We present a methodology to extract points of interest (POIs) data from OpenStreetMap (OSM) for application in travel demand models. We use custom taglists to identify and assign POI elements to typical activities used in travel demand models. We then compare the extracted OSM data with official sources and point out that the OSM data quality depends on the type of POI and that it generally matches the quality of official sources. It can therefore be used in travel demand models. However, we recommend that plausibility checks should be done to ensure a certain quality. Further, we present a methodology for calculating attractiveness measures for typical activities from single POIs and national trip generation guidelines. We show that the quality of these calculated measures is good enough for them to be used in travel demand models. Using our approach, therefore, allows the quick, automated, and flexible generation of attractiveness measures for travel demand models.

No abstract

No abstract

OpenStreetMap (OSM) data are geographical data that are easy and open to access and therefore used for a large set of applications including travel demand modeling. However, often there is a limited awareness about the shortcomings of volunteered geographic information data, such as OSM. One important issue for the application in travel demand modeling is the completeness of OSM elements, particularly points of interest (POI), since it directly influences the predictions of trip distributions. This might cause unreliable model sensitivities and end up in wrong predictions leading to expensive misinterpretations of the effects of policy measures. Because of a lack of large-scale real-world data, a detailed assessment of the quality of POI from OSM has not been done yet. Therefore, in this work, we assess the quality of POI from OSM for use within travel demand models using surveyed real-world data from 49 areas in Germany. We perform a descriptive and a model-based analysis using spatial, demographic, and intrinsic indicators for two common trip purpose categories used in travel demand modeling. We show that the completeness of POI data in OSM depends on the category of POI. We further show that intrinsic indicators and indicators calculated based on data from other sources (e.g., land use or census data) are able to detect quality deficiencies of OSM data.

Autonomous busses and on-demand (OD) services have the potential to improve the public transport system. However, research on potential traffic impacts is still ongoing, mainly because of a lack of existing use cases of autonomous driving as part of public transport. The availability of revealed preference data for mode choice decisions is thus very limited. Therefore, we conducted a stated choice experiment to assess mode choice preferences with regard to use cases as the main mode of transport and as the solution for the first and last mile. We also distinguished between OD and schedule-based (sched.) services. The target population of the survey is the population of Baden-Württemberg, a state in southwestern Germany. The responses of 1,434 people were analyzed using a nested logit approach. On this basis, we established exemplary utility functions and descriptively derived recommendations for efficient forms of deploying autonomous busses in addition to already existing well-developed public transport systems. It was found that, under the given conditions, public transport pass owners without a car in their household would be the most interested in using autonomous busses. Car owners without a smartphone see less benefit. It was also shown that the recruiting method of the respondents is crucial. Those reached via social media were significantly more positive than those contacted via an online panel. Further evaluations show that autonomous busses are rated similarly to existing public transport and consequently have particularly high potential on medium distances, especially if their deployment leads to shorter access routes.