=* for prediction. The additional adapted betweenness centrality indicator was calculated to identify highly frequented street segments in each city by simulating several thousands of car trips in each city. In order to consider the geographic context, the original centrality indicator calculation was adapted to consider the spatial configuration of the city by including population distribution and relevant POIs during the calculation. Finally, Twitter data was used to account for the spatio-temporal distribution of human activity within the city. Using only the timestamp and geolocation of the tweets, the number of tweets in the vicinity of a street segment aggregated by the time of day was used as an indicator. The quality of the different models was evaluated with the help of the coefficient of determination (R2), the root mean square error (RMSE) and the mean absolute error (MAE). In all cities, the Twitter indicators improved the model, although this effect was only visible for certain road types. The Twitter indicators improved the accuracy especially for construction sites and motorways. For medium sized roads such as residential streets, the prediction did not improve. The centrality indicator improved the model as well but to a lesser extent. Best results were achieved in Berlin with an RMSE of 6.58 and R2 of 0.82. To use the modelled traffic speed data in route planning, an experimental traffic integration was implemented in openrouteservice using which traffic speed data can be passed to openrouteservice as a CSV file. Each row contains the traffic speed at a certain hour of the day and for a certain OSM street segment specified by its OSM way id along with a start and end node. The data is structured the same way as the Uber Movement data making it possible to either integrate the raw Uber data or the modelled traffic speed. The effect of using external traffic speed data on the travel time estimation was evaluated by calculating multiple random car trips within different cities and at different times of the day and comparing it to the estimated travel time of the Google Routing API as well as the original openrouteservice implementation. In addition, the raw as well as the modelled traffic data were compared. The comparison between travel times in Google and openrouteservice showed regional differences in the accuracy of estimated travel times. These differences could be partly alleviated by incorporating raw or modeled traffic speed information. Future research on traffic speed modelling using open data includes further development of the models and their transferability to other cities for which no Uber data is available. In this regard, the potential of deep learning approaches should be evaluated. Since Twitter has stopped providing their API for free, data from other social media platforms needs to be integrated. The potential for this is high though, since only the timestamp and geolocation of each tweet are used making the general approach easily transferable. The methods, results and software generated within this study are relevant to the FOSS4G community - in particular to the FOSS4G academic track - since it combines scientific analysis in the form of traffic speed modelling with open-source software development by integrating the results in openrouteservice. The source code for the prototypical traffic integration is available on GitHub (https://github.com/GIScience/openrouteservice). The source code of the traffic speed model will be published along with the paper.

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OpenStreetMap provides a lot of valuable information about urban green spaces, but the numerous and conceptually overlapping OSM tags that describe such features lead to spatially heterogenous representations in OSM. We developed an exploratory data analysis methodology to identify locally relevant OSM tags for mapping green spaces in a specific area and compared the extracted OSM features to administrative data to evaluate the level of completeness in regard to urban green spaces.

Urban green spaces provide a variety of important ecosystem services such as micro-climate regulation, increase of biodiversity and the provision of recreational and cultural services for citizens. Thus, they are an important factor for the quality of life in cities (Bolund and Hunhammar, 1999). However, in order to take advantage of these services citizens need to have sufficient information about the location and qualities of nearby green spaces. Within the project “meinGrün” we are addressing this issue by developing a web-based recommendation service which helps citizens find suitable green spaces that satisfy their personal needs. OpenStreetMap (OSM) plays an important role in this project, since it provides a lot of valuable information about urban green spaces such as their location and the amenities they provide (playgrounds, benches, toilets etc.) However, its spatially heterogeneous data quality, especially in regard to the level of completeness, provides challenges for its usage in a recommendation system. Therefore, the integration of OSM data for our purposes requires a prior assessment of the completeness of urban green spaces. The completeness of certain geographic objects is one of the main fields of investigation in regard to OSM data quality. In recent years several studies investigated the completeness of OSM data with respect to the road network (Barrington-Leigh and Millard-Ball, 2017), buildings (Hecht et al., 2013) or land use features (Jokar Arsanjani et al., 2015). Urban green spaces, on the other hand, were rarely the focus of completeness studies. Ali et al. (2016) developed a method to quantify the plausibility of vegetation-related tags being assigned to specific OSM features and Lopes et al. (2017) evaluated the potential of OSM for extracting information about natural local climate zones. Since both of these studies do not explicitly address the completeness of urban green spaces, we developed a new methodology for this purpose. In contrast to buildings and highways, this poses unique challenges due to the variety of vegetation-related OSM tags and the many different forms of urban vegetation ranging from large parks over private gardens to roadside greenery. OSM tags that describe natural objects are numerous and sometimes conceptually overlapping e.g. some features could be tagged as leisure=park or leisure=garden. This leads to different representations of urban green spaces in OSM across different geographical regions. Defining one set of relevant OSM tags to measure the completeness of urban green spaces that can be applied everywhere is therefore not possible. To solve this issue, we developed an explorative data analysis methodology based on OSM and satellite imagery to identify locally relevant OSM tags that indicate urban green spaces. The analysis is based on statistical and graphical methods to evaluate the association between a certain OSM tag and the presence of vegetation. After the relevant tags have been identified, features representing green spaces are extracted from OSM and compared to an administrative data set to assess the level of completeness. As a basis for this comparison, the study area is divided into patches of homogenous land use based on natural and human-made barriers such as the road network, rivers or objects that mark changes in land use (fences, walls, etc.). On this basis, features from both data sets are joined and the level of completeness is assessed using different extrinsic data quality measures. In our talk we will present our methodology along with the results of the completeness assessment for the City of Dresden, which is a pilot city of “meinGrün”, a project funded by the Federal Ministry of Transport and Digital Infrastructure (BMVI). References: Ali, A., Sirilertworakul, N., Zipf, A., Mobasheri, A., 2016. Guided classification system for conceptual overlapping classes in OpenStreetMap. ISPRS Int. J. Geo-Inf. 5, 87. Barrington-Leigh, C., Millard-Ball, A., 2017. The world’s user-generated road map is more than 80% complete. PLOS ONE 12, e0180698. https://doi.org/10.1371/journal.pone.0180698 Bolund, P., Hunhammar, S., 1999. Ecosystem services in urban areas. Ecol. Econ. 29, 293–301. https://doi.org/10.1016/S0921-8009(99)00013-0 Hecht, R., Kunze, C., Hahmann, S., 2013. Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS Int. J. Geo-Inf. 2, 1066–1091. Jokar Arsanjani, J., Mooney, P., Zipf, A., Schauss, A., 2015. Quality Assessment of the Contributed Land Use Information from OpenStreetMap Versus Authoritative Datasets, in: Jokar Arsanjani, J., Zipf, A., Mooney, P., Helbich, M. (Eds.), OpenStreetMap in GIScience: Experiences, Research, and Applications, Lecture Notes in Geoinformation and Cartography. Springer International Publishing, Cham, pp. 37–58. https://doi.org/10.1007/978-3-319-14280-7_3 Lopes, P., Fonte, C., See, L., Bechtel, B., 2017. Using OpenStreetMap data to assist in the creation of LCZ maps, in: 2017 Joint Urban Remote Sensing Event (JURSE). IEEE, pp. 1–4.