A Neural Network Model for Road Traffic Flow Estimation

Habtie, Ayalew Belay; Abraham, Ajith; Midekso, Dida

doi:10.1007/978-3-319-27400-3_27

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Habtie et al proposed the deployment of a neural network based model in order to forecast traffic flows on urban road networks. Their experimental sessions relied on simulation and real world data, and highlighted the model's potential for producing accurate results (Habtie et al, 2016). Neural networks have also been used successfully in other smart city application areas.…”

Section: Smart City Projectsmentioning

confidence: 99%

Big data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2020

Technological Forecasting and Social Change

179

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Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

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Section: Smart City Projectsmentioning

confidence: 99%

Big data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2020

Technological Forecasting and Social Change

179

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“…In this work, the open source traffic simulator known as 'Simulation of Urban MObility' or 'SUMO' has been used and adapted to reflect the needs of the WAAS simulation [21,22]. SUMO has been available since 2001 and was developed by the Institute of Transportation Systems at the Deutsches Zentrum für Luft und Raumfahrt (DLR) for evaluating modifications to road infrastructure and transport policy, examples are: optimizing traffic light timings [26], forecasting traffic density [27,28] and evaluating wireless in vehicle systems known as "Vehicle-to-X" (V2X) infrastructure [29] .…”

Section: Sumo Traffic Simulatormentioning

confidence: 99%

A system for the generation of synthetic Wide Area Aerial surveillance imagery

Griffith

Mishra

Ralph

et al. 2018

Simulation Modelling Practice and Theory

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The development, benchmarking and validation of aerial Persistent Surveillance (PS) algorithms requires access to specialist Wide Area Aerial Surveillance (WAAS) datasets. Such datasets are difficult to obtain and are often extremely large both in spatial resolution and temporal duration. This paper outlines an approach to the simulation of complex urban environments and demonstrates the viability of using this approach for the generation of simulated sensor data, corresponding to the use of wide area imaging systems for surveillance and reconnaissance applications. This provides a costeffective method to generate datasets for vehicle tracking algorithms and anomaly detection methods. The system fuses the Simulation of Urban Mobility (SUMO) traffic simulator with a MATLAB controller and an image generator to create scenes containing uninterrupted door-to-door journeys across large areas of the urban environment. This 'pattern-of-life' approach provides three-dimensional visual information with natural movement and traffic flows. This can then be used to provide simulated sensor measurements (e.g. visual band and infrared video imagery) and automatic access to ground-truth data for the evaluation of multi-target tracking systems.

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