To improve the prediction accuracy of traffic flow under the influence of nearby time traffic flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed. Considering the macroscopic periodic characteristics of traffic flow,...
The results indicate that the genetic algorithm's variable order achieves the best performance for the ArcGIS Hub and Frementon Bridge Cycle datasets, fixed order one preprocessing for the Traffic Prediction dataset and variable order using the genetic algorithm for the PeMS08 dataset. Fixed order 2...
Vehicle miles driven (BVM) Cars/TaxiLCVHGVPedalBuses/CoachMotorcycles 251.3 57.8 16.9 3.6 1.9 2.9 Source: https://roadtraffic.dft.gov.uk/summaryCasualties by gender Once again, in 2023 KSI’s on our roads were found to be overwhelmingly male. Overall, in 2023: 75% of fatalities and 61%...
we present the plans of a driver-assistance system, which will be capable of road lane and traffic sign detection by using an OPEN-CV. open-source opencv project lane-finding finalyearproject lane-detection final-year-project final-project semester-project finalproject road-detection lane-lines-de...
Slippery road weather conditions are prevalent in many regions and cause a regular risk for traffic. Still, there has been less research on how autonomous vehicles could detect slippery driving conditions on the road to drive safely. In this work, we pro
Traffic flow prediction for urban road network is influenced by historical traffic flow and traffic flow at adjacent intersections, which has complex spatio-temporal correlation.For the lack of correlation analysis on traffic flow data, capturing small c
To improve the prediction accuracy of traffic flow under the influence of nearby time traffic flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed. Considering the macroscopic periodic characteristics of traffic flow,...
Deep learning prediction models have emerged as the most widely used for the development of intelligent transportation systems (ITS), and their success is strongly reliant on the volume and quality of training data. However, traffic datasets are often small due to the limitations of the resources ...
The KMeans clustering algorithm is used to filter the prior frame to make it more suitable for the dataset and to improve the detection accuracy. To improve the generalization ability of the model, a label smoothing algorithm is introduced. In addition, the structure reparameterization method is ...
Data Preprocessing: Scripts to clean and prepare the dataset for training. Model Development: A robust LSTM model for time series forecasting. Model Evaluation: Performance metrics to assess prediction accuracy. Web Application: An interactive Streamlit app for traffic flow predictions. Data Sources Hist...