A5008079276- Traffic Prediction and Management Techniques
- Human Mobility and Location-Based Analysis
- Transportation Planning and Optimization
- Air Quality Monitoring and Forecasting
- Handwritten Text Recognition Techniques
- Data Management and Algorithms
- Biometric Identification and Security
- Time Series Analysis and Forecasting
- Vehicle License Plate Recognition
Pakistan Institute of Engineering and Applied Sciences
2022
With the advent of Internet Things (IoT), it has become possible to have a variety data sets generated through numerous types sensors deployed across large urban areas, thus empowering notion smart cities. In cities, various may fall into different administrative domains and be accessible exposed Application Program Interfaces (APIs). such setups, for traffic prediction in Intelligent Transport Systems (ITS), one major prerequisites is integration heterogeneous sources within preprocessing...
Predicting a trip's travel time is essential for route planning and navigation applications.The majority of research based on international data that does not apply to Pakistan's road conditions.We designed complete pipeline mining trajectories from sensors data.On this data, we employed state-of-the-art approaches, including shallow artificial neural network, deep multi-layered perceptron, long-shortterm memory, explore the issue prediction frequent routes.The experimental results...
Travel Time Prediction (TTP) has become an essential service that people use in daily commutes. With the precise TTP, individuals, logistic companies, and transport authorities can better manage their activities operations. This paper presents a novel Hybridized Deep Feature Space (HDFS) based TTP ensemble model (HDFS-TTP) for accurate travel time prediction. In first step, extensive endogenous exogenous data sources are augmented with traffic obtained using sensors. Next, we used Principal...
With the advent of Big Data technology and Internet Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part ITS plays a pivotal role in congestion avoidance route planning. The novel data sources such as smartphones in-vehicle navigation applications allow traffic conditions smart cities to be analyzed forecast more reliably than ever. Such massive amount geospatial provides rich source...
Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning deep models are combined using an ensemble approach. This research mainly contributes by proposing model hybridized feature spaces obtained from bidirectional...
In smart cities of the future, data will be generated, integrated, processed and utilized from heterogeneous sources at varying levels complexity. For urban traffic planning in cities, one biggest challenges is congestion prediction its avoidance. Traffic a complex phenomenon it manifestation various contributing factors. addition to vehicular mobility, properties road network, weather, holidays peak hours play significant role especially on arterial roads within city. this paper, we...