Category Archives: Uncategorized

New CEUS paper published – presenting our open-source library Trackintel

Over the past years, MIE lab has been developing an open-source Python library for analyzing human mobility data. Trackintel provides a standardized pipeline for loading, preprocessing, and analyzing tracking data, as shown in the graphic below.  In the paper titled “Trackintel: An open-source Python library for human mobility analysis”, we describe the functionality of the library and demonstrate it in a case study on several datasets.
The paper is available open-access.

New TR_C paper online – “Conserved quantities in human mobility”

Our new paper entitled “Conserved quantities in human mobility: From locations to trips” was accepted at Transportation Research Part C: Emerging Technologies and is now available online.

We use two high-resolution user-labelled datasets from ~3800 individuals to analyse individuals’ activity–travel behaviour over the long term. We find that individuals maintain a conserved quantity in the number of essential travel mode and activity location combinations over time. A typical individual maintains 15 mode–location combinations, of which 7 are travelled with a private vehicle every 5 weeks. The dynamics of this stability reveal that the exploration speed of locations is faster than the one for travel modes, and they can both be well-modelled using a power-law fit that slows down over time.

Our findings enrich the understanding of the long-term intra-person variability in activity–travel behaviour and open new possibilities for designing mobility simulation models.

Check out the open-access paper online!

New CEUS paper online: “Graph based mobility profiling”

Our new paper on “Graph based mobility profiling” was accepted at Computers, Environment and Urban Systems (CEUS) and is now available online. We propose a graph based workflow to identify groups of persons with similar mobility behavior based on person specific graphs that describe the mobility behavior. Our approach is privacy friendly, does not depend on a specific clustering algorithm, is robust against the choice of hyperparameters, does not require specific labels in the dataset, and is not limited to specific types of tracking data. We show in the paper how this can be used to evaluate the impact of new mobility offers.

The paper is open access and the source code of the project is available on our Github.

New TGIS Paper is Online – “Street-level Traffic Flow and Context Sensing Analysis”

Our new paper entitled “Street-level traffic flow and context sensing analysis through semantic integration of multisource geospatial data” by Yatao Zhang and Martin Raubal is now online at TGIS.

Sensing urban spaces from multisource geospatial data is vital to understanding the transportation system in the urban context. However, the complexity of urban context and its indirect interaction with traffic flow deepen the difficulty of exploring their relationship. This study proposes a geo-semantic framework first to generate semantic representations of multi-hierarchical urban context and street-level traffic flow, and then investigate their mutual correlation and predictability using a novel semantic matching method. The results demonstrate that each street is associated with its multi-hierarchical spatial signatures of urban context and street-level temporal signatures of traffic flow. The correlation between urban context and traffic flow displays higher values after semantic matching than those in multi-hierarchies. Moreover, we found that utilizing traffic flow to predict urban context results in better accuracy than the reversed prediction. The results of signature analysis and relationship exploration can contribute to a deeper understanding of context-aware transportation research.

Kick-off of the e-bike city lighthouse project

What if 50% of the existing urban road space was allocated to e-bikes and the slow modes? Seven chairs at the department for civil engineering at ETH join forces to analyze the opportunities and effects of an urban future giving priority to cycling, micromobility and public transport. Our group will contribute with the developement of spatial optimization methods for redistributing the street lanes to design the new bike lane network, considering constraints such as (multi-modal) accessibility and exposure levels of the lanes.

New SIGSPATIAL Paper Is Online – “Improving next location prediction”

We are delighted to announce that our paper “How do you go where? Improving next location prediction by learning travel mode information using transformers” by MIE Lab members Ye Hong, Henry Martin, and Martin Raubal is now online at arXiv and will be presented at ACM SIGSPATIAL, November 1–4, 2022, Seattle, WA, USA conference.

In this work, we propose a transformer decoder-based neural network to predict the next location an individual will visit based on her historical locations, time, and travel modes. In particular, we design an auxiliary task to jointly predict the next travel mode, with the aim of guiding the learning process of the network. We conduct extensive experiments on two real-world GPS tracking datasets and conclude that considering additional aspects of travel behaviour significantly increases the performance of next location prediction. The overall architecture of the proposed model is shown below.

Challenge award at HackZurich for “The Energymanager”

We participated at the largest Hackathon in Europe, HackZurich 2022, with a team of former and current PhD student of our group, and our app was awarded the challenge price by the company Sensirion! We developed a tool that utilises temperature sensors and building floor plans to serve as a digital energy consultant (for details, see here).

Best paper award at AGILE conference

Our paper “Unlocking Social Network analysis methods for studying human mobility” received the Best Paper Award at the 25th AGILE Conference on Geographic Information Science! Nina Wiedemann presented the paper in Vilnius, Lithuania, and it was elected by the audience out of three papers pre-​selected by the conference committee. In the paper we demonstrate how network modelling methods can help to gain insights into the topology and spatial layout of mobility graphs. For more details, see the publication in the conference proceedings.

New article in Renewable & Sustainable Energy Reviews published

How well can you charge your battery electric vehicle with solar power generated from your own roof?

Our latest journal article “Using rooftop photovoltaic generation to cover individual electric vehicle demand—A detailed case study” shows that this works surprisingly well even without a dedicated battery home storage.
The article was also featured in the ETH News and other media outlets. Check out the press release or the journal article published in Renewable & Sustainable Energy Reviews (open access).

3rd place in the Traffic4cast 2021 extended challenge

Nina Wiedemann of MIE Lab achieved 3rd place in the NeurIPS 2021 Traffic4cast competition!
In this year’s extended challenge, the task was to predict traffic volume and speed in unknown cities, based on movie-format spatio-temporal data. Our team could improve the generalisation ability of the convolutional neural network with a patch-based approach. For more details on the method, check out our preprint on arXiv.