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Urban Transportation Planning: Artificial Neural Network Applications
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Author(s): Sitesh Kumar Singh (College of Engineering, National University of Science and Technology, Oman), Bader Eddin Alasali (College of Engineering, National University of Science and Technology, Oman)and Yuvraj Belbase (Maple Leaf Food, Canada)
Copyright: 2025
Pages: 36
Source title:
Expert Artificial Neural Network Applications for Science and Engineering
Source Author(s)/Editor(s): Lingala Syam Sundar (Prince Mohamamd Bin Fahd University, Saudia Arabia), Deepanraj Balakrishnan (Prince Mohammad Bin Fahd University, Saudi Arabia)and Antonio C.M. Sousa (University of Aveiro, Portugal)
DOI: 10.4018/979-8-3693-7250-0.ch017
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Abstract
The system complexities in urban transportation are increasing. It is difficult for modular planning methods to predict traffic changes, people traveling patterns and mobility needs to develop for a sustainable context. To address this, there has been a paradigm shift in the adoption of Artificial Neural Networks (ANNs). ANNs are capable of understanding patterns of traffic data, and thus can be employed to predict traffic flow, forecast public transit demand or determine the accident risk. The ability to predict these trends enables the country to proactively manage traffic, make smart investments into local safety infrastructure and optimize its public transport system. ANNs are good at discerning patterns in massive data sets and evolving when conditions change. There are, however, difficulties in terms of data availability model interpretability and computational requirements. Given the ongoing improvements in data generation and computational resources, ANNs can revolutionize urban transport planning, creating tomorrow's cities of better functioning, sustainable, and equitable.
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