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.

Received Best Vision Paper Award at the ACM SIGSPATIAL’22 Conference

Our paper “Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis” won the Best Vision Paper Award at the 30th ACM SIGSPATIAL Conference in Seattle, USA. This year’s vision paper selection has been very competitive given the large number of submissions. Among the six selected vision papers invited for presentation, our paper, presented by Dr. Yanan Xin, received the best vision paper award. In this paper, we envision opportunities for utilizing causal inference to enhance the interpretability and robustness of deep learning methods and address challenges in mobility analysis. This direction will help us build safer, more efficient, and more sustainable future transportation systems. For more information, check out our paper and a pre-recorded video presentation.

The vision paper also highlights what we aim to achieve through our project “Interpretable and Robust Machine Learning for Mobility Analysis”. Stay tuned for more exciting results coming from the project!