A novel intelligent traffic recovery model for emergency vehicles based on context-aware reinforcement learning

Kiani, Farzad; Saraç, Ömer Faruk

doi:10.1016/j.ins.2022.11.057

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…A promising direction would be to explore a contextaware approach to enhance model adaptability and accuracy under various real-world scenarios. For instance, drawing inspiration from studies like [46], we could develop a model that dynamically adjusts to the fluctuating conditions of the environment, providing a more holistic and precise classification performance.…”

Section: A Discussionmentioning

confidence: 99%

Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

Taherinavid,

Moravvej,

Chen

et al. 2024

IEEE Access

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The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an ever-evolving digital landscape.

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Section: A Discussionmentioning

confidence: 99%

Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

Taherinavid,

Moravvej,

Chen

et al. 2024

IEEE Access

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“…Looking forward, our work will include coping with dynamic changes in resource allocation, task prioritization under emergency situations [31], and other issues. Furthermore, we hope to extend our approach to more realistic application scenarios, ensuring its adaptability to diverse and evolving vehicle environments.…”

Section: Discussionmentioning

confidence: 99%

Traffic-Aware Optimization of Task Offloading and Content Caching in the Internet of Vehicles

Wang,

Qiao

et al. 2023

Applied Sciences

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Emerging in-vehicle applications seek to improve travel experiences, but the rising number of vehicles results in more computational tasks and redundant content requests, leading to resource waste. Efficient compute offloading and content caching strategies are crucial for the Internet of Vehicles (IoV) to optimize performance in time latency and energy consumption. This paper proposes a joint task offloading and content caching optimization method based on forecasting traffic streams, called TOCC. First, temporal and spatial correlations are extracted from the preprocessed dataset using the Forecasting Open Source Tool (FOST) and integrated to predict the traffic stream to obtain the number of tasks in the region at the next moment. To obtain a suitable joint optimization strategy for task offloading and content caching, the multi-objective problem of minimizing delay and energy consumption is decomposed into multiple single-objective problems using an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) via the Tchebycheff weight aggregation method, and a set of Pareto-optimal solutions is obtained. Finally, the experimental results verify the effectiveness of the TOCC strategy. Compared with other methods, its latency is up to 29% higher and its energy consumption is up to 83% higher.

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“…By training the raw location GPS data (location data not on the road route) with the model, the accuracy of the latitude and longitude values that should be on the road line has been increased. This model can also be used efficiently in many new trending technologies, such as the Internet of Things [15], smart networks [16], and autonomous vehicles [17].…”

Section: Contributionmentioning

confidence: 99%

Enhancing GPS Accuracy with Machine Learning: A Comparative Analysis of Algorithms

Zontul,

Ersan,

Yelmen

et al. 2024

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A novel intelligent traffic recovery model for emergency vehicles based on context-aware reinforcement learning

Cited by 12 publications

References 28 publications

Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

Traffic-Aware Optimization of Task Offloading and Content Caching in the Internet of Vehicles

Enhancing GPS Accuracy with Machine Learning: A Comparative Analysis of Algorithms

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