M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction
Guangyin Jin, Sicong Lai, Xiaoshuai Hao, Jinlei Zhang, Mingtao Zhang
Abstract
Achieving accurate traffic prediction is a fundamental but crucial task in the development of current intelligent transportation systems. These limitations pose significant challenges for the efficient deployment and operation of deep learning models on large-scale datasets. To address these challenges, we propose a cost-effective graph-free Multilayer Perceptron (MLP) based model M3-Net for traffic prediction. Extensive experiments conducted on multiple real datasets demonstrate the superiority of the proposed model in terms of prediction performance and lightweight deployment. Our code is available at https://github.com/jinguangyin/M3_NET
Topics & Concepts
Computer scienceSoftware deploymentTask (project management)Artificial intelligenceMachine learningPredictive modellingCode (set theory)Key (lock)PerceptronArtificial neural networkData miningMultilayer perceptronIntelligent transportation systemDeep learningData modelingPerformance predictionReal-time computingSource codeTraining setTraffic Prediction and Management TechniquesAdvanced Data and IoT TechnologiesTraffic control and management