Kategorie: News

Study accepted for publication in TRIP đŸ„ł

Our study, “Mobility in pandemic times: Exploring changes and long-term effects of COVID-19 on urban mobility behavior”, has been accepted for publication in Transportation Research Interdisciplinary Perspectives (TRIP) and we are very excited about it. Thanks to the reviewer and the editor, whose feedback helped improve our paper greatly.

Find the article under https://www.sciencedirect.com/science/article/pii/S2590198222001282

Use-Case study submitted for publication: Mobility in pandemic times

We are proud to announce that a study using the data provided and integrated on the data platform prototype has been submitted for publication.

The study „Mobility in pandemic times: Exploring changes and long-term effects of COVID-19 on urban mobility behavior“ is a longitudinal analysis of changing urban mobility behaviors before and during the COVID-19 pandemic.

Key findings are:

  • During the pandemic individuals traveled shorter distances, traveled less often, and switched to active modes.
  • Public modes suffered a long-lasting regression, while bike travel is the central modal beneficiary of the pandemic.
  • Each pandemic wave created unique behavioral response patterns.
  • Results hint to long-term effects constituting a “new normal” of an entirely altered mobility landscape.

On 14 March, Robin Kellermann gave an interview to taz about the study, https://taz.de/Interview-mit-Mobilitaetsforscher/!5838610/ (german only)

We are working on articles!

The Mobility Data Hub project team is actively working on two scientific articles.

One article will present the current state of development of our mobility data platform, following the Design Resaerch Approach (DSR) to design and evaluate the platform. This article will be submitted for „17. Internationale Tagung Wirtschaftsinformatik“ (WI22, Feb 2022) under the thematic track of „ICT and Responsible Consumption as well as Production“.

A second article will illustrate the feasiblity of our Mobility Data Hub by an exemplary analysis of the effects induced by the COVID-19 pandemic on Berlin citizens‘ mobility behavior. Possessing an extensive source of longitudinal mobility data, we will explore modal, spatial and behavioral changes of mobility patterns within pandemic times. The article will be submitted to a renowned Open Access transport studies journal.

Welcome Daniel Conde!

Daniel Conde has joined our group as a doctoral researcher. He was involved in the project since April 2021, finishing his Master thesis on the topic: „Data Science und Corona: Änderung des MobilitĂ€tsverhaltens in Berlin“.

Now, we are looking forward to continuing our work together and are sending you a warm welcome!

Research Seminar on Information Systems

On August 14th, our undergraduate research seminar on Information Systems ended. Students had been working on projects from different areas within the Information Systems science, some of which focussing on topics directly related to the mobility data hub.

The research project tasks involved mapping the platform ecosystem, designing of a possible architecture, and preparing and analyzing first relevant data sets (i.e. GPS-based mode choices and the public transport timetable information).

The seminar was conducted by Prof. Natalia Kliewer and David RĂ¶ĂŸler at Freie UniversitĂ€t Berlin.

Doctoral Colloquium on Fare Revenue Forecasting in Public Transportation

In late June, Nicki KĂ€mpf and Jonas Krembsler presented first results from the research project “ReComMeND“ of fare revenue forecasting in public transportation in Berlin as part of our doctoral colloquium. The research project is funded by the IFAF Berlin and supported by Berliner Verkehrsbetriebe AöR, Internationaler Controller Verein e.V., Lufthansa Systems GmbH & Co. KG and Lufthansa Industry Solutions GmbH.

For their research, they use data based on monthly fare revenues for different product segments. The results will be used in a research project in public transport with the goal of automating revenue controlling and implementing data-driven decision-making in the existing controlling processes.

The focus of their study is to obtain suitable and reliable predictions: on the one hand with autoregressive methods such as ARIMA, SARIMA as well as Holt-Winters Exponential Smoothing and on the other hand with methods that include exogenous variables such as SARIMAX, MLR, LASSO, Ridge, Random Forests, Gradient Boosting, and Neural Networks. The data concerning exogenous variables are freely available and cover a wide range from tourism data to labor market development and weather data.

In their work, the researchers discuss the different methods and compare the prediction results by means of common accuracy measures. The goal is to evaluate a wide range of different methods in order to decide in which situations they out- or underperform other methods.

Besides simple prediction accuracy, another part of the study is the feature selection and interpretation of their impact. They address automatic feature selection using traditional approaches such as AIC optimization, a rolling window cross-validation approach optimizing the cv-error, and algorithmic approaches such as LASSO or Bayesian optimization. The researchers discuss the interpretability of the results and the advantages and disadvantages of different approaches.

Close collaboration with BUA project „Berlin Open Science Platform“ (BOS)

The Berlin Mobility Data Hub has established a close cooperation with another project funded by the Berlin University Alliance (BUA), the „Berlin Open Science Platform“ (BOS), coordinated by the Quality and Usability Lab of TU Berlin. In similarity to our project, BOS aims to establish a data platform that gathers and integrates available data within the BUA. Beyond that, the platform aims to provide analytical features within the platform. Despite our project having a more specific thematic focus (mobility & transport), we regularly meet with the BOS project team in order to exchange ideas on the technical side of developing our platforms.

Acquisition of longitudinal data set (Covid-19 and Mobility in Berlin)

The project team has acquired a highly interesting data set consisting of longitudinal data from individual Berlin-based app users who tracked their daily mobility with the help of the MotionTag technology. These data comprise of about 200.000 user days in the period 01/2019-03/2021 that allow us a) to analyse users‘ change of mobility patterns during the Corona crisis, and b) to feed the Berlin Mobility Data Hub with highly relevant data. In short, besides the analytical value of gaining more insights how Berliners changed their everyday mobility in pandemic times, the acquisition of this data set will act as a showcase for illustrating the functional potential of the hub itself.

Stay tuned for more on this!