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Found 5 papers, 0 papers with code
Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories
While there's an assumption of higher performance of multi-task over sing-task learners, the results of this study does not hold such an assumption and shows, in the context of mode and trip purpose inference from GPS trajectory data, a multi-task learning approach does not bring any considerable advantage over single-task learners.
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning
This study investigates pedestrian crossing behaviour, as an important element of urban dynamics that is expected to be affected by the presence of automated vehicles.
DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis
Pedestrian's road crossing behaviour is one of the important aspects of urban dynamics that will be affected by the introduction of autonomous vehicles.
A semi-supervised deep residual network for mode detection in Wi-Fi signals
Due to their ubiquitous and pervasive nature, Wi-Fi networks have the potential to collect large-scale, low-cost, and disaggregate data on multimodal transportation.
Mobility Mode Detection Using WiFi Signals
We utilize Wi-Fi communications from smartphones to predict their mobility mode, i. e. walking, biking and driving.
