Location & approach

By car

The campus of the Technical University of Dortmund is located near the freeway junction Dortmund West, where the Sauerland line A45 crosses the Ruhr expressway B1/A40. The Dortmund-Eichlinghofen exit on the A45 leads to the South Campus, the Dortmund-Dorstfeld exit on the A40 leads to the North Campus. The university is signposted at both exits.

By bus and train

The "Dortmund Universität" S-Bahn station is located directly on the North Campus. From there, the S-Bahn line S1 runs every 20 or 30 minutes to Dortmund main station and in the opposite direction to Düsseldorf main station via Bochum, Essen and Duisburg. In addition, the university can be reached by bus lines 445, 447 and 462. Timetable information can be found on the homepage of the Rhine-Ruhr transport association, and DSW21 also offer an interactive route network map.

The H-Bahn

One of the landmarks of the TU Dortmund is the H-Bahn. Line 1 runs every 10 minutes between Dortmund Eichlinghofen and the Technology Center via Campus South and Dortmund University S, while Line 2 commutes every 5 minutes between Campus North and Campus South. It covers this distance in two minutes.

By plane

From Dortmund Airport, the AirportExpress takes just over 20 minutes to Dortmund Central Station and from there to the university by S-Bahn. A wider range of international flight connections is offered by Düsseldorf Airport, about 60 kilometers away, which can be reached directly by S-Bahn from the university's train station.

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Location & approach

By car

By bus and train

The H-Bahn

By plane

ServicePortal UniMail

New publication in Geographical Analysis

10/12/2023

Machine learning methods can offer a useful addition in transport and spatial research: The article published by Katja Schimohr, Philipp Doebler (Faculty of Statistics) and Joachim Scheiner in the current volume of Geographical Analysis deals with the modelling of the spatial distribution of bikesharing trips in Cologne. For this purpose, it compares a generalised additive model (GAM) with the XGBoost algorithm from the field of Machine Learning. In both methods, measures are taken to account for spatial autocorrelation. While the results of the GAM give an easier to interpret insight into the factors influencing bikesharing use, XGBoost leads to more accurate predictions. Both methods are well suited for spatial modelling of demand, although the choice of method should be made depending on the objective of the study.

DOI: https://doi.org/10.1111/gean.12354

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