New #bicycle 🚲 research #preprint using yours truly:
Skåntorp et al. (2024). Data-driven bicycle driving cycles via mixed-integer programming
"we utilized the #KalmanFilter from the #MovingPandas library"
http://dx.doi.org/10.13140/RG.2.2.19928.10240
For the full list of publications we're aware of, check out:
#MixedIntegerProgramming #MovementDataAnalysis #MobilityDataAnalytics #MobilityDataScience #cycling #ActiveMobility #TrajectoryData
Finally completed the first version of full circle #OGC #MovingFeatures support in @movingpandas :
Read MF-JSON (MovingPoint or Trajectory encoding) as TrajectoryCollection and write it back out to MF-JSON MovingPoint encoding ... interopterability success 🎉
https://github.com/movingpandas/movingpandas/blob/main/tutorials/8-ogc-moving-features.ipynb
#MovementDataAnalytics #MobilityDataManagement #TrajectoryData #MFJSON
More #transportation research using #MovingPandas 🤩
Golze, J., & Sester, M. (2024). Determining user specific #semantics of locations extracted from #TrajectoryData. Transportation Research Procedia, 78, 215-221. - "stop points are extracted from the GPS #trajectories using the #Python framework MovingPandas"
https://www.sciencedirect.com/science/article/pii/S2352146524000814
Just found another new preprint / review on this topic:
#DeepLearning for #TrajectoryData Management and Mining: A Survey and Beyond
https://arxiv.org/pdf/2403.14151.pdf
@movingpandas mentioned 👍
#MovementDataScience #mobilitydatascience #gischat #giscience #mobility #geoai
If you liked our last year's short paper on #DeepLearning from #TrajectoryData, you'll love our new #preprint even more:
📝 "MobilityDL: A Review of Deep Learning From Trajectory Data"
https://arxiv.org/abs/2402.00732
#MobilityDataScience #SpatialDataScience #MovementData #Mobility #MachineLearning #arxiv #GISChat #OpenScience
Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This review paper provides the first comprehensive overview of deep learning approaches for trajectory data. We have identified eight specific mobility use cases which we analyze with regards to the deep learning models and the training data used. Besides a comprehensive quantitative review of the literature since 2018, the main contribution of our work is the data-centric analysis of recent work in this field, placing it along the mobility data continuum which ranges from detailed dense trajectories of individual movers (quasi-continuous tracking data), to sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).
MovingPandas
Anita Graser 🇪🇺🇺🇦🇬🇪