Traffic volume prediction using aerial imagery and sparse data from road counts

Ganji, Arman; Zhang, Mingqian; Hatzopoulou, Marianne

doi:10.1016/j.trc.2022.103739

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…The success of ITS is mainly based on the platform utilized for accessing, collecting, and processing precise data from the atmosphere [8]. RS (both satellite and terrestrial) was an appropriate technique to efficiently collect data on a large scale, having an accuracy level that gratifies the ITS demand.…”

Section: Introductionmentioning

confidence: 99%

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Ahmed¹,

Al-Zebari²,

Zebari³

et al. 2023

Computers, Materials &Amp; Continua

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Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. At the initial stage, the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder (CNN-AE) model for productive and accurate traffic flow. Next, the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization (BADSO) algorithm. The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

show abstract

Section: Introductionmentioning

confidence: 99%

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Ahmed¹,

Al-Zebari²,

Zebari³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In addition to the high installation cost, traffic sensors have high operating and maintenance costs 3 . To achieve cost efficiency, researchers have proposed some optimal and near-optimal traffic sensor placement methods, [4][5][6][7] .…”

mentioning

confidence: 99%

Traffic monitoring system design considering multi-hazard disaster risks

Gazzea

Miraki

Alisan

et al. 2023

Sci Rep

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Roadways are critical infrastructure in our society, providing services for people through and between cities. However, they are prone to closures and disruptions, especially after extreme weather events like hurricanes. At the same time, traffic flow data are a fundamental type of information for any transportation system. In this paper, we tackle the problem of traffic sensor placement on roadways to address two tasks at the same time. The first task is traffic data estimation in ordinary situations, which is vital for traffic monitoring and city planning. We design a graph-based method to estimate traffic flow on roads where sensors are not present. The second one is enhanced observability of roadways in case of extreme weather events. We propose a satellite-based multi-domain risk assessment to locate roads at high risk of closures. Vegetation and flood hazards are taken into account. We formalize the problem as a search method over the network to suggest the minimum number and location of traffic sensors to place while maximizing the traffic estimation capabilities and observability of the risky areas of a city.

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An autonomous navigation approach for unmanned vehicle in off-road environment with self-supervised traversal cost prediction

Zhou

Zhang

et al. 2023

Appl Intell

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Traffic volume prediction using aerial imagery and sparse data from road counts

Cited by 8 publications

References 23 publications

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Traffic monitoring system design considering multi-hazard disaster risks

An autonomous navigation approach for unmanned vehicle in off-road environment with self-supervised traversal cost prediction

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