A Data Science Solution for Mining Weather Data and Transportation Data for Smart Cities

Nguyen, Ben; Shinnie, Mark J.D.; Leung, Carson K.; Kuzie, Michael R.; Kokilev, Nikola N.; Kaur, Sukhmandeep

doi:10.1109/compsac54236.2022.00266

Cited by 6 publications

(2 citation statements)

References 45 publications

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“…We filter the detected vehicles and keep ones having prediction confidence greater than 70%. 1 We also distinguish parked and moving vehicles. We leverage one property of the CCTV cameras, which is having a fixed view during large time periods.…”

Section: Vehicle Counting and Traffic Estimationmentioning

confidence: 99%

“…Cities face serious challenges maintaining smooth access to the road network in such adverse weather conditions. The accumulation of snow on the roads affects vehicle mobility, causes traffic congestion, and alters the daily course of life [1], [2]. Furthermore, failing to remove snow actively prevents access to vital services and public transport.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Computer Vision-Based Framework for Snow Removal Operation Routing

Karaa,

Ghazzai,

Massoud

et al. 2024

IEEE Open J. Circuits Syst.

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During snowfall, the utility of the road infrastructure is critical. Roads must be effectively cleared to ensure access to important locations and services. In this paper, we present an end-to-end framework for snow removal vehicle routing based on road priority. We offer an artificial intelligence-based image-based approach for estimating snow depth and traffic volume on roads. For segments monitored by CCTV cameras, we exploit images and supervised learning models to perform this task. For unmonitored roads, we use the Graph Convolutional Network architecture to predict parameters in a semi-supervised manner. Following that, we assign priority weights to all graph edges as a function of image-based attributes and road categories. We test the method using a real-world example, simulating snow removal within a study area in Montreal, Quebec, Canada. As input for the framework, we collect CCTV image data and combine it with a 2D map. As a result, more efficient snow removal operation can be achieved by optimizing the trajectories of trucks based on the computer vision module outputs.INDEX TERMS Snow removal, road service prioritization, graph analytics, computer vision, vehicle routing.

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Section: Vehicle Counting and Traffic Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%