Ruben Verborgh

What Public Transit API Logs Tell Us about Travel Flows

In the field of smart cities, researchers need an indication of how people move in and between cities. Yet, getting statistics of travel flows within public transit systems has proven to be troublesome. In order to get an indication of public transit travel flows in Belgium, we analyzed the query logs of the iRail API, a highly expressive route planning API for the Belgian railways. We were able to study 100k to 500k requests for each month between October 2012 and November 2015, which is between 0.56% and 1.66% of the amount of monthly passengers. Using data visualizations, we illustrate the commuting patterns in Belgium and confirm that Brussels, the capital, acts as a central hub. The Flemish region appears to be polycentric, while in the Walloon region, everything converges on Brussels. The findings correspond to the real travel demand, according to experts of the passenger federation Trein Tram Bus. We conclude that query logs of route planners are of high importance in getting an indication of travel flows. However, better travel intentions would be acquirable using dedicated HTTP POST requests.

Generating public transport data based on population distributions for RDF benchmarking

When benchmarking RDF data management systems such as public transport route planners, system evaluation needs to happen under various realistic circumstances, which requires a wide range of datasets with different properties. Real-world datasets are almost ideal, as they offer these realistic circumstances, but they are often hard to obtain and inflexible for testing. For these reasons, synthetic dataset generators are typically preferred over real-world datasets due to their intrinsic flexibility. Unfortunately, many synthetic dataset that are generated within benchmarks are insufficiently realistic, raising questions about the generalizability of benchmark results to real-world scenarios. In order to benchmark geospatial and temporal RDF data management systems such as route planners with sufficient external validity and depth, we designed PODiGG, a highly configurable generation algorithm for synthetic public transport datasets with realistic geospatial and temporal characteristics comparable to those of their real-world variants. The algorithm is inspired by real-world public transit network design and scheduling methodologies. This article discusses the design and implementation of PODiGG and validates the properties of its generated datasets. Our findings show that the generator achieves a sufficient level of realism, based on the existing coherence metric and new metrics we introduce specifically for the public transport domain. Thereby, PODiGG provides a flexible foundation for benchmarking RDF data management systems with geospatial and temporal data.