Category Archives: Uncategorized

New TR_C paper online – Context-aware next location prediction

Our new paper entitled “Context-aware multi-head self-attentional neural network model for next location prediction” was accepted at Transportation Research Part C: Emerging Technologies and is now available (open-access!) online.

We present a multi-head self-attentional (MHSA) neural network that integrates location features, temporal features, and functional land use contexts for next location prediction. This comprehensive approach effectively captures movement-related spatio-temporal information, leading to state-of-the-art performance on GNSS mobility datasets.

Our analysis demonstrates that training the model on population data yields superior results by learning from collective movement patterns, surpassing the capabilities of individual-level models. Moreover, we emphasize the significance of recent past movements and weekly periodicity, showing that learning from a subset of historical mobility is sufficient to obtain an accurate location prediction result.

The proposed model represents a pivotal advancement in accurate and interpretable individual mobility prediction, and can be readily applied in downstream applications, including planning on-demand transport services, implementing mobility incentives, and suggesting alternative mobility options.

Check out the paper online and the corresponding code on Github!

We invite you to join our workshop on Reproducibility in Tracking Data Analysis and Mobility Research at ACM SIGSPATIAL

This year at the ACM SIGSPATIAL conference, we are hosting a workshop on Reproducibility in Tracking Data Analysis and Mobility Research (https://github.com/mie-lab/reprotrack)!

Considering the fast methodological advances in spatial data science, the topic of reproducibility is more important than ever before. To foster common standards and transparency, we aim to bring researchers together in this session to discuss challenges and future pathways for reproducible spatial data science, with a focus on mobility data. The workshop is planned as a particularly interactive session, including a hands-on tutorial on tracking data preprocessing where you can bring your own data.

Please sign up here if you plan to attend the workshop. We hope to see you there on Monday, November 13th, in Hamburg!

New IJGIS Paper is online – using context data to improve traffic forecasting

Our new paper entitled “Incorporating multimodal context information into traffic speed forecasting through graph deep learning” is now online at IJGIS.

In this work, we propose a multimodal context-based graph convolutional neural network (MCGCN) model to fuse context data into traffic speed prediction, including spatial and temporal contexts. The proposed model comprises three modules, i.e., (a) hierarchical spatial embedding to learn spatial representations by organizing spatial contexts from different dimensions, (b) multivariate temporal modeling to learn temporal representations by capturing dependencies of multivariate temporal contexts and (c) attention-based multimodal fusion to integrate traffic speed with the spatial and temporal context representations for multi-step speed prediction. We conduct extensive experiments in Singapore. Compared to the baseline model (STGCN), our results demonstrate the importance of multimodal contexts with the mean-absolute-error improvement of 0.29 km/h, 0.45 km/h and 0.89 km/h in 30-min, 60-min and 120-min speed prediction, respectively. We also explore how different contexts affect traffic speed forecasting, providing references for stakeholders to understand the relationship between context information and transportation systems. Check out the open-access paper online!

Paper published in the Journal of LBS

Our paper titled “Influence of tracking duration on the privacy of individual mobility graphs” was published by the Journal of Location Based Services! In this work, we use a GPS tracking dataset and analyze how the tracking duration affects the risk for users to be re-identified; i.e., by matching to previously stored tracking data. It is well known that the tracking data of a user is quite unique and can be matched to stored data easily; however, we study the risk of a representation of tracking data that is already privatised, namely so-called location graphs. Location graphs do not reveal the geocoordinates or time stamps of the places that a user visited, but just the topology of the mobility behaviour. Nevertheless, users can be re-identified with a top-1 accuracy of up to 20%, and the re-identification risk strongly depends on the tracking duration of the user, as well as the duration of the stored data (pool), as shown in the figure below. Check out our paper and code for more information!

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.