2 research outputs found
SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos
In recent years, distracted driving has garnered considerable attention as it
continues to pose a significant threat to public safety on the roads. This has
increased the need for innovative solutions that can identify and eliminate
distracted driving behavior before it results in fatal accidents. In this
paper, we propose a Signal-Based anomaly detection algorithm that segments
videos into anomalies and non-anomalies using a deep CNN-LSTM classifier to
precisely estimate the start and end times of an anomalous driving event. In
the phase of anomaly detection and analysis, driver pose background estimation,
mask extraction, and signal activity spikes are utilized. A Deep CNN-LSTM
classifier was applied to candidate anomalies to detect and classify final
anomalies. The proposed method achieved an overlap score of 0.5424 and ranked
9th on the public leader board in the AI City Challenge 2023, according to
experimental validation results
Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network
The provision of paratransit services is one option to meet the
transportation needs of Vulnerable Road Users (VRUs). Like any other means of
transportation, paratransit has obstacles such as high operational costs and
longer trip times. As a result, customers are dissatisfied, and paratransit
operators have a low approval rating. Researchers have undertaken various
studies over the years to better understand the travel behaviors of paratransit
customers and how they are operated. According to the findings of these
researches, paratransit operators confront the challenge of determining the
optimal route for their trips in order to save travel time. Depending on the
nature of the challenge, most research used different optimization techniques
to solve these routing problems. As a result, the goal of this study is to use
Graph Convolutional Neural Networks (GCNs) to assist paratransit operators in
researching various operational scenarios in a strategic setting in order to
optimize routing, minimize operating costs and minimize their users' travel
time. The study was carried out by using a randomized simulated dataset to help
determine the decision to make in terms of fleet composition and capacity under
different situations. For the various scenarios investigated, the GCN assisted
in determining the minimum optimal gap