A Novel Framework for Handling Sparse Data in Traffic Forecast

• URL: http://arxiv.org/abs/2301.05292v1
• Date: Thu, 12 Jan 2023 21:07:04 GMT
• Title: A Novel Framework for Handling Sparse Data in Traffic Forecast
• Authors: Nikolaos Zygouras and Dimitrios Gunopulos
• Abstract summary: A set of sparse and time evolving traffic reports is generated for each road. In this paper we present a deep learning framework that encodes the sparse recent traffic information and forecasts the future traffic condition.
• Score: 1.6549126148958608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ever increasing amount of GPS-equipped vehicles provides in real-time valuable traffic information for the roads traversed by the moving vehicles. In this way, a set of sparse and time evolving traffic reports is generated for each road. These time series are a valuable asset in order to forecast the future traffic condition. In this paper we present a deep learning framework that encodes the sparse recent traffic information and forecasts the future traffic condition. Our framework consists of a recurrent part and a decoder. The recurrent part employs an attention mechanism that encodes the traffic reports that are available at a particular time window. The decoder is responsible to forecast the future traffic condition.

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