Design of Artificial Neural Networks for Traffic Forecasting in the Context of Smart Mobility Solutions

Christian Anschütz (), Jan Ibisch, Katharina Ebner and Stefan Smolnik
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Christian Anschütz: University of Hagen, Chair of Business Information Systems
Jan Ibisch: University of Hagen, Chair of Business Information Systems
Katharina Ebner: University of Hagen, Chair of Business Information Systems
Stefan Smolnik: University of Hagen, Chair of Business Information Systems

A chapter in Innovation Through Information Systems, 2021, pp 136-149 from Springer

Abstract: Abstract In this paper, artificial neural networks (ANNs) are developed to predict traffic volumes using traffic sensor data from the city of Darmstadt as a basis for future smart mobility solutions. After processing the acquired sensor data, information about the current traffic situation can be derived and events such as rush hour, weekends or holidays can be identified. Based on current research findings in the field of traffic forecasting using neural networks, our work shows the first best practices for modeling the traffic volume and an associated traffic forecast. A Long Short-Term Memory (LSTM) network is shown to be superior to a Deep Neural Network (DNN) in terms of prediction quality and prediction horizon. Furthermore, it is discussed whether the enrichment of the training data with additional time and weather data enables an increase of the forecast accuracy. In the sense of a design-theoretical approach, design requirements and design principles for the development of an ANN in a traffic-specific context are derived.

Keywords: Artificial neural networks; Long Short-Term Memory; Deep neural network; Traffic forecasting; Smart mobility (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-86797-3_10

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DOI: 10.1007/978-3-030-86797-3_10

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