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1. Weighted Mobility

   https://doi.org/10.1002/adma.202001537  

   Snyder, G_Jeffrey; Snyder, Alemayouh_H; Wood, Maxwell; Gurunathan, Ramya; Snyder, Berhanu_H; Niu, Changning (May 2020, Advanced Materials)

   Abstract Engineering semiconductor devices requires an understanding of charge carrier mobility. Typically, mobilities are estimated using Hall effect and electrical resistivity meausrements, which are are routinely performed at room temperature and below, in materials with mobilities greater than 1 cm²V^‐1s^‐1. With the availability of combined Seebeck coefficient and electrical resistivity measurement systems, it is now easy to measure the weighted mobility (electron mobility weighted by the density of electronic states). A simple method to calculate the weighted mobility from Seebeck coefficient and electrical resistivity measurements is introduced, which gives good results at room temperature and above, and for mobilities as low as 10⁻³cm²V^‐1s^‐1,Here, μ_wis the weighted mobility, ρ is the electrical resistivity measured in mΩ cm,Tis the absolute temperature in K,Sis the Seebeck coefficient, andk_B/e = 86.3 µV K^–1. Weighted mobility analysis can elucidate the electronic structure and scattering mechanisms in materials and is particularly helpful in understanding and optimizing thermoelectric systems. 

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2. Smart Urban Mobility: When Mobility Systems Meet Smart Data

   Mahrez, Zineb; Sabir, Essaid; Badidi, Elarbi; Saad, Walid; Sadik, Mohamed (January 2021, IEEE transactions on intelligent transportation systems)
3. Human Mobility Challenge: Are Transformers Effective for Human Mobility Prediction?

   https://doi.org/10.1145/3681771.3700130  

   Kong, Ruochen; Amiri, Hossein; Liu, Yueyang; Kennedy, Lance; Gupta, Misha; Kim, Joon-Seok; Züfle, Andreas (October 2024, ACM)

   Transformer-based models are popular for time series forecasting and spatiotemporal prediction due to their ability to infer semantic correlations in long sequences. However, for human mobility prediction, temporal correlations, such as location patterns at the same time on previous days or weeks, are essential. While positional encodings help retain order, the self-attention mechanism causes a loss of temporal detail. To validate this claim, we used a simple approach in the 2nd ACM SIGSPATIAL Human Mobility Prediction Challenge, predicting locations based on past patterns weighted by reliability scores for missing data. Our simple approach was among the top 10 competitors and significantly outperformed the Transformer-based model that won the 2023 challenge. 

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4. Measuring Upward Mobility

   https://doi.org/10.1257/aer.20220316  

   Ray, Debraj; Genicot, Garance (November 2023, American Economic Review)

   We conceptualize and measure upward mobility over income or wealth. At the core of our exercise is the Growth Progressivity Axiom: transfers of instantaneous growth rates from relatively rich to poor individuals increases upward mobility. This axiom, along with mild auxiliary restrictions, identifies an “upward mobility kernel” with a single free parameter, in which mobility is linear in individual growth rates, with geometrically declining weights on baseline incomes. We extend this kernel to trajectories over intervals. The analysis delivers an upward mobility index that does not rely on panel data. That significantly expands our analytical scope to data-poor settings. (JEL D31, D63, I32, O15, O40) 

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5. Mobility in the Sky: Performance and Mobility Analysis for Cellular-Connected UAVs

   https://doi.org/10.1109/TCOMM.2020.2973629  

   Amer, Ramy; Saad, Walid; Marchetti, Nicola (May 2020, IEEE Transactions on Communications)