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Found 8 papers, 2 papers with code
Where Would I Go Next? Large Language Models as Human Mobility Predictors
We posit that our research marks a significant paradigm shift in human mobility modelling, transitioning from building complex domain-specific models to harnessing general-purpose LLMs that yield accurate predictions through language instructions.
Dynamic Spatial Propagation Network for Depth Completion
Our solution is to estimate independent affinity matrices in each SPN iteration, but it is over-parameterized and heavy calculation.
CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning
Cycling is a promising sustainable mode for commuting and leisure in cities, however, the fear of getting hit or fall reduces its wide expansion as a commuting mode.
WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning
Despite the significance of this subject, it is still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used for practice.
URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision
How can deep learning and Artificial Intelligence (AI) untangle the complexities of informality to advance urban modelling and our understanding of cities?
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning
We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present.
Impact of weather conditions on macroscopic urban travel times
This study aims to investigate the impact of different intensities of rain, snow and temperature levels on macroscopic travel times in the Greater London area (UK) during the period 1 October–10 December 2009.
Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification
Understanding travel behaviour and travel demand is of constant importance to transportation communities and agencies in every country.
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