Accident risk prediction based on heterogeneous sparse data: New dataset and insights

S Moosavi, MH Samavatian, S Parthasarathy… - Proceedings of the 27th …, 2019 - dl.acm.org
Reducing traffic accidents is an important public safety challenge, therefore, accident
analysis and prediction has been a topic of much research over the past few decades. Using …

semi-Traj2Graph identifying fine-grained driving style with GPS trajectory data via multi-task learning

C Chen, Q Liu, X Wang, C Liao… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Driving behaviour understanding is of vital importance in improving transportation safety and
promoting the development of Intelligent Transportation Systems (ITS). As a long-standing …

Context-aware driver risk prediction with telematics data

S Moosavi, R Ramnath - Accident Analysis & Prevention, 2023 - Elsevier
Driving risk prediction is crucial for safety and risk mitigation. While traditional methods rely
on demographic information for insurance pricing, they may not fully capture actual driving …

Trajectory annotation by discovering driving patterns

S Moosavi, B Omidvar-Tehrani… - Proceedings of the 3rd acm …, 2017 - dl.acm.org
The ubiquity and variety of available sensors has enabled the collection of voluminous
datasets of car trajectories that enable analysts to make sense of driving patterns and …

Driving style representation in convolutional recurrent neural network model of driver identification

S Moosavi, PD Mahajan, S Parthasarathy… - arXiv preprint arXiv …, 2021 - arxiv.org
Identifying driving styles is the task of analyzing the behavior of drivers in order to capture
variations that will serve to discriminate different drivers from each other. This task has …

Reinforced feature extraction and multi-resolution learning for driver mobility fingerprint identification

M Tabatabaie, S He, X Yang - … of the 29th International Conference on …, 2021 - dl.acm.org
Taking into account the availability of the historical GPS trajectories of drivers, given a new
GPS trajectory, Driver mobility fingerprint (DMF) identification aims at (i) determining whether …

Driver state modeling through latent variable state space framework in the wild

A Tavakoli, S Boker, A Heydarian - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Analyzing the impact of the environment on drivers' stress level and workload is of high
importance for designing human-centered driver-vehicle interaction systems and to …

IoT-Based Assessment of a Driver's Stress Level

V Mattioli, L Davoli, L Belli, S Gambetta… - Sensors (Basel …, 2024 - pmc.ncbi.nlm.nih.gov
Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (eg, road
accidents) by providing prompt assistance when anomalies are detected while driving …

Understanding self-reported stress among drivers and designing stress monitor using heart rate variability

N Ahmed, RJ Rony - Quality and User Experience, 2021 - Springer
Driving stress can impact the driving performance that has an impact on the overall driving
experiences. It is a vital area to focus on when the traffic scenario is challenging in terms of …

DriveR: Towards Generating a Dynamic Road Safety Map with Causal Contexts

D Das, S Chakraborty, B Mitra - Proceedings of the ACM on Human …, 2024 - dl.acm.org
Road safety remains a critical global concern, with millions of crashes reported annually.
Understanding the safety of individual road junctions is vital, especially in areas prone to …