TraMPa: Transportation Modeling Using Publicly Available Data: An Evolution for Model Input Data

• Applicants: Professor Dr. Constantinos Antoniou; Professor Dr.-Ing. Rolf Moeckel
• Subject Area: Traffic and Transport Systems, Intelligent and Automated Traffic
• Term: from 2019 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 415208373

TraMPa investigates the use of openly available data for the development of transportation models. The proposed project builds upon the ever-growing amount of data that can be obtained by a diverse number of open data sources available. The analysis will be based on different levels of open source data availability to perform validation, sensitivity analyses and meta-model analyses for the investigation of the model performance based on real-world scenarios.This project aims at using openly available data as much as possible for model development, model calibration and model validation. A core finding will be how far openly available data may replace traditional data sources in transport modeling. Data sources to be tested in this project include all transport-related data that can be obtained openly online, legally and free of charge. This includes data that can be accessed through an Application Programming Interface (API), web-scraping or direct download (open data). Traditional data, in contrast, may be inaccessible to other researchers or cost money. By exploring the potential of open data, this proposal aims at establishing evidence how open data may support a more transparent and reproducible transport modeling approach. An Open Model will be compared with a Traditional Model that has been implemented for the Munich metropolitan area, the State of Maryland in the USA and Cape Town in South Africa. By comparing two traditional models with open models (and hybrids thereof) for the same study areas and the same scenarios, it will be possible to quantify the potential of openly available data to replace traditional data sources. It is perceivable that the Open Model will even perform better than the Traditional Model, as it is hypothesized that the data used for the Open Model is less biased and less error-filled than conventional data used for the Traditional Model. In order to allow for a better understanding of the effect that open data could have in the definition of the Open Transportation Model, different mixes of traditional data and open data will be tested by validation, sensitivity analyses and meta analyses to examine the performance of the models estimated based on Key Performance Indicators (KPIs). This comparison of the Open Model with the Traditional Model (and hybrids thereof) will help explore the impact that levels of data availability and penetration have on transportation models. Should this research show that the Open Model outperforms the Traditional Model, the implications on data collection, model design and required funding could be substantial.

• DFG Programme: Research Grants