As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches in time and space. Here, we propose a high-resolution estimation of traffic emissions, which is based entirely on open data, such as the road network and points of interest derived from OpenStreetMap (OSM). We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel. Our estimated total annual GHG emissions were 7.3 million t CO₂ equivalent. The highest emissions were estimated for the motorways and major roads connecting the city center with the outskirts. The application of the approach to Berlin showed that the method could reflect the traffic pattern. As the input data is freely available, the approach can be applied to other study areas within Germany with little additional effort.

The Equality-Generalized Travelling Salesman Problem (E-GTSP), which is an extension of the Travelling Salesman Problem (TSP), is stated as follows: given groups of points within a city, like banks, supermarkets, etc., find a minimum cost Hamiltonian cycle that visits each group exactly once. It can model many real-life combinatorial optimization scenarios more efficiently than TSP. This study presents five spatially driven search-algorithms for possible transformation of E-GTSP to TSP by considering the spatial spread of points in a given urban city. Presented algorithms are tested over 15 different cities, classified by their street-network’s fractal-dimension. Obtained results denote that the R-Search algorithm, which selects the points from each group based on their radial separation with respect to the start–end point, is the best search criterion for any E-GTSP to TSP conversion modelled for a city street network. An 8.8% length error has been reported for this algorithm.

Traffic prediction is a topic of increasing importance for research and applications in the domain of routing and navigation. Unfortunately, open data are rarely available for this purpose. To overcome this, the authors explored the possibility of using geo-tagged social media data (Twitter), land-use and land-cover point of interest data (from OpenStreetMap) and an adapted betweenness centrality measure as feature spaces to predict the traffic congestion of eleven world cities. The presented framework and workflow are termed as SocialMedia2Traffic. Traffic congestion was predicted at four tile spatial resolutions and compared with Uber Movement data. The overall precision of the forecast for highly traffic-congested regions was approximately 81%. Different data processing steps including ways to aggregate data points, different proxies and machine learning approaches were compared. The lack of a universal definition on a global scale to classify road segments by speed bins into different traffic congestion classes has been identified to be a major limitation of the transferability of the framework. Overall, SocialMedia2Traffic further improves the usability of the tested feature space for traffic prediction. A further benefit is the agnostic nature of the social media platform’s approach.

Number of studies covering major data aspects of OpenStreetMap (OSM) for developed cities and countries are available in scientific literature. However, this is not the case for developing ones mainly because of low data availability in OSM. This study presents a time-series spatial analysis of Turkey OSM dataset, a developing country, between the year 2007 and 2015 to understand how the dataset has developed with time and space. Five different socio-economic factors of the region are tested to find their relationship, if any, with dataset growth. An east-west spatial trend in data density is observed within the country. Population Density and Literacy Level of the region are found be the factors controlling it. It has also been observed that the street network of the region has followed the Exploration and Densification evolutionary model. High participation inequality is found within the OSM mappers, with only 5 of them responsible for the country’s 50% geo-data upload. Furthermore, it is found that these mappers use other Volunteered Geographic Information (VGI) and government open-dataset to feed into OSM. This study is believed to bring some high level insights of OSM for a developing country which would be useful for geographers, open-data policy makers, VGI projects planners and data-curators to structure and deploy similar future projects.

Time-dependent traffic speed information at a street level is important for routing services to estimate accurate travel 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 derived from OSM tags, since comprehensive traffic speed data is not openly available. In this study, a method was developed to model traffic speed by hour of day at a street level using open data from OpenStreetMap, Twitter and population data. The modelled traffic speed data was subsequently integrated into the open-source routing engine openrouteservice to improve travel time estimation in route planning. Machine learning models were trained for ten cities worldwide using traffic speed data from Uber Movement as reference data. Different indicators based on geolocation and timestamp of Twitter data as well as a geographically adapted betweeness centrality indicator were evaluated for their potential to improve prediction accuracy. In all cities, the Twitter indicators improved the model, although this effect was only visible for certain road types. The centrality indicator improved the model as well but to a lesser extent. The Google Routing API was used as reference to evaluate the accuracy in travel time estimation. Deviations in travel times were regionally different and were partly alleviated by including the raw traffic data by Uber or the modelled traffic speed data in openrouteservice.