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Roadside traffic sensor deployment strategy using modified graph-searching algorithm for full link observability
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AI
DOI：10.1080/03081060.2025.2585032.png)
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Abstract
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Traffic sensors are essential for monitoring traffic loadings in a transportation network. However, limited budgets present a significant challenge in strategically configuring traffic sensor deployment, which involves determining the optimal number and locations for these sensors. To address this issue, this research proposes a straightforward method for traffic agencies. This method, built upon a graph-searching algorithm incorporating allocation, recursion, and reverse searching mechanisms, can apply to any network structure. Unlike conventional techniques, the proposed algorithm identifies unequipped traffic links rather than equipped ones, ensuring full link flow observability with the minimal required sensors. This approach simplifies finding the optimal sensor deployment strategy, thereby enhancing solution efficiency. The proposed model is evaluated using simplified and realistic transportation networks. The results illustrate that seeking a set of unequipped links as a roadside traffic sensor deployment strategy is available and efficient, and partially/critically equipped links can reasonably infer the complete traffic link flows.
Keywords:
Traffic sensor
deployment strategy
graph-searching algorithm
link flow observability
unequipped
Journal
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IF:
1.8
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63
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1.4K
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