TL;DR
The methodology offers a cost-effective, privacy-preserving, and interpretable approach for congestion monitoring using ubiquitous smartphone sensors, with real-time, edge-deployable inference and applications in Advanced Traveler Information Systems (ATIS) and intelligent transportation systems in resource-constrained urban environments.
Traffic demand at urban intersections fluctuates drastically across different signal phases, while conventional fixed-time signal control strategies fail to dynamically adjust green light durations in response to short-term traffic variations. This study addresses two core research questions: whether higher single-poin...
The sustained growth of urban populations has worsened chronic traffic congestion, with detrimental impacts on multiple dimensions of urban livability, including increased commute times, reduced road safety, and the degradation of local air quality. Thus, traffic speed prediction (TSP) is an important component of inte...
Traffic congestion is a persistent challenge in rapidly urbanizing regions, including Penang Island, Malaysia, where growing vehicle demand increasingly exceeds road capacity. Quantitative understanding of traffic dynamics is therefore essential for evidence-based traffic management and infrastructure planning. While p...
Traffic congestion prediction focuses on evaluations upcoming road traffic states by analyzing past and real-time data such as vehicle velocity, traffic throughput and road occupancy. However, many existing models have limited adaptability to rapidly changing urban traffic conditions. To address these limitations, the...
This research aims to provide a smart city architecture that can detect accidents and track traffic in realtime using edge-cloud computing, deep learning-based video analytics, and IoT sensing and exhibits low response time, robustness under varying traffic and lighting conditions, and outstanding detection accuracy.
Congestion in urban roads is one of the largest dilemmas in contemporary cities which causes an increment in the travel. it is cheaper, less consuming fuel and causing pollution. The standard traffic control systems are cycle-based. Existing intelligent traffic systems rely on manual monitoring and thus could not cope...
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